diff --git a/doc/userguide/models/fs2000ns_steadystate.m b/doc/userguide/models/fs2000ns_steadystate.m
index 3720ec68e6157cb1f3ab910d2e08d94552c818fa..f9991e4d4544e9ae19b124e5c349fabb55a79817 100644
--- a/doc/userguide/models/fs2000ns_steadystate.m
+++ b/doc/userguide/models/fs2000ns_steadystate.m
@@ -1,53 +1,53 @@
% computes the steady state of fs2000 analyticaly
% largely inspired by the program of F. Schorfheide
function [ys,check] = fs2000ns_steadystate(ys,exe)
- global M_
-
- alp = M_.params(1);
- bet = M_.params(2);
- gam = M_.params(3);
- mst = M_.params(4);
- rho = M_.params(5);
- psi = M_.params(6);
- del = M_.params(7);
+global M_
- check = 0;
-
- dA = exp(gam);
- gst = 1/dA;
- m = mst;
-
- khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
- xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
- nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
- n = xist/(nust+xist);
- P = xist + nust;
- k = khst*n;
- l = psi*mst*n/( (1-psi)*(1-n) );
- c = mst/P;
- d = l - mst + 1;
- y = k^alp*n^(1-alp)*gst^alp;
- R = mst/bet;
- W = l/n;
- ist = y-c;
- q = 1 - d;
- e = 1;
-
- P_obs = 1;
- Y_obs = 1;
-
- ys =[
-m
-P
-c
-e
-W
-R
-k
-d
-n
-l
-Y_obs
-P_obs
-y
-dA ];
\ No newline at end of file
+alp = M_.params(1);
+bet = M_.params(2);
+gam = M_.params(3);
+mst = M_.params(4);
+rho = M_.params(5);
+psi = M_.params(6);
+del = M_.params(7);
+
+check = 0;
+
+dA = exp(gam);
+gst = 1/dA;
+m = mst;
+
+khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
+xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
+nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
+n = xist/(nust+xist);
+P = xist + nust;
+k = khst*n;
+l = psi*mst*n/( (1-psi)*(1-n) );
+c = mst/P;
+d = l - mst + 1;
+y = k^alp*n^(1-alp)*gst^alp;
+R = mst/bet;
+W = l/n;
+ist = y-c;
+q = 1 - d;
+e = 1;
+
+P_obs = 1;
+Y_obs = 1;
+
+ys =[
+ m
+ P
+ c
+ e
+ W
+ R
+ k
+ d
+ n
+ l
+ Y_obs
+ P_obs
+ y
+ dA ];
\ No newline at end of file
diff --git a/doc/userguide/models/fsdat.m b/doc/userguide/models/fsdat.m
index aba209b908a794044e570302331b8efe9eaac673..ef1279c0b39764c26ded95083e89500f17aeb45d 100644
--- a/doc/userguide/models/fsdat.m
+++ b/doc/userguide/models/fsdat.m
@@ -1,198 +1,198 @@
data_q = [
-18.02 1474.5 150.2
-17.94 1538.2 150.9
-18.01 1584.5 151.4
-18.42 1644.1 152
-18.73 1678.6 152.7
-19.46 1693.1 153.3
-19.55 1724 153.9
-19.56 1758.2 154.7
-19.79 1760.6 155.4
-19.77 1779.2 156
-19.82 1778.8 156.6
-20.03 1790.9 157.3
-20.12 1846 158
-20.1 1882.6 158.6
-20.14 1897.3 159.2
-20.22 1887.4 160
-20.27 1858.2 160.7
-20.34 1849.9 161.4
-20.39 1848.5 162
-20.42 1868.9 162.8
-20.47 1905.6 163.6
-20.56 1959.6 164.3
-20.62 1994.4 164.9
-20.78 2020.1 165.7
-21 2030.5 166.5
-21.2 2023.6 167.2
-21.33 2037.7 167.9
-21.62 2033.4 168.7
-21.71 2066.2 169.5
-22.01 2077.5 170.2
-22.15 2071.9 170.9
-22.27 2094 171.7
-22.29 2070.8 172.5
-22.56 2012.6 173.1
-22.64 2024.7 173.8
-22.77 2072.3 174.5
-22.88 2120.6 175.3
-22.92 2165 176.045
-22.91 2223.3 176.727
-22.94 2221.4 177.481
-23.03 2230.95 178.268
-23.13 2279.22 179.694
-23.22 2265.48 180.335
-23.32 2268.29 181.094
-23.4 2238.57 181.915
-23.45 2251.68 182.634
-23.51 2292.02 183.337
-23.56 2332.61 184.103
-23.63 2381.01 184.894
-23.75 2422.59 185.553
-23.81 2448.01 186.203
-23.87 2471.86 186.926
-23.94 2476.67 187.68
-24 2508.7 188.299
-24.07 2538.05 188.906
-24.12 2586.26 189.631
-24.29 2604.62 190.362
-24.35 2666.69 190.954
-24.41 2697.54 191.56
-24.52 2729.63 192.256
-24.64 2739.75 192.938
-24.77 2808.88 193.467
-24.88 2846.34 193.994
-25.01 2898.79 194.647
-25.17 2970.48 195.279
-25.32 3042.35 195.763
-25.53 3055.53 196.277
-25.79 3076.51 196.877
-26.02 3102.36 197.481
-26.14 3127.15 197.967
-26.31 3129.53 198.455
-26.6 3154.19 199.012
-26.9 3177.98 199.572
-27.21 3236.18 199.995
-27.49 3292.07 200.452
-27.75 3316.11 200.997
-28.12 3331.22 201.538
-28.39 3381.86 201.955
-28.73 3390.23 202.419
-29.14 3409.65 202.986
-29.51 3392.6 203.584
-29.94 3386.49 204.086
-30.36 3391.61 204.721
-30.61 3422.95 205.419
-31.02 3389.36 206.13
-31.5 3481.4 206.763
-31.93 3500.95 207.362
-32.27 3523.8 208
-32.54 3533.79 208.642
-33.02 3604.73 209.142
-33.2 3687.9 209.637
-33.49 3726.18 210.181
-33.95 3790.44 210.737
-34.36 3892.22 211.192
-34.94 3919.01 211.663
-35.61 3907.08 212.191
-36.29 3947.11 212.708
-37.01 3908.15 213.144
-37.79 3922.57 213.602
-38.96 3879.98 214.147
-40.13 3854.13 214.7
-41.05 3800.93 215.135
-41.66 3835.21 215.652
-42.41 3907.02 216.289
-43.19 3952.48 216.848
-43.69 4044.59 217.314
-44.15 4072.19 217.776
-44.77 4088.49 218.338
-45.57 4126.39 218.917
-46.32 4176.28 219.427
-47.07 4260.08 219.956
-47.66 4329.46 220.573
-48.63 4328.33 221.201
-49.42 4345.51 221.719
-50.41 4510.73 222.281
-51.27 4552.14 222.933
-52.35 4603.65 223.583
-53.51 4605.65 224.152
-54.65 4615.64 224.737
-55.82 4644.93 225.418
-56.92 4656.23 226.117
-58.18 4678.96 226.754
-59.55 4566.62 227.389
-61.01 4562.25 228.07
-62.59 4651.86 228.689
-64.15 4739.16 229.155
-65.37 4696.82 229.674
-66.65 4753.02 230.301
-67.87 4693.76 230.903
-68.86 4615.89 231.395
-69.72 4634.88 231.906
-70.66 4612.08 232.498
-71.44 4618.26 233.074
-72.08 4662.97 233.546
-72.83 4763.57 234.028
-73.48 4849 234.603
-74.19 4939.23 235.153
-75.02 5053.56 235.605
-75.58 5132.87 236.082
-76.25 5170.34 236.657
-76.81 5203.68 237.232
-77.63 5257.26 237.673
-78.25 5283.73 238.176
-78.76 5359.6 238.789
-79.45 5393.57 239.387
-79.81 5460.83 239.861
-80.22 5466.95 240.368
-80.84 5496.29 240.962
-81.45 5526.77 241.539
-82.09 5561.8 242.009
-82.68 5618 242.52
-83.33 5667.39 243.12
-84.09 5750.57 243.721
-84.67 5785.29 244.208
-85.56 5844.05 244.716
-86.66 5878.7 245.354
-87.44 5952.83 245.966
-88.45 6010.96 246.46
-89.39 6055.61 247.017
-90.13 6087.96 247.698
-90.88 6093.51 248.374
-92 6152.59 248.928
-93.18 6171.57 249.564
-94.14 6142.1 250.299
-95.11 6078.96 251.031
-96.27 6047.49 251.65
-97 6074.66 252.295
-97.7 6090.14 253.033
-98.31 6105.25 253.743
-99.13 6175.69 254.338
-99.79 6214.22 255.032
-100.17 6260.74 255.815
-100.88 6327.12 256.543
-101.84 6327.93 257.151
-102.35 6359.9 257.785
-102.83 6393.5 258.516
-103.51 6476.86 259.191
-104.13 6524.5 259.738
-104.71 6600.31 260.351
-105.39 6629.47 261.04
-106.09 6688.61 261.692
-106.75 6717.46 262.236
-107.24 6724.2 262.847
-107.75 6779.53 263.527
-108.29 6825.8 264.169
-108.91 6882 264.681
-109.24 6983.91 265.258
-109.74 7020 265.887
-110.23 7093.12 266.491
-111 7166.68 266.987
-111.43 7236.5 267.545
-111.76 7311.24 268.171
-112.08 7364.63 268.815
-];
+ 18.02 1474.5 150.2
+ 17.94 1538.2 150.9
+ 18.01 1584.5 151.4
+ 18.42 1644.1 152
+ 18.73 1678.6 152.7
+ 19.46 1693.1 153.3
+ 19.55 1724 153.9
+ 19.56 1758.2 154.7
+ 19.79 1760.6 155.4
+ 19.77 1779.2 156
+ 19.82 1778.8 156.6
+ 20.03 1790.9 157.3
+ 20.12 1846 158
+ 20.1 1882.6 158.6
+ 20.14 1897.3 159.2
+ 20.22 1887.4 160
+ 20.27 1858.2 160.7
+ 20.34 1849.9 161.4
+ 20.39 1848.5 162
+ 20.42 1868.9 162.8
+ 20.47 1905.6 163.6
+ 20.56 1959.6 164.3
+ 20.62 1994.4 164.9
+ 20.78 2020.1 165.7
+ 21 2030.5 166.5
+ 21.2 2023.6 167.2
+ 21.33 2037.7 167.9
+ 21.62 2033.4 168.7
+ 21.71 2066.2 169.5
+ 22.01 2077.5 170.2
+ 22.15 2071.9 170.9
+ 22.27 2094 171.7
+ 22.29 2070.8 172.5
+ 22.56 2012.6 173.1
+ 22.64 2024.7 173.8
+ 22.77 2072.3 174.5
+ 22.88 2120.6 175.3
+ 22.92 2165 176.045
+ 22.91 2223.3 176.727
+ 22.94 2221.4 177.481
+ 23.03 2230.95 178.268
+ 23.13 2279.22 179.694
+ 23.22 2265.48 180.335
+ 23.32 2268.29 181.094
+ 23.4 2238.57 181.915
+ 23.45 2251.68 182.634
+ 23.51 2292.02 183.337
+ 23.56 2332.61 184.103
+ 23.63 2381.01 184.894
+ 23.75 2422.59 185.553
+ 23.81 2448.01 186.203
+ 23.87 2471.86 186.926
+ 23.94 2476.67 187.68
+ 24 2508.7 188.299
+ 24.07 2538.05 188.906
+ 24.12 2586.26 189.631
+ 24.29 2604.62 190.362
+ 24.35 2666.69 190.954
+ 24.41 2697.54 191.56
+ 24.52 2729.63 192.256
+ 24.64 2739.75 192.938
+ 24.77 2808.88 193.467
+ 24.88 2846.34 193.994
+ 25.01 2898.79 194.647
+ 25.17 2970.48 195.279
+ 25.32 3042.35 195.763
+ 25.53 3055.53 196.277
+ 25.79 3076.51 196.877
+ 26.02 3102.36 197.481
+ 26.14 3127.15 197.967
+ 26.31 3129.53 198.455
+ 26.6 3154.19 199.012
+ 26.9 3177.98 199.572
+ 27.21 3236.18 199.995
+ 27.49 3292.07 200.452
+ 27.75 3316.11 200.997
+ 28.12 3331.22 201.538
+ 28.39 3381.86 201.955
+ 28.73 3390.23 202.419
+ 29.14 3409.65 202.986
+ 29.51 3392.6 203.584
+ 29.94 3386.49 204.086
+ 30.36 3391.61 204.721
+ 30.61 3422.95 205.419
+ 31.02 3389.36 206.13
+ 31.5 3481.4 206.763
+ 31.93 3500.95 207.362
+ 32.27 3523.8 208
+ 32.54 3533.79 208.642
+ 33.02 3604.73 209.142
+ 33.2 3687.9 209.637
+ 33.49 3726.18 210.181
+ 33.95 3790.44 210.737
+ 34.36 3892.22 211.192
+ 34.94 3919.01 211.663
+ 35.61 3907.08 212.191
+ 36.29 3947.11 212.708
+ 37.01 3908.15 213.144
+ 37.79 3922.57 213.602
+ 38.96 3879.98 214.147
+ 40.13 3854.13 214.7
+ 41.05 3800.93 215.135
+ 41.66 3835.21 215.652
+ 42.41 3907.02 216.289
+ 43.19 3952.48 216.848
+ 43.69 4044.59 217.314
+ 44.15 4072.19 217.776
+ 44.77 4088.49 218.338
+ 45.57 4126.39 218.917
+ 46.32 4176.28 219.427
+ 47.07 4260.08 219.956
+ 47.66 4329.46 220.573
+ 48.63 4328.33 221.201
+ 49.42 4345.51 221.719
+ 50.41 4510.73 222.281
+ 51.27 4552.14 222.933
+ 52.35 4603.65 223.583
+ 53.51 4605.65 224.152
+ 54.65 4615.64 224.737
+ 55.82 4644.93 225.418
+ 56.92 4656.23 226.117
+ 58.18 4678.96 226.754
+ 59.55 4566.62 227.389
+ 61.01 4562.25 228.07
+ 62.59 4651.86 228.689
+ 64.15 4739.16 229.155
+ 65.37 4696.82 229.674
+ 66.65 4753.02 230.301
+ 67.87 4693.76 230.903
+ 68.86 4615.89 231.395
+ 69.72 4634.88 231.906
+ 70.66 4612.08 232.498
+ 71.44 4618.26 233.074
+ 72.08 4662.97 233.546
+ 72.83 4763.57 234.028
+ 73.48 4849 234.603
+ 74.19 4939.23 235.153
+ 75.02 5053.56 235.605
+ 75.58 5132.87 236.082
+ 76.25 5170.34 236.657
+ 76.81 5203.68 237.232
+ 77.63 5257.26 237.673
+ 78.25 5283.73 238.176
+ 78.76 5359.6 238.789
+ 79.45 5393.57 239.387
+ 79.81 5460.83 239.861
+ 80.22 5466.95 240.368
+ 80.84 5496.29 240.962
+ 81.45 5526.77 241.539
+ 82.09 5561.8 242.009
+ 82.68 5618 242.52
+ 83.33 5667.39 243.12
+ 84.09 5750.57 243.721
+ 84.67 5785.29 244.208
+ 85.56 5844.05 244.716
+ 86.66 5878.7 245.354
+ 87.44 5952.83 245.966
+ 88.45 6010.96 246.46
+ 89.39 6055.61 247.017
+ 90.13 6087.96 247.698
+ 90.88 6093.51 248.374
+ 92 6152.59 248.928
+ 93.18 6171.57 249.564
+ 94.14 6142.1 250.299
+ 95.11 6078.96 251.031
+ 96.27 6047.49 251.65
+ 97 6074.66 252.295
+ 97.7 6090.14 253.033
+ 98.31 6105.25 253.743
+ 99.13 6175.69 254.338
+ 99.79 6214.22 255.032
+ 100.17 6260.74 255.815
+ 100.88 6327.12 256.543
+ 101.84 6327.93 257.151
+ 102.35 6359.9 257.785
+ 102.83 6393.5 258.516
+ 103.51 6476.86 259.191
+ 104.13 6524.5 259.738
+ 104.71 6600.31 260.351
+ 105.39 6629.47 261.04
+ 106.09 6688.61 261.692
+ 106.75 6717.46 262.236
+ 107.24 6724.2 262.847
+ 107.75 6779.53 263.527
+ 108.29 6825.8 264.169
+ 108.91 6882 264.681
+ 109.24 6983.91 265.258
+ 109.74 7020 265.887
+ 110.23 7093.12 266.491
+ 111 7166.68 266.987
+ 111.43 7236.5 267.545
+ 111.76 7311.24 268.171
+ 112.08 7364.63 268.815
+ ];
%GDPD GDPQ GPOP
series = zeros(193,2);
diff --git a/dynare++/extern/matlab/dynare_simul.m b/dynare++/extern/matlab/dynare_simul.m
index 32ac8dc57e081cd243b8ba5c8e92f6623815b145..a77fc458bda3e290d705cbe22d04feb990c3fee9 100644
--- a/dynare++/extern/matlab/dynare_simul.m
+++ b/dynare++/extern/matlab/dynare_simul.m
@@ -80,71 +80,71 @@ eval(['load ' fname]);
% set prefix, shocks, ystart
if ischar(varargin{2})
- prefix = varargin{2};
- if length(varargin) == 3
- shocks = varargin{3};
- ystart = NaN;
- elseif length(varargin) == 4
- shocks = varargin{3};
- ystart = varargin{4};
- else
- error('Wrong number of parameters.');
- end
+ prefix = varargin{2};
+ if length(varargin) == 3
+ shocks = varargin{3};
+ ystart = NaN;
+ elseif length(varargin) == 4
+ shocks = varargin{3};
+ ystart = varargin{4};
+ else
+ error('Wrong number of parameters.');
+ end
else
- prefix = 'dyn';
- if length(varargin) == 2
- shocks = varargin{2};
- ystart = NaN;
- elseif length(varargin) == 3
- shocks = varargin{2};
- ystart = varargin{3};
- else
- error('Wrong number of parameters.');
- end
+ prefix = 'dyn';
+ if length(varargin) == 2
+ shocks = varargin{2};
+ ystart = NaN;
+ elseif length(varargin) == 3
+ shocks = varargin{2};
+ ystart = varargin{3};
+ else
+ error('Wrong number of parameters.');
+ end
end
% load all needed variables but prefix_g_*
if (exist([prefix '_nstat']))
- nstat = eval([prefix '_nstat']);
+ nstat = eval([prefix '_nstat']);
else
- error(['Could not find variable ' prefix '_nstat in workspace']);
+ error(['Could not find variable ' prefix '_nstat in workspace']);
end
if (exist([prefix '_npred']))
- npred = eval([prefix '_npred']);
+ npred = eval([prefix '_npred']);
else
- error(['Could not find variable ' prefix '_npred in workspace']);
+ error(['Could not find variable ' prefix '_npred in workspace']);
end
if (exist([prefix '_nboth']))
- nboth = eval([prefix '_nboth']);
+ nboth = eval([prefix '_nboth']);
else
- error(['Could not find variable ' prefix '_nboth in workspace']);
+ error(['Could not find variable ' prefix '_nboth in workspace']);
end
if (exist([prefix '_nforw']))
- nforw = eval([prefix '_nforw']);
+ nforw = eval([prefix '_nforw']);
else
- error(['Could not find variable ' prefix '_nforw in workspace']);
+ error(['Could not find variable ' prefix '_nforw in workspace']);
end
if (exist([prefix '_ss']))
- ss = eval([prefix '_ss']);
+ ss = eval([prefix '_ss']);
else
- error(['Could not find variable ' prefix '_ss in workspace']);
+ error(['Could not find variable ' prefix '_ss in workspace']);
end
if (exist([prefix '_vcov_exo']))
- vcov_exo = eval([prefix '_vcov_exo']);
+ vcov_exo = eval([prefix '_vcov_exo']);
else
- error(['Could not find variable ' prefix '_vcov_exo in workspace']);
+ error(['Could not find variable ' prefix '_vcov_exo in workspace']);
end
nexog = size(vcov_exo,1);
if isnan(ystart)
- ystart = ss;
+ ystart = ss;
end
% newer version of dynare++ doesn't return prefix_g_0, we make it here if
% it does not exist in workspace
g_zero = [prefix '_g_0'];
if (~ exist(g_zero))
- eval([ g_zero '= zeros(nstat+npred+nboth+nforw,1);']);
+ eval([ g_zero '= zeros(nstat+npred+nboth+nforw,1);']);
end
% make derstr a string of comma seperated existing prefix_g_*
@@ -152,13 +152,13 @@ derstr = [',' g_zero];
order = 1;
cont = 1;
while cont == 1
- g_ord = [prefix '_g_' num2str(order)];
- if (exist(g_ord))
- derstr = [derstr ',' g_ord];
- order = order + 1;
- else
- cont = 0;
- end
+ g_ord = [prefix '_g_' num2str(order)];
+ if (exist(g_ord))
+ derstr = [derstr ',' g_ord];
+ order = order + 1;
+ else
+ cont = 0;
+ end
end
% set seed
diff --git a/dynare++/sylv/matlab/gensylv.m b/dynare++/sylv/matlab/gensylv.m
index 56f5f0e73b78018bb5147455656c06250fd799de..d16bde364042bf7eeb26525de1c5472043e9a41e 100644
--- a/dynare++/sylv/matlab/gensylv.m
+++ b/dynare++/sylv/matlab/gensylv.m
@@ -58,19 +58,18 @@ function [err, X, varargout] = gensylv(order, A, B, C, D)
% in Windows, ensure that aa_gensylv.dll is loaded, this prevents
% clearing the function and a successive Matlab crash
if strcmp('PCWIN', computer)
- if ~ libisloaded('aa_gensylv')
- loadlibrary('aa_gensylv', 'dummy');
- end
+ if ~ libisloaded('aa_gensylv')
+ loadlibrary('aa_gensylv', 'dummy');
+ end
end
% launch aa_gensylv
if nargout == 2
- X = aa_gensylv(order, A, B, C, D);
+ X = aa_gensylv(order, A, B, C, D);
elseif nargout == 3
- [X, par] = aa_gensylv(order, A, B, C, D);
- varargout(1) = {par};
+ [X, par] = aa_gensylv(order, A, B, C, D);
+ varargout(1) = {par};
else
- error('Must have 2 or 3 output arguments.');
+ error('Must have 2 or 3 output arguments.');
end
err = 0;
-
\ No newline at end of file
diff --git a/examples/NK_baseline_steadystate.m b/examples/NK_baseline_steadystate.m
index f7254b8fbb9b33f38029bdb2e7ec55dc2540f833..48c8d60f7b1656ea8d195cff324b18bd3f9bbaf2 100644
--- a/examples/NK_baseline_steadystate.m
+++ b/examples/NK_baseline_steadystate.m
@@ -17,8 +17,8 @@ global M_
% read out parameters to access them with their name
NumberOfParameters = M_.param_nbr;
for ii = 1:NumberOfParameters
- paramname = deblank(M_.param_names(ii,:));
- eval([ paramname ' = M_.params(' int2str(ii) ');']);
+ paramname = deblank(M_.param_names(ii,:));
+ eval([ paramname ' = M_.params(' int2str(ii) ');']);
end
% initialize indicator
check = 0;
@@ -68,8 +68,8 @@ vw=(1-thetaw)/(1-thetaw*PI^((1-chiw)*eta)*mu_z^eta)*PIstarw^(-eta);
tempvaromega=alppha/(1-alppha)*w/r*mu_z*mu_I;
[ld,fval,exitflag]=fzero(@(ld)(1-betta*thetaw*mu_z^(eta-1)*PI^(-(1-chiw)*(1-eta)))/(1-betta*thetaw*mu_z^(eta*(1+gammma))*PI^(eta*(1-chiw)*(1+gammma)))...
--(eta-1)/eta*wstar/(varpsi*PIstarw^(-eta*gammma)*ld^gammma)*((1-h*mu_z^(-1))^(-1)-betta*h*(mu_z-h)^(-1))*...
-((mu_A*mu_z^(-1)*vp^(-1)*tempvaromega^alppha-tempvaromega*(1-(1-delta)*(mu_z*mu_I)^(-1)))*ld-vp^(-1)*Phi)^(-1),0.25,options);
+ -(eta-1)/eta*wstar/(varpsi*PIstarw^(-eta*gammma)*ld^gammma)*((1-h*mu_z^(-1))^(-1)-betta*h*(mu_z-h)^(-1))*...
+ ((mu_A*mu_z^(-1)*vp^(-1)*tempvaromega^alppha-tempvaromega*(1-(1-delta)*(mu_z*mu_I)^(-1)))*ld-vp^(-1)*Phi)^(-1),0.25,options);
if exitflag <1
%indicate the SS computation was not sucessful; this would also be detected by Dynare
%setting the indicator here shows how to use this functionality to
@@ -95,11 +95,11 @@ g2=epsilon/(epsilon-1)*g1;
%% end own model equations
for iter = 1:length(M_.params) %update parameters set in the file
- eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
+ eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
end
NumberOfEndogenousVariables = M_.orig_endo_nbr; %auxiliary variables are set automatically
for ii = 1:NumberOfEndogenousVariables
- varname = deblank(M_.endo_names(ii,:));
- eval(['ys(' int2str(ii) ') = ' varname ';']);
+ varname = deblank(M_.endo_names(ii,:));
+ eval(['ys(' int2str(ii) ') = ' varname ';']);
end
diff --git a/examples/fsdat_simul.m b/examples/fsdat_simul.m
index 56c0e4cd56a8bb96fb579587fb4218afb626c56e..f6ad30c85be4e71227913664bd14cce25ac504eb 100644
--- a/examples/fsdat_simul.m
+++ b/examples/fsdat_simul.m
@@ -1,416 +1,416 @@
% Generated data, used by fs2000.mod
gy_obs =[
- 1.0030045
- 1.0002599
- 0.99104664
- 1.0321162
- 1.0223545
- 1.0043614
- 0.98626929
- 1.0092127
- 1.0357197
- 1.0150827
- 1.0051548
- 0.98465775
- 0.99132132
- 0.99904153
- 1.0044641
- 1.0179198
- 1.0113462
- 0.99409421
- 0.99904293
- 1.0448336
- 0.99932433
- 1.0057004
- 0.99619787
- 1.0267504
- 1.0077645
- 1.0058026
- 1.0025891
- 0.9939097
- 0.99604693
- 0.99908569
- 1.0151094
- 0.99348134
- 1.0039124
- 1.0145805
- 0.99800868
- 0.98578138
- 1.0065771
- 0.99843919
- 0.97979062
- 0.98413351
- 0.96468174
- 1.0273857
- 1.0225211
- 0.99958667
- 1.0111157
- 1.0099585
- 0.99480311
- 1.0079265
- 0.98924573
- 1.0070613
- 1.0075706
- 0.9937151
- 1.0224711
- 1.0018891
- 0.99051863
- 1.0042944
- 1.0184055
- 0.99419508
- 0.99756624
- 1.0015983
- 0.9845772
- 1.0004407
- 1.0116237
- 0.9861885
- 1.0073094
- 0.99273355
- 1.0013224
- 0.99777979
- 1.0301686
- 0.96809556
- 0.99917088
- 0.99949253
- 0.96590004
- 1.0083938
- 0.96662298
- 1.0221454
- 1.0069792
- 1.0343996
- 1.0066531
- 1.0072525
- 0.99743563
- 0.99723703
- 1.000372
- 0.99013917
- 1.0095223
- 0.98864268
- 0.98092242
- 0.98886488
- 1.0030341
- 1.01894
- 0.99155059
- 0.99533235
- 0.99734316
- 1.0047356
- 1.0082737
- 0.98425116
- 0.99949212
- 1.0055899
- 1.0065075
- 0.99385069
- 0.98867975
- 0.99804843
- 1.0184038
- 0.99301902
- 1.0177222
- 1.0051924
- 1.0187852
- 1.0098985
- 1.0097172
- 1.0145811
- 0.98721038
- 1.0361722
- 1.0105821
- 0.99469309
- 0.98626785
- 1.013871
- 0.99858924
- 0.99302637
- 1.0042186
- 0.99623745
- 0.98545708
- 1.0225435
- 1.0011861
- 1.0130321
- 0.97861347
- 1.0228193
- 0.99627435
- 1.0272779
- 1.0075172
- 1.0096762
- 1.0129306
- 0.99966549
- 1.0262882
- 1.0026914
- 1.0061475
- 1.009523
- 1.0036127
- 0.99762992
- 0.99092634
- 1.0058469
- 0.99887292
- 1.0060653
- 0.98673557
- 0.98895709
- 0.99111967
- 0.990118
- 0.99788054
- 0.97054709
- 1.0099157
- 1.0107431
- 0.99518695
- 1.0114048
- 0.99376019
- 1.0023369
- 0.98783327
- 1.0051727
- 1.0100462
- 0.98607387
- 1.0000064
- 0.99692442
- 1.012225
- 0.99574078
- 0.98642833
- 0.99008207
- 1.0197359
- 1.0112849
- 0.98711069
- 0.99402748
- 1.0242141
- 1.0135349
- 0.99842505
- 1.0130714
- 0.99887044
- 1.0059058
- 1.0185998
- 1.0073314
- 0.98687706
- 1.0084551
- 0.97698964
- 0.99482714
- 1.0015302
- 1.0105331
- 1.0261767
- 1.0232822
- 1.0084176
- 0.99785167
- 0.99619733
- 1.0055223
- 1.0076326
- 0.99205461
- 1.0030587
- 1.0137012
- 1.0145878
- 1.0190297
- 1.0000681
- 1.0153894
- 1.0140649
- 1.0007236
- 0.97961463
- 1.0125257
- 1.0169503
- 1.0197363
- 1.0221185
+ 1.0030045
+ 1.0002599
+ 0.99104664
+ 1.0321162
+ 1.0223545
+ 1.0043614
+ 0.98626929
+ 1.0092127
+ 1.0357197
+ 1.0150827
+ 1.0051548
+ 0.98465775
+ 0.99132132
+ 0.99904153
+ 1.0044641
+ 1.0179198
+ 1.0113462
+ 0.99409421
+ 0.99904293
+ 1.0448336
+ 0.99932433
+ 1.0057004
+ 0.99619787
+ 1.0267504
+ 1.0077645
+ 1.0058026
+ 1.0025891
+ 0.9939097
+ 0.99604693
+ 0.99908569
+ 1.0151094
+ 0.99348134
+ 1.0039124
+ 1.0145805
+ 0.99800868
+ 0.98578138
+ 1.0065771
+ 0.99843919
+ 0.97979062
+ 0.98413351
+ 0.96468174
+ 1.0273857
+ 1.0225211
+ 0.99958667
+ 1.0111157
+ 1.0099585
+ 0.99480311
+ 1.0079265
+ 0.98924573
+ 1.0070613
+ 1.0075706
+ 0.9937151
+ 1.0224711
+ 1.0018891
+ 0.99051863
+ 1.0042944
+ 1.0184055
+ 0.99419508
+ 0.99756624
+ 1.0015983
+ 0.9845772
+ 1.0004407
+ 1.0116237
+ 0.9861885
+ 1.0073094
+ 0.99273355
+ 1.0013224
+ 0.99777979
+ 1.0301686
+ 0.96809556
+ 0.99917088
+ 0.99949253
+ 0.96590004
+ 1.0083938
+ 0.96662298
+ 1.0221454
+ 1.0069792
+ 1.0343996
+ 1.0066531
+ 1.0072525
+ 0.99743563
+ 0.99723703
+ 1.000372
+ 0.99013917
+ 1.0095223
+ 0.98864268
+ 0.98092242
+ 0.98886488
+ 1.0030341
+ 1.01894
+ 0.99155059
+ 0.99533235
+ 0.99734316
+ 1.0047356
+ 1.0082737
+ 0.98425116
+ 0.99949212
+ 1.0055899
+ 1.0065075
+ 0.99385069
+ 0.98867975
+ 0.99804843
+ 1.0184038
+ 0.99301902
+ 1.0177222
+ 1.0051924
+ 1.0187852
+ 1.0098985
+ 1.0097172
+ 1.0145811
+ 0.98721038
+ 1.0361722
+ 1.0105821
+ 0.99469309
+ 0.98626785
+ 1.013871
+ 0.99858924
+ 0.99302637
+ 1.0042186
+ 0.99623745
+ 0.98545708
+ 1.0225435
+ 1.0011861
+ 1.0130321
+ 0.97861347
+ 1.0228193
+ 0.99627435
+ 1.0272779
+ 1.0075172
+ 1.0096762
+ 1.0129306
+ 0.99966549
+ 1.0262882
+ 1.0026914
+ 1.0061475
+ 1.009523
+ 1.0036127
+ 0.99762992
+ 0.99092634
+ 1.0058469
+ 0.99887292
+ 1.0060653
+ 0.98673557
+ 0.98895709
+ 0.99111967
+ 0.990118
+ 0.99788054
+ 0.97054709
+ 1.0099157
+ 1.0107431
+ 0.99518695
+ 1.0114048
+ 0.99376019
+ 1.0023369
+ 0.98783327
+ 1.0051727
+ 1.0100462
+ 0.98607387
+ 1.0000064
+ 0.99692442
+ 1.012225
+ 0.99574078
+ 0.98642833
+ 0.99008207
+ 1.0197359
+ 1.0112849
+ 0.98711069
+ 0.99402748
+ 1.0242141
+ 1.0135349
+ 0.99842505
+ 1.0130714
+ 0.99887044
+ 1.0059058
+ 1.0185998
+ 1.0073314
+ 0.98687706
+ 1.0084551
+ 0.97698964
+ 0.99482714
+ 1.0015302
+ 1.0105331
+ 1.0261767
+ 1.0232822
+ 1.0084176
+ 0.99785167
+ 0.99619733
+ 1.0055223
+ 1.0076326
+ 0.99205461
+ 1.0030587
+ 1.0137012
+ 1.0145878
+ 1.0190297
+ 1.0000681
+ 1.0153894
+ 1.0140649
+ 1.0007236
+ 0.97961463
+ 1.0125257
+ 1.0169503
+ 1.0197363
+ 1.0221185
-];
+ ];
gp_obs =[
- 1.0079715
- 1.0115853
- 1.0167502
- 1.0068957
- 1.0138189
- 1.0258364
- 1.0243817
- 1.017373
- 1.0020171
- 1.0003742
- 1.0008974
- 1.0104804
- 1.0116393
- 1.0114294
- 0.99932124
- 0.99461459
- 1.0170349
- 1.0051446
- 1.020639
- 1.0051964
- 1.0093042
- 1.007068
- 1.01086
- 0.99590086
- 1.0014883
- 1.0117332
- 0.9990095
- 1.0108284
- 1.0103672
- 1.0036722
- 1.0005124
- 1.0190331
- 1.0130978
- 1.007842
- 1.0285436
- 1.0322054
- 1.0213403
- 1.0246486
- 1.0419306
- 1.0258867
- 1.0156316
- 0.99818589
- 0.9894107
- 1.0127584
- 1.0146882
- 1.0136529
- 1.0340107
- 1.0343652
- 1.02971
- 1.0077932
- 1.0198114
- 1.013971
- 1.0061083
- 1.0089573
- 1.0037926
- 1.0082071
- 0.99498155
- 0.99735772
- 0.98765026
- 1.006465
- 1.0196088
- 1.0053233
- 1.0119974
- 1.0188066
- 1.0029302
- 1.0183459
- 1.0034218
- 1.0158799
- 0.98824798
- 1.0274357
- 1.0168832
- 1.0180641
- 1.0294657
- 0.98864091
- 1.0358326
- 0.99889969
- 1.0178322
- 0.99813566
- 1.0073549
- 1.0215985
- 1.0084245
- 1.0080939
- 1.0157021
- 1.0075815
- 1.0032633
- 1.0117871
- 1.0209276
- 1.0077569
- 0.99680958
- 1.0120266
- 1.0017625
- 1.0138811
- 1.0198358
- 1.0059629
- 1.0115416
- 1.0319473
- 1.0167074
- 1.0116111
- 1.0048627
- 1.0217622
- 1.0125221
- 1.0142045
- 0.99792469
- 0.99823971
- 0.99561547
- 0.99850373
- 0.9898464
- 1.0030963
- 1.0051373
- 1.0004213
- 1.0144117
- 0.97185592
- 0.9959518
- 1.0073529
- 1.0051603
- 0.98642572
- 0.99433423
- 1.0112131
- 1.0007695
- 1.0176867
- 1.0134363
- 0.99926191
- 0.99879835
- 0.99878754
- 1.0331374
- 1.0077797
- 1.0127221
- 1.0047393
- 1.0074106
- 0.99784213
- 1.0056495
- 1.0057708
- 0.98817494
- 0.98742176
- 0.99930555
- 1.0000687
- 1.0129754
- 1.009529
- 1.0226731
- 1.0149534
- 1.0164295
- 1.0239469
- 1.0293458
- 1.026199
- 1.0197525
- 1.0126818
- 1.0054473
- 1.0254423
- 1.0069461
- 1.0153135
- 1.0337515
- 1.0178187
- 1.0240469
- 1.0079489
- 1.0186953
- 1.0008628
- 1.0113799
- 1.0140118
- 1.0168007
- 1.011441
- 0.98422774
- 0.98909729
- 1.0157859
- 1.0151586
- 0.99756232
- 0.99497777
- 1.0102841
- 1.0221659
- 0.9937759
- 0.99877193
- 1.0079433
- 0.99667692
- 1.0095959
- 1.0128804
- 1.0156949
- 1.0111951
- 1.0228887
- 1.0122083
- 1.0190197
- 1.0074927
- 1.0268096
- 0.99689352
- 0.98948474
- 1.0024938
- 1.0105543
- 1.014116
- 1.0141217
- 1.0056504
- 1.0101026
- 1.0105069
- 0.99619053
- 1.0059439
- 0.99449473
- 0.99482458
- 1.0037702
- 1.0068087
- 0.99575975
- 1.0030815
- 1.0334014
- 0.99879386
- 0.99625634
- 1.0171195
- 0.99233844
+ 1.0079715
+ 1.0115853
+ 1.0167502
+ 1.0068957
+ 1.0138189
+ 1.0258364
+ 1.0243817
+ 1.017373
+ 1.0020171
+ 1.0003742
+ 1.0008974
+ 1.0104804
+ 1.0116393
+ 1.0114294
+ 0.99932124
+ 0.99461459
+ 1.0170349
+ 1.0051446
+ 1.020639
+ 1.0051964
+ 1.0093042
+ 1.007068
+ 1.01086
+ 0.99590086
+ 1.0014883
+ 1.0117332
+ 0.9990095
+ 1.0108284
+ 1.0103672
+ 1.0036722
+ 1.0005124
+ 1.0190331
+ 1.0130978
+ 1.007842
+ 1.0285436
+ 1.0322054
+ 1.0213403
+ 1.0246486
+ 1.0419306
+ 1.0258867
+ 1.0156316
+ 0.99818589
+ 0.9894107
+ 1.0127584
+ 1.0146882
+ 1.0136529
+ 1.0340107
+ 1.0343652
+ 1.02971
+ 1.0077932
+ 1.0198114
+ 1.013971
+ 1.0061083
+ 1.0089573
+ 1.0037926
+ 1.0082071
+ 0.99498155
+ 0.99735772
+ 0.98765026
+ 1.006465
+ 1.0196088
+ 1.0053233
+ 1.0119974
+ 1.0188066
+ 1.0029302
+ 1.0183459
+ 1.0034218
+ 1.0158799
+ 0.98824798
+ 1.0274357
+ 1.0168832
+ 1.0180641
+ 1.0294657
+ 0.98864091
+ 1.0358326
+ 0.99889969
+ 1.0178322
+ 0.99813566
+ 1.0073549
+ 1.0215985
+ 1.0084245
+ 1.0080939
+ 1.0157021
+ 1.0075815
+ 1.0032633
+ 1.0117871
+ 1.0209276
+ 1.0077569
+ 0.99680958
+ 1.0120266
+ 1.0017625
+ 1.0138811
+ 1.0198358
+ 1.0059629
+ 1.0115416
+ 1.0319473
+ 1.0167074
+ 1.0116111
+ 1.0048627
+ 1.0217622
+ 1.0125221
+ 1.0142045
+ 0.99792469
+ 0.99823971
+ 0.99561547
+ 0.99850373
+ 0.9898464
+ 1.0030963
+ 1.0051373
+ 1.0004213
+ 1.0144117
+ 0.97185592
+ 0.9959518
+ 1.0073529
+ 1.0051603
+ 0.98642572
+ 0.99433423
+ 1.0112131
+ 1.0007695
+ 1.0176867
+ 1.0134363
+ 0.99926191
+ 0.99879835
+ 0.99878754
+ 1.0331374
+ 1.0077797
+ 1.0127221
+ 1.0047393
+ 1.0074106
+ 0.99784213
+ 1.0056495
+ 1.0057708
+ 0.98817494
+ 0.98742176
+ 0.99930555
+ 1.0000687
+ 1.0129754
+ 1.009529
+ 1.0226731
+ 1.0149534
+ 1.0164295
+ 1.0239469
+ 1.0293458
+ 1.026199
+ 1.0197525
+ 1.0126818
+ 1.0054473
+ 1.0254423
+ 1.0069461
+ 1.0153135
+ 1.0337515
+ 1.0178187
+ 1.0240469
+ 1.0079489
+ 1.0186953
+ 1.0008628
+ 1.0113799
+ 1.0140118
+ 1.0168007
+ 1.011441
+ 0.98422774
+ 0.98909729
+ 1.0157859
+ 1.0151586
+ 0.99756232
+ 0.99497777
+ 1.0102841
+ 1.0221659
+ 0.9937759
+ 0.99877193
+ 1.0079433
+ 0.99667692
+ 1.0095959
+ 1.0128804
+ 1.0156949
+ 1.0111951
+ 1.0228887
+ 1.0122083
+ 1.0190197
+ 1.0074927
+ 1.0268096
+ 0.99689352
+ 0.98948474
+ 1.0024938
+ 1.0105543
+ 1.014116
+ 1.0141217
+ 1.0056504
+ 1.0101026
+ 1.0105069
+ 0.99619053
+ 1.0059439
+ 0.99449473
+ 0.99482458
+ 1.0037702
+ 1.0068087
+ 0.99575975
+ 1.0030815
+ 1.0334014
+ 0.99879386
+ 0.99625634
+ 1.0171195
+ 0.99233844
-];
+ ];
diff --git a/matlab/@dynTimeIndex/subsref.m b/matlab/@dynTimeIndex/subsref.m
index 6c8aa79d4279dcbbb5610c729dcc6e0af7521d9d..bae6edc565f22864879776606588c7cb96d2d6e7 100644
--- a/matlab/@dynTimeIndex/subsref.m
+++ b/matlab/@dynTimeIndex/subsref.m
@@ -4,7 +4,7 @@ function B = subsref(A,S) % --*-- Unitary tests --*--
%
% Overloads the subsref method for dynTimeIndex class. This method only allows to get
% the value of the field `index`.
-
+
% Copyright (C) 2013 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/AHessian.m b/matlab/AHessian.m
index 561c380a00da5f3872fb84227c0c73d6dcdc6762..be59603fdc00ea28d2babfb41278fcc09bbf7d95 100644
--- a/matlab/AHessian.m
+++ b/matlab/AHessian.m
@@ -26,106 +26,106 @@ function [AHess, DLIK, LIK] = AHessian(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,start,mf,ka
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- k = size(DT,3); % number of structural parameters
- smpl = size(Y,2); % Sample size.
- pp = size(Y,1); % Maximum number of observed variables.
- mm = size(T,2); % Number of state variables.
- a = zeros(mm,1); % State vector.
- Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
- t = 0; % Initialization of the time index.
- oldK = 0;
- notsteady = 1; % Steady state flag.
- F_singular = 1;
+k = size(DT,3); % number of structural parameters
+smpl = size(Y,2); % Sample size.
+pp = size(Y,1); % Maximum number of observed variables.
+mm = size(T,2); % Number of state variables.
+a = zeros(mm,1); % State vector.
+Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
+t = 0; % Initialization of the time index.
+oldK = 0;
+notsteady = 1; % Steady state flag.
+F_singular = 1;
lik = zeros(smpl,1); % Initialization of the vector gathering the densities.
LIK = Inf; % Default value of the log likelihood.
if nargout > 1,
DLIK = zeros(k,1); % Initialization of the score.
end
- AHess = zeros(k,k); % Initialization of the Hessian
- Da = zeros(mm,k); % State vector.
- Dv = zeros(length(mf),k);
-
+AHess = zeros(k,k); % Initialization of the Hessian
+Da = zeros(mm,k); % State vector.
+Dv = zeros(length(mf),k);
+
% for ii = 1:k
% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
% end
-
- while notsteady && t<smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- F = P(mf,mf) + H;
- if rcond(F) < kalman_tol
- if ~all(abs(F(:))<kalman_tol)
- return
- else
- a = T*a;
- P = T*P*transpose(T)+Om;
- end
+
+while notsteady && t<smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
+ F = P(mf,mf) + H;
+ if rcond(F) < kalman_tol
+ if ~all(abs(F(:))<kalman_tol)
+ return
else
- F_singular = 0;
- iF = inv(F);
- K = P(:,mf)*iF;
- lik(t) = log(det(F))+transpose(v)*iF*v;
-
- [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
-
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii) - DYss(mf,ii);
- Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
- if t>=start && nargout > 1
- DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
- end
- end
- vecDPmf = reshape(DP(mf,mf,:),[],k);
-% iPmf = inv(P(mf,mf));
- if t>=start
- AHess = AHess + Dv'*iF*Dv + .5*(vecDPmf' * kron(iF,iF) * vecDPmf);
- end
- a = T*(a+K*v);
- P = T*(P-K*P(mf,:))*transpose(T)+Om;
- DP = DP1;
+ a = T*a;
+ P = T*P*transpose(T)+Om;
end
- notsteady = max(max(abs(K-oldK))) > riccati_tol;
- oldK = K;
- end
+ else
+ F_singular = 0;
+ iF = inv(F);
+ K = P(:,mf)*iF;
+ lik(t) = log(det(F))+transpose(v)*iF*v;
- if F_singular
- error('The variance of the forecast error remains singular until the end of the sample')
+ [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
+
+ for ii = 1:k
+ Dv(:,ii) = -Da(mf,ii) - DYss(mf,ii);
+ Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
+ if t>=start && nargout > 1
+ DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
+ end
+ end
+ vecDPmf = reshape(DP(mf,mf,:),[],k);
+ % iPmf = inv(P(mf,mf));
+ if t>=start
+ AHess = AHess + Dv'*iF*Dv + .5*(vecDPmf' * kron(iF,iF) * vecDPmf);
+ end
+ a = T*(a+K*v);
+ P = T*(P-K*P(mf,:))*transpose(T)+Om;
+ DP = DP1;
end
+ notsteady = max(max(abs(K-oldK))) > riccati_tol;
+ oldK = K;
+end
-
- if t < smpl
- t0 = t+1;
- while t < smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
- Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
- if t>=start && nargout >1
- DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
- end
- end
- if t>=start
- AHess = AHess + Dv'*iF*Dv;
- end
- a = T*(a+K*v);
- lik(t) = transpose(v)*iF*v;
- end
- AHess = AHess + .5*(smpl-t0+1)*(vecDPmf' * kron(iF,iF) * vecDPmf);
- if nargout > 1
+if F_singular
+ error('The variance of the forecast error remains singular until the end of the sample')
+end
+
+
+if t < smpl
+ t0 = t+1;
+ while t < smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
for ii = 1:k
-% DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
+ Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
+ Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
+ if t>=start && nargout >1
+ DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
+ end
end
+ if t>=start
+ AHess = AHess + Dv'*iF*Dv;
+ end
+ a = T*(a+K*v);
+ lik(t) = transpose(v)*iF*v;
+ end
+ AHess = AHess + .5*(smpl-t0+1)*(vecDPmf' * kron(iF,iF) * vecDPmf);
+ if nargout > 1
+ for ii = 1:k
+ % DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
end
- lik(t0:smpl) = lik(t0:smpl) + log(det(F));
-% for ii = 1:k;
-% for jj = 1:ii
-% H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
-% end
-% end
- end
-
+ end
+ lik(t0:smpl) = lik(t0:smpl) + log(det(F));
+ % for ii = 1:k;
+ % for jj = 1:ii
+ % H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
+ % end
+ % end
+end
+
AHess = -AHess;
if nargout > 1,
DLIK = DLIK/2;
@@ -134,12 +134,12 @@ end
lik = (lik + pp*log(2*pi))/2;
LIK = sum(lik(start:end)); % Minus the log-likelihood.
-% end of main function
-
+ % end of main function
+
function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
- k = size(DT,3);
- tmp = P-K*P(mf,:);
+k = size(DT,3);
+tmp = P-K*P(mf,:);
for ii = 1:k
DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
@@ -152,4 +152,3 @@ end
% end of computeDKalman
-
\ No newline at end of file
diff --git a/matlab/AIM/SPAimerr.m b/matlab/AIM/SPAimerr.m
index 2894a89a78e7cd9b54dec4a8e68193f8a7ca3418..fb20705275cecaef85877a5a9cdda1a0e434426e 100644
--- a/matlab/AIM/SPAimerr.m
+++ b/matlab/AIM/SPAimerr.m
@@ -29,7 +29,7 @@ function e = SPAimerr(c);
% Journal of Economic Dynamics and Control, 2010, vol. 34, issue 3,
% pages 472-489
- if(c==1) e='Aim: unique solution.';
+if(c==1) e='Aim: unique solution.';
elseif(c==2) e='Aim: roots not correctly computed by real_schur.';
elseif(c==3) e='Aim: too many big roots.';
elseif(c==35) e='Aim: too many big roots, and q(:,right) is singular.';
diff --git a/matlab/AIM/SPAmalg.m b/matlab/AIM/SPAmalg.m
index 41307b445e118ca8721e93952706d9be876bdc5e..19cc958fe5fac04b2fedab08287ec2b78c123f51 100644
--- a/matlab/AIM/SPAmalg.m
+++ b/matlab/AIM/SPAmalg.m
@@ -1,5 +1,5 @@
function [b,rts,ia,nexact,nnumeric,lgroots,aimcode] = ...
- SPAmalg(h,neq,nlag,nlead,condn,uprbnd)
+ SPAmalg(h,neq,nlag,nlead,condn,uprbnd)
% [b,rts,ia,nexact,nnumeric,lgroots,aimcode] = ...
% SPAmalg(h,neq,nlag,nlead,condn,uprbnd)
%
diff --git a/matlab/AIM/SPBuild_a.m b/matlab/AIM/SPBuild_a.m
index eb12473a8afef4a70b8088a7fc40233508253ed1..b4d61ad92bfdfeba4337a278533d9535e6185bd0 100644
--- a/matlab/AIM/SPBuild_a.m
+++ b/matlab/AIM/SPBuild_a.m
@@ -41,9 +41,9 @@ hs(:,left) = -hs(:,right)\hs(:,left);
a = zeros(qcols,qcols);
if(qcols > neq)
- eyerows = 1:qcols-neq;
- eyecols = neq+1:qcols;
- a(eyerows,eyecols) = eye(qcols-neq);
+ eyerows = 1:qcols-neq;
+ eyecols = neq+1:qcols;
+ a(eyerows,eyecols) = eye(qcols-neq);
end
hrows = qcols-neq+1:qcols;
a(hrows,:) = hs(:,left);
@@ -56,9 +56,9 @@ a(hrows,:) = hs(:,left);
js = 1:qcols;
zerocols = sum(abs(a)) == 0;
while( any(zerocols) )
- a(:,zerocols) = [];
- a(zerocols,:) = [];
- js(zerocols) = [];
- zerocols = sum(abs(a)) == 0;
+ a(:,zerocols) = [];
+ a(zerocols,:) = [];
+ js(zerocols) = [];
+ zerocols = sum(abs(a)) == 0;
end
ia = length(js);
diff --git a/matlab/AIM/SPCopy_w.m b/matlab/AIM/SPCopy_w.m
index 714a1e27466116e2d35df8838c535cbd8e519009..d7da8c647e66eaee01fa590bd7bb670b2874d7e9 100644
--- a/matlab/AIM/SPCopy_w.m
+++ b/matlab/AIM/SPCopy_w.m
@@ -30,7 +30,7 @@ function q = SPCopy_w(q,w,js,iq,qrows)
if(iq < qrows)
- lastrows = iq+1:qrows;
- wrows = 1:length(lastrows);
- q(lastrows,js) = w(:,wrows)';
+ lastrows = iq+1:qrows;
+ wrows = 1:length(lastrows);
+ q(lastrows,js) = w(:,wrows)';
end
diff --git a/matlab/AIM/SPEigensystem.m b/matlab/AIM/SPEigensystem.m
index 16855401a521704a22778f62c1dce3edcaa39f99..2ef1676ce1e474936dfc90103471bf9237b4f1bb 100644
--- a/matlab/AIM/SPEigensystem.m
+++ b/matlab/AIM/SPEigensystem.m
@@ -39,22 +39,22 @@ opts.disp=0;
% [mag,k] = sort(-mag);
% rts = rts(k);
%catch
- %disp('Catch in SPE');
- %pause(0.5);
- %aStr=datestr(clock);
- %eval(['save ' regexprep(aStr,' ','') ' a']);
- try
- [w,d]=eig(a');
- catch
- lasterr
- w=[];rts=[];lgroots=[];
- flag_trouble=1;
- return
- end
- rts = diag(d);
- mag = abs(rts);
- [mag,k] = sort(-mag);
- rts = rts(k);
+%disp('Catch in SPE');
+%pause(0.5);
+%aStr=datestr(clock);
+%eval(['save ' regexprep(aStr,' ','') ' a']);
+try
+ [w,d]=eig(a');
+catch
+ lasterr
+ w=[];rts=[];lgroots=[];
+ flag_trouble=1;
+ return
+end
+rts = diag(d);
+mag = abs(rts);
+[mag,k] = sort(-mag);
+rts = rts(k);
%end
flag_trouble=0;
diff --git a/matlab/AIM/SPExact_shift.m b/matlab/AIM/SPExact_shift.m
index c9034e319bd3d309b4e49ebb53d6fa28a1e261d1..876d9ca4f468d86c40fe4667b4f6ab0e9dff7393 100644
--- a/matlab/AIM/SPExact_shift.m
+++ b/matlab/AIM/SPExact_shift.m
@@ -36,12 +36,12 @@ right = qcols+1:qcols+neq;
zerorows = find( sum(abs( hs(:,right)' ))==0 );
while( any(zerorows) && iq <= qrows )
- nz = length(zerorows);
- q(iq+1:iq+nz,:) = hs(zerorows,left);
- hs(zerorows,:) = SPShiftright(hs(zerorows,:),neq);
- iq = iq + nz;
- nexact = nexact + nz;
- zerorows = find( sum(abs( hs(:,right)' ))==0 );
+ nz = length(zerorows);
+ q(iq+1:iq+nz,:) = hs(zerorows,left);
+ hs(zerorows,:) = SPShiftright(hs(zerorows,:),neq);
+ iq = iq + nz;
+ nexact = nexact + nz;
+ zerorows = find( sum(abs( hs(:,right)' ))==0 );
end
h=full(hs);
diff --git a/matlab/AIM/SPNumeric_shift.m b/matlab/AIM/SPNumeric_shift.m
index cc1e1825f7588a1481d6fabe986c86b8d1b60616..27049ca0cc41a629fe0a2d6c54fbe7e1044705a8 100644
--- a/matlab/AIM/SPNumeric_shift.m
+++ b/matlab/AIM/SPNumeric_shift.m
@@ -37,14 +37,14 @@ right = qcols+1:qcols+neq;
zerorows = find( abs(diag(R)) <= condn );
while( any(zerorows) && iq <= qrows )
- h=sparse(h);
- Q=sparse(Q);
- h = Q'*h;
- nz = length(zerorows);
- q(iq+1:iq+nz,:) = h(zerorows,left);
- h(zerorows,:) = SPShiftright( h(zerorows,:), neq );
- iq = iq + nz;
- nnumeric = nnumeric + nz;
- [Q,R,E] = qr( full(h(:,right)) );
- zerorows = find( abs(diag(R)) <= condn );
+ h=sparse(h);
+ Q=sparse(Q);
+ h = Q'*h;
+ nz = length(zerorows);
+ q(iq+1:iq+nz,:) = h(zerorows,left);
+ h(zerorows,:) = SPShiftright( h(zerorows,:), neq );
+ iq = iq + nz;
+ nnumeric = nnumeric + nz;
+ [Q,R,E] = qr( full(h(:,right)) );
+ zerorows = find( abs(diag(R)) <= condn );
end
diff --git a/matlab/AIM/SPObstruct.m b/matlab/AIM/SPObstruct.m
index 8620ca1b9b562ae859f342dfa093bc4337b29a68..ea8252856c52c375d8343604f677d335c1b5a792 100644
--- a/matlab/AIM/SPObstruct.m
+++ b/matlab/AIM/SPObstruct.m
@@ -52,16 +52,16 @@ qs(1:rc,1:cc) = sparse(cofb);
qcols = neq*(nlag+nlead);
if( nlead > 1 )
- for i = 1:nlead-1
- rows = i*neq + (1:neq);
- qs(rows,:) = SPShiftright( qs((rows-neq),:), neq );
- end
+ for i = 1:nlead-1
+ rows = i*neq + (1:neq);
+ qs(rows,:) = SPShiftright( qs((rows-neq),:), neq );
+ end
end
l = (1: neq*nlag);
r = (neq*nlag+1: neq*(nlag+nlead));
- qs(:,l) = - qs(:,r) \ qs(:,l);
+qs(:,l) = - qs(:,r) \ qs(:,l);
minus = 1: neq*(nlag+1);
plus = neq*(nlag+1)+1: neq*(nlag+1+nlead);
diff --git a/matlab/AIM/SPReduced_form.m b/matlab/AIM/SPReduced_form.m
index 3d5c8899078e52eb3f401904bad949351c48d73c..8d529fc4e127c48cd25dc7320b6182829664eaca 100644
--- a/matlab/AIM/SPReduced_form.m
+++ b/matlab/AIM/SPReduced_form.m
@@ -38,7 +38,7 @@ if(nonsing)
b = qs(1:neq,1:bcols);
b = full(b);
else %rescale by dividing row by maximal qr element
- %'inverse condition number small, rescaling '
+ %'inverse condition number small, rescaling '
themax=max(abs(qs(:,right)),[],2);
oneover = diag(1 ./ themax);
nonsing = rcond(full(oneover *qs(:,right))) > condn;
diff --git a/matlab/AIM_first_order_solver.m b/matlab/AIM_first_order_solver.m
index fb3a99dc6a499affeb56d82eff1498d78365f98a..51993d119db696c64ba3682c7d47db391bfc36af 100644
--- a/matlab/AIM_first_order_solver.m
+++ b/matlab/AIM_first_order_solver.m
@@ -67,35 +67,35 @@ function [dr,info]=AIM_first_order_solver(jacobia,M,dr,qz_criterium)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
- info = 0;
-
- [dr,aimcode]=dynAIMsolver1(jacobia,M,dr);
- if aimcode ~=1
- info(1) = convertAimCodeToInfo(aimCode); %convert to be in the 100 range
- info(2) = 1.0e+8;
- return
- end
- A = kalman_transition_matrix(dr,M.nstatic+(1:M.nspred), 1:M.nspred,...
- M.exo_nbr);
- dr.eigval = eig(A);
- disp(dr.eigval)
- nd = size(dr.kstate,1);
- nba = nd-sum( abs(dr.eigval) < qz_criterium );
+info = 0;
+
+[dr,aimcode]=dynAIMsolver1(jacobia,M,dr);
+
+if aimcode ~=1
+ info(1) = convertAimCodeToInfo(aimCode); %convert to be in the 100 range
+ info(2) = 1.0e+8;
+ return
+end
+A = kalman_transition_matrix(dr,M.nstatic+(1:M.nspred), 1:M.nspred,...
+ M.exo_nbr);
+dr.eigval = eig(A);
+disp(dr.eigval)
+nd = size(dr.kstate,1);
+nba = nd-sum( abs(dr.eigval) < qz_criterium );
- nsfwrd = M.nsfwrd;
+nsfwrd = M.nsfwrd;
- if nba ~= nsfwrd
- temp = sort(abs(dr.eigval));
- if nba > nsfwrd
- temp = temp(nd-nba+1:nd-nsfwrd)-1-qz_criterium;
- info(1) = 3;
- elseif nba < nsfwrd;
- temp = temp(nd-nsfwrd+1:nd-nba)-1-qz_criterium;
- info(1) = 4;
- end
- info(2) = temp'*temp;
- return
+if nba ~= nsfwrd
+ temp = sort(abs(dr.eigval));
+ if nba > nsfwrd
+ temp = temp(nd-nba+1:nd-nsfwrd)-1-qz_criterium;
+ info(1) = 3;
+ elseif nba < nsfwrd;
+ temp = temp(nd-nsfwrd+1:nd-nba)-1-qz_criterium;
+ info(1) = 4;
end
+ info(2) = temp'*temp;
+ return
+end
diff --git a/matlab/DsgeSmoother.m b/matlab/DsgeSmoother.m
index 08b7011e93cc1d050444fe2c660f4e0645b2c891..d3894bc0881a5cc0d74637f23a9db6d31371c2a8 100644
--- a/matlab/DsgeSmoother.m
+++ b/matlab/DsgeSmoother.m
@@ -169,7 +169,7 @@ elseif options_.lik_init == 3 % Diffuse Kalman filter
kalman_algo = 3;
else
if ~all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
- %Augment state vector (follows Section 6.4.3 of DK (2012))
+ %Augment state vector (follows Section 6.4.3 of DK (2012))
expanded_state_vector_for_univariate_filter=1;
T = blkdiag(T,zeros(vobs));
np = size(T,1);
@@ -240,32 +240,32 @@ if kalman_algo == 1 || kalman_algo == 3
end
if kalman_algo == 2 || kalman_algo == 4
- if ~all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
- if ~expanded_state_vector_for_univariate_filter
- %Augment state vector (follows Section 6.4.3 of DK (2012))
- expanded_state_vector_for_univariate_filter=1;
- Z = [Z, eye(vobs)];
- ST = blkdiag(ST,zeros(vobs));
- np = size(ST,1);
- Q = blkdiag(Q,H);
- R1 = blkdiag(R,eye(vobs));
- if kalman_algo == 4
- %recompute Schur state space transformation with
- %expanded state space
- [Pstar,Pinf] = compute_Pinf_Pstar(mf,ST,R1,Q,options_.qz_criterium);
- else
- Pstar = blkdiag(Pstar,H);
- if ~isempty(Pinf)
- Pinf = blkdiag(Pinf,zeros(vobs));
- end
- end
- %now reset H to 0
- H = zeros(vobs,vobs);
+ if ~all(all(abs(H-diag(diag(H)))<1e-14))% ie, the covariance matrix is diagonal...
+ if ~expanded_state_vector_for_univariate_filter
+ %Augment state vector (follows Section 6.4.3 of DK (2012))
+ expanded_state_vector_for_univariate_filter=1;
+ Z = [Z, eye(vobs)];
+ ST = blkdiag(ST,zeros(vobs));
+ np = size(ST,1);
+ Q = blkdiag(Q,H);
+ R1 = blkdiag(R,eye(vobs));
+ if kalman_algo == 4
+ %recompute Schur state space transformation with
+ %expanded state space
+ [Pstar,Pinf] = compute_Pinf_Pstar(mf,ST,R1,Q,options_.qz_criterium);
else
- %do nothing, state vector was already expanded
+ Pstar = blkdiag(Pstar,H);
+ if ~isempty(Pinf)
+ Pinf = blkdiag(Pinf,zeros(vobs));
+ end
end
+ %now reset H to 0
+ H = zeros(vobs,vobs);
+ else
+ %do nothing, state vector was already expanded
end
-
+ end
+
[alphahat,epsilonhat,etahat,ahat,P,aK,PK,decomp,state_uncertainty] = missing_DiffuseKalmanSmootherH3_Z(ST, ...
Z,R1,Q,diag(H), ...
Pinf,Pstar,data1,vobs,np,smpl,data_index, ...
diff --git a/matlab/GetPosteriorMeanVariance.m b/matlab/GetPosteriorMeanVariance.m
index d1e2935873b0e5a969446e4c2572c31756469140..285b9448023e727a5ad558f2312ddb7720c2e2ab 100644
--- a/matlab/GetPosteriorMeanVariance.m
+++ b/matlab/GetPosteriorMeanVariance.m
@@ -16,38 +16,38 @@ function [mean,variance] = GetPosteriorMeanVariance(M,drop)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
- MetropolisFolder = CheckPath('metropolis',M.dname);
- FileName = M.fname;
- BaseName = [MetropolisFolder filesep FileName];
- load_last_mh_history_file(MetropolisFolder, FileName);
- NbrDraws = sum(record.MhDraws(:,1));
- NbrFiles = sum(record.MhDraws(:,2));
- NbrBlocks = record.Nblck;
- mean = 0;
- variance = 0;
-
- NbrKeptDraws = 0;
- for i=1:NbrBlocks
- NbrDrawsCurrentBlock = 0;
- for j=1:NbrFiles
- o = load([BaseName '_mh' int2str(j) '_blck' int2str(i),'.mat']);
- NbrDrawsCurrentFile = size(o.x2,1);
- if NbrDrawsCurrentBlock + NbrDrawsCurrentFile <= drop*NbrDraws
- NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
- continue
- elseif NbrDrawsCurrentBlock < drop*NbrDraws
- FirstDraw = ceil(drop*NbrDraws - NbrDrawsCurrentBlock + 1);
- x2 = o.x2(FirstDraw:end,:);
- else
- x2 = o.x2;
- end
- NbrKeptDrawsCurrentFile = size(x2,1);
- %recursively compute mean and variance
- mean = (NbrKeptDraws*mean + sum(x2)')/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
- x2Demeaned = bsxfun(@minus,x2,mean');
- variance = (NbrKeptDraws*variance + x2Demeaned'*x2Demeaned)/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
+
+MetropolisFolder = CheckPath('metropolis',M.dname);
+FileName = M.fname;
+BaseName = [MetropolisFolder filesep FileName];
+load_last_mh_history_file(MetropolisFolder, FileName);
+NbrDraws = sum(record.MhDraws(:,1));
+NbrFiles = sum(record.MhDraws(:,2));
+NbrBlocks = record.Nblck;
+mean = 0;
+variance = 0;
+
+NbrKeptDraws = 0;
+for i=1:NbrBlocks
+ NbrDrawsCurrentBlock = 0;
+ for j=1:NbrFiles
+ o = load([BaseName '_mh' int2str(j) '_blck' int2str(i),'.mat']);
+ NbrDrawsCurrentFile = size(o.x2,1);
+ if NbrDrawsCurrentBlock + NbrDrawsCurrentFile <= drop*NbrDraws
NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
- NbrKeptDraws = NbrKeptDraws + NbrKeptDrawsCurrentFile;
+ continue
+ elseif NbrDrawsCurrentBlock < drop*NbrDraws
+ FirstDraw = ceil(drop*NbrDraws - NbrDrawsCurrentBlock + 1);
+ x2 = o.x2(FirstDraw:end,:);
+ else
+ x2 = o.x2;
end
+ NbrKeptDrawsCurrentFile = size(x2,1);
+ %recursively compute mean and variance
+ mean = (NbrKeptDraws*mean + sum(x2)')/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
+ x2Demeaned = bsxfun(@minus,x2,mean');
+ variance = (NbrKeptDraws*variance + x2Demeaned'*x2Demeaned)/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
+ NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
+ NbrKeptDraws = NbrKeptDraws + NbrKeptDrawsCurrentFile;
end
+end
diff --git a/matlab/PosteriorIRF.m b/matlab/PosteriorIRF.m
index c0d393d4e9ab60446922d762c1288a5f93d7e82a..1d51f5967b6cd92d367d42858d1d741722aba8ec 100644
--- a/matlab/PosteriorIRF.m
+++ b/matlab/PosteriorIRF.m
@@ -368,101 +368,101 @@ end
% PosteriorIRF_core2.m function.
if ~options_.nograph && ~options_.no_graph.posterior
-% Save the local variables.
-localVars=[];
+ % Save the local variables.
+ localVars=[];
-Check=options_.TeX;
-if (Check)
- localVars.varlist_TeX=varlist_TeX;
-end
+ Check=options_.TeX;
+ if (Check)
+ localVars.varlist_TeX=varlist_TeX;
+ end
-localVars.nvar=nvar;
-localVars.MeanIRF=MeanIRF;
-localVars.tit=tit;
-localVars.nn=nn;
-localVars.MAX_nirfs_dsgevar=MAX_nirfs_dsgevar;
-localVars.HPDIRF=HPDIRF;
-localVars.varlist=varlist;
-localVars.MaxNumberOfPlotPerFigure=MaxNumberOfPlotPerFigure;
-if options_.dsge_var
- localVars.HPDIRFdsgevar=HPDIRFdsgevar;
- localVars.MeanIRFdsgevar = MeanIRFdsgevar;
-end
+ localVars.nvar=nvar;
+ localVars.MeanIRF=MeanIRF;
+ localVars.tit=tit;
+ localVars.nn=nn;
+ localVars.MAX_nirfs_dsgevar=MAX_nirfs_dsgevar;
+ localVars.HPDIRF=HPDIRF;
+ localVars.varlist=varlist;
+ localVars.MaxNumberOfPlotPerFigure=MaxNumberOfPlotPerFigure;
+ if options_.dsge_var
+ localVars.HPDIRFdsgevar=HPDIRFdsgevar;
+ localVars.MeanIRFdsgevar = MeanIRFdsgevar;
+ end
-% The files .TeX are genereted in sequential way always!
-
-% The files .TeX are generated in sequential way always!
-subplotnum = 0;
-tit_TeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
-if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fidTeX = fopen([DirectoryName filesep M_.fname '_BayesianIRF.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by PosteriorIRF.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
- fprintf(fidTeX,' \n');
- titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
-
- for ii=irf_shocks_indx
- figunumber = 0;
-
- for jj=1:nvar
- if max(abs(MeanIRF(:,jj,ii))) >= options_.impulse_responses.plot_threshold
- subplotnum = subplotnum+1;
-
- if subplotnum == 1
- fprintf(fidTeX,'\\begin{figure}[H]\n');
+ % The files .TeX are genereted in sequential way always!
+
+ % The files .TeX are generated in sequential way always!
+ subplotnum = 0;
+ tit_TeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fidTeX = fopen([DirectoryName filesep M_.fname '_BayesianIRF.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by PosteriorIRF.m (Dynare).\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
+ fprintf(fidTeX,' \n');
+ titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
+
+ for ii=irf_shocks_indx
+ figunumber = 0;
+
+ for jj=1:nvar
+ if max(abs(MeanIRF(:,jj,ii))) >= options_.impulse_responses.plot_threshold
+ subplotnum = subplotnum+1;
+
+ if subplotnum == 1
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ end
+ name = deblank(varlist(jj,:));
+ texname = deblank(varlist_TeX(jj,:));
+ fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],name,['$' texname '$']);
end
- name = deblank(varlist(jj,:));
- texname = deblank(varlist_TeX(jj,:));
- fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],name,['$' texname '$']);
+ if subplotnum == MaxNumberOfPlotPerFigure || (jj == nvar && subplotnum> 0)
+ figunumber = figunumber+1;
+
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s/%s_Bayesian_IRF_%s_%d}\n',options_.figures.textwidth*min(subplotnum/nn,1),DirectoryName,M_.fname,deblank(tit(ii,:)),figunumber);
+ if options_.relative_irf
+ fprintf(fidTeX,['\\caption{Bayesian relative IRF.}']);
+ else
+ fprintf(fidTeX,'\\caption{Bayesian IRF: Orthogonalized shock to $%s$.}\n',deblank(tit_TeX(ii,:)));
+ end
+ fprintf(fidTeX,'\\label{Fig:BayesianIRF:%s:%d}\n',deblank(tit(ii,:)),figunumber);
+ fprintf(fidTeX,'\\end{figure}\n');
+ fprintf(fidTeX,' \n');
+
+ subplotnum = 0;
+ end
end
- if subplotnum == MaxNumberOfPlotPerFigure || (jj == nvar && subplotnum> 0)
- figunumber = figunumber+1;
-
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s/%s_Bayesian_IRF_%s_%d}\n',options_.figures.textwidth*min(subplotnum/nn,1),DirectoryName,M_.fname,deblank(tit(ii,:)),figunumber);
- if options_.relative_irf
- fprintf(fidTeX,['\\caption{Bayesian relative IRF.}']);
- else
- fprintf(fidTeX,'\\caption{Bayesian IRF: Orthogonalized shock to $%s$.}\n',deblank(tit_TeX(ii,:)));
- end
- fprintf(fidTeX,'\\label{Fig:BayesianIRF:%s:%d}\n',deblank(tit(ii,:)),figunumber);
- fprintf(fidTeX,'\\end{figure}\n');
- fprintf(fidTeX,' \n');
-
- subplotnum = 0;
- end
end
+ fprintf(fidTeX,'%% End of TeX file.\n');
+ fclose(fidTeX);
end
- fprintf(fidTeX,'%% End of TeX file.\n');
- fclose(fidTeX);
-end
-% The others file format are generated in parallel by PosteriorIRF_core2!
+ % The others file format are generated in parallel by PosteriorIRF_core2!
-% Comment for testing!
-if ~isoctave
- if isnumeric(options_.parallel) || (M_.exo_nbr*ceil(size(varlist,1)/MaxNumberOfPlotPerFigure))<8,
- [fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
- else
- isRemoteOctave = 0;
- for indPC=1:length(options_.parallel),
- isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
- end
- if isRemoteOctave
+ % Comment for testing!
+ if ~isoctave
+ if isnumeric(options_.parallel) || (M_.exo_nbr*ceil(size(varlist,1)/MaxNumberOfPlotPerFigure))<8,
[fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
else
- globalVars = struct('M_',M_, ...
- 'options_', options_);
+ isRemoteOctave = 0;
+ for indPC=1:length(options_.parallel),
+ isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
+ end
+ if isRemoteOctave
+ [fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
+ else
+ globalVars = struct('M_',M_, ...
+ 'options_', options_);
- [fout] = masterParallel(options_.parallel, 1, M_.exo_nbr,NamFileInput,'PosteriorIRF_core2', localVars, globalVars, options_.parallel_info);
+ [fout] = masterParallel(options_.parallel, 1, M_.exo_nbr,NamFileInput,'PosteriorIRF_core2', localVars, globalVars, options_.parallel_info);
+ end
end
+ else
+ [fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
end
-else
- [fout] = PosteriorIRF_core2(localVars,1,M_.exo_nbr,0);
-end
-% END parallel code!
+ % END parallel code!
end
diff --git a/matlab/PosteriorIRF_core2.m b/matlab/PosteriorIRF_core2.m
index abf1c3cbdbd786adcaa94b2c5c17b7c605f4ae98..9c08726d5b7f49332fd60e0d91de8cecc513a681 100644
--- a/matlab/PosteriorIRF_core2.m
+++ b/matlab/PosteriorIRF_core2.m
@@ -135,7 +135,7 @@ for i=fpar:npar,
plot(1:options_.irf,HPDIRFdsgevar(:,1,j,i),'--k','linewidth',1)
plot(1:options_.irf,HPDIRFdsgevar(:,2,j,i),'--k','linewidth',1)
end
- % plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
+ % plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
box on
axis tight
xlim([1 options_.irf]);
@@ -162,7 +162,7 @@ for i=fpar:npar,
if whoiam,
fprintf('Done! \n');
waitbarString = [ 'Exog. shocks ' int2str(i) '/' int2str(npar) ' done.'];
-% fMessageStatus((i-fpar+1)/(npar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
+ % fMessageStatus((i-fpar+1)/(npar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
dyn_waitbar((i-fpar+1)/(npar-fpar+1),[],waitbarString);
end
end% loop over exo_var
diff --git a/matlab/UnivariateSpectralDensity.m b/matlab/UnivariateSpectralDensity.m
index ab00d18568b41fd57ad8500d3993206effd5b9e8..0039cae30fd155b363569eba15c433e521d30e53 100644
--- a/matlab/UnivariateSpectralDensity.m
+++ b/matlab/UnivariateSpectralDensity.m
@@ -112,7 +112,7 @@ tneg = exp(-sqrt(-1)*freqs); %negative frequencies
if options_.one_sided_hp_filter
error('UnivariateSpectralDensity:: spectral density estimate not available with one-sided HP filter')
elseif options_.hp_filter == 0 && ~options_.bandpass.indicator %do not filter
- filter_gain=ones(ngrid,1);
+ filter_gain=ones(ngrid,1);
elseif ~(options_.hp_filter == 0 && ~options_.bandpass.indicator) && options_.bandpass.indicator %filter with bandpass
filter_gain = zeros(1,ngrid);
lowest_periodicity=options_.bandpass.passband(2);
diff --git a/matlab/WriteShockDecomp2Excel.m b/matlab/WriteShockDecomp2Excel.m
index dff5c79bbb7baaf2a94facf758ed287b924234ad..e71b2772dec3417317506fca7992b735d8ece26c 100644
--- a/matlab/WriteShockDecomp2Excel.m
+++ b/matlab/WriteShockDecomp2Excel.m
@@ -60,7 +60,7 @@ if nargin==8 ,
end
if isfield(opts_decomp,'fig_name')
fig_name = opts_decomp.fig_name;
-% fig_name = ['_' fig_name];
+ % fig_name = ['_' fig_name];
fig_name1 = [fig_name];
fig_name = [fig_name '_'];
end
diff --git a/matlab/add_filter_subtitle.m b/matlab/add_filter_subtitle.m
index 65258751793cc66945e27c822291c9589dd16e05..a11dbe55257f6cb3679b2ca674dc7a516e303c10 100644
--- a/matlab/add_filter_subtitle.m
+++ b/matlab/add_filter_subtitle.m
@@ -1,7 +1,7 @@
function title=add_filter_subtitle(title,options_)
if ~options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator %do not filter
- %nothing to add here
+ %nothing to add here
elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && options_.bandpass.indicator
title = [title ' (Bandpass filter, (' ...
num2str(options_.bandpass.passband(1)),' ',num2str(options_.bandpass.passband(2)), '))'];
diff --git a/matlab/add_path_to_mex_files.m b/matlab/add_path_to_mex_files.m
index e35d29a6b9a3d959b49b5030a7660cfd420ee942..13140bba5b03243ea71ca3e4f5fd9044665c4c34 100644
--- a/matlab/add_path_to_mex_files.m
+++ b/matlab/add_path_to_mex_files.m
@@ -1,5 +1,5 @@
function mexpath = add_path_to_mex_files(dynareroot, modifypath)
-
+
% Copyright (C) 2015-2017 Dynare Team
%
% This file is part of Dynare.
@@ -20,7 +20,7 @@ function mexpath = add_path_to_mex_files(dynareroot, modifypath)
if nargin<2
modifypath = true;
end
-
+
if exist('OCTAVE_VERSION')
if ispc() && strcmpi(computer(), 'i686-w64-mingw32')
mexpath = {[dynareroot '../mex/octave32/']};
diff --git a/matlab/annualized_shock_decomposition.m b/matlab/annualized_shock_decomposition.m
index 9590699b3b6276c3025782838d3318977e82a4cb..fe0b9cc4ec5e9cff396a2291360175ea6f161306 100644
--- a/matlab/annualized_shock_decomposition.m
+++ b/matlab/annualized_shock_decomposition.m
@@ -59,11 +59,11 @@ aux0 = aux;
cumfix = q2a.cumfix;
% usual shock decomp
if isstruct(oo_)
-% z = oo_.shock_decomposition;
- myopts=options_;
- myopts.plot_shock_decomp.type='qoq';
- myopts.plot_shock_decomp.realtime=0;
- [z, junk] = plot_shock_decomposition(M_,oo_,myopts,[]);
+ % z = oo_.shock_decomposition;
+ myopts=options_;
+ myopts.plot_shock_decomp.type='qoq';
+ myopts.plot_shock_decomp.realtime=0;
+ [z, junk] = plot_shock_decomposition(M_,oo_,myopts,[]);
else
z = oo_;
end
@@ -177,147 +177,147 @@ end
% realtime
if realtime_ && isstruct(oo_) && isfield(oo_, 'realtime_shock_decomposition'),
-init=1;
-for i=t0:4:t1,
- yr=floor(i/4);
- za=[];
- gza=[];
+ init=1;
+ for i=t0:4:t1,
+ yr=floor(i/4);
+ za=[];
+ gza=[];
myopts=options_;
myopts.plot_shock_decomp.type='qoq';
myopts.plot_shock_decomp.realtime=1;
myopts.plot_shock_decomp.vintage=i;
[z, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,[]);
z = z(i_var,:,:);
-if isstruct(aux)
- if ischar(aux0.y)
- [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux0.y);
- aux.y=y_aux;
- aux.yss=steady_state_aux;
- end
- yaux=aux.y;
-end
+ if isstruct(aux)
+ if ischar(aux0.y)
+ [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux0.y);
+ aux.y=y_aux;
+ aux.yss=steady_state_aux;
+ end
+ yaux=aux.y;
+ end
nterms = size(z,2);
-
-% z = oo_.realtime_shock_decomposition.(['time_' int2str(i)]);
-% z = z(i_var,:,:);
-
- for j=1:nvar
- for k =nterms:-1:1,
-% if k<nterms
-% ztmp = squeeze(sum(z(j,[1:k-1,k+1:end-1],t0-4:end)));
-% else
+
+ % z = oo_.realtime_shock_decomposition.(['time_' int2str(i)]);
+ % z = z(i_var,:,:);
+
+ for j=1:nvar
+ for k =nterms:-1:1,
+ % if k<nterms
+ % ztmp = squeeze(sum(z(j,[1:k-1,k+1:end-1],t0-4:end)));
+ % else
ztmp = squeeze(z(j,k,min((t0-3):-4:1):end));
-% end
- if isstruct(aux),
- aux.y = squeeze(yaux(j,k,min((t0-3):-4:1):end));
+ % end
+ if isstruct(aux),
+ aux.y = squeeze(yaux(j,k,min((t0-3):-4:1):end));
+ end
+ [za(j,k,:), steady_state_a(j,1), gza(j,k,:), steady_state_ga(j,1)] = ...
+ quarterly2annual(ztmp,steady_state(j),GYTREND0,var_type,islog,aux);
+ % if k<nterms
+ % za(j,k,:) = za(j,end,:) - za(j,k,:);
+ % gza(j,k,:) = gza(j,end,:) - gza(j,k,:);
+ % end
+
end
- [za(j,k,:), steady_state_a(j,1), gza(j,k,:), steady_state_ga(j,1)] = ...
- quarterly2annual(ztmp,steady_state(j),GYTREND0,var_type,islog,aux);
-% if k<nterms
-% za(j,k,:) = za(j,end,:) - za(j,k,:);
-% gza(j,k,:) = gza(j,end,:) - gza(j,k,:);
-% end
- end
-
- ztmp=squeeze(za(j,:,:));
+ ztmp=squeeze(za(j,:,:));
- if cumfix==0,
- zscale = sum(ztmp(1:end-1,:))./ztmp(end,:);
- ztmp(1:end-1,:) = ztmp(1:end-1,:)./repmat(zscale,[nterms-1,1]);
- else
- zres = ztmp(end,:)-sum(ztmp(1:end-1,:));
- ztmp(end-1,:) = ztmp(end-1,:) + zres;
+ if cumfix==0,
+ zscale = sum(ztmp(1:end-1,:))./ztmp(end,:);
+ ztmp(1:end-1,:) = ztmp(1:end-1,:)./repmat(zscale,[nterms-1,1]);
+ else
+ zres = ztmp(end,:)-sum(ztmp(1:end-1,:));
+ ztmp(end-1,:) = ztmp(end-1,:) + zres;
+ end
+
+ gztmp=squeeze(gza(j,:,:));
+ if cumfix==0,
+ gscale = sum(gztmp(1:end-1,:))./ gztmp(end,:);
+ gztmp(1:end-1,:) = gztmp(1:end-1,:)./repmat(gscale,[nterms-1,1]);
+ else
+ gres = gztmp(end,:) - sum(gztmp(1:end-1,:));
+ gztmp(end-1,:) = gztmp(end-1,:)+gres;
+ end
+
+ za(j,:,:) = ztmp;
+ gza(j,:,:) = gztmp;
end
- gztmp=squeeze(gza(j,:,:));
- if cumfix==0,
- gscale = sum(gztmp(1:end-1,:))./ gztmp(end,:);
- gztmp(1:end-1,:) = gztmp(1:end-1,:)./repmat(gscale,[nterms-1,1]);
+ if q2a.plot ==1,
+ z=gza;
+ elseif q2a.plot == 2
+ z=za;
else
- gres = gztmp(end,:) - sum(gztmp(1:end-1,:));
- gztmp(end-1,:) = gztmp(end-1,:)+gres;
+ z=cat(1,za,gza);
end
- za(j,:,:) = ztmp;
- gza(j,:,:) = gztmp;
- end
-
- if q2a.plot ==1,
- z=gza;
- elseif q2a.plot == 2
- z=za;
- else
- z=cat(1,za,gza);
- end
-
- if init==1,
- oo_.annualized_realtime_shock_decomposition.pool = z;
- else
- oo_.annualized_realtime_shock_decomposition.pool(:,:,yr) = z(:,:,end-nfrcst);
- end
- oo_.annualized_realtime_shock_decomposition.(['yr_' int2str(yr)]) = z;
+ if init==1,
+ oo_.annualized_realtime_shock_decomposition.pool = z;
+ else
+ oo_.annualized_realtime_shock_decomposition.pool(:,:,yr) = z(:,:,end-nfrcst);
+ end
+ oo_.annualized_realtime_shock_decomposition.(['yr_' int2str(yr)]) = z;
- if opts.forecast
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr)]) = z(:,:,end-nfrcst:end);
- if init>nfrcst
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)]) = ...
- oo_.annualized_realtime_shock_decomposition.pool(:,:,yr-nfrcst:end) - ...
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-nfrcst)]);
- % fix others
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end-1,:) = ...
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end-1,:) + ...
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end,:);
- % fix total
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end,:) = ...
- oo_.annualized_realtime_shock_decomposition.pool(:,end,yr-nfrcst:end);
- if i==t1
- for my_forecast_=(nfrcst-1):-1:1,
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)]) = ...
- oo_.annualized_realtime_shock_decomposition.pool(:,:,yr-my_forecast_:yr) - ...
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,:,1:my_forecast_+1);
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end-1,:) = ...
- oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end,1:my_forecast_+1);
- oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end,:) = ...
- oo_.annualized_realtime_shock_decomposition.pool(:,end,yr-my_forecast_:yr);
+ if opts.forecast
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr)]) = z(:,:,end-nfrcst:end);
+ if init>nfrcst
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)]) = ...
+ oo_.annualized_realtime_shock_decomposition.pool(:,:,yr-nfrcst:end) - ...
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-nfrcst)]);
+ % fix others
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end-1,:) = ...
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end-1,:) + ...
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end,:);
+ % fix total
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-nfrcst)])(:,end,:) = ...
+ oo_.annualized_realtime_shock_decomposition.pool(:,end,yr-nfrcst:end);
+ if i==t1
+ for my_forecast_=(nfrcst-1):-1:1,
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)]) = ...
+ oo_.annualized_realtime_shock_decomposition.pool(:,:,yr-my_forecast_:yr) - ...
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,:,1:my_forecast_+1);
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end-1,:) = ...
+ oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end,1:my_forecast_+1);
+ oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(yr-my_forecast_)])(:,end,:) = ...
+ oo_.annualized_realtime_shock_decomposition.pool(:,end,yr-my_forecast_:yr);
+ end
end
end
end
- end
-% ztmp=oo_.realtime_shock_decomposition.pool(:,:,21:29)-oo_.realtime_forecast_shock_decomposition.time_21;
+ % ztmp=oo_.realtime_shock_decomposition.pool(:,:,21:29)-oo_.realtime_forecast_shock_decomposition.time_21;
- init=init+1;
-end
+ init=init+1;
+ end
-switch realtime_
-
- case 0
+ switch realtime_
+
+ case 0
z = oo_.annualized_shock_decomposition;
-
- case 1 % realtime
+
+ case 1 % realtime
if vintage_
z = oo_.annualized_realtime_shock_decomposition.(['yr_' int2str(floor(vintage_/4))]);
else
z = oo_.annualized_realtime_shock_decomposition.pool;
end
-
- case 2 % conditional
+
+ case 2 % conditional
if vintage_
z = oo_.annualized_realtime_conditional_shock_decomposition.(['yr_' int2str(floor(vintage_/4))]);
else
error();
end
- case 3 % forecast
+ case 3 % forecast
if vintage_
z = oo_.annualized_realtime_forecast_shock_decomposition.(['yr_' int2str(floor(vintage_/4))]);
else
error()
end
-end
+ end
end
if q2a.plot ==0,
diff --git a/matlab/backward/simul_backward_model.m b/matlab/backward/simul_backward_model.m
index 4cf801fbdb6532dd9a7e82ea76f2d2e9efe866b3..5327e6435c323f432928d9c08c63ef901004e6cd 100644
--- a/matlab/backward/simul_backward_model.m
+++ b/matlab/backward/simul_backward_model.m
@@ -91,8 +91,8 @@ end
if DynareOptions.linear
DynareOutput = simul_backward_linear_model(initial_conditions, sample_size, DynareOptions, ...
- DynareModel, DynareOutput, innovations);
+ DynareModel, DynareOutput, innovations);
else
DynareOutput = simul_backward_nonlinear_model(initial_conditions, sample_size, DynareOptions, ...
- DynareModel, DynareOutput, innovations);
+ DynareModel, DynareOutput, innovations);
end
\ No newline at end of file
diff --git a/matlab/basic_plan.m b/matlab/basic_plan.m
index c7b4cb360330ab126ae612deb0595570381f26c8..233e795eb1182b5c6112b3356ea19efd9bc7620c 100644
--- a/matlab/basic_plan.m
+++ b/matlab/basic_plan.m
@@ -31,60 +31,60 @@ function plan = basic_plan(plan, exogenous, expectation_type, date, value)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
- error(['in basic_plan the third argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
- end
- exogenous = strtrim(exogenous);
- ix = find(strcmp(exogenous, plan.exo_names));
- if isempty(ix)
- error(['in basic_plan the second argument ' exogenous ' is not an exogenous variable']);
- end;
- sdate = length(date);
- if sdate > 1
- if date(1) < plan.date(1) || date(end) > plan.date(end)
- error(['in basic_plan the fourth argument (date=' date ') must lay inside the plan.date ' plan.date]);
- end
- else
- if date < plan.date(1) || date > plan.date(end)
- error(['in basic_plan the fourth argument (date=' date ') must lay iside the plan.date ' plan.date]);
- end
- end
- if length(date) ~= length(value)
- error(['in basic_plan the number of dates (' int2str(length(date)) ') is not equal to the numbers of shock (' int2str(length(value)) ') for exogenous variable ' exogenous]);
- end
- if ~isempty(plan.options_cond_fcst_.controlled_varexo)
- common_var = find(ix == plan.options_cond_fcst_.controlled_varexo);
- if ~isempty(common_var)
- common_date = intersect(date, plan.constrained_date_{common_var});
- if ~isempty(common_date)
- [date_, i_date] = setdiff(date, common_date);
- value = value(i_date);
- if common_date.length > 1
- the_dates = [cell2mat(strings(common_date(1))) ':' cell2mat(strings(common_date(end)))];
- else
- the_dates = cell2mat(strings(common_date));
- end
- warning(['Impossible case: ' plan.exo_names{plan.options_cond_fcst_.controlled_varexo(common_var)} ' is used both as a shock and as an endogenous variable to control the path of ' plan.endo_names{plan.constrained_vars_(common_var)} ' at the dates ' the_dates]);
- warning('This shock will not be considered');
- end
- end
- end
- if isempty(plan.shock_vars_)
- plan.shock_vars_ = ix;
- if strcmp(expectation_type, 'perfect_foresight')
- plan.shock_perfect_foresight_ = 1;
- else
- plan.shock_perfect_foresight_ = 0;
- end
- else
- plan.shock_vars_ = [plan.shock_vars_ ; ix];
- if strcmp(expectation_type, 'perfect_foresight')
- plan.shock_perfect_foresight_ = [plan.shock_perfect_foresight_ ; 1];
- else
- plan.shock_perfect_foresight_ = [plan.shock_perfect_foresight_ ; 0];
- end
- end
- plan.shock_date_{length(plan.shock_date_) + 1} = date;
- plan.shock_str_date_{length(plan.shock_str_date_) + 1} = strings(date);
- plan.shock_int_date_{length(plan.shock_int_date_) + 1} = date - plan.date(1) + 1;
- plan.shock_paths_{length(plan.shock_paths_) + 1} = value;
+if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
+ error(['in basic_plan the third argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
+end
+exogenous = strtrim(exogenous);
+ix = find(strcmp(exogenous, plan.exo_names));
+if isempty(ix)
+ error(['in basic_plan the second argument ' exogenous ' is not an exogenous variable']);
+end;
+sdate = length(date);
+if sdate > 1
+ if date(1) < plan.date(1) || date(end) > plan.date(end)
+ error(['in basic_plan the fourth argument (date=' date ') must lay inside the plan.date ' plan.date]);
+ end
+else
+ if date < plan.date(1) || date > plan.date(end)
+ error(['in basic_plan the fourth argument (date=' date ') must lay iside the plan.date ' plan.date]);
+ end
+end
+if length(date) ~= length(value)
+ error(['in basic_plan the number of dates (' int2str(length(date)) ') is not equal to the numbers of shock (' int2str(length(value)) ') for exogenous variable ' exogenous]);
+end
+if ~isempty(plan.options_cond_fcst_.controlled_varexo)
+ common_var = find(ix == plan.options_cond_fcst_.controlled_varexo);
+ if ~isempty(common_var)
+ common_date = intersect(date, plan.constrained_date_{common_var});
+ if ~isempty(common_date)
+ [date_, i_date] = setdiff(date, common_date);
+ value = value(i_date);
+ if common_date.length > 1
+ the_dates = [cell2mat(strings(common_date(1))) ':' cell2mat(strings(common_date(end)))];
+ else
+ the_dates = cell2mat(strings(common_date));
+ end
+ warning(['Impossible case: ' plan.exo_names{plan.options_cond_fcst_.controlled_varexo(common_var)} ' is used both as a shock and as an endogenous variable to control the path of ' plan.endo_names{plan.constrained_vars_(common_var)} ' at the dates ' the_dates]);
+ warning('This shock will not be considered');
+ end
+ end
+end
+if isempty(plan.shock_vars_)
+ plan.shock_vars_ = ix;
+ if strcmp(expectation_type, 'perfect_foresight')
+ plan.shock_perfect_foresight_ = 1;
+ else
+ plan.shock_perfect_foresight_ = 0;
+ end
+else
+ plan.shock_vars_ = [plan.shock_vars_ ; ix];
+ if strcmp(expectation_type, 'perfect_foresight')
+ plan.shock_perfect_foresight_ = [plan.shock_perfect_foresight_ ; 1];
+ else
+ plan.shock_perfect_foresight_ = [plan.shock_perfect_foresight_ ; 0];
+ end
+end
+plan.shock_date_{length(plan.shock_date_) + 1} = date;
+plan.shock_str_date_{length(plan.shock_str_date_) + 1} = strings(date);
+plan.shock_int_date_{length(plan.shock_int_date_) + 1} = date - plan.date(1) + 1;
+plan.shock_paths_{length(plan.shock_paths_) + 1} = value;
diff --git a/matlab/bvar_forecast.m b/matlab/bvar_forecast.m
index d070884b4b3baade7aae42560d1fcb827c0b80cc..5319ab8be32a51c38b885fcc89ed73902540a4f2 100644
--- a/matlab/bvar_forecast.m
+++ b/matlab/bvar_forecast.m
@@ -120,9 +120,9 @@ dyn_graph=dynare_graph_init(sprintf('BVAR forecasts (nlags = %d)', nlags), ny, {
for i = 1:ny
dyn_graph=dynare_graph(dyn_graph,[ sims_no_shock_median(:, i) ...
- sims_no_shock_up_conf(:, i) sims_no_shock_down_conf(:, i) ...
- sims_with_shocks_up_conf(:, i) sims_with_shocks_down_conf(:, i) ], ...
- options_.varobs{i});
+ sims_no_shock_up_conf(:, i) sims_no_shock_down_conf(:, i) ...
+ sims_with_shocks_up_conf(:, i) sims_with_shocks_down_conf(:, i) ], ...
+ options_.varobs{i});
end
dyn_saveas(dyn_graph.fh,[OutputDirectoryName '/' M_.fname '_BVAR_forecast_',num2str(nlags)],options_.nodisplay,options_.graph_format)
diff --git a/matlab/check_matlab_path.m b/matlab/check_matlab_path.m
index 3817def6be90e02781b2a85254c74ca36fcfa03d..a5156df9192329ada27ac10a3ae119b5ffba7a4c 100644
--- a/matlab/check_matlab_path.m
+++ b/matlab/check_matlab_path.m
@@ -1,5 +1,5 @@
function check_matlab_path(change_path_flag)
-
+
% Copyright (C) 2015-2016 Dynare Team
%
% This file is part of Dynare.
@@ -107,40 +107,40 @@ else
end
function q = path2cell(p)
- % Converts the output of path() to a cell
- s = strfind(p,pathsep);
- n = length(s)+1;
- q = cell(n,1);
- q(1) = {p(1:s(1)-1)};
- q(n) = {p(s(end)+1:end)};
- for i=2:n-1
- q(i) = {p(s(i-1)+1:s(i)-1)};
- end
-
+% Converts the output of path() to a cell
+s = strfind(p,pathsep);
+n = length(s)+1;
+q = cell(n,1);
+q(1) = {p(1:s(1)-1)};
+q(n) = {p(s(end)+1:end)};
+for i=2:n-1
+ q(i) = {p(s(i-1)+1:s(i)-1)};
+end
+
function flist = getallroutinenames(p, excludedsubfolders)
- if nargin<2
- excludedsubfolders = {};
- end
- flist={};
- %get m-files in this directory
- dd = dir([p,filesep '*.m']);
- temp=struct2cell(dd);
- flist=[flist temp(1,:)];
- %deal with subdirectories
- dlist=getalldirectories(p,excludedsubfolders); %first call with excluded directories
- for ii=1:length(dlist)
- flist=[flist getallroutinenames([ p filesep dlist{ii}])]; %recursive calls without subfolders
- end
-
+if nargin<2
+ excludedsubfolders = {};
+end
+flist={};
+%get m-files in this directory
+dd = dir([p,filesep '*.m']);
+temp=struct2cell(dd);
+flist=[flist temp(1,:)];
+%deal with subdirectories
+dlist=getalldirectories(p,excludedsubfolders); %first call with excluded directories
+for ii=1:length(dlist)
+ flist=[flist getallroutinenames([ p filesep dlist{ii}])]; %recursive calls without subfolders
+end
+
function dlist = getalldirectories(p,excluded_directories)
- if nargin<2
- excluded_directories = {};
- end
- dd = dir(p);
- dir_result=struct2cell(dd);
- directory_indicator=cell2mat(dir_result(4,:));
- dlist = dir_result(1,directory_indicator==1 & ~strcmp('.',dir_result(1,:)) & ~strcmp('..',dir_result(1,:)) & ~ismember(dir_result(1,:),excluded_directories));
+if nargin<2
+ excluded_directories = {};
+end
+dd = dir(p);
+dir_result=struct2cell(dd);
+directory_indicator=cell2mat(dir_result(4,:));
+dlist = dir_result(1,directory_indicator==1 & ~strcmp('.',dir_result(1,:)) & ~strcmp('..',dir_result(1,:)) & ~ismember(dir_result(1,:),excluded_directories));
function n = position(i, currentpath)
- n = length(strfind(currentpath(1:i), pathsep));
\ No newline at end of file
+n = length(strfind(currentpath(1:i), pathsep));
\ No newline at end of file
diff --git a/matlab/check_posterior_sampler_options.m b/matlab/check_posterior_sampler_options.m
index 1980c0a7986bc0d88c3979ef7ea583d019b5b0f4..d20f85a1619b36dbdb8befb8ee23799bc772c08b 100644
--- a/matlab/check_posterior_sampler_options.m
+++ b/matlab/check_posterior_sampler_options.m
@@ -43,334 +43,334 @@ if init,
switch posterior_sampler_options.posterior_sampling_method
- case 'random_walk_metropolis_hastings'
- posterior_sampler_options.parallel_bar_refresh_rate=50;
- posterior_sampler_options.serial_bar_refresh_rate=3;
- posterior_sampler_options.parallel_bar_title='RWMH';
- posterior_sampler_options.serial_bar_title='RW Metropolis-Hastings';
-
- % default options
- posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.rwmh);
-
- % user defined options
- if ~isempty(options_.posterior_sampler_options.sampling_opt)
- options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
- for i=1:rows(options_list)
- switch options_list{i,1}
-
- case 'proposal_distribution'
- if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
- strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
- error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
- 'rand_multivariate_student or rand_multivariate_normal as options']);
- else
- posterior_sampler_options.proposal_distribution=options_list{i,2};
- end
-
-
- case 'student_degrees_of_freedom'
- if options_list{i,2} <= 0
- error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
- else
- posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
- end
-
- case 'use_mh_covariance_matrix'
- % indicates to use the covariance matrix from previous iterations to
- % define the covariance of the proposal distribution
- % default = 0
- posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
- options_.use_mh_covariance_matrix = options_list{i,2};
- case 'scale_file'
- % load optimal_mh_scale parameter if previous run was with mode_compute=6
- % will overwrite jscale from set_prior.m
- if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
- tmp = load(options_list{i,2},'Scale');
- global bayestopt_
- bayestopt_.mh_jscale = tmp.Scale;
- options_.mh_jscale = tmp.Scale;
- bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
-% options_.mh_init_scale = 2*options_.mh_jscale;
- else
- error('initial_estimation_checks:: The specified mh_scale_file does not exist.')
- end
- case 'save_tmp_file'
- posterior_sampler_options.save_tmp_file = options_list{i,2};
- otherwise
- warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
+ case 'random_walk_metropolis_hastings'
+ posterior_sampler_options.parallel_bar_refresh_rate=50;
+ posterior_sampler_options.serial_bar_refresh_rate=3;
+ posterior_sampler_options.parallel_bar_title='RWMH';
+ posterior_sampler_options.serial_bar_title='RW Metropolis-Hastings';
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.rwmh);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+
+ case 'proposal_distribution'
+ if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
+ strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
+ error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
+ 'rand_multivariate_student or rand_multivariate_normal as options']);
+ else
+ posterior_sampler_options.proposal_distribution=options_list{i,2};
+ end
+
+
+ case 'student_degrees_of_freedom'
+ if options_list{i,2} <= 0
+ error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
+ else
+ posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
+ end
+
+ case 'use_mh_covariance_matrix'
+ % indicates to use the covariance matrix from previous iterations to
+ % define the covariance of the proposal distribution
+ % default = 0
+ posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
+ options_.use_mh_covariance_matrix = options_list{i,2};
+ case 'scale_file'
+ % load optimal_mh_scale parameter if previous run was with mode_compute=6
+ % will overwrite jscale from set_prior.m
+ if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
+ tmp = load(options_list{i,2},'Scale');
+ global bayestopt_
+ bayestopt_.mh_jscale = tmp.Scale;
+ options_.mh_jscale = tmp.Scale;
+ bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
+ % options_.mh_init_scale = 2*options_.mh_jscale;
+ else
+ error('initial_estimation_checks:: The specified mh_scale_file does not exist.')
end
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+ otherwise
+ warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
end
end
-
- case 'tailored_random_block_metropolis_hastings'
- posterior_sampler_options.parallel_bar_refresh_rate=5;
- posterior_sampler_options.serial_bar_refresh_rate=1;
- posterior_sampler_options.parallel_bar_title='TaRB-MH';
- posterior_sampler_options.serial_bar_title='TaRB Metropolis-Hastings';
-
- % default options
- posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.tarb);
-
- % user defined options
- if ~isempty(options_.posterior_sampler_options.sampling_opt)
- options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
- for i=1:rows(options_list)
+ end
+
+ case 'tailored_random_block_metropolis_hastings'
+ posterior_sampler_options.parallel_bar_refresh_rate=5;
+ posterior_sampler_options.serial_bar_refresh_rate=1;
+ posterior_sampler_options.parallel_bar_title='TaRB-MH';
+ posterior_sampler_options.serial_bar_title='TaRB Metropolis-Hastings';
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.tarb);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+
+ switch options_list{i,1}
+
+ case 'proposal_distribution'
+ if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
+ strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
+ error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
+ 'rand_multivariate_student or rand_multivariate_normal as options']);
+ else
+ posterior_sampler_options.proposal_distribution=options_list{i,2};
+ end
+
+
+ case 'student_degrees_of_freedom'
+ if options_list{i,2} <= 0
+ error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
+ else
+ posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
+ end
- switch options_list{i,1}
-
- case 'proposal_distribution'
- if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
- strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
- error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
- 'rand_multivariate_student or rand_multivariate_normal as options']);
- else
- posterior_sampler_options.proposal_distribution=options_list{i,2};
- end
-
-
- case 'student_degrees_of_freedom'
- if options_list{i,2} <= 0
- error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
- else
- posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
- end
-
- case 'mode_compute'
- posterior_sampler_options.mode_compute=options_list{i,2};
-
- case 'optim'
- posterior_sampler_options.optim_opt=options_list{i,2};
+ case 'mode_compute'
+ posterior_sampler_options.mode_compute=options_list{i,2};
+
+ case 'optim'
+ posterior_sampler_options.optim_opt=options_list{i,2};
- case 'new_block_probability'
- if options_list{i,2}<0 || options_list{i,2}>1
- error('check_posterior_sampler_options:: The tarb new_block_probability must be between 0 and 1!')
- else
- posterior_sampler_options.new_block_probability=options_list{i,2};
- end
- case 'scale_file'
- % load optimal_mh_scale parameter if previous run was with mode_compute=6
- % will overwrite jscale from set_prior.m
- if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
- tmp = load(options_list{i,2},'Scale');
- global bayestopt_
- bayestopt_.mh_jscale = tmp.Scale;
- options_.mh_jscale = tmp.Scale;
- bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
-% options_.mh_init_scale = 2*options_.mh_jscale;
- else
- error('initial_estimation_checks:: The specified scale_file does not exist.')
- end
- case 'save_tmp_file'
- posterior_sampler_options.save_tmp_file = options_list{i,2};
-
- otherwise
- warning(['tarb_sampler: Unknown option (' options_list{i,1} ')!'])
-
+ case 'new_block_probability'
+ if options_list{i,2}<0 || options_list{i,2}>1
+ error('check_posterior_sampler_options:: The tarb new_block_probability must be between 0 and 1!')
+ else
+ posterior_sampler_options.new_block_probability=options_list{i,2};
+ end
+ case 'scale_file'
+ % load optimal_mh_scale parameter if previous run was with mode_compute=6
+ % will overwrite jscale from set_prior.m
+ if exist(options_list{i,2},'file') || exist([options_list{i,2},'.mat'],'file')
+ tmp = load(options_list{i,2},'Scale');
+ global bayestopt_
+ bayestopt_.mh_jscale = tmp.Scale;
+ options_.mh_jscale = tmp.Scale;
+ bayestopt_.jscale = ones(size(bounds.lb,1),1)*tmp.Scale;
+ % options_.mh_init_scale = 2*options_.mh_jscale;
+ else
+ error('initial_estimation_checks:: The specified scale_file does not exist.')
end
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+
+ otherwise
+ warning(['tarb_sampler: Unknown option (' options_list{i,1} ')!'])
end
end
- case 'independent_metropolis_hastings'
- posterior_sampler_options.parallel_bar_refresh_rate=50;
- posterior_sampler_options.serial_bar_refresh_rate=3;
- posterior_sampler_options.parallel_bar_title='IMH';
- posterior_sampler_options.serial_bar_title='Ind. Metropolis-Hastings';
-
- % default options
- posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.imh);
-
- % user defined options
- if ~isempty(options_.posterior_sampler_options.sampling_opt)
- options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
- for i=1:rows(options_list)
- switch options_list{i,1}
-
- case 'proposal_distribution'
- if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
- strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
- error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
- 'rand_multivariate_student or rand_multivariate_normal as options']);
- else
- posterior_sampler_options.proposal_distribution=options_list{i,2};
- end
-
-
- case 'student_degrees_of_freedom'
- if options_list{i,2} <= 0
- error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
- else
- posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
- end
-
- case 'use_mh_covariance_matrix'
- % indicates to use the covariance matrix from previous iterations to
- % define the covariance of the proposal distribution
- % default = 0
- posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
- options_.use_mh_covariance_matrix = options_list{i,2};
-
- case 'save_tmp_file'
- posterior_sampler_options.save_tmp_file = options_list{i,2};
-
- otherwise
- warning(['imh_sampler: Unknown option (' options_list{i,1} ')!'])
+ end
+
+ case 'independent_metropolis_hastings'
+ posterior_sampler_options.parallel_bar_refresh_rate=50;
+ posterior_sampler_options.serial_bar_refresh_rate=3;
+ posterior_sampler_options.parallel_bar_title='IMH';
+ posterior_sampler_options.serial_bar_title='Ind. Metropolis-Hastings';
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.imh);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+
+ case 'proposal_distribution'
+ if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
+ strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
+ error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
+ 'rand_multivariate_student or rand_multivariate_normal as options']);
+ else
+ posterior_sampler_options.proposal_distribution=options_list{i,2};
+ end
+
+
+ case 'student_degrees_of_freedom'
+ if options_list{i,2} <= 0
+ error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
+ else
+ posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
end
+
+ case 'use_mh_covariance_matrix'
+ % indicates to use the covariance matrix from previous iterations to
+ % define the covariance of the proposal distribution
+ % default = 0
+ posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
+ options_.use_mh_covariance_matrix = options_list{i,2};
+
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+
+ otherwise
+ warning(['imh_sampler: Unknown option (' options_list{i,1} ')!'])
end
end
-
-
- case 'slice'
- posterior_sampler_options.parallel_bar_refresh_rate=1;
- posterior_sampler_options.serial_bar_refresh_rate=1;
- posterior_sampler_options.parallel_bar_title='SLICE';
- posterior_sampler_options.serial_bar_title='SLICE';
-
- % default options
- posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.slice);
-
- % user defined options
- if ~isempty(options_.posterior_sampler_options.sampling_opt)
- options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
- for i=1:rows(options_list)
- switch options_list{i,1}
- case 'rotated'
- % triggers rotated slice iterations using a covariance
- % matrix from initial burn-in iterations
- % must be associated with:
- % <use_mh_covariance_matrix> or <slice_initialize_with_mode>
- % default = 0
- posterior_sampler_options.rotated = options_list{i,2};
-
- case 'mode'
- % for multimodal posteriors, provide the list of modes as a
- % matrix, ordered by column, i.e. [x1 x2 x3] for three
- % modes x1 x2 x3
- % MR note: not sure this is possible with the
- % read_key_value_string ???
- % if this is not possible <mode_files> does to job in any case
- % This will automatically trigger <rotated>
- % default = []
- tmp_mode = options_list{i,2};
- for j=1:size(tmp_mode,2),
- posterior_sampler_options.mode(j).m = tmp_mode(:,j);
- end
-
- case 'mode_files'
- % for multimodal posteriors provide the name of
- % a file containing a variable array xparams = [nparam * nmodes]
- % one column per mode. With this info, the code will automatically
- % set the <mode> option.
- % This will automatically trigger <rotated>
- % default = []
- posterior_sampler_options.mode_files = options_list{i,2};
-
- case 'slice_initialize_with_mode'
- % the default for slice is to set mode_compute = 0 in the
- % preprocessor and start the chain(s) from a random location in the prior.
- % This option first runs the optimizer and then starts the
- % chain from the mode. Associated with optios <rotated>, it will
- % use invhess from the mode to perform rotated slice
- % iterations.
- % default = 0
- posterior_sampler_options.slice_initialize_with_mode = options_list{i,2};
-
- case 'initial_step_size'
- % sets the initial size of the interval in the STEPPING-OUT PROCEDURE
- % the initial_step_size must be a real number in [0, 1],
- % and it sets the size as a proportion of the prior bounds,
- % i.e. the size will be initial_step_size*(UB-LB)
- % slice sampler requires prior_truncation > 0!
- % default = 0.8
- if options_list{i,2}<=0 || options_list{i,2}>=1
- error('check_posterior_sampler_options:: slice initial_step_size must be between 0 and 1')
- else
- posterior_sampler_options.initial_step_size=options_list{i,2};
- end
- case 'use_mh_covariance_matrix'
- % in association with <rotated> indicates to use the
- % covariance matrix from previous iterations to define the
- % rotated slice
- % default = 0
- posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
- options_.use_mh_covariance_matrix = options_list{i,2};
-
- case 'save_tmp_file'
- posterior_sampler_options.save_tmp_file = options_list{i,2};
-
- otherwise
- warning(['slice_sampler: Unknown option (' options_list{i,1} ')!'])
+ end
+
+
+ case 'slice'
+ posterior_sampler_options.parallel_bar_refresh_rate=1;
+ posterior_sampler_options.serial_bar_refresh_rate=1;
+ posterior_sampler_options.parallel_bar_title='SLICE';
+ posterior_sampler_options.serial_bar_title='SLICE';
+
+ % default options
+ posterior_sampler_options = add_fields_(posterior_sampler_options,options_.posterior_sampler_options.slice);
+
+ % user defined options
+ if ~isempty(options_.posterior_sampler_options.sampling_opt)
+ options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'rotated'
+ % triggers rotated slice iterations using a covariance
+ % matrix from initial burn-in iterations
+ % must be associated with:
+ % <use_mh_covariance_matrix> or <slice_initialize_with_mode>
+ % default = 0
+ posterior_sampler_options.rotated = options_list{i,2};
+
+ case 'mode'
+ % for multimodal posteriors, provide the list of modes as a
+ % matrix, ordered by column, i.e. [x1 x2 x3] for three
+ % modes x1 x2 x3
+ % MR note: not sure this is possible with the
+ % read_key_value_string ???
+ % if this is not possible <mode_files> does to job in any case
+ % This will automatically trigger <rotated>
+ % default = []
+ tmp_mode = options_list{i,2};
+ for j=1:size(tmp_mode,2),
+ posterior_sampler_options.mode(j).m = tmp_mode(:,j);
+ end
+
+ case 'mode_files'
+ % for multimodal posteriors provide the name of
+ % a file containing a variable array xparams = [nparam * nmodes]
+ % one column per mode. With this info, the code will automatically
+ % set the <mode> option.
+ % This will automatically trigger <rotated>
+ % default = []
+ posterior_sampler_options.mode_files = options_list{i,2};
+
+ case 'slice_initialize_with_mode'
+ % the default for slice is to set mode_compute = 0 in the
+ % preprocessor and start the chain(s) from a random location in the prior.
+ % This option first runs the optimizer and then starts the
+ % chain from the mode. Associated with optios <rotated>, it will
+ % use invhess from the mode to perform rotated slice
+ % iterations.
+ % default = 0
+ posterior_sampler_options.slice_initialize_with_mode = options_list{i,2};
+
+ case 'initial_step_size'
+ % sets the initial size of the interval in the STEPPING-OUT PROCEDURE
+ % the initial_step_size must be a real number in [0, 1],
+ % and it sets the size as a proportion of the prior bounds,
+ % i.e. the size will be initial_step_size*(UB-LB)
+ % slice sampler requires prior_truncation > 0!
+ % default = 0.8
+ if options_list{i,2}<=0 || options_list{i,2}>=1
+ error('check_posterior_sampler_options:: slice initial_step_size must be between 0 and 1')
+ else
+ posterior_sampler_options.initial_step_size=options_list{i,2};
end
+ case 'use_mh_covariance_matrix'
+ % in association with <rotated> indicates to use the
+ % covariance matrix from previous iterations to define the
+ % rotated slice
+ % default = 0
+ posterior_sampler_options.use_mh_covariance_matrix = options_list{i,2};
+ options_.use_mh_covariance_matrix = options_list{i,2};
+
+ case 'save_tmp_file'
+ posterior_sampler_options.save_tmp_file = options_list{i,2};
+
+ otherwise
+ warning(['slice_sampler: Unknown option (' options_list{i,1} ')!'])
end
end
-
- % slice posterior sampler does not require mode or hessian to run
- % needs to be set to 1 to skip parts in dynare_estimation_1.m
- % requiring posterior maximization/calibrated smoother before MCMC
- options_.mh_posterior_mode_estimation=1;
-
- if ~ posterior_sampler_options.slice_initialize_with_mode
+ end
+
+ % slice posterior sampler does not require mode or hessian to run
+ % needs to be set to 1 to skip parts in dynare_estimation_1.m
+ % requiring posterior maximization/calibrated smoother before MCMC
+ options_.mh_posterior_mode_estimation=1;
+
+ if ~ posterior_sampler_options.slice_initialize_with_mode
% by default, slice sampler should trigger
% mode_compute=0 and
% mh_replic=100 (much smaller than the default mh_replic=20000 of RWMH)
- options_.mode_compute = 0;
- options_.cova_compute = 0;
- else
- if (isequal(options_.mode_compute,0) && isempty(options_.mode_file) )
- skipline()
- disp('check_posterior_sampler_options:: You have specified the option "slice_initialize_with_mode"')
- disp('check_posterior_sampler_options:: to initialize the slice sampler using mode information')
- disp('check_posterior_sampler_options:: but no mode file nor posterior maximization is selected,')
- error('check_posterior_sampler_options:: The option "slice_initialize_with_mode" is inconsistent with mode_compute=0 or empty mode_file.')
- else
- options_.mh_posterior_mode_estimation=0;
- end
- end
-
- if any(isinf(bounds.lb)) || any(isinf(bounds.ub)),
+ options_.mode_compute = 0;
+ options_.cova_compute = 0;
+ else
+ if (isequal(options_.mode_compute,0) && isempty(options_.mode_file) )
skipline()
- disp('some priors are unbounded and prior_trunc is set to zero')
- error('The option "slice" is inconsistent with prior_trunc=0.')
+ disp('check_posterior_sampler_options:: You have specified the option "slice_initialize_with_mode"')
+ disp('check_posterior_sampler_options:: to initialize the slice sampler using mode information')
+ disp('check_posterior_sampler_options:: but no mode file nor posterior maximization is selected,')
+ error('check_posterior_sampler_options:: The option "slice_initialize_with_mode" is inconsistent with mode_compute=0 or empty mode_file.')
+ else
+ options_.mh_posterior_mode_estimation=0;
end
-
- % moreover slice must be associated to:
- % options_.mh_posterior_mode_estimation = 0;
- % this is done below, but perhaps preprocessing should do this?
-
- if ~isempty(posterior_sampler_options.mode)
- % multimodal case
- posterior_sampler_options.rotated = 1;
- posterior_sampler_options.WR=[];
+ end
+
+ if any(isinf(bounds.lb)) || any(isinf(bounds.ub)),
+ skipline()
+ disp('some priors are unbounded and prior_trunc is set to zero')
+ error('The option "slice" is inconsistent with prior_trunc=0.')
+ end
+
+ % moreover slice must be associated to:
+ % options_.mh_posterior_mode_estimation = 0;
+ % this is done below, but perhaps preprocessing should do this?
+
+ if ~isempty(posterior_sampler_options.mode)
+ % multimodal case
+ posterior_sampler_options.rotated = 1;
+ posterior_sampler_options.WR=[];
+ end
+ % posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
+
+
+ posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
+ if options_.load_mh_file,
+ posterior_sampler_options.slice_initialize_with_mode = 0;
+ else
+ if ~posterior_sampler_options.slice_initialize_with_mode,
+ posterior_sampler_options.invhess=[];
end
- % posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
-
-
- posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
- if options_.load_mh_file,
- posterior_sampler_options.slice_initialize_with_mode = 0;
- else
- if ~posterior_sampler_options.slice_initialize_with_mode,
- posterior_sampler_options.invhess=[];
- end
+ end
+
+ if ~isempty(posterior_sampler_options.mode_files), % multimodal case
+ modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
+ load(modes, 'xparams')
+ if size(xparams,2)<2,
+ error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
end
-
- if ~isempty(posterior_sampler_options.mode_files), % multimodal case
- modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
- load(modes, 'xparams')
- if size(xparams,2)<2,
- error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
- end
- for j=1:size(xparams,2),
- mode(j).m=xparams(:,j);
- end
- posterior_sampler_options.mode = mode;
- posterior_sampler_options.rotated = 1;
- posterior_sampler_options.WR=[];
+ for j=1:size(xparams,2),
+ mode(j).m=xparams(:,j);
end
-
- otherwise
- error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
+ posterior_sampler_options.mode = mode;
+ posterior_sampler_options.rotated = 1;
+ posterior_sampler_options.WR=[];
+ end
+
+ otherwise
+ error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
end
return
diff --git a/matlab/chol_SE.m b/matlab/chol_SE.m
index 0ec5a900bf4a23fb44f5f20a852b1d8220f8ede2..0e516c4960b4ba179e69285db7a276e432a66474 100644
--- a/matlab/chol_SE.m
+++ b/matlab/chol_SE.m
@@ -229,7 +229,7 @@ for j = 1:n-1
end
% Calculate delta and add to the diagonal. delta=max{0,-A(j,j) + max{normj,taugam},delta_previous}
% where normj=sum of |A(i,j)|,for i=1,n, delta_previous is the delta computed at the previous iter and taugam is tau1*gamma.
-
+
normj=sum(abs(A(j+1:n,j)));
delta = max([0;delta;-A(j,j)+normj;-A(j,j)+taugam]); % get adjustment based on formula on bottom of p. 309 of Eskow/Schnabel (1991)
diff --git a/matlab/cli/prior.m b/matlab/cli/prior.m
index 9e044506646b29224efcada72ef6969ce8af48c1..6395a7e4443e65b2e829c00b5949f984ce8be65d 100644
--- a/matlab/cli/prior.m
+++ b/matlab/cli/prior.m
@@ -53,11 +53,11 @@ end
if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
% Prior over measurement errors are defined...
- if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
- % ... But the list of observed variabled is not yet defined.
- warning('Prior detected on measurement erros, but no list of observed variables (varobs is missing)!')
- return
- end
+ if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
+ % ... But the list of observed variabled is not yet defined.
+ warning('Prior detected on measurement erros, but no list of observed variables (varobs is missing)!')
+ return
+ end
end
% Fill or update bayestopt_ structure
@@ -114,7 +114,7 @@ if ismember('optimize', varargin) % Prior optimization.
end
if ismember('moments', varargin) % Prior simulations (2nd order moments).
- % Set estimated parameters to the prior mode...
+ % Set estimated parameters to the prior mode...
xparam1 = BayesOptions.p5;
% ... Except for uniform priors (use the prior mean)!
k = find(isnan(xparam1));
diff --git a/matlab/collect_latex_files.m b/matlab/collect_latex_files.m
index 5c4f2b7d643d419c558053edbcde65783be13ca7..9220d2564606b9916843711cdf497da389d59e9f 100644
--- a/matlab/collect_latex_files.m
+++ b/matlab/collect_latex_files.m
@@ -45,10 +45,10 @@ TeX_Files=dir([M_.fname,'*.tex']);
for ii=1:length(TeX_Files)
[pathstr,f_name,ext] = fileparts(TeX_Files(ii).name);
if ~strcmp(TeX_Files(ii).name,f_name_binder) && ...
- ~strcmp(TeX_Files(ii).name,[M_.fname,'_dynamic.tex']) && ...
- ~strcmp(TeX_Files(ii).name,[M_.fname,'_static.tex']) && ...
- ~strcmp(TeX_Files(ii).name,[M_.fname,'_original.tex']) && ...
- ~strcmp(TeX_Files(ii).name,[M_.fname,'_TeX_binder.tex'])
+ ~strcmp(TeX_Files(ii).name,[M_.fname,'_dynamic.tex']) && ...
+ ~strcmp(TeX_Files(ii).name,[M_.fname,'_static.tex']) && ...
+ ~strcmp(TeX_Files(ii).name,[M_.fname,'_original.tex']) && ...
+ ~strcmp(TeX_Files(ii).name,[M_.fname,'_TeX_binder.tex'])
fprintf(fid,'%s \n',['\include{',f_name,'}']);
end
end
diff --git a/matlab/compute_moments_varendo.m b/matlab/compute_moments_varendo.m
index f2e26744016d9f2a0fa571df2b8eb8960e173fb9..a74b55fe301e155f6df37a635d92488c187ffa87 100644
--- a/matlab/compute_moments_varendo.m
+++ b/matlab/compute_moments_varendo.m
@@ -129,7 +129,7 @@ if M_.exo_nbr > 1
headers = char(' ',headers);
lh = size(deblank(var_list_),2)+2;
dyntable(options_,title,headers,deblank(var_list_),100* ...
- temp,lh,8,2);
+ temp,lh,8,2);
if options_.TeX
headers=M_.exo_names_tex;
headers = char(' ',headers);
@@ -166,7 +166,7 @@ if M_.exo_nbr > 1
headers = char(' ',headers);
lh = size(deblank(var_list_),2)+2;
dyntable(options_,title_print,headers,deblank(var_list_),100* ...
- temp(:,:,step_iter),lh,8,2);
+ temp(:,:,step_iter),lh,8,2);
if options_.TeX
headers=M_.exo_names_tex;
headers = char(' ',headers);
diff --git a/matlab/compute_overall_acceptance_ratio.m b/matlab/compute_overall_acceptance_ratio.m
index d3b323eb309659191033067caf984802170a5bf5..937579e91006bacd569763baaf2fa60b4d9bd70d 100644
--- a/matlab/compute_overall_acceptance_ratio.m
+++ b/matlab/compute_overall_acceptance_ratio.m
@@ -16,7 +16,7 @@ function overallacceptanceratio = compute_overall_acceptance_ratio(MetropolisFol
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
BaseName = [MetropolisFolder filesep ModelName];
mh_history_files = dir([BaseName '_mh_history_*.mat']);
diff --git a/matlab/convergence_diagnostics/McMCDiagnostics.m b/matlab/convergence_diagnostics/McMCDiagnostics.m
index c42467d31a21ba42988bf1e314748f6872e447f4..ff6c1089da2618e152e725abf6dedec557e16811 100644
--- a/matlab/convergence_diagnostics/McMCDiagnostics.m
+++ b/matlab/convergence_diagnostics/McMCDiagnostics.m
@@ -130,7 +130,7 @@ end
if nblck == 1 % Brooks and Gelman tests need more than one block
convergence_diagnostics_geweke=zeros(npar,4+2*length(options_.convergence.geweke.taper_steps));
if any(options_.convergence.geweke.geweke_interval<0) || any(options_.convergence.geweke.geweke_interval>1) || length(options_.convergence.geweke.geweke_interval)~=2 ...
- || (options_.convergence.geweke.geweke_interval(2)-options_.convergence.geweke.geweke_interval(1)<0)
+ || (options_.convergence.geweke.geweke_interval(2)-options_.convergence.geweke.geweke_interval(1)<0)
fprintf('\nCONVERGENCE DIAGNOSTICS: Invalid option for geweke_interval. Using the default of [0.2 0.5].\n')
options_.convergence.geweke.geweke_interval=[0.2 0.5];
end
@@ -168,7 +168,7 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
end
[results_vec, results_struct] = geweke_moments(param_draws,options_);
convergence_diagnostics_geweke(jj,:)=results_vec;
-
+
param_draws1 = param_draws(first_obs_begin_sample:last_obs_begin_sample,:);
param_draws2 = param_draws(first_obs_end_sample:end,:);
[results_vec1] = geweke_moments(param_draws1,options_);
@@ -183,7 +183,7 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
if options_.TeX
Geweke_tex_header=char('Parameter', 'Mean', 'Std', 'No\ Taper');
additional_header={[' & \multicolumn{2}{c}{Posterior} & \multicolumn{',num2str(1+length(options_.convergence.geweke.taper_steps)),'}{c}{p-values} \\'],
- ['\cmidrule(r{.75em}){2-3} \cmidrule(r{.75em}){4-',num2str(4+length(options_.convergence.geweke.taper_steps)),'}']};
+ ['\cmidrule(r{.75em}){2-3} \cmidrule(r{.75em}){4-',num2str(4+length(options_.convergence.geweke.taper_steps)),'}']};
for ii=1:length(options_.convergence.geweke.taper_steps)
Geweke_tex_header=char(Geweke_tex_header,[num2str(options_.convergence.geweke.taper_steps(ii)),'\%%\ Taper']);
end
@@ -196,7 +196,7 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
if options_.convergence.rafterylewis.indicator
if any(options_.convergence.rafterylewis.qrs<0) || any(options_.convergence.rafterylewis.qrs>1) || length(options_.convergence.rafterylewis.qrs)~=3 ...
- || (options_.convergence.rafterylewis.qrs(1)-options_.convergence.rafterylewis.qrs(2)<=0)
+ || (options_.convergence.rafterylewis.qrs(1)-options_.convergence.rafterylewis.qrs(2)<=0)
fprintf('\nCONVERGENCE DIAGNOSTICS: Invalid option for raftery_lewis_qrs. Using the default of [0.025 0.005 0.95].\n')
options_.convergence.rafterylewis.qrs=[0.025 0.005 0.95];
end
@@ -219,7 +219,7 @@ if nblck == 1 % Brooks and Gelman tests need more than one block
dyn_latex_table(M_,options_,my_title,'raftery_lewis',headers,labels_Raftery_Lewis_tex,raftery_data_mat,lh,10,0);
end
end
-
+
return;
end
diff --git a/matlab/convergence_diagnostics/geweke_chi2_test.m b/matlab/convergence_diagnostics/geweke_chi2_test.m
index 7ed38313981fbfaabe39ddcd76f434021236054e..5009b5f26f994dae6a36d8dfde2f7922bf5b90fb 100644
--- a/matlab/convergence_diagnostics/geweke_chi2_test.m
+++ b/matlab/convergence_diagnostics/geweke_chi2_test.m
@@ -58,17 +58,17 @@ function results_struct = geweke_chi2_test(results1,results2,results_struct,opti
% drew on MATLAB programs written by Siddartha Chib
for k=1:length(options.convergence.geweke.taper_steps)+1;
- NSE=[results1(:,3+(k-1)*2) results2(:,3+(k-1)*2)];
- means=[results1(:,1) results2(:,1)];
- diff_Means=means(:,1)-means(:,2);
- sum_of_weights=sum(1./(NSE.^2),2);
- pooled_mean=sum(means./(NSE.^2),2)./sum_of_weights;
- pooled_NSE=1./sqrt(sum_of_weights);
+ NSE=[results1(:,3+(k-1)*2) results2(:,3+(k-1)*2)];
+ means=[results1(:,1) results2(:,1)];
+ diff_Means=means(:,1)-means(:,2);
+ sum_of_weights=sum(1./(NSE.^2),2);
+ pooled_mean=sum(means./(NSE.^2),2)./sum_of_weights;
+ pooled_NSE=1./sqrt(sum_of_weights);
- test_stat=diff_Means.^2./sum(NSE.^2,2);
- p = 1-chi2cdf(test_stat,1);
- results_struct.pooled_mean(:,k) = pooled_mean;
- results_struct.pooled_nse(:,k) = pooled_NSE;
- results_struct.prob_chi2_test(:,k) = p;
+ test_stat=diff_Means.^2./sum(NSE.^2,2);
+ p = 1-chi2cdf(test_stat,1);
+ results_struct.pooled_mean(:,k) = pooled_mean;
+ results_struct.pooled_nse(:,k) = pooled_NSE;
+ results_struct.prob_chi2_test(:,k) = p;
end;
diff --git a/matlab/convergence_diagnostics/geweke_moments.m b/matlab/convergence_diagnostics/geweke_moments.m
index 242a9d55016796ca844792e31fa87acda779be87..eceaa7ee12514cb8efabd970b1830ed7003d235d 100644
--- a/matlab/convergence_diagnostics/geweke_moments.m
+++ b/matlab/convergence_diagnostics/geweke_moments.m
@@ -53,7 +53,7 @@ function [results_vec, results_struct] = geweke_moments(draws,Dynareoptions)
% based on code by James P. LeSage, who in turn
% drew on MATLAB programs written by Siddartha Chib
-
+
ndraw = size(draws,1);
n_groups=100;
taper_steps=Dynareoptions.convergence.geweke.taper_steps;
diff --git a/matlab/convergence_diagnostics/mcmc_ifac.m b/matlab/convergence_diagnostics/mcmc_ifac.m
index 3688153f98f3b3b2d3dddf6658f47055bc3da807..7f231842be33cffcaf0732820e167622ebc5af30 100644
--- a/matlab/convergence_diagnostics/mcmc_ifac.m
+++ b/matlab/convergence_diagnostics/mcmc_ifac.m
@@ -1,71 +1,71 @@
-function Ifac = mcmc_ifac(X, Nc)
-% function Ifac = mcmc_ifac(X, Nc)
-% Compute inefficiency factor of a MCMC sample X based on a Parzen Window
-%
-% INPUTS
-% X: time series
-% Nc: # of lags
-%
-% OUTPUTS
-% Ifac: inefficiency factor of MCMC sample
-%
-% SPECIAL REQUIREMENTS
-% none
-% ALGORITHM:
-% Inefficiency factors are computed as
-% \[
-% Ifac = 1 + 2\sum\limits_{i=1}^{Nc} {\hat \rho(i)}
-% \]
-% where $\hat \rho(i)$ denotes the autocorrelation at lag i and the terms
-% of the sum are truncated using a Parzen window.
-%
-% For inefficiency factors, see Section 6.1 of Paolo Giordani, Michael Pitt, and Robert Kohn (2011):
-% "Bayesian Inference for Time Series State Space Models" in : John Geweke, Gary Koop,
-% Herman van Dijk (editors): "The Oxford Handbook of Bayesian
-% Econometrics", Oxford University Press
-%
-% The Parzen-Window is given by
-% \[
-% k(x) = \left\{ {\begin{array}{*{20}{c}}
-% {1 - 6{x^2} + 6|x|^3} \text{ for } 0 \leqslant |x| \leqslant \frac{1}{2}} \\
-% {2(1-|x|^3) \text{ for } \frac{1}{2} \leqslant |x| \leqslant 1} \\
-% {0 \text{ otherwise}}
-% \end{array}} \right.
-% \]
-% See Donald W.K Andrews (1991): "Heteroskedasticity and autocorrelation
-% consistent covariance matrix estimation", Econometrica, 59(3), p. 817-858
-
-
-% Copyright (C) 2015-16 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-Nc = floor(min(Nc, length(X)/2));
-if mod(Nc,2),
- Nc=Nc-1;
-end
-AcorrXSIM = dyn_autocorr(X(:), Nc);
-%
-%Calculate the Parzen Weight
-Parzen=zeros(Nc+1,1);
-for i=1: Nc/2+1
- Parzen(i)=1 - 6*(i/Nc)^2+ 6*(i/Nc)^3;
-end
-for i=(Nc/2)+1: Nc+1
- Parzen(i)=2 * (1-(i/Nc))^3;
-end
-Parzen=Parzen';
-Ifac= 1+2*sum(Parzen(:).* AcorrXSIM);
+function Ifac = mcmc_ifac(X, Nc)
+% function Ifac = mcmc_ifac(X, Nc)
+% Compute inefficiency factor of a MCMC sample X based on a Parzen Window
+%
+% INPUTS
+% X: time series
+% Nc: # of lags
+%
+% OUTPUTS
+% Ifac: inefficiency factor of MCMC sample
+%
+% SPECIAL REQUIREMENTS
+% none
+% ALGORITHM:
+% Inefficiency factors are computed as
+% \[
+% Ifac = 1 + 2\sum\limits_{i=1}^{Nc} {\hat \rho(i)}
+% \]
+% where $\hat \rho(i)$ denotes the autocorrelation at lag i and the terms
+% of the sum are truncated using a Parzen window.
+%
+% For inefficiency factors, see Section 6.1 of Paolo Giordani, Michael Pitt, and Robert Kohn (2011):
+% "Bayesian Inference for Time Series State Space Models" in : John Geweke, Gary Koop,
+% Herman van Dijk (editors): "The Oxford Handbook of Bayesian
+% Econometrics", Oxford University Press
+%
+% The Parzen-Window is given by
+% \[
+% k(x) = \left\{ {\begin{array}{*{20}{c}}
+% {1 - 6{x^2} + 6|x|^3} \text{ for } 0 \leqslant |x| \leqslant \frac{1}{2}} \\
+% {2(1-|x|^3) \text{ for } \frac{1}{2} \leqslant |x| \leqslant 1} \\
+% {0 \text{ otherwise}}
+% \end{array}} \right.
+% \]
+% See Donald W.K Andrews (1991): "Heteroskedasticity and autocorrelation
+% consistent covariance matrix estimation", Econometrica, 59(3), p. 817-858
+
+
+% Copyright (C) 2015-16 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+Nc = floor(min(Nc, length(X)/2));
+if mod(Nc,2),
+ Nc=Nc-1;
+end
+AcorrXSIM = dyn_autocorr(X(:), Nc);
+%
+%Calculate the Parzen Weight
+Parzen=zeros(Nc+1,1);
+for i=1: Nc/2+1
+ Parzen(i)=1 - 6*(i/Nc)^2+ 6*(i/Nc)^3;
+end
+for i=(Nc/2)+1: Nc+1
+ Parzen(i)=2 * (1-(i/Nc))^3;
+end
+Parzen=Parzen';
+Ifac= 1+2*sum(Parzen(:).* AcorrXSIM);
diff --git a/matlab/convergence_diagnostics/raftery_lewis.m b/matlab/convergence_diagnostics/raftery_lewis.m
index c2a162d875d684f2330b042f4befdecc74fb96aa..d6e403c5b1ebb9493cfd450a2ef166cd6fe6843d 100644
--- a/matlab/convergence_diagnostics/raftery_lewis.m
+++ b/matlab/convergence_diagnostics/raftery_lewis.m
@@ -142,17 +142,17 @@ for ind_1 = 1:2
for ind_3 = 1:2
if tran(ind_1,ind_2,ind_3) ~= 0
fitted = (tran(ind_1,ind_2,1) + tran(ind_1,ind_2,2))*(tran(1,ind_2,ind_3) + tran(2,ind_2,ind_3))/...
- (tran(1,ind_2,1) + tran(1,ind_2,2) + tran(2,ind_2,1) + tran(2,ind_2,2));
+ (tran(1,ind_2,1) + tran(1,ind_2,2) + tran(2,ind_2,1) + tran(2,ind_2,2));
focus = tran(ind_1,ind_2,ind_3);
g2 = g2 + log(focus/fitted)*focus;
end
end; % end of for i3
end; % end of for i2
end; % end of for i1
-g2 = g2*2;
-bic = g2 - log(n_obs-2)*2;
+ g2 = g2*2;
+ bic = g2 - log(n_obs-2)*2;
-end
+ end
function [g2, bic] = independence_chain_test(d)
diff --git a/matlab/convert_dyn_45_to_44.m b/matlab/convert_dyn_45_to_44.m
index ba3dbc671fc55d793993e98462b5b37ac448a13b..12306cccc862c206ff22ce198e1f10970d7f76f7 100644
--- a/matlab/convert_dyn_45_to_44.m
+++ b/matlab/convert_dyn_45_to_44.m
@@ -147,11 +147,11 @@ end
if ~isempty(options_.nk) && options_.nk ~= 0 && ~isempty(bayestopt_)
if ~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)) %no Bayesian estimation
positions_in_decision_order=oo_.dr.inv_order_var(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list));
- if options_.loglinear == 1 %logged steady state must be used
- constant_all_variables=log(oo_.dr.ys(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list)));
- elseif options_.loglinear == 0 %unlogged steady state must be used
- constant_all_variables=oo_.dr.ys(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list));
- end
+ if options_.loglinear == 1 %logged steady state must be used
+ constant_all_variables=log(oo_.dr.ys(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list)));
+ elseif options_.loglinear == 0 %unlogged steady state must be used
+ constant_all_variables=oo_.dr.ys(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list));
+ end
if ~(options_.selected_variables_only && ~(options_.forecast > 0)) %happens only when selected_variables_only is not used
oo_.FilteredVariablesKStepAhead(:,positions_in_decision_order,:)=oo_.FilteredVariablesKStepAhead-constant_all_variables;
if ~isempty(PK) %get K-step ahead variances
@@ -182,11 +182,11 @@ end
%Deal with OSR
if ~isempty(M_.osr.variable_weights)
- evalin('base','optim_weights_=M_.osr.variable_weights')
+ evalin('base','optim_weights_=M_.osr.variable_weights')
end
if ~isempty(M_.osr.variable_indices)
- evalin('base','obj_var_=M_.osr.variable_indices')
+ evalin('base','obj_var_=M_.osr.variable_indices')
end
if ~isempty(M_.osr.param_names)
- evalin('base','osr_params_=char(M_.osr.param_names)')
+ evalin('base','osr_params_=char(M_.osr.param_names)')
end
diff --git a/matlab/convert_oo_.m b/matlab/convert_oo_.m
index ef5f72280d148177844f706ab6ad48184ad80ddc..948fc708da72c277763369cc8f60266add4fdf05 100644
--- a/matlab/convert_oo_.m
+++ b/matlab/convert_oo_.m
@@ -81,5 +81,5 @@ else
end
eval(['oo_ = convert_dyn_' strrep(new_from_ver, '.', '') '_to_' ...
- strrep(to_ver, '.', '') '(M_, options_, oo_);']);
+ strrep(to_ver, '.', '') '(M_, options_, oo_);']);
end
diff --git a/matlab/covariance_mc_analysis.m b/matlab/covariance_mc_analysis.m
index 60753407b41dead7f5fad6daaa99137a9a713938..1d0139d43582424a4d90f93a8bd42a41e98036d9 100644
--- a/matlab/covariance_mc_analysis.m
+++ b/matlab/covariance_mc_analysis.m
@@ -110,11 +110,11 @@ end
if options_.estimation.moments_posterior_density.indicator
[p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
- posterior_moments(tmp,1,mh_conf_sig);
+ posterior_moments(tmp,1,mh_conf_sig);
oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.density.(var1).(var2) = density;
else
[p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
- posterior_moments(tmp,0,mh_conf_sig);
+ posterior_moments(tmp,0,mh_conf_sig);
end
oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var1).(var2) = p_mean;
oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Median.(var1).(var2) = p_median;
diff --git a/matlab/cubature_with_gaussian_weight.m b/matlab/cubature_with_gaussian_weight.m
index ecc69230dd75847af8841ef993767f254b52f8bf..43f05b61c26c239fbed95fc2437ff55a165b4303 100644
--- a/matlab/cubature_with_gaussian_weight.m
+++ b/matlab/cubature_with_gaussian_weight.m
@@ -52,7 +52,7 @@ function [nodes, weights] = cubature_with_gaussian_weight(d,n,method)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
-
+
% Set default.
if nargin<3 || isempty(method)
method = 'Stroud';
@@ -117,16 +117,16 @@ end
function v = e(n,i)
- v = zeros(n,1);
- v(i) = 1;
-
+v = zeros(n,1);
+v(i) = 1;
+
function m = ee(n,i,j)
- m = zeros(n,4);
- m(:,1) = e(n,i)+e(n,j);
- m(:,2) = e(n,i)-e(n,j);
- m(:,3) = -m(:,2);
- m(:,4) = -m(:,1);
-
+m = zeros(n,4);
+m(:,1) = e(n,i)+e(n,j);
+m(:,2) = e(n,i)-e(n,j);
+m(:,3) = -m(:,2);
+m(:,4) = -m(:,1);
+
%@test:1
%$ % Set problem
%$ d = 4;
diff --git a/matlab/datatomfile.m b/matlab/datatomfile.m
index 4f6a5ff49acd04aabdefa4194da65c2e7e77f9c2..c2ea03ef684f5a6d39676c1295131d4502e2c91f 100644
--- a/matlab/datatomfile.m
+++ b/matlab/datatomfile.m
@@ -65,8 +65,8 @@ for i=1:n
end
end
stack = dbstack;
- fprintf(fid,'%% Dataset generated by %s.\n',stack(2).file);
- fprintf(fid,['%% ' datestr(now,0) '\n']);
+fprintf(fid,'%% Dataset generated by %s.\n',stack(2).file);
+fprintf(fid,['%% ' datestr(now,0) '\n']);
% Save the selected data.
for i = 1:n
fprintf(fid,[strtrim(names(i,:)), ' = ['],'\n') ;
diff --git a/matlab/delete_mh_history_files.m b/matlab/delete_mh_history_files.m
index 64b720ec9ba9f7dd666f2f272b93de9df0240c61..635bb9c94d9b0211b889882eb847e846e72102f1 100644
--- a/matlab/delete_mh_history_files.m
+++ b/matlab/delete_mh_history_files.m
@@ -1,5 +1,5 @@
function info = delete_mh_history_files(MetropolisFolder, ModelName)
-
+
% Copyright (C) 2013 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/discretionary_policy_1.m b/matlab/discretionary_policy_1.m
index 931d3b596f634a87d3814eca5c1bfc622cc91587..09584e864240ec14548336c8138482e4e3a466da 100644
--- a/matlab/discretionary_policy_1.m
+++ b/matlab/discretionary_policy_1.m
@@ -39,7 +39,7 @@ if isfield(M_,'orig_model')
M_.maximum_lag = orig_model.maximum_lag;
M_.maximum_endo_lag = orig_model.maximum_endo_lag;
else
- M_.orig_model = M_;
+ M_.orig_model = M_;
end
beta = get_optimal_policy_discount_factor(M_.params,M_.param_names);
@@ -53,10 +53,10 @@ if isfield(M_,'orig_model')
MaxLead = orig_model.maximum_lead;
MaxLag = orig_model.maximum_lag;
else
- endo_names = M_.endo_names;
- endo_nbr = M_.endo_nbr;
- MaxLag=M_.maximum_lag;
- MaxLead=M_.maximum_lead;
+ endo_names = M_.endo_names;
+ endo_nbr = M_.endo_nbr;
+ MaxLag=M_.maximum_lag;
+ MaxLead=M_.maximum_lead;
lead_lag_incidence = M_.lead_lag_incidence;
end
@@ -64,7 +64,7 @@ end
if options_.steadystate_flag
% explicit steady state file
[junk,M_.params,info] = evaluate_steady_state_file(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_, ...
- options_,0);
+ options_,0);
end
[U,Uy,W] = feval([M_.fname,'_objective_static'],zeros(endo_nbr,1),[], M_.params);
if any(any(Uy~=0))
@@ -99,29 +99,29 @@ if instr_nbr==0
error('discretionary_policy:: There are no available instruments, because the model has as many equations as variables.')
end
if size(Instruments,1)~= instr_nbr
- error('discretionary_policy:: There are more declared instruments than omitted equations.')
+ error('discretionary_policy:: There are more declared instruments than omitted equations.')
end
instr_id=nan(instr_nbr,1);
for j=1:instr_nbr
- vj=deblank(Instruments(j,:));
- vj_id=strmatch(vj,endo_names,'exact');
- if ~isempty(vj_id)
- instr_id(j)=vj_id;
- else
- error([mfilename,':: instrument ',vj,' not found'])
- end
+ vj=deblank(Instruments(j,:));
+ vj_id=strmatch(vj,endo_names,'exact');
+ if ~isempty(vj_id)
+ instr_id(j)=vj_id;
+ else
+ error([mfilename,':: instrument ',vj,' not found'])
+ end
end
Indices={'lag','0','lead'};
iter=1;
for j=1:numel(Indices)
eval(['A',Indices{j},'=zeros(eq_nbr,endo_nbr);'])
- if strcmp(Indices{j},'0')||(strcmp(Indices{j},'lag') && MaxLag)||(strcmp(Indices{j},'lead') && MaxLead)
- [junk,row,col]=find(lead_lag_incidence(iter,:));
- eval(['A',Indices{j},'(:,row)=jacobia_(:,col);'])
- iter=iter+1;
- end
+ if strcmp(Indices{j},'0')||(strcmp(Indices{j},'lag') && MaxLag)||(strcmp(Indices{j},'lead') && MaxLead)
+ [junk,row,col]=find(lead_lag_incidence(iter,:));
+ eval(['A',Indices{j},'(:,row)=jacobia_(:,col);'])
+ iter=iter+1;
+ end
end
B=jacobia_(:,nnz(iyv)+1:end);
@@ -131,17 +131,17 @@ solve_maxit = options_.dp.maxit;
discretion_tol = options_.discretionary_tol;
if ~isempty(Hold)
- [H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium,Hold);
+ [H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium,Hold);
else
- [H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium);
+ [H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium);
end
if info
dr=[];
return
else
- Hold=H; %save previous solution
- % Hold=[]; use this line if persistent command is not used.
+ Hold=H; %save previous solution
+ % Hold=[]; use this line if persistent command is not used.
end
% set the state
dr=oo_.dr;
@@ -159,7 +159,7 @@ dr.ys=ys; % <--- dr.ys =zeros(NewEndo_nbr,1);
function ys=NondistortionarySteadyState(M_)
if exist([M_.fname,'_steadystate.m'],'file')
- eval(['ys=',M_.fname,'_steadystate.m;'])
+ eval(['ys=',M_.fname,'_steadystate.m;'])
else
- ys=zeros(M_.endo_nbr,1);
+ ys=zeros(M_.endo_nbr,1);
end
diff --git a/matlab/discretionary_policy_engine.m b/matlab/discretionary_policy_engine.m
index 154b66426e19f42b90f33e12b348dae1b53f983b..7189dabef440f526cff1cb2fd99ec1415440af9e 100644
--- a/matlab/discretionary_policy_engine.m
+++ b/matlab/discretionary_policy_engine.m
@@ -253,7 +253,7 @@ end
function v = SylvesterHessenbergSchur(d,g,h)
%
-% DSYLHS Solves a discrete time sylvester equation using the
+% DSYLHS Solves a discrete time sylvester equation using the
% Hessenberg-Schur algorithm
%
% v = DSYLHS(g,d,h) computes the matrix v that satisfies the
@@ -301,7 +301,7 @@ if i< n,
i = i+1;
else
A = [w-g*h(i,i) (-g*h(i+1,i));...
- -g*h(i,i+1) w-g*h(i+1,i+1)];
+ -g*h(i,i+1) w-g*h(i+1,i+1)];
C = [d(:,i); d(:,i+1)];
X = A\C;
v(:,i) = X(1:m,:);
@@ -320,9 +320,9 @@ while i<n,
i = i+1;
else
A = [w - g*h(i,i) (-g*h(i+1,i)); ...
- -g*h(i,i+1) w - g*h(i+1,i+1)];
+ -g*h(i,i+1) w - g*h(i+1,i+1)];
C = [d(:,i) + temp*h(1:b,i); ...
- d(:,i+1) + temp*h(1:b,i+1)];
+ d(:,i+1) + temp*h(1:b,i+1)];
X = A\C;
v(:,i) = X(1:m,:);
v(:,i+1) = X(m+1:2*m, :);
diff --git a/matlab/disp_dr.m b/matlab/disp_dr.m
index a23c8f4be221500f2d154bb9a7ed09538d3f317a..673838a6e646175d136e286e516b78263fdee7e4 100644
--- a/matlab/disp_dr.m
+++ b/matlab/disp_dr.m
@@ -256,10 +256,10 @@ error(sprintf('Could not find aux var: %s', M_.endo_names(aux_index, :)))
end
function [str,flag]=get_print_string(str,x,value_format_zero,value_format_float,flag,options_)
- if abs(x) >= options_.dr_display_tol
- flag = 1;
- str = [str sprintf(value_format_float,x)];
- else
- str = [str sprintf(value_format_zero,0)];
- end
+if abs(x) >= options_.dr_display_tol
+ flag = 1;
+ str = [str sprintf(value_format_float,x)];
+else
+ str = [str sprintf(value_format_zero,0)];
+end
end
\ No newline at end of file
diff --git a/matlab/disp_identification.m b/matlab/disp_identification.m
index 5bdb3e8e558122acf7b7b5f7cfcb6848ed9efccc..6f89e429d69fc448386006a14c267b7e099f5d3a 100644
--- a/matlab/disp_identification.m
+++ b/matlab/disp_identification.m
@@ -133,23 +133,23 @@ if any(idemodel.ino),
end
end
end
-% if npar>(j+1),
-% [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))'>(1-1.e-10));
-% else
-% [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))>(1-1.e-10));
-% end
-% if ~isempty(jpair),
-% for jx=j+1:npar,
-% ixp = find(jx==(jpair+j));
-% if ~isempty(ixp)
-% if SampleSize > 1,
-% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
-% else
-% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10)!' ])
-% end
-% end
-% end
-% end
+ % if npar>(j+1),
+ % [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))'>(1-1.e-10));
+ % else
+ % [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))>(1-1.e-10));
+ % end
+ % if ~isempty(jpair),
+ % for jx=j+1:npar,
+ % ixp = find(jx==(jpair+j));
+ % if ~isempty(ixp)
+ % if SampleSize > 1,
+ % disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
+ % else
+ % disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10)!' ])
+ % end
+ % end
+ % end
+ % end
end
if ~any(idemodel.ino) && ~any(any(idemodel.ind0==0))
@@ -167,10 +167,10 @@ if any(idemoments.ino),
end
% disp('WARNING !!!')
% disp(['The rank of J (moments) is deficient for ', num2str(length(find(idemoments.ino))/SampleSize*100),'% of MC runs!' ]),
-% indno=[];
-% for j=1:SampleSize, indno=[indno;idemoments.indno{j}]; end
-% freqno = mean(indno)*100;
-% ifreq=find(freqno);
+ % indno=[];
+ % for j=1:SampleSize, indno=[indno;idemoments.indno{j}]; end
+ % freqno = mean(indno)*100;
+ % ifreq=find(freqno);
% disp('MOMENT RANK FAILURE DUE TO COLLINEARITY OF PARAMETERS:');
skipline()
for j=1:npar,
@@ -214,25 +214,25 @@ if any(idemoments.ino),
end
end
end
-% if npar>(j+1),
-% [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))'>(1-1.e-10));
-% else
-% [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))>(1-1.e-10));
-% end
-% if ~isempty(jpair),
-% for jx=j+1:npar,
-% ixp = find(jx==(jpair+j));
-% if ~isempty(ixp)
-% if SampleSize > 1
-% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
-% else
-% disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
-% end
-% end
-% end
-% end
-% end
-% end
+ % if npar>(j+1),
+ % [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))'>(1-1.e-10));
+ % else
+ % [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))>(1-1.e-10));
+ % end
+ % if ~isempty(jpair),
+ % for jx=j+1:npar,
+ % ixp = find(jx==(jpair+j));
+ % if ~isempty(ixp)
+ % if SampleSize > 1
+ % disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
+ % else
+ % disp([' [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
+ % end
+ % end
+ % end
+ % end
+ % end
+ % end
end
if ~any(idemoments.ino) && ~any(any(idemoments.ind0==0))
skipline()
diff --git a/matlab/disp_moments.m b/matlab/disp_moments.m
index 07e4b3c7d370bbbed71cde2ddeed7cb2220bc982..8bbf1f567ca6dbac61fc4e7c45b3729dafb498bd 100644
--- a/matlab/disp_moments.m
+++ b/matlab/disp_moments.m
@@ -98,7 +98,7 @@ if options_.nocorr == 0
end
if options_.noprint == 0 && length(options_.conditional_variance_decomposition)
- fprintf('\nSTOCH_SIMUL: conditional_variance_decomposition requires theoretical moments, i.e. periods=0.\n')
+ fprintf('\nSTOCH_SIMUL: conditional_variance_decomposition requires theoretical moments, i.e. periods=0.\n')
end
ar = options_.ar;
@@ -156,7 +156,7 @@ if ~options_.nodecomposition
if ~options_.noprint %options_.nomoments == 0
skipline()
title='VARIANCE DECOMPOSITION SIMULATING ONE SHOCK AT A TIME (in percent)';
-
+
title=add_filter_subtitle(title,options_);
headers = M_.exo_names;
@@ -181,12 +181,12 @@ if ~options_.nodecomposition
end
end
-
+
warning(warning_old_state);
end
function y=get_filtered_time_series(y,m,options_)
-
+
if options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpass.indicator
[hptrend,y] = sample_hp_filter(y,options_.hp_filter);
elseif ~options_.hp_filter && options_.one_sided_hp_filter && ~options_.bandpass.indicator
@@ -200,5 +200,5 @@ elseif ~options_.hp_filter && ~options_.one_sided_hp_filter && ~options_.bandpa
else
error('disp_moments:: You cannot use more than one filter at the same time')
end
-
+
end
\ No newline at end of file
diff --git a/matlab/disp_th_moments.m b/matlab/disp_th_moments.m
index 45d217611cc79637aeb142c4a6b0362df29c46ee..77e38c5e7554607f0643266b5861232a75992562 100644
--- a/matlab/disp_th_moments.m
+++ b/matlab/disp_th_moments.m
@@ -86,7 +86,7 @@ if size(stationary_vars, 1) > 0
headers = char(' ',headers);
lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
dyntable(options_,title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
- :)),100* ...
+ :)),100* ...
oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
if options_.TeX
headers=M_.exo_names_tex;
@@ -110,7 +110,7 @@ if size(stationary_vars, 1) > 0
if options_.noprint == 0
display_conditional_variance_decomposition(oo_.conditional_variance_decomposition,conditional_variance_steps,...
- ivar,M_,options_);
+ ivar,M_,options_);
end
end
end
diff --git a/matlab/display_conditional_variance_decomposition.m b/matlab/display_conditional_variance_decomposition.m
index 597aa0b1b95a9671d121a717b6a2ee50e8e306bd..2a82c7e2fd58f5ca46cdfff4fb9cdc6f47563081 100644
--- a/matlab/display_conditional_variance_decomposition.m
+++ b/matlab/display_conditional_variance_decomposition.m
@@ -56,10 +56,10 @@ for i=1:length(Steps)
dyntable(options_,'',headers,...
deblank(M_.endo_names(SubsetOfVariables,:)),...
vardec_i,lh,8,2);
- if options_.TeX
- labels_TeX = deblank(M_.endo_names_tex(SubsetOfVariables,:));
- headers_TeX=char('',deblank(M_.exo_names_tex));
- lh = size(labels_TeX,2)+2;
- dyn_latex_table(M_,options_,[title,'; Period ' int2str(Steps(i))],['th_var_decomp_cond_h',int2str(Steps(i))],headers_TeX,labels_TeX,vardec_i,lh,8,2);
- end
+ if options_.TeX
+ labels_TeX = deblank(M_.endo_names_tex(SubsetOfVariables,:));
+ headers_TeX=char('',deblank(M_.exo_names_tex));
+ lh = size(labels_TeX,2)+2;
+ dyn_latex_table(M_,options_,[title,'; Period ' int2str(Steps(i))],['th_var_decomp_cond_h',int2str(Steps(i))],headers_TeX,labels_TeX,vardec_i,lh,8,2);
+ end
end
\ No newline at end of file
diff --git a/matlab/display_estimation_results_table.m b/matlab/display_estimation_results_table.m
index 152ea006b95605375357470ddb4f317498a0deb9..52ffacfda6b763d0b0792a3cc0165560286353af 100644
--- a/matlab/display_estimation_results_table.m
+++ b/matlab/display_estimation_results_table.m
@@ -64,13 +64,13 @@ if np
name = bayestopt_.name{ip};
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name, ...
- bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
- pnames(bayestopt_.pshape(ip)+1,:), ...
- bayestopt_.p2(ip));
+ header_width,name, ...
+ bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
+ pnames(bayestopt_.pshape(ip)+1,:), ...
+ bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n', ...
- header_width,name,xparam1(ip),stdh(ip),tstath(ip));
+ header_width,name,xparam1(ip),stdh(ip),tstath(ip));
end
eval(['oo_.' field_name '_mode.parameters.' name ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.parameters.' name ' = stdh(ip);']);
@@ -87,9 +87,9 @@ if nvx
name = deblank(M_.exo_names(k,:));
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name,bayestopt_.p1(ip),xparam1(ip), ...
- stdh(ip),pnames(bayestopt_.pshape(ip)+1,:), ...
- bayestopt_.p2(ip));
+ header_width,name,bayestopt_.p1(ip),xparam1(ip), ...
+ stdh(ip),pnames(bayestopt_.pshape(ip)+1,:), ...
+ bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width,name,xparam1(ip),stdh(ip),tstath(ip));
end
@@ -99,8 +99,8 @@ if nvx
ip = ip+1;
end
skipline()
- end
- if nvn
+end
+if nvn
disp('standard deviation of measurement errors')
disp(tit1)
ip = nvx+1;
@@ -108,10 +108,10 @@ if nvx
name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name,bayestopt_.p1(ip), ...
- xparam1(ip),stdh(ip), ...
- pnames(bayestopt_.pshape(ip)+1,:), ...
- bayestopt_.p2(ip));
+ header_width,name,bayestopt_.p1(ip), ...
+ xparam1(ip),stdh(ip), ...
+ pnames(bayestopt_.pshape(ip)+1,:), ...
+ bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width,name,xparam1(ip),stdh(ip),tstath(ip))
end
@@ -120,7 +120,7 @@ if nvx
ip = ip+1;
end
skipline()
- end
+end
if ncx
disp('correlation of shocks')
@@ -133,8 +133,8 @@ if ncx
NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
- pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip));
+ header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
+ pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width,name,xparam1(ip),stdh(ip),tstath(ip));
end
@@ -158,8 +158,8 @@ if ncn
NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
- header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
- pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip));
+ header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
+ pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width,name,xparam1(ip),stdh(ip),tstath(ip));
end
@@ -305,8 +305,8 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
TeXBegin_ML(fidTeX,3,'standard deviation of measurement errors',table_title,LaTeXtitle)
ip = nvx+1;
for i=1:nvn
- idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)},M_.endo_names);
- fprintf(fidTeX,'$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n',...
+ idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)},M_.endo_names);
+ fprintf(fidTeX,'$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n',...
deblank(M_.endo_names_tex(idx,:)), ...
xparam1(ip),...
stdh(ip),...
@@ -347,7 +347,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
tstath(ip));
ip = ip+1;
end
- TeXEnd(fidTeX)
+ TeXEnd(fidTeX)
end
end
@@ -356,49 +356,49 @@ end
%% subfunctions:
%
function TeXBegin_Bayesian(fid,fnum,title)
- fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
- fprintf(fid,['%% RESULTS FROM POSTERIOR MAXIMIZATION (' title ')\n']);
- fprintf(fid,['%% ' datestr(now,0)]);
- fprintf(fid,' \n');
- fprintf(fid,' \n');
- fprintf(fid,'\\begin{center}\n');
- fprintf(fid,'\\begin{longtable}{llcccc} \n');
- fprintf(fid,['\\caption{Results from posterior maximization (' title ')}\\\\\n ']);
- fprintf(fid,['\\label{Table:Posterior:' int2str(fnum) '}\\\\\n']);
- fprintf(fid,'\\toprule \n');
- fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
- fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
- fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
- fprintf(fid,'\\midrule \\endfirsthead \n');
- fprintf(fid,'\\caption{(continued)}\\\\\n ');
- fprintf(fid,'\\bottomrule \n');
- fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
- fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
- fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
- fprintf(fid,'\\midrule \\endhead \n');
- fprintf(fid,'\\bottomrule \\multicolumn{6}{r}{(Continued on next page)}\\endfoot \n');
- fprintf(fid,'\\bottomrule\\endlastfoot \n');
+fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
+fprintf(fid,['%% RESULTS FROM POSTERIOR MAXIMIZATION (' title ')\n']);
+fprintf(fid,['%% ' datestr(now,0)]);
+fprintf(fid,' \n');
+fprintf(fid,' \n');
+fprintf(fid,'\\begin{center}\n');
+fprintf(fid,'\\begin{longtable}{llcccc} \n');
+fprintf(fid,['\\caption{Results from posterior maximization (' title ')}\\\\\n ']);
+fprintf(fid,['\\label{Table:Posterior:' int2str(fnum) '}\\\\\n']);
+fprintf(fid,'\\toprule \n');
+fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
+fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
+fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
+fprintf(fid,'\\midrule \\endfirsthead \n');
+fprintf(fid,'\\caption{(continued)}\\\\\n ');
+fprintf(fid,'\\bottomrule \n');
+fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
+fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
+fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
+fprintf(fid,'\\midrule \\endhead \n');
+fprintf(fid,'\\bottomrule \\multicolumn{6}{r}{(Continued on next page)}\\endfoot \n');
+fprintf(fid,'\\bottomrule\\endlastfoot \n');
+
+function TeXBegin_ML(fid,fnum,title,table_title,LaTeXtitle)
+fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
+fprintf(fid,['%% RESULTS FROM ' table_title ' MAXIMIZATION (' title ')\n']);
+fprintf(fid,['%% ' datestr(now,0)]);
+fprintf(fid,' \n');
+fprintf(fid,' \n');
+fprintf(fid,'\\begin{center}\n');
+fprintf(fid,'\\begin{longtable}{llcc} \n');
+fprintf(fid,['\\caption{Results from ' table_title ' maximization (' title ')}\\\\\n ']);
+fprintf(fid,['\\label{Table:' LaTeXtitle ':' int2str(fnum) '}\\\\\n']);
+fprintf(fid,'\\toprule \n');
+fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
+fprintf(fid,'\\midrule \\endfirsthead \n');
+fprintf(fid,'\\caption{(continued)}\\\\\n ');
+fprintf(fid,'\\toprule \n');
+fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
+fprintf(fid,'\\midrule \\endhead \n');
+fprintf(fid,'\\bottomrule \\multicolumn{4}{r}{(Continued on next page)} \\endfoot \n');
+fprintf(fid,'\\bottomrule \\endlastfoot \n');
- function TeXBegin_ML(fid,fnum,title,table_title,LaTeXtitle)
- fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
- fprintf(fid,['%% RESULTS FROM ' table_title ' MAXIMIZATION (' title ')\n']);
- fprintf(fid,['%% ' datestr(now,0)]);
- fprintf(fid,' \n');
- fprintf(fid,' \n');
- fprintf(fid,'\\begin{center}\n');
- fprintf(fid,'\\begin{longtable}{llcc} \n');
- fprintf(fid,['\\caption{Results from ' table_title ' maximization (' title ')}\\\\\n ']);
- fprintf(fid,['\\label{Table:' LaTeXtitle ':' int2str(fnum) '}\\\\\n']);
- fprintf(fid,'\\toprule \n');
- fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
- fprintf(fid,'\\midrule \\endfirsthead \n');
- fprintf(fid,'\\caption{(continued)}\\\\\n ');
- fprintf(fid,'\\toprule \n');
- fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
- fprintf(fid,'\\midrule \\endhead \n');
- fprintf(fid,'\\bottomrule \\multicolumn{4}{r}{(Continued on next page)} \\endfoot \n');
- fprintf(fid,'\\bottomrule \\endlastfoot \n');
-
function TeXEnd(fid)
fprintf(fid,'\\end{longtable}\n ');
fprintf(fid,'\\end{center}\n');
diff --git a/matlab/display_problematic_vars_Jacobian.m b/matlab/display_problematic_vars_Jacobian.m
index a4278208b74d604232022e387289eafdb0772533..26cfef45235053d208d3849203d3f624591fefaf 100644
--- a/matlab/display_problematic_vars_Jacobian.m
+++ b/matlab/display_problematic_vars_Jacobian.m
@@ -62,25 +62,25 @@ if strcmp(type,'dynamic')
fprintf('Derivative of Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(var_index,:)),deblank(M_.endo_names(var_index,:)),x(var_index))
end
elseif problemcol(ii)<=max(max(M_.lead_lag_incidence)) && var_index>M_.orig_endo_nbr %auxiliary vars
- if M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).type ==6 %Ramsey Lagrange Multiplier
- if problemrow(ii)<=aux_eq_nbr
- eq_nbr=problemrow(ii);
- fprintf('Derivative of Auxiliary Equation %d with respect to %s of Langrange multiplier of equation %s (initial value: %g) \n',eq_nbr,type_string,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
- else
- eq_nbr=problemrow(ii)-aux_eq_nbr;
- fprintf('Derivative of Equation %d with respect to %s of Langrange multiplier of equation %s (initial value: %g) \n',eq_nbr,type_string,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
- end
+ if M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).type ==6 %Ramsey Lagrange Multiplier
+ if problemrow(ii)<=aux_eq_nbr
+ eq_nbr=problemrow(ii);
+ fprintf('Derivative of Auxiliary Equation %d with respect to %s of Langrange multiplier of equation %s (initial value: %g) \n',eq_nbr,type_string,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
else
- if problemrow(ii)<=aux_eq_nbr
- eq_nbr=problemrow(ii);
- orig_var_index=M_.aux_vars(1,var_index-M_.orig_endo_nbr).orig_index;
- fprintf('Derivative of Auxiliary Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(orig_var_index))
- else
- eq_nbr=problemrow(ii)-aux_eq_nbr;
- orig_var_index=M_.aux_vars(1,var_index-M_.orig_endo_nbr).orig_index;
- fprintf('Derivative of Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(orig_var_index))
- end
+ eq_nbr=problemrow(ii)-aux_eq_nbr;
+ fprintf('Derivative of Equation %d with respect to %s of Langrange multiplier of equation %s (initial value: %g) \n',eq_nbr,type_string,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
end
+ else
+ if problemrow(ii)<=aux_eq_nbr
+ eq_nbr=problemrow(ii);
+ orig_var_index=M_.aux_vars(1,var_index-M_.orig_endo_nbr).orig_index;
+ fprintf('Derivative of Auxiliary Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(orig_var_index))
+ else
+ eq_nbr=problemrow(ii)-aux_eq_nbr;
+ orig_var_index=M_.aux_vars(1,var_index-M_.orig_endo_nbr).orig_index;
+ fprintf('Derivative of Equation %d with respect to %s Variable %s (initial value of %s: %g) \n',eq_nbr,type_string,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(orig_var_index))
+ end
+ end
elseif problemcol(ii)>max(max(M_.lead_lag_incidence)) && var_index<=M_.exo_nbr
if problemrow(ii)<=aux_eq_nbr
eq_nbr=problemrow(ii);
@@ -117,15 +117,15 @@ elseif strcmp(type,'static')
fprintf('Derivative of Equation %d with respect to Lagrange multiplier of equation %d (initial value: %g) \n',eq_nbr,M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).eq_nbr,x(problemcol(ii)))
end
else
- if problemrow(ii)<=aux_eq_nbr
- eq_nbr=problemrow(ii);
- orig_var_index=M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).orig_index;
- fprintf('Derivative of Auxiliary Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(problemcol(ii)))
- else
- eq_nbr=problemrow(ii)-aux_eq_nbr;
- orig_var_index=M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).orig_index;
- fprintf('Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(problemcol(ii)))
- end
+ if problemrow(ii)<=aux_eq_nbr
+ eq_nbr=problemrow(ii);
+ orig_var_index=M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).orig_index;
+ fprintf('Derivative of Auxiliary Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(problemcol(ii)))
+ else
+ eq_nbr=problemrow(ii)-aux_eq_nbr;
+ orig_var_index=M_.aux_vars(1,problemcol(ii)-M_.orig_endo_nbr).orig_index;
+ fprintf('Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',eq_nbr,deblank(M_.endo_names(orig_var_index,:)),deblank(M_.endo_names(orig_var_index,:)),x(problemcol(ii)))
+ end
end
end
end
diff --git a/matlab/distributions/lpdfgweibull.m b/matlab/distributions/lpdfgweibull.m
index 3f2df819d298a2f99bfddff04a1a2355e47747fc..e8ca18c2e890e53cdbbd7e2dff0748be90f6d935 100644
--- a/matlab/distributions/lpdfgweibull.m
+++ b/matlab/distributions/lpdfgweibull.m
@@ -77,7 +77,7 @@ if isempty(idx), return, end
jdx = find( abs(a-1)<1e-12 & x>=c & (x-c)<1e-12) ;
ldens(jdx) = 1.0;
-
+
if ~isempty(idx)
x0 = x(idx)-c(idx);
x1 = x0./b(idx);
diff --git a/matlab/dr_block.m b/matlab/dr_block.m
index 47c20add23faaf5f244d4a513a2e1ebc8ef31c2a..d2e35a1057cf44ec25a44fe5ead550d86e44ea04 100644
--- a/matlab/dr_block.m
+++ b/matlab/dr_block.m
@@ -54,7 +54,7 @@ function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
info = 0;
verbose = 0;
if nargin > 5
- verbose = varargin{1};
+ verbose = varargin{1};
end
%verbose = options_.verbosity;
if options_.order > 1
@@ -134,7 +134,7 @@ for i = 1:Size;
end;
switch block_type
case 1
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Evaluate Forward
if maximum_lag > 0 && n_pred > 0
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
@@ -175,7 +175,7 @@ for i = 1:Size;
l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
selector_tm1 = M_.block_structure.block(i).tm1;
-
+
ghx_other = - B \ (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1);
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
end;
@@ -198,17 +198,17 @@ for i = 1:Size;
else
exo = dr.exo_var;
if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -B \ (fx_tp1 * l_x * l_u_sv + (fx_t) * l_u );
+ l_u_sv = dr.ghu(dr.state_var,:);
+ l_x = dr.ghx(data(i).other_endogenous,:);
+ l_u = dr.ghu(data(i).other_endogenous,:);
+ ghu = -B \ (fx_tp1 * l_x * l_u_sv + (fx_t) * l_u );
else
ghu = [];
end
end
end
case 2
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Evaluate Backward
if maximum_lead > 0 && n_fwrd > 0
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(3,:));
@@ -233,7 +233,7 @@ for i = 1:Size;
ghu = - inv(jacob(indx_r, indx_c)) * data(i).g1_x;
end
case 3
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Solve Forward single equation
if maximum_lag > 0 && n_pred > 0
data(i).eigval = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
@@ -246,9 +246,9 @@ for i = 1:Size;
%First order approximation
if task ~= 1
if (maximum_lag > 0)
- ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
+ ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
else
- ghx = 0;
+ ghx = 0;
end;
if other_endo_nbr
fx = data(i).g1_o;
@@ -289,19 +289,19 @@ for i = 1:Size;
ghu = - fu / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
end;
else
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- exo = dr.exo_var;
- else
- ghu = [];
- end
+ if other_endo_nbr > 0
+ l_u_sv = dr.ghu(dr.state_var,:);
+ l_x = dr.ghx(data(i).other_endogenous,:);
+ l_u = dr.ghu(data(i).other_endogenous,:);
+ ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
+ exo = dr.exo_var;
+ else
+ ghu = [];
+ end
end
end
case 4
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Solve Backward single equation
if maximum_lead > 0 && n_fwrd > 0
data(i).eigval = - jacob(1 , n_pred + n - n_fwrd + 1 : n_pred + n) / jacob(1 , n_pred + n + 1 : n_pred + n + n_fwrd) ;
@@ -315,11 +315,11 @@ for i = 1:Size;
dr.full_rank = dr.full_rank && full_rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case 6
- %% ------------------------------------------------------------------
- %Solve Forward complete
+ %% ------------------------------------------------------------------
+ %Solve Forward complete
if (maximum_lag > 0)
ghx = - jacob(: , n_pred + 1 : n_pred + n_static ...
- + n_pred + n_both) \ jacob(: , 1 : n_pred);
+ + n_pred + n_both) \ jacob(: , 1 : n_pred);
else
ghx = 0;
end;
@@ -373,19 +373,19 @@ for i = 1:Size;
ghu = - fu / jacob(: , n_pred + 1 : n_pred + n_static + n_pred + n_both);
end;
else
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- exo = dr.exo_var;
- else
- ghu = [];
- end
+ if other_endo_nbr > 0
+ l_u_sv = dr.ghu(dr.state_var,:);
+ l_x = dr.ghx(data(i).other_endogenous,:);
+ l_u = dr.ghu(data(i).other_endogenous,:);
+ ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
+ exo = dr.exo_var;
+ else
+ ghu = [];
+ end
end
end
case 7
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%Solve Backward complete
if maximum_lead > 0 && n_fwrd > 0
data(i).eigval = eig(- jacob(: , n_pred + n - n_fwrd + 1: n_pred + n))/ ...
@@ -401,7 +401,7 @@ for i = 1:Size;
dr.full_rank = dr.full_rank && full_rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case {5,8}
- %% ------------------------------------------------------------------
+ %% ------------------------------------------------------------------
%The lead_lag_incidence contains columns in the following order:
% static variables, backward variable, mixed variables and forward variables
%
@@ -483,7 +483,7 @@ for i = 1:Size;
nba = nyf;
end
else
- sorted_roots = sort(abs(data(i).eigval));
+ sorted_roots = sort(abs(data(i).eigval));
if nba > nyf
temp = sorted_roots(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
info(1) = 3;
@@ -497,8 +497,8 @@ for i = 1:Size;
end
indx_stable_root = 1: (nd - nyf); %=> index of stable roots
indx_explosive_root = n_pred + n_both + 1:nd; %=> index of explosive roots
- % derivatives with respect to dynamic state variables
- % forward variables
+ % derivatives with respect to dynamic state variables
+ % forward variables
Z = w';
Z11t = Z(indx_stable_root, indx_stable_root)';
Z21 = Z(indx_explosive_root, indx_stable_root);
@@ -555,7 +555,7 @@ for i = 1:Size;
if other_endo_nbr
if n_static > 0
- fx = Q' * data(i).g1_o;
+ fx = Q' * data(i).g1_o;
else
fx = data(i).g1_o;
end;
@@ -594,8 +594,8 @@ for i = 1:Size;
[err, ghx_other] = gensylv(1, A_, B_, C_, -D_);
end;
if options_.aim_solver ~= 1 && options_.use_qzdiv
- % Necessary when using Sims' routines for QZ
- ghx_other = real(ghx_other);
+ % Necessary when using Sims' routines for QZ
+ ghx_other = real(ghx_other);
end
dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
@@ -640,13 +640,13 @@ for i = 1:Size;
if options_.loglinear
error('The loglinear option is not yet supported in first order approximation for a block decomposed model');
-% k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
-% klag = dr.kstate(k,[1 2]);
-% k1 = dr.order_var;
-%
-% ghx = repmat(1./dr.ys(k1),1,size(ghx,2)).*ghx.* ...
-% repmat(dr.ys(k1(klag(:,1)))',size(ghx,1),1);
-% ghu = repmat(1./dr.ys(k1),1,size(ghu,2)).*ghu;
+ % k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
+ % klag = dr.kstate(k,[1 2]);
+ % k1 = dr.order_var;
+ %
+ % ghx = repmat(1./dr.ys(k1),1,size(ghx,2)).*ghx.* ...
+ % repmat(dr.ys(k1(klag(:,1)))',size(ghx,1),1);
+ % ghu = repmat(1./dr.ys(k1),1,size(ghu,2)).*ghu;
end
@@ -659,16 +659,16 @@ for i = 1:Size;
%exogenous deterministic variables
if exo_det_nbr > 0
error('Deterministic exogenous variables are not yet implemented in first order approximation for a block decomposed model');
-% f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
-% f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
-% fudet = data(i).g1_xd;
-% M1 = inv(f0+[zeros(n,n_static) f1*gx zeros(n,nyf-n_both)]);
-% M2 = M1*f1;
-% dr.ghud = cell(M_.exo_det_length,1);
-% dr.ghud{1} = -M1*fudet;
-% for i = 2:M_.exo_det_length
-% dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
-% end
+ % f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
+ % f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
+ % fudet = data(i).g1_xd;
+ % M1 = inv(f0+[zeros(n,n_static) f1*gx zeros(n,nyf-n_both)]);
+ % M2 = M1*f1;
+ % dr.ghud = cell(M_.exo_det_length,1);
+ % dr.ghud{1} = -M1*fudet;
+ % for i = 2:M_.exo_det_length
+ % dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
+ % end
end
end
end;
@@ -686,20 +686,20 @@ for i = 1:Size;
data(i).pol.i_ghu = exo;
end;
- if (verbose)
+ if (verbose)
disp('dr.ghx');
dr.ghx
disp('dr.ghu');
dr.ghu
- end;
-
+ end;
+
end;
M_.block_structure.block = data ;
if (verbose)
- disp('dr.ghx');
- disp(real(dr.ghx));
- disp('dr.ghu');
- disp(real(dr.ghu));
+ disp('dr.ghx');
+ disp(real(dr.ghx));
+ disp('dr.ghu');
+ disp(real(dr.ghu));
end;
if (task == 1)
return;
diff --git a/matlab/dsge_likelihood.m b/matlab/dsge_likelihood.m
index 30bb87b5fba62a3bade3b0fe46b9110ea98656b4..b8ee614b93a2f215db9bbad84dbf03ee0e814e92 100644
--- a/matlab/dsge_likelihood.m
+++ b/matlab/dsge_likelihood.m
@@ -149,7 +149,7 @@ xparam1 = xparam1(:);
if DynareOptions.estimation_dll
[fval,exit_flag,SteadyState,trend_coeff,info,params,H,Q] ...
= logposterior(xparam1,DynareDataset, DynareOptions,Model, ...
- EstimatedParameters,BayesInfo,DynareResults);
+ EstimatedParameters,BayesInfo,DynareResults);
mexErrCheck('logposterior', exit_flag);
Model.params = params;
if ~isequal(Model.H,0)
@@ -268,8 +268,8 @@ end
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85 || info(1) == 86
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85 || info(1) == 86
%meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
@@ -323,8 +323,8 @@ if BayesInfo.with_trend
[trend_addition, trend_coeff]=compute_trend_coefficients(Model,DynareOptions,DynareDataset.vobs,DynareDataset.nobs);
trend = repmat(constant,1,DynareDataset.nobs)+trend_addition;
else
- trend_coeff = zeros(DynareDataset.vobs,1);
- trend = repmat(constant,1,DynareDataset.nobs);
+ trend_coeff = zeros(DynareDataset.vobs,1);
+ trend = repmat(constant,1,DynareDataset.nobs);
end
% Get needed informations for kalman filter routines.
@@ -372,8 +372,8 @@ switch DynareOptions.lik_init
if kalman_algo == 0
kalman_algo = 3;
elseif ~((kalman_algo == 3) || (kalman_algo == 4))
- error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
- 'to 0 (default), 3 or 4'])
+ error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
+ 'to 0 (default), 3 or 4'])
end
[Pstar,Pinf] = compute_Pinf_Pstar(Z,T,R,Q,DynareOptions.qz_criterium,[1:length(T)]);
Z =zeros(length(BayesInfo.mf),size(T,1));
@@ -387,9 +387,9 @@ switch DynareOptions.lik_init
Pstar0 = Pstar; % store Pstar
if no_missing_data_flag
[dLIK,dlik,a,Pstar] = kalman_filter_d(Y, 1, size(Y,2), ...
- zeros(mm,1), Pinf, Pstar, ...
- kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
- T,R,Q,H,Z,mm,pp,rr);
+ zeros(mm,1), Pinf, Pstar, ...
+ kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
+ T,R,Q,H,Z,mm,pp,rr);
else
[dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations, ...
Y, 1, size(Y,2), ...
@@ -438,12 +438,12 @@ switch DynareOptions.lik_init
end
[dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DatasetInfo.missing.aindex,...
- DatasetInfo.missing.number_of_observations,...
- DatasetInfo.missing.no_more_missing_observations, ...
- Y, 1, size(Y,2), ...
- zeros(mmm,1), Pinf, Pstar, ...
- kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
- T,R,Q,H1,Z,mmm,pp,rr);
+ DatasetInfo.missing.number_of_observations,...
+ DatasetInfo.missing.no_more_missing_observations, ...
+ Y, 1, size(Y,2), ...
+ zeros(mmm,1), Pinf, Pstar, ...
+ kalman_tol, diffuse_kalman_tol, riccati_tol, DynareOptions.presample, ...
+ T,R,Q,H1,Z,mmm,pp,rr);
diffuse_periods = size(dlik,1);
end
if isnan(dLIK),
@@ -541,9 +541,9 @@ if analytic_derivation,
full_Hess = derivatives_info.full_Hess;
end
if full_Hess,
- D2T = derivatives_info.D2T;
- D2Om = derivatives_info.D2Om;
- D2Yss = derivatives_info.D2Yss;
+ D2T = derivatives_info.D2T;
+ D2Om = derivatives_info.D2Om;
+ D2Yss = derivatives_info.D2Yss;
end
if isfield(derivatives_info,'no_DLIK'),
no_DLIK = derivatives_info.no_DLIK;
@@ -556,7 +556,7 @@ if analytic_derivation,
DP=zeros([size(T),length(xparam1)]);
if full_Hess,
for j=1:size(D2Yss,1),
- tmp(j,:,:) = blkdiag(zeros(offset,offset), squeeze(D2Yss(j,:,:)));
+ tmp(j,:,:) = blkdiag(zeros(offset,offset), squeeze(D2Yss(j,:,:)));
end
D2Yss = tmp;
D2H=sparse(size(D2Om,1),size(D2Om,2)); %zeros([size(H),length(xparam1),length(xparam1)]);
@@ -564,58 +564,58 @@ if analytic_derivation,
jcount=0;
end
if DynareOptions.lik_init==1,
- for i=1:EstimatedParameters.nvx
- k =EstimatedParameters.var_exo(i,1);
- DQ(k,k,i) = 2*sqrt(Q(k,k));
- dum = lyapunov_symm(T,DOm(:,:,i),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
-% kk = find(abs(dum) < 1e-12);
-% dum(kk) = 0;
- DP(:,:,i)=dum;
- if full_Hess
- for j=1:i,
- jcount=jcount+1;
- dum = lyapunov_symm(T,dyn_unvech(D2Om(:,jcount)),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
-% kk = (abs(dum) < 1e-12);
-% dum(kk) = 0;
- D2P(:,jcount)=dyn_vech(dum);
-% D2P(:,:,j,i)=dum;
- end
+ for i=1:EstimatedParameters.nvx
+ k =EstimatedParameters.var_exo(i,1);
+ DQ(k,k,i) = 2*sqrt(Q(k,k));
+ dum = lyapunov_symm(T,DOm(:,:,i),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
+ % kk = find(abs(dum) < 1e-12);
+ % dum(kk) = 0;
+ DP(:,:,i)=dum;
+ if full_Hess
+ for j=1:i,
+ jcount=jcount+1;
+ dum = lyapunov_symm(T,dyn_unvech(D2Om(:,jcount)),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
+ % kk = (abs(dum) < 1e-12);
+ % dum(kk) = 0;
+ D2P(:,jcount)=dyn_vech(dum);
+ % D2P(:,:,j,i)=dum;
+ end
+ end
end
end
- end
offset = EstimatedParameters.nvx;
for i=1:EstimatedParameters.nvn
k = EstimatedParameters.var_endo(i,1);
DH(k,k,i+offset) = 2*sqrt(H(k,k));
if full_Hess
- D2H(k,k,i+offset,i+offset) = 2;
+ D2H(k,k,i+offset,i+offset) = 2;
end
end
offset = offset + EstimatedParameters.nvn;
if DynareOptions.lik_init==1,
- for j=1:EstimatedParameters.np
- dum = lyapunov_symm(T,DT(:,:,j+offset)*Pstar*T'+T*Pstar*DT(:,:,j+offset)'+DOm(:,:,j+offset),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
-% kk = find(abs(dum) < 1e-12);
-% dum(kk) = 0;
- DP(:,:,j+offset)=dum;
- if full_Hess
- DTj = DT(:,:,j+offset);
- DPj = dum;
- for i=1:j+offset,
- jcount=jcount+1;
- DTi = DT(:,:,i);
- DPi = DP(:,:,i);
- D2Tij = reshape(D2T(:,jcount),size(T));
- D2Omij = dyn_unvech(D2Om(:,jcount));
- tmp = D2Tij*Pstar*T' + T*Pstar*D2Tij' + DTi*DPj*T' + DTj*DPi*T' + T*DPj*DTi' + T*DPi*DTj' + DTi*Pstar*DTj' + DTj*Pstar*DTi' + D2Omij;
- dum = lyapunov_symm(T,tmp,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
-% dum(abs(dum)<1.e-12) = 0;
- D2P(:,jcount) = dyn_vech(dum);
-% D2P(:,:,j+offset,i) = dum;
- end
+ for j=1:EstimatedParameters.np
+ dum = lyapunov_symm(T,DT(:,:,j+offset)*Pstar*T'+T*Pstar*DT(:,:,j+offset)'+DOm(:,:,j+offset),DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
+ % kk = find(abs(dum) < 1e-12);
+ % dum(kk) = 0;
+ DP(:,:,j+offset)=dum;
+ if full_Hess
+ DTj = DT(:,:,j+offset);
+ DPj = dum;
+ for i=1:j+offset,
+ jcount=jcount+1;
+ DTi = DT(:,:,i);
+ DPi = DP(:,:,i);
+ D2Tij = reshape(D2T(:,jcount),size(T));
+ D2Omij = dyn_unvech(D2Om(:,jcount));
+ tmp = D2Tij*Pstar*T' + T*Pstar*D2Tij' + DTi*DPj*T' + DTj*DPi*T' + T*DPj*DTi' + T*DPi*DTj' + DTi*Pstar*DTj' + DTj*Pstar*DTi' + D2Omij;
+ dum = lyapunov_symm(T,tmp,DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold,[],DynareOptions.debug);
+ % dum(abs(dum)<1.e-12) = 0;
+ D2P(:,jcount) = dyn_vech(dum);
+ % D2P(:,:,j+offset,i) = dum;
+ end
+ end
end
end
- end
if analytic_derivation==1,
analytic_deriv_info={analytic_derivation,DT,DYss,DOm,DH,DP,asy_Hess};
else
@@ -848,14 +848,14 @@ else
end
if DynareOptions.endogenous_prior==1
- if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
- error('Endogenous prior not supported with non-stationary models')
- else
- [lnpriormom] = endogenous_prior(Y,Pstar,BayesInfo,H);
- fval = (likelihood-lnprior-lnpriormom);
- end
+ if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
+ error('Endogenous prior not supported with non-stationary models')
+ else
+ [lnpriormom] = endogenous_prior(Y,Pstar,BayesInfo,H);
+ fval = (likelihood-lnprior-lnpriormom);
+ end
else
- fval = (likelihood-lnprior);
+ fval = (likelihood-lnprior);
end
if DynareOptions.prior_restrictions.status
diff --git a/matlab/dsge_simulated_theoretical_correlation.m b/matlab/dsge_simulated_theoretical_correlation.m
index 265336dbb05bb1f2f9342aecbdc1b616eda0d734..23485a8d500da5b0642bf4d3de981edddff2a849 100644
--- a/matlab/dsge_simulated_theoretical_correlation.m
+++ b/matlab/dsge_simulated_theoretical_correlation.m
@@ -16,7 +16,7 @@ function [nvar,vartan,CorrFileNumber] = dsge_simulated_theoretical_correlation(S
% nvar [integer] nvar is the number of stationary variables.
% vartan [char] array of characters (with nvar rows).
% CorrFileNumber [integer] scalar, number of prior or posterior data files (for correlation).
-
+
% Copyright (C) 2007-2015 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/dsge_simulated_theoretical_variance_decomposition.m b/matlab/dsge_simulated_theoretical_variance_decomposition.m
index a8ddadec6d811cafe453afb6be7f4614eb70bd5d..1cb8f0d3140b07994e5c75e353a7129102c23260 100644
--- a/matlab/dsge_simulated_theoretical_variance_decomposition.m
+++ b/matlab/dsge_simulated_theoretical_variance_decomposition.m
@@ -119,17 +119,17 @@ for file = 1:NumberOfDrawsFiles
if file==1 && linee==1
[tmp, stationary_vars] = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
if isempty(stationary_vars)
- fprintf('\ndsge_simulated_theoretical_variance_decomposition:: All requested endogenous variables have a unit root and thus infinite variance.\n')
- fprintf('dsge_simulated_theoretical_variance_decomposition:: No decomposition is performed.\n')
- only_non_stationary_vars=1;
+ fprintf('\ndsge_simulated_theoretical_variance_decomposition:: All requested endogenous variables have a unit root and thus infinite variance.\n')
+ fprintf('dsge_simulated_theoretical_variance_decomposition:: No decomposition is performed.\n')
+ only_non_stationary_vars=1;
end
end
if only_non_stationary_vars
- for i=1:nvar
+ for i=1:nvar
for j=1:nexo
Decomposition_array(linea,(i-1)*nexo+j) = NaN;
end
- end
+ end
else
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
for i=1:nvar
diff --git a/matlab/dsge_var_likelihood.m b/matlab/dsge_var_likelihood.m
index 94f8e21ac46d93c0675a06f5c6779bafedcab149..c37fca0589000caf782d2e1b4044335549da43ae 100644
--- a/matlab/dsge_var_likelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -158,8 +158,8 @@ end
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85
%meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
@@ -265,7 +265,7 @@ if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model
prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
NumberOfParameters ...
+1-(1:NumberOfObservedVariables)'))); %last term in denominator of third line of (A.2), DS (2004)
- %Compute minus log likelihood according to (A.2), DS (2004)
+ %Compute minus log likelihood according to (A.2), DS (2004)
lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ... %first term in numerator of second line of (A.2), DS (2004)
+ .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMA_u_tilde)) ... %second term in numerator of second line of (A.2), DS (2004)
- .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ... %first term in denominator of second line of (A.2), DS (2004)
@@ -278,7 +278,7 @@ else% Evaluation of the likelihood of the dsge-var model when the dsge prior wei
PHI_star = iGXX*transpose(GYX);
%Compute minus log likelihood according to (33), DS (2004) (where the last term in the trace operator has been multiplied out)
lik = NumberOfObservations * ( log(det(SIGMA_u_star)) + NumberOfObservedVariables*log(2*pi) + ...
- trace(inv(SIGMA_u_star)*(mYY - transpose(mYX*PHI_star) - mYX*PHI_star + transpose(PHI_star)*mXX*PHI_star)/NumberOfObservations));
+ trace(inv(SIGMA_u_star)*(mYY - transpose(mYX*PHI_star) - mYX*PHI_star + transpose(PHI_star)*mXX*PHI_star)/NumberOfObservations));
lik = .5*lik;% Minus likelihood
SIGMA_u_tilde=SIGMA_u_star;
PHI_tilde=PHI_star;
diff --git a/matlab/dyn_first_order_solver.m b/matlab/dyn_first_order_solver.m
index cb5e407a833c14cf36940598f853feb3b2d819eb..0adf0e2c0ecc393347d9db2ed26d34d0728154c1 100644
--- a/matlab/dyn_first_order_solver.m
+++ b/matlab/dyn_first_order_solver.m
@@ -179,7 +179,7 @@ if task ~= 1 && (DynareOptions.dr_cycle_reduction || DynareOptions.dr_logarithmi
if n_current < DynareModel.endo_nbr
if DynareOptions.dr_cycle_reduction
error(['The cycle reduction algorithme can''t be used when the ' ...
- 'coefficient matrix for current variables isn''t invertible'])
+ 'coefficient matrix for current variables isn''t invertible'])
elseif DynareOptions.dr_logarithmic_reduction
error(['The logarithmic reduction algorithme can''t be used when the ' ...
'coefficient matrix for current variables isn''t invertible'])
diff --git a/matlab/dyn_forecast.m b/matlab/dyn_forecast.m
index 8189b7e5979241922936785bad140ad03305ac38..e5faf86afb6e3898801bdc54e663a57ead84ad55 100644
--- a/matlab/dyn_forecast.m
+++ b/matlab/dyn_forecast.m
@@ -43,7 +43,7 @@ if nargin<6 && options.prefilter
elseif nargin==6
mean_varobs=dataset_info.descriptive.mean';
end
-
+
info = 0;
oo=make_ex_(M,options,oo);
@@ -92,7 +92,7 @@ switch task
for i = 1:M.endo_nbr
v_name = deblank(M.endo_names(i,:));
y0(i,:) = y_smoothed.(v_name)(end-maximum_lag+1:end); %includes steady state or mean, but simult_ will subtract only steady state
- % 2. Subtract mean/steady state and add steady state; takes care of prefiltering
+ % 2. Subtract mean/steady state and add steady state; takes care of prefiltering
if isfield(oo.Smoother,'Constant') && isfield(oo.Smoother.Constant,v_name)
y0(i,:)=y0(i,:)-oo.Smoother.Constant.(v_name)(end-maximum_lag+1:end); %subtract mean or steady state
if options.loglinear
@@ -153,10 +153,10 @@ else
end
if isequal(M.H,0)
[yf,int_width] = simultxdet(y0,ex,oo.exo_det_simul,...
- options.order,var_list,M,oo,options);
+ options.order,var_list,M,oo,options);
else
[yf,int_width,int_width_ME] = simultxdet(y0,ex,oo.exo_det_simul,...
- options.order,var_list,M,oo,options);
+ options.order,var_list,M,oo,options);
end
end
diff --git a/matlab/dyn_latex_table.m b/matlab/dyn_latex_table.m
index 63fb6095f6d392da82b8f39a46d38bf80df9ee05..ca14e351f5f398ba2bc8d603fa2692d07b03c93b 100644
--- a/matlab/dyn_latex_table.m
+++ b/matlab/dyn_latex_table.m
@@ -27,7 +27,7 @@ OutputDirectoryName = CheckPath('Output',M_.dname);
%% get width of label column
if ~isempty(label_width)
label_width = max(size(deblank(char(headers(1,:),labels)),2)+2, ...
- label_width);
+ label_width);
else %use default length
label_width = max(size(deblank(char(headers(1,:),labels)),2))+2;
end
diff --git a/matlab/dyn_ramsey_static.m b/matlab/dyn_ramsey_static.m
index 964bede382c949bca72303aaa4002f2e4c3ad78b..d20582c3b5095ea850ffc695802f42bc9bfbc771 100644
--- a/matlab/dyn_ramsey_static.m
+++ b/matlab/dyn_ramsey_static.m
@@ -39,7 +39,7 @@ function [steady_state,params,check] = dyn_ramsey_static(ys_init,M,options_,oo)
params = M.params;
check = 0;
options_.steadystate.nocheck = 1; %locally disable checking because Lagrange multipliers are not accounted for in evaluate_steady_state_file
-% dyn_ramsey_static_1 is a subfunction
+ % dyn_ramsey_static_1 is a subfunction
nl_func = @(x) dyn_ramsey_static_1(x,M,options_,oo);
% check_static_model is a subfunction
@@ -120,7 +120,7 @@ if options_.steadystate_flag
ys_init(k_inst) = x; %set instrument, the only value required for steady state computation, to current value
[x,params,check] = evaluate_steady_state_file(ys_init,... %returned x now has size endo_nbr as opposed to input size of n_instruments
[oo.exo_steady_state; ...
- oo.exo_det_steady_state], ...
+ oo.exo_det_steady_state], ...
M,options_,~options_.steadystate.nocheck);
if any(imag(x(1:M.orig_endo_nbr))) %return with penalty
resids=1+sum(abs(imag(x(1:M.orig_endo_nbr)))); %return with penalty
@@ -154,12 +154,12 @@ Uyy = reshape(Uyy,endo_nbr,endo_nbr);
% set multipliers and auxiliary variables that
% depends on multipliers to 0 to compute residuals
if (options_.bytecode)
- [chck, res, junk] = bytecode('static',xx,[oo.exo_steady_state oo.exo_det_steady_state], ...
- params, 'evaluate');
- fJ = junk.g1;
+ [chck, res, junk] = bytecode('static',xx,[oo.exo_steady_state oo.exo_det_steady_state], ...
+ params, 'evaluate');
+ fJ = junk.g1;
else
- [res,fJ] = feval([fname '_static'],xx,[oo.exo_steady_state oo.exo_det_steady_state], ...
- params);
+ [res,fJ] = feval([fname '_static'],xx,[oo.exo_steady_state oo.exo_det_steady_state], ...
+ params);
end
% index of multipliers and corresponding equations
% the auxiliary variables before the Lagrange multipliers are treated
diff --git a/matlab/dyn_risky_steadystate_solver.m b/matlab/dyn_risky_steadystate_solver.m
index a7e09b1bd3ca4fc09221c83a3feb32e33089c0f3..1292523a4a9ae003c26d1fe79233508526859400 100644
--- a/matlab/dyn_risky_steadystate_solver.m
+++ b/matlab/dyn_risky_steadystate_solver.m
@@ -1,273 +1,311 @@
function [dr,info] = dyn_risky_steadystate_solver(ys0,M, ...
dr,options,oo)
- %@info:
- %! @deftypefn {Function File} {[@var{dr},@var{info}] =} dyn_risky_steadystate_solver (@var{ys0},@var{M},@var{dr},@var{options},@var{oo})
- %! @anchor{dyn_risky_steadystate_solver}
- %! @sp 1
- %! Computes the second order risky steady state and first and second order reduced form of the DSGE model.
- %! @sp 2
- %! @strong{Inputs}
- %! @sp 1
- %! @table @ @var
- %! @item ys0
- %! Vector containing a guess value for the risky steady state
- %! @item M
- %! Matlab's structure describing the model (initialized by @code{dynare}).
- %! @item dr
- %! Matlab's structure describing the reduced form solution of the model.
- %! @item options
- %! Matlab's structure describing the options (initialized by @code{dynare}).
- %! @item oo
- %! Matlab's structure gathering the results (initialized by @code{dynare}).
- %! @end table
- %! @sp 2
- %! @strong{Outputs}
- %! @sp 1
- %! @table @ @var
- %! @item dr
- %! Matlab's structure describing the reduced form solution of the model.
- %! @item info
- %! Integer scalar, error code.
- %! @sp 1
- %! @table @ @code
- %! @item info==0
- %! No error.
- %! @item info==1
- %! The model doesn't determine the current variables uniquely.
- %! @item info==2
- %! MJDGGES returned an error code.
- %! @item info==3
- %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
- %! @item info==4
- %! Blanchard & Kahn conditions are not satisfied: indeterminacy.
- %! @item info==5
- %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
- %! @item info==6
- %! The jacobian evaluated at the deterministic steady state is complex.
- %! @item info==19
- %! The steadystate routine thrown an exception (inconsistent deep parameters).
- %! @item info==20
- %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
- %! @item info==21
- %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
- %! @item info==22
- %! The steady has NaNs.
- %! @item info==23
- %! M_.params has been updated in the steadystate routine and has complex valued scalars.
- %! @item info==24
- %! M_.params has been updated in the steadystate routine and has some NaNs.
- %! @end table
- %! @end table
- %! @end deftypefn
- %@eod:
-
- % Copyright (C) 2001-2012 Dynare Team
- %
- % This file is part of Dynare.
- %
- % Dynare is free software: you can redistribute it and/or modify
- % it under the terms of the GNU General Public License as published by
- % the Free Software Foundation, either version 3 of the License, or
- % (at your option) any later version.
- %
- % Dynare is distributed in the hope that it will be useful,
- % but WITHOUT ANY WARRANTY; without even the implied warranty of
- % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- % GNU General Public License for more details.
- %
- % You should have received a copy of the GNU General Public License
- % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+%@info:
+%! @deftypefn {Function File} {[@var{dr},@var{info}] =} dyn_risky_steadystate_solver (@var{ys0},@var{M},@var{dr},@var{options},@var{oo})
+%! @anchor{dyn_risky_steadystate_solver}
+%! @sp 1
+%! Computes the second order risky steady state and first and second order reduced form of the DSGE model.
+%! @sp 2
+%! @strong{Inputs}
+%! @sp 1
+%! @table @ @var
+%! @item ys0
+%! Vector containing a guess value for the risky steady state
+%! @item M
+%! Matlab's structure describing the model (initialized by @code{dynare}).
+%! @item dr
+%! Matlab's structure describing the reduced form solution of the model.
+%! @item options
+%! Matlab's structure describing the options (initialized by @code{dynare}).
+%! @item oo
+%! Matlab's structure gathering the results (initialized by @code{dynare}).
+%! @end table
+%! @sp 2
+%! @strong{Outputs}
+%! @sp 1
+%! @table @ @var
+%! @item dr
+%! Matlab's structure describing the reduced form solution of the model.
+%! @item info
+%! Integer scalar, error code.
+%! @sp 1
+%! @table @ @code
+%! @item info==0
+%! No error.
+%! @item info==1
+%! The model doesn't determine the current variables uniquely.
+%! @item info==2
+%! MJDGGES returned an error code.
+%! @item info==3
+%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
+%! @item info==4
+%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
+%! @item info==5
+%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
+%! @item info==6
+%! The jacobian evaluated at the deterministic steady state is complex.
+%! @item info==19
+%! The steadystate routine thrown an exception (inconsistent deep parameters).
+%! @item info==20
+%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
+%! @item info==21
+%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
+%! @item info==22
+%! The steady has NaNs.
+%! @item info==23
+%! M_.params has been updated in the steadystate routine and has complex valued scalars.
+%! @item info==24
+%! M_.params has been updated in the steadystate routine and has some NaNs.
+%! @end table
+%! @end table
+%! @end deftypefn
+%@eod:
+
+% Copyright (C) 2001-2012 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+
+info = 0;
+lead_lag_incidence = M.lead_lag_incidence;
+order_var = dr.order_var;
+endo_nbr = M.endo_nbr;
+exo_nbr = M.exo_nbr;
+
+M.var_order_endo_names = M.endo_names(dr.order_var,:);
+
+[junk,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
+dr.i_fwrd_f = i_fwrd_f;
+nd = nnz(lead_lag_incidence) + M.exo_nbr;
+dr.nd = nd;
+kk = reshape(1:nd^2,nd,nd);
+kkk = reshape(1:nd^3,nd^2,nd);
+dr.i_fwrd2_f = kk(i_fwrd_f,i_fwrd_f);
+dr.i_fwrd2a_f = kk(i_fwrd_f,:);
+dr.i_fwrd3_f = kkk(dr.i_fwrd2_f,:);
+dr.i_uu = kk(end-exo_nbr+1:end,end-exo_nbr+1:end);
+if options.k_order_solver
+ func = @risky_residuals_k_order;
+else
+ func = @risky_residuals;
+end
+if isfield(options,'portfolio') && options.portfolio == 1
+ pm = portfolio_model_structure(M,options);
- info = 0;
- lead_lag_incidence = M.lead_lag_incidence;
- order_var = dr.order_var;
- endo_nbr = M.endo_nbr;
- exo_nbr = M.exo_nbr;
-
- M.var_order_endo_names = M.endo_names(dr.order_var,:);
-
- [junk,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
- dr.i_fwrd_f = i_fwrd_f;
- nd = nnz(lead_lag_incidence) + M.exo_nbr;
- dr.nd = nd;
- kk = reshape(1:nd^2,nd,nd);
- kkk = reshape(1:nd^3,nd^2,nd);
- dr.i_fwrd2_f = kk(i_fwrd_f,i_fwrd_f);
- dr.i_fwrd2a_f = kk(i_fwrd_f,:);
- dr.i_fwrd3_f = kkk(dr.i_fwrd2_f,:);
- dr.i_uu = kk(end-exo_nbr+1:end,end-exo_nbr+1:end);
- if options.k_order_solver
- func = @risky_residuals_k_order;
- else
- func = @risky_residuals;
- end
-
- if isfield(options,'portfolio') && options.portfolio == 1
- pm = portfolio_model_structure(M,options);
-
- x0 = ys0(pm.v_p);
- n = length(x0);
- [x, info] = solve1(@risky_residuals_ds,x0,1:n,1:n,0,options.gstep, ...
- options.solve_tolf,options.solve_tolx, ...
- options.steady.maxit,options.debug,pm,M,dr, ...
- options,oo);
- if info
- error('DS approach can''t be computed')
- end
- %[x, info] = csolve(@risky_residuals_ds,x0,[],1e-10,100,M,dr,options,oo);
- % ys0(l_var) = x;
- [resids,dr1] = risky_residuals_ds(x,pm,M,dr,options,oo);
- ys1 = dr1.ys;
- else
- pm = model_structure(M,options);
- end
-
- [ys, info] = solve1(func,ys0,1:endo_nbr,1:endo_nbr,0,options.gstep, ...
- options.solve_tolf,options.solve_tolx, ...
- options.steady.maxit,options.debug,pm,M,dr,options,oo);
- % [ys, info] = csolve(func,ys0,[],1e-10,100,M,dr,options,oo);
+ x0 = ys0(pm.v_p);
+ n = length(x0);
+ [x, info] = solve1(@risky_residuals_ds,x0,1:n,1:n,0,options.gstep, ...
+ options.solve_tolf,options.solve_tolx, ...
+ options.steady.maxit,options.debug,pm,M,dr, ...
+ options,oo);
if info
- error('RSS approach can''t be computed')
- end
- dr.ys = ys;
-
- [resid,dr] = func(ys,pm,M,dr,options,oo);
- dr.ghs2 = zeros(M.endo_nbr,1);
-
- for i=1:M.endo_nbr
- if isfield(options,'portfolio') && options.portfolio == 1
- disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys1(i), ...
- ys(i)))
- else
- disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys(i)))
- end
+ error('DS approach can''t be computed')
end
+ %[x, info] = csolve(@risky_residuals_ds,x0,[],1e-10,100,M,dr,options,oo);
+ % ys0(l_var) = x;
+ [resids,dr1] = risky_residuals_ds(x,pm,M,dr,options,oo);
+ ys1 = dr1.ys;
+else
+ pm = model_structure(M,options);
+end
+[ys, info] = solve1(func,ys0,1:endo_nbr,1:endo_nbr,0,options.gstep, ...
+ options.solve_tolf,options.solve_tolx, ...
+ options.steady.maxit,options.debug,pm,M,dr,options,oo);
+% [ys, info] = csolve(func,ys0,[],1e-10,100,M,dr,options,oo);
+if info
+ error('RSS approach can''t be computed')
end
+dr.ys = ys;
-function [resid,dr] = risky_residuals(ys,pm,M,dr,options,oo)
-
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- if M.exo_nbr == 0
- oo.exo_steady_state = [] ;
- end
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
-
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
- end
+[resid,dr] = func(ys,pm,M,dr,options,oo);
+dr.ghs2 = zeros(M.endo_nbr,1);
+for i=1:M.endo_nbr
if isfield(options,'portfolio') && options.portfolio == 1
- pm = portfolio_model_structure(M,options);
- x = ys(pm.v_p);
- dr = first_step_ds(x,pm,M,dr,options,oo);
- dr.ys = ys;
+ disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys1(i), ...
+ ys(i)))
else
- pm = model_structure(M,options);
- [dr,info] = dyn_first_order_solver(d1,M,dr,options,0);
- if info
- print_info(info,options.noprint,options);
- end
- dr = dyn_second_order_solver(d1,d2,dr,M,...
- options.threads.kronecker.A_times_B_kronecker_C,...
- options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
+ disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys(i)))
end
-
- gu1 = dr.ghu(pm.i_fwrd_g,:);
+end
- resid = resid1+0.5*(d1(:,pm.i_fwrd_f1)*dr.ghuu(pm.i_fwrd_g,:)+ ...
- d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
end
-function [resid,dr] = risky_residuals_ds(x,pm,M,dr,options,oo)
-
- v_p = pm.v_p;
- v_np = pm.v_np;
-
- % computing steady state of non-portfolio variables consistent with
- % assumed portfolio
- dr.ys(v_p) = x;
- ys0 = dr.ys(v_np);
- f_h =str2func([M.fname '_static']);
- [dr.ys(v_np),info] = csolve(@ds_static_model,ys0,[],1e-10,100,f_h,x,pm.eq_np,v_np,v_p, ...
- M.endo_nbr,M.exo_nbr,M.params);
+function [resid,dr] = risky_residuals(ys,pm,M,dr,options,oo)
+
+lead_lag_incidence = M.lead_lag_incidence;
+iyv = lead_lag_incidence';
+iyv = iyv(:);
+iyr0 = find(iyv) ;
+
+if M.exo_nbr == 0
+ oo.exo_steady_state = [] ;
+end
+
+z = repmat(ys,1,3);
+z = z(iyr0) ;
+[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+if ~isreal(d1) || ~isreal(d2)
+ pause
+end
+
+if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+end
+
+if isfield(options,'portfolio') && options.portfolio == 1
+ pm = portfolio_model_structure(M,options);
+ x = ys(pm.v_p);
+ dr = first_step_ds(x,pm,M,dr,options,oo);
+ dr.ys = ys;
+else
+ pm = model_structure(M,options);
+ [dr,info] = dyn_first_order_solver(d1,M,dr,options,0);
if info
- error('can''t compute non-portfolio steady state')
+ print_info(info,options.noprint,options);
end
-
- dr_np = first_step_ds(x,pm,M,dr,options,oo);
+ dr = dyn_second_order_solver(d1,d2,dr,M,...
+ options.threads.kronecker.A_times_B_kronecker_C,...
+ options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
+end
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- z = repmat(dr.ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
-
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
- end
+gu1 = dr.ghu(pm.i_fwrd_g,:);
-
- gu1 = dr_np.ghu(pm.i_fwrd_g,:);
+resid = resid1+0.5*(d1(:,pm.i_fwrd_f1)*dr.ghuu(pm.i_fwrd_g,:)+ ...
+ d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
+end
- resid = resid1+0.5*(d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
+function [resid,dr] = risky_residuals_ds(x,pm,M,dr,options,oo)
- resid = resid(pm.eq_p)
+v_p = pm.v_p;
+v_np = pm.v_np;
+
+% computing steady state of non-portfolio variables consistent with
+% assumed portfolio
+dr.ys(v_p) = x;
+ys0 = dr.ys(v_np);
+f_h =str2func([M.fname '_static']);
+[dr.ys(v_np),info] = csolve(@ds_static_model,ys0,[],1e-10,100,f_h,x,pm.eq_np,v_np,v_p, ...
+ M.endo_nbr,M.exo_nbr,M.params);
+if info
+ error('can''t compute non-portfolio steady state')
+end
+
+dr_np = first_step_ds(x,pm,M,dr,options,oo);
+
+lead_lag_incidence = M.lead_lag_incidence;
+iyv = lead_lag_incidence';
+iyv = iyv(:);
+iyr0 = find(iyv) ;
+
+z = repmat(dr.ys,1,3);
+z = z(iyr0) ;
+[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+if ~isreal(d1) || ~isreal(d2)
+ pause
+end
+
+if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+end
+
+
+gu1 = dr_np.ghu(pm.i_fwrd_g,:);
+
+resid = resid1+0.5*(d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
+
+resid = resid(pm.eq_p)
end
function dr_np = first_step_ds(x,pm,M,dr,options,oo)
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
+lead_lag_incidence = M.lead_lag_incidence;
+iyv = lead_lag_incidence';
+iyv = iyv(:);
+iyr0 = find(iyv) ;
+
+ys = dr.ys;
+ys(pm.v_p) = x;
+
+z = repmat(ys,1,3);
+z = z(iyr0) ;
+[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+if ~isreal(d1) || ~isreal(d2)
+ pause
+end
+
+if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+end
- ys = dr.ys;
- ys(pm.v_p) = x;
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
+d1_np = d1(pm.eq_np,pm.i_d1_np);
+d2_np = d2(pm.eq_np,pm.i_d2_np);
+
+[dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
+if info
+ print_info(info, 0, options);
+ return
+end
+
+dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
+ options.threads.kronecker.A_times_B_kronecker_C,...
+ options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
+end
+
+function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
+exo_nbr = M.exo_nbr;
+endo_nbr = M.endo_nbr;
+
+iyv = M.lead_lag_incidence';
+iyv = iyv(:);
+iyr0 = find(iyv) ;
+
+if exo_nbr == 0
+ oo.exo_steady_state = [] ;
+end
+
+z = repmat(ys,1,3);
+z = z(iyr0) ;
+[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+
+if isfield(options,'portfolio') && options.portfolio == 1
+ eq_np = pm.eq_np;
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
- end
+ d1_np = d1(eq_np,pm.i_d1_np);
+ d2_np = d2(eq_np,pm.i_d2_np);
- d1_np = d1(pm.eq_np,pm.i_d1_np);
- d2_np = d2(pm.eq_np,pm.i_d2_np);
+ M_np = pm.M_np;
+ dr_np = pm.dr_np;
[dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
if info
@@ -280,271 +318,233 @@ function dr_np = first_step_ds(x,pm,M,dr,options,oo)
options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
end
-function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
- exo_nbr = M.exo_nbr;
- endo_nbr = M.endo_nbr;
-
- iyv = M.lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- if exo_nbr == 0
- oo.exo_steady_state = [] ;
- end
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+i_fwrd_f1 = pm.i_fwrd_f1;
+i_fwrd_f2 = pm.i_fwrd_f2;
+i_fwrd_f3 = pm.i_fwrd_f3;
+i_fwrd_g = pm.i_fwrd_g;
+gu1 = dr_np.ghu(i_fwrd_g,:);
+ghuu = dr_np.ghuu;
+
+resid = resid1+0.5*(d1(:,i_fwrd_f1)*ghuu(i_fwrd_g,:)+d2(:,i_fwrd_f2)* ...
+ kron(gu1,gu1))*vec(M.Sigma_e);
+
+if nargout > 1
+ [resid1,d1,d2,d3] = feval([M.fname '_dynamic'],z,...
[oo.exo_simul ...
oo.exo_det_simul], M.params, dr.ys, 2);
+
- if isfield(options,'portfolio') && options.portfolio == 1
- eq_np = pm.eq_np;
-
- d1_np = d1(eq_np,pm.i_d1_np);
- d2_np = d2(eq_np,pm.i_d2_np);
-
- M_np = pm.M_np;
- dr_np = pm.dr_np;
-
- [dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
- if info
- print_info(info, 0, options);
- return
- end
-
- dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
- options.threads.kronecker.A_times_B_kronecker_C,...
- options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
- end
+ [a,b,c] = find(d2(eq_np,pm.i_d2_np));
+ d2_np = [a b c];
- i_fwrd_f1 = pm.i_fwrd_f1;
- i_fwrd_f2 = pm.i_fwrd_f2;
- i_fwrd_f3 = pm.i_fwrd_f3;
- i_fwrd_g = pm.i_fwrd_g;
- gu1 = dr_np.ghu(i_fwrd_g,:);
- ghuu = dr_np.ghuu;
+ [a,b,c] = find(d3(eq_np,pm.i_d3_np));
+ d3_np = [a b c];
- resid = resid1+0.5*(d1(:,i_fwrd_f1)*ghuu(i_fwrd_g,:)+d2(:,i_fwrd_f2)* ...
- kron(gu1,gu1))*vec(M.Sigma_e);
-
- if nargout > 1
- [resid1,d1,d2,d3] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
-
-
- [a,b,c] = find(d2(eq_np,pm.i_d2_np));
- d2_np = [a b c];
-
- [a,b,c] = find(d3(eq_np,pm.i_d3_np));
- d3_np = [a b c];
-
- options.order = 3;
- % space holder, unused by k_order_pertrubation
- dr_np.ys = dr.ys(pm.v_np);
- nu2 = exo_nbr*(exo_nbr+1)/2;
- nu3 = exo_nbr*(exo_nbr+1)*(exo_nbr+2)/3;
- M_np.NZZDerivatives = [nnz(d1_np); nnz(d2_np); nnz(d3_np)];
- [err,g_0, g_1, g_2, g_3] = k_order_perturbation(dr_np,M_np,options,d1_np,d2_np,d3_np);
- mexErrCheck('k_order_perturbation', err);
-
- gu1 = g_1(i_fwrd_g,end-exo_nbr+1:end);
- ghuu = unfold2(g_2(:,end-nu2+1:end),exo_nbr);
- ghsuu = get_ghsuu(g_3,size(g_1,2),exo_nbr);
-
- i_fwrd1_f2 = pm.i_fwrd1_f2;
- i_fwrd1_f3 = pm.i_fwrd1_f3;
- n = size(d1,2);
- d1b = d1 + 0.5*( ...
- d1(:,i_fwrd_f1)*...
- d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e))...
- + 0.5*d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e)));
- format short
- kk1 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
- nnz(M.lead_lag_incidence)+[1; 2]]
- kk2 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
- nnz(M.lead_lag_incidence)+[3; 4]]
- format short
- gu1
- kron(gu1,gu1)*vec(M.Sigma_e)
- disp(d1(:,:))
- disp(d1b(:,:))
- aa2=d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e));
- aa3=d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e));
- disp(d3(4,7+6*n+6*n*n))
- disp(d3(4,8+16*n+17*n*n)) %8,17,18
- disp(d3(4,8+17*n+16*n*n)) %8,17,18
- disp(d3(4,7*n+17+17*n*n)) %8,17,18
- disp(d3(4,7*n+18+16*n*n)) %8,17,18
- disp(d3(4,7*n*n+16*n+18)) %8,17,18
- disp(d3(4,7*n*n+17+17*n)) %8,17,18
- pause
- disp(aa2(:,kk1))
- disp(aa2(:,kk2))
- disp(aa3(:,kk1))
- disp(aa3(:,kk2))
- [dr,info] = dyn_first_order_solver(d1b,M,dr,options,0);
- if info
- print_info(info, 0, options);
- return
- end
-
- disp_dr(dr,dr.order_var,[]);
-
+ options.order = 3;
+ % space holder, unused by k_order_pertrubation
+ dr_np.ys = dr.ys(pm.v_np);
+ nu2 = exo_nbr*(exo_nbr+1)/2;
+ nu3 = exo_nbr*(exo_nbr+1)*(exo_nbr+2)/3;
+ M_np.NZZDerivatives = [nnz(d1_np); nnz(d2_np); nnz(d3_np)];
+ [err,g_0, g_1, g_2, g_3] = k_order_perturbation(dr_np,M_np,options,d1_np,d2_np,d3_np);
+ mexErrCheck('k_order_perturbation', err);
+
+ gu1 = g_1(i_fwrd_g,end-exo_nbr+1:end);
+ ghuu = unfold2(g_2(:,end-nu2+1:end),exo_nbr);
+ ghsuu = get_ghsuu(g_3,size(g_1,2),exo_nbr);
+
+ i_fwrd1_f2 = pm.i_fwrd1_f2;
+ i_fwrd1_f3 = pm.i_fwrd1_f3;
+ n = size(d1,2);
+ d1b = d1 + 0.5*( ...
+ d1(:,i_fwrd_f1)*...
+ d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e))...
+ + 0.5*d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e)));
+ format short
+ kk1 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
+ nnz(M.lead_lag_incidence)+[1; 2]]
+ kk2 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
+ nnz(M.lead_lag_incidence)+[3; 4]]
+ format short
+ gu1
+ kron(gu1,gu1)*vec(M.Sigma_e)
+ disp(d1(:,:))
+ disp(d1b(:,:))
+ aa2=d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e));
+ aa3=d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e));
+ disp(d3(4,7+6*n+6*n*n))
+ disp(d3(4,8+16*n+17*n*n)) %8,17,18
+ disp(d3(4,8+17*n+16*n*n)) %8,17,18
+ disp(d3(4,7*n+17+17*n*n)) %8,17,18
+ disp(d3(4,7*n+18+16*n*n)) %8,17,18
+ disp(d3(4,7*n*n+16*n+18)) %8,17,18
+ disp(d3(4,7*n*n+17+17*n)) %8,17,18
+ pause
+ disp(aa2(:,kk1))
+ disp(aa2(:,kk2))
+ disp(aa3(:,kk1))
+ disp(aa3(:,kk2))
+ [dr,info] = dyn_first_order_solver(d1b,M,dr,options,0);
+ if info
+ print_info(info, 0, options);
+ return
end
+
+ disp_dr(dr,dr.order_var,[]);
+
+end
end
function y=unfold2(x,n)
- y = zeros(size(x,1),n*n);
- k = 1;
- for i=1:n
- for j=i:n
- y(:,(i-1)*n+j) = x(:,k);
- if i ~= j
- y(:,(j-1)*n+i) = x(:,k);
- end
- k = k+1;
+y = zeros(size(x,1),n*n);
+k = 1;
+for i=1:n
+ for j=i:n
+ y(:,(i-1)*n+j) = x(:,k);
+ if i ~= j
+ y(:,(j-1)*n+i) = x(:,k);
end
+ k = k+1;
end
end
+end
function y=unfold3(x,n)
- y = zeros(size(x,1),n*n*n);
- k = 1;
- for i=1:n
- for j=i:n
- for m=j:n
- y(:,(i-1)*n*n+(j-1)*n+m) = x(:,k);
- y(:,(i-1)*n*n+(m-1)*n+j) = x(:,k);
- y(:,(j-1)*n*n+(i-1)*n+m) = x(:,k);
- y(:,(j-1)*n*n+(m-1)*n+i) = x(:,k);
- y(:,(m-1)*n*n+(i-1)*n+j) = x(:,k);
- y(:,(m-1)*n*n+(j-1)*n+i) = x(:,k);
-
- k = k+1;
- end
+y = zeros(size(x,1),n*n*n);
+k = 1;
+for i=1:n
+ for j=i:n
+ for m=j:n
+ y(:,(i-1)*n*n+(j-1)*n+m) = x(:,k);
+ y(:,(i-1)*n*n+(m-1)*n+j) = x(:,k);
+ y(:,(j-1)*n*n+(i-1)*n+m) = x(:,k);
+ y(:,(j-1)*n*n+(m-1)*n+i) = x(:,k);
+ y(:,(m-1)*n*n+(i-1)*n+j) = x(:,k);
+ y(:,(m-1)*n*n+(j-1)*n+i) = x(:,k);
+
+ k = k+1;
end
end
end
+end
function pm = model_structure(M,options)
- lead_index = M.maximum_endo_lag+2;
- lead_lag_incidence = M.lead_lag_incidence;
- dr = struct();
- dr = set_state_space(dr,M,options);
- pm.i_fwrd_g = find(lead_lag_incidence(lead_index,dr.order_var)');
-
- i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,dr.order_var));
- pm.i_fwrd_f1 = i_fwrd_f1;
- n = nnz(lead_lag_incidence)+M.exo_nbr;
- ih = reshape(1:n*n,n,n);
- i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
- pm.i_fwrd_f2 = i_fwrd_f2(:);
- i_fwrd1_f2 = ih(i_fwrd_f1,:);
- pm.i_fwrd1_f2 = i_fwrd1_f2(:);
-
- ih = reshape(1:n*n*n,n,n,n);
- i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
- pm.i_fwrd_f3 = i_fwrd_f3(:);
- i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
- pm.i_fwrd1_f3 = i_fwrd1_f3(:);
+lead_index = M.maximum_endo_lag+2;
+lead_lag_incidence = M.lead_lag_incidence;
+dr = struct();
+dr = set_state_space(dr,M,options);
+pm.i_fwrd_g = find(lead_lag_incidence(lead_index,dr.order_var)');
+
+i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,dr.order_var));
+pm.i_fwrd_f1 = i_fwrd_f1;
+n = nnz(lead_lag_incidence)+M.exo_nbr;
+ih = reshape(1:n*n,n,n);
+i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
+pm.i_fwrd_f2 = i_fwrd_f2(:);
+i_fwrd1_f2 = ih(i_fwrd_f1,:);
+pm.i_fwrd1_f2 = i_fwrd1_f2(:);
+
+ih = reshape(1:n*n*n,n,n,n);
+i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
+pm.i_fwrd_f3 = i_fwrd_f3(:);
+i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
+pm.i_fwrd1_f3 = i_fwrd1_f3(:);
end
function pm = portfolio_model_structure(M,options)
- i_d3_np = [];
- i_d3_p = [];
-
- lead_index = M.maximum_endo_lag+2;
- lead_lag_incidence = M.lead_lag_incidence;
- eq_tags = M.equations_tags;
- n_tags = size(eq_tags,1);
- eq_p = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
- 'portfolio'),1));
- pm.eq_p = eq_p;
- pm.eq_np = setdiff(1:M.endo_nbr,eq_p);
- v_p = zeros(n_tags,1);
- for i=1:n_tags
- v_p(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
- length(cell2mat(eq_tags(i,3)))));
- end
- if any(lead_lag_incidence(lead_index,v_p))
- error(['portfolio variables appear in the model as forward ' ...
- 'variable'])
- end
- pm.v_p = v_p;
- v_np = setdiff(1:M.endo_nbr,v_p);
- pm.v_np = v_np;
- lli_np = lead_lag_incidence(:,v_np)';
- k = find(lli_np);
- lead_lag_incidence_np = lli_np;
- lead_lag_incidence_np(k) = 1:nnz(lli_np);
- lead_lag_incidence_np = lead_lag_incidence_np';
- pm.lead_lag_incidence_np = lead_lag_incidence_np;
- i_d1_np = [nonzeros(lli_np); nnz(lead_lag_incidence)+(1:M.exo_nbr)'];
- pm.i_d1_np = i_d1_np;
-
- n = nnz(lead_lag_incidence)+M.exo_nbr;
- ih = reshape(1:n*n,n,n);
- i_d2_np = ih(i_d1_np,i_d1_np);
- pm.i_d2_np = i_d2_np(:);
-
- ih = reshape(1:n*n*n,n,n,n);
- i_d3_np = ih(i_d1_np,i_d1_np,i_d1_np);
- pm.i_d3_np = i_d3_np(:);
-
- M_np = M;
- M_np.lead_lag_incidence = lead_lag_incidence_np;
- M_np.lead_lag_incidence = lead_lag_incidence_np;
- M_np.endo_nbr = length(v_np);
- M_np.endo_names = M.endo_names(v_np,:);
- dr_np = struct();
- dr_np = set_state_space(dr_np,M_np,options);
- pm.dr_np = dr_np;
- M_np.var_order_endo_names = M_np.endo_names(dr_np.order_var,:);
- pm.M_np = M_np;
- pm.i_fwrd_g = find(lead_lag_incidence_np(lead_index,dr_np.order_var)');
-
- i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,:));
- pm.i_fwrd_f1 = i_fwrd_f1;
- n = nnz(lead_lag_incidence)+M.exo_nbr;
- ih = reshape(1:n*n,n,n);
- i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
- pm.i_fwrd_f2 = i_fwrd_f2(:);
- i_fwrd1_f2 = ih(i_fwrd_f1,:);
- pm.i_fwrd1_f2 = i_fwrd1_f2(:);
-
- ih = reshape(1:n*n*n,n,n,n);
- i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
- pm.i_fwrd_f3 = i_fwrd_f3(:);
- i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
- pm.i_fwrd1_f3 = i_fwrd1_f3(:);
+i_d3_np = [];
+i_d3_p = [];
+
+lead_index = M.maximum_endo_lag+2;
+lead_lag_incidence = M.lead_lag_incidence;
+eq_tags = M.equations_tags;
+n_tags = size(eq_tags,1);
+eq_p = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
+ 'portfolio'),1));
+pm.eq_p = eq_p;
+pm.eq_np = setdiff(1:M.endo_nbr,eq_p);
+v_p = zeros(n_tags,1);
+for i=1:n_tags
+ v_p(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
+ length(cell2mat(eq_tags(i,3)))));
+end
+if any(lead_lag_incidence(lead_index,v_p))
+ error(['portfolio variables appear in the model as forward ' ...
+ 'variable'])
+end
+pm.v_p = v_p;
+v_np = setdiff(1:M.endo_nbr,v_p);
+pm.v_np = v_np;
+lli_np = lead_lag_incidence(:,v_np)';
+k = find(lli_np);
+lead_lag_incidence_np = lli_np;
+lead_lag_incidence_np(k) = 1:nnz(lli_np);
+lead_lag_incidence_np = lead_lag_incidence_np';
+pm.lead_lag_incidence_np = lead_lag_incidence_np;
+i_d1_np = [nonzeros(lli_np); nnz(lead_lag_incidence)+(1:M.exo_nbr)'];
+pm.i_d1_np = i_d1_np;
+
+n = nnz(lead_lag_incidence)+M.exo_nbr;
+ih = reshape(1:n*n,n,n);
+i_d2_np = ih(i_d1_np,i_d1_np);
+pm.i_d2_np = i_d2_np(:);
+
+ih = reshape(1:n*n*n,n,n,n);
+i_d3_np = ih(i_d1_np,i_d1_np,i_d1_np);
+pm.i_d3_np = i_d3_np(:);
+
+M_np = M;
+M_np.lead_lag_incidence = lead_lag_incidence_np;
+M_np.lead_lag_incidence = lead_lag_incidence_np;
+M_np.endo_nbr = length(v_np);
+M_np.endo_names = M.endo_names(v_np,:);
+dr_np = struct();
+dr_np = set_state_space(dr_np,M_np,options);
+pm.dr_np = dr_np;
+M_np.var_order_endo_names = M_np.endo_names(dr_np.order_var,:);
+pm.M_np = M_np;
+pm.i_fwrd_g = find(lead_lag_incidence_np(lead_index,dr_np.order_var)');
+
+i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,:));
+pm.i_fwrd_f1 = i_fwrd_f1;
+n = nnz(lead_lag_incidence)+M.exo_nbr;
+ih = reshape(1:n*n,n,n);
+i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
+pm.i_fwrd_f2 = i_fwrd_f2(:);
+i_fwrd1_f2 = ih(i_fwrd_f1,:);
+pm.i_fwrd1_f2 = i_fwrd1_f2(:);
+
+ih = reshape(1:n*n*n,n,n,n);
+i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
+pm.i_fwrd_f3 = i_fwrd_f3(:);
+i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
+pm.i_fwrd1_f3 = i_fwrd1_f3(:);
end
function r=ds_static_model(y0,f_h,p0,eq_np,v_np,v_p,endo_nbr,exo_nbr,params)
- ys = zeros(endo_nbr,1);
- ys(v_p) = p0;
- ys(v_np) = y0;
- r = f_h(ys,zeros(exo_nbr,1),params);
- r = r(eq_np);
+ys = zeros(endo_nbr,1);
+ys(v_p) = p0;
+ys(v_np) = y0;
+r = f_h(ys,zeros(exo_nbr,1),params);
+r = r(eq_np);
end
function ghsuu = get_ghsuu(g,ns,nx)
- nxx = nx*(nx+1)/2;
- m1 = 0;
- m2 = ns*(ns+1)/2;
- kk = 1:(nx*nx);
- ghsuu = zeros(size(g,1),(ns*nx*nx));
-
- for i=1:n
- j = m1+(1:m2);
- k = j(end-nxx+1:end);
- ghsuu(:,kk) = unfold2(g(:,k),nx);
- m1 = m1+m2;
- m2 = m2 - (n-i+1);
- kk = kk + nx*nx;
- end
+nxx = nx*(nx+1)/2;
+m1 = 0;
+m2 = ns*(ns+1)/2;
+kk = 1:(nx*nx);
+ghsuu = zeros(size(g,1),(ns*nx*nx));
+
+for i=1:n
+ j = m1+(1:m2);
+ k = j(end-nxx+1:end);
+ ghsuu(:,kk) = unfold2(g(:,k),nx);
+ m1 = m1+m2;
+ m2 = m2 - (n-i+1);
+ kk = kk + nx*nx;
+end
end
\ No newline at end of file
diff --git a/matlab/dyn_saveas.m b/matlab/dyn_saveas.m
index a31c724b2e13e654e0e63e09fa9a557812b90c6b..fb70bab75599ccaf516c139c7aa0d7d066bbd363 100644
--- a/matlab/dyn_saveas.m
+++ b/matlab/dyn_saveas.m
@@ -53,19 +53,19 @@ if any(strcmp('fig',cellstr(graph_format)))
error('Octave cannot create fig files!')
else
if nodisplay
-% THE FOLLOWING LINES COULD BE USED IF BUGS/PROBLEMS ARE REPORTED USING LINE 60
-% set(h,'Units','Normalized')
-% mypos=get(h,'Position');
-% set(h,'Position',[-1 -1 mypos(3:4)])
-% set(h, 'Visible','on');
+ % THE FOLLOWING LINES COULD BE USED IF BUGS/PROBLEMS ARE REPORTED USING LINE 60
+ % set(h,'Units','Normalized')
+ % mypos=get(h,'Position');
+ % set(h,'Position',[-1 -1 mypos(3:4)])
+ % set(h, 'Visible','on');
set(h,'CreateFcn','set(gcf, ''Visible'',''on'')') ;
end
saveas(h,[fname '.fig']);
end
end
if any(strcmp('none',cellstr(graph_format)))
-% don't save
-% check here as a reminder that none is an option to graph_format
+ % don't save
+ % check here as a reminder that none is an option to graph_format
end
if nodisplay
close(h);
diff --git a/matlab/dyn_second_order_solver.m b/matlab/dyn_second_order_solver.m
index dd2d2bfb8ee849ba80fc47f28ffbf509ddad68c4..37e13f8412ee21ff289a22ecd6c446aac5363719 100644
--- a/matlab/dyn_second_order_solver.m
+++ b/matlab/dyn_second_order_solver.m
@@ -48,135 +48,135 @@ function dr = dyn_second_order_solver(jacobia,hessian_mat,dr,M_,threads_ABC,thre
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
- dr.ghxx = [];
- dr.ghuu = [];
- dr.ghxu = [];
- dr.ghs2 = [];
- Gy = dr.Gy;
-
- kstate = dr.kstate;
- nstatic = M_.nstatic;
- nfwrd = M_.nfwrd;
- nspred = M_.nspred;
- nboth = M_.nboth;
- nsfwrd = M_.nsfwrd;
- order_var = dr.order_var;
- nd = size(kstate,1);
- lead_lag_incidence = M_.lead_lag_incidence;
-
- np = nd - nsfwrd;
-
- k1 = nonzeros(lead_lag_incidence(:,order_var)');
- kk = [k1; length(k1)+(1:M_.exo_nbr+M_.exo_det_nbr)'];
- nk = size(kk,1);
- kk1 = reshape([1:nk^2],nk,nk);
- kk1 = kk1(kk,kk);
- % reordering second order derivatives
- hessian_mat = hessian_mat(:,kk1(:));
-
- zx = zeros(np,np);
- zu=zeros(np,M_.exo_nbr);
- zx(1:np,:)=eye(np);
- k0 = [1:M_.endo_nbr];
- gx1 = dr.ghx;
- hu = dr.ghu(nstatic+[1:nspred],:);
- k0 = find(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)');
- zx = [zx; gx1(k0,:)];
- zu = [zu; dr.ghu(k0,:)];
- k1 = find(lead_lag_incidence(M_.maximum_endo_lag+2,order_var)');
- zu = [zu; gx1(k1,:)*hu];
- zx = [zx; gx1(k1,:)*Gy];
- zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
- zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
- [nrzx,nczx] = size(zx);
-
- [rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,threads_BC);
- mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
- rhs = -rhs;
-
- %lhs
- n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
- A = zeros(M_.endo_nbr,M_.endo_nbr);
- B = zeros(M_.endo_nbr,M_.endo_nbr);
- A(:,k0) = jacobia(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
- % variables with the highest lead
- k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
- % Jacobian with respect to the variables with the highest lead
- fyp = jacobia(:,kstate(k1,3)+nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)));
- B(:,nstatic+M_.npred+1:end) = fyp;
- [junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
- A(1:M_.endo_nbr,nstatic+1:nstatic+nspred)=...
- A(1:M_.endo_nbr,nstatic+[1:nspred])+fyp*gx1(k1,1:nspred);
- C = Gy;
- D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
-
-
- [err, dr.ghxx] = gensylv(2,A,B,C,D);
- mexErrCheck('gensylv', err);
-
- %ghxu
- %rhs
- hu = dr.ghu(nstatic+1:nstatic+nspred,:);
- [rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,zu,threads_BC);
- mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
-
- hu1 = [hu;zeros(np-nspred,M_.exo_nbr)];
- [nrhx,nchx] = size(Gy);
- [nrhu1,nchu1] = size(hu1);
-
- [abcOut,err] = A_times_B_kronecker_C(dr.ghxx,Gy,hu1,threads_ABC);
- mexErrCheck('A_times_B_kronecker_C', err);
- B1 = B*abcOut;
- rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
-
-
- %lhs
- dr.ghxu = A\rhs;
-
- %ghuu
- %rhs
- [rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zu,threads_BC);
- mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
-
- [B1, err] = A_times_B_kronecker_C(B*dr.ghxx,hu1,threads_ABC);
- mexErrCheck('A_times_B_kronecker_C', err);
- rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
-
- %lhs
- dr.ghuu = A\rhs;
-
- % dr.ghs2
- % derivatives of F with respect to forward variables
- % reordering predetermined variables in diminishing lag order
- O1 = zeros(M_.endo_nbr,nstatic);
- O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-nspred);
- LHS = zeros(M_.endo_nbr,M_.endo_nbr);
- LHS(:,k0) = jacobia(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
- RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
- gu = dr.ghu;
- guu = dr.ghuu;
- E = eye(M_.endo_nbr);
- kh = reshape([1:nk^2],nk,nk);
- kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
- E1 = [eye(nspred); zeros(kp-nspred,nspred)];
- H = E1;
- hxx = dr.ghxx(nstatic+[1:nspred],:);
- [junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
- k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)';
- [B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC);
- mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
- RHS = RHS + jacobia(:,k2)*guu(k2a,:)+B1;
-
- % LHS
- LHS = LHS + jacobia(:,k2)*(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)*H O2(k2a,:)]);
-
- RHS = RHS*M_.Sigma_e(:);
- dr.fuu = RHS;
- %RHS = -RHS-dr.fbias;
- RHS = -RHS;
- dr.ghs2 = LHS\RHS;
-
- % deterministic exogenous variables
- if M_.exo_det_nbr > 0
- end
+
+dr.ghxx = [];
+dr.ghuu = [];
+dr.ghxu = [];
+dr.ghs2 = [];
+Gy = dr.Gy;
+
+kstate = dr.kstate;
+nstatic = M_.nstatic;
+nfwrd = M_.nfwrd;
+nspred = M_.nspred;
+nboth = M_.nboth;
+nsfwrd = M_.nsfwrd;
+order_var = dr.order_var;
+nd = size(kstate,1);
+lead_lag_incidence = M_.lead_lag_incidence;
+
+np = nd - nsfwrd;
+
+k1 = nonzeros(lead_lag_incidence(:,order_var)');
+kk = [k1; length(k1)+(1:M_.exo_nbr+M_.exo_det_nbr)'];
+nk = size(kk,1);
+kk1 = reshape([1:nk^2],nk,nk);
+kk1 = kk1(kk,kk);
+% reordering second order derivatives
+hessian_mat = hessian_mat(:,kk1(:));
+
+zx = zeros(np,np);
+zu=zeros(np,M_.exo_nbr);
+zx(1:np,:)=eye(np);
+k0 = [1:M_.endo_nbr];
+gx1 = dr.ghx;
+hu = dr.ghu(nstatic+[1:nspred],:);
+k0 = find(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)');
+zx = [zx; gx1(k0,:)];
+zu = [zu; dr.ghu(k0,:)];
+k1 = find(lead_lag_incidence(M_.maximum_endo_lag+2,order_var)');
+zu = [zu; gx1(k1,:)*hu];
+zx = [zx; gx1(k1,:)*Gy];
+zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
+zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
+[nrzx,nczx] = size(zx);
+
+[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,threads_BC);
+mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
+rhs = -rhs;
+
+%lhs
+n = M_.endo_nbr+sum(kstate(:,2) > M_.maximum_endo_lag+1 & kstate(:,2) < M_.maximum_endo_lag+M_.maximum_endo_lead+1);
+A = zeros(M_.endo_nbr,M_.endo_nbr);
+B = zeros(M_.endo_nbr,M_.endo_nbr);
+A(:,k0) = jacobia(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
+% variables with the highest lead
+k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
+% Jacobian with respect to the variables with the highest lead
+fyp = jacobia(:,kstate(k1,3)+nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)));
+B(:,nstatic+M_.npred+1:end) = fyp;
+[junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
+A(1:M_.endo_nbr,nstatic+1:nstatic+nspred)=...
+ A(1:M_.endo_nbr,nstatic+[1:nspred])+fyp*gx1(k1,1:nspred);
+C = Gy;
+D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
+
+
+[err, dr.ghxx] = gensylv(2,A,B,C,D);
+mexErrCheck('gensylv', err);
+
+%ghxu
+%rhs
+hu = dr.ghu(nstatic+1:nstatic+nspred,:);
+[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,zu,threads_BC);
+mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
+
+hu1 = [hu;zeros(np-nspred,M_.exo_nbr)];
+[nrhx,nchx] = size(Gy);
+[nrhu1,nchu1] = size(hu1);
+
+[abcOut,err] = A_times_B_kronecker_C(dr.ghxx,Gy,hu1,threads_ABC);
+mexErrCheck('A_times_B_kronecker_C', err);
+B1 = B*abcOut;
+rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
+
+
+%lhs
+dr.ghxu = A\rhs;
+
+%ghuu
+%rhs
+[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zu,threads_BC);
+mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
+
+[B1, err] = A_times_B_kronecker_C(B*dr.ghxx,hu1,threads_ABC);
+mexErrCheck('A_times_B_kronecker_C', err);
+rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B1;
+
+%lhs
+dr.ghuu = A\rhs;
+
+% dr.ghs2
+% derivatives of F with respect to forward variables
+% reordering predetermined variables in diminishing lag order
+O1 = zeros(M_.endo_nbr,nstatic);
+O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-nspred);
+LHS = zeros(M_.endo_nbr,M_.endo_nbr);
+LHS(:,k0) = jacobia(:,nonzeros(lead_lag_incidence(M_.maximum_endo_lag+1,order_var)));
+RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
+gu = dr.ghu;
+guu = dr.ghuu;
+E = eye(M_.endo_nbr);
+kh = reshape([1:nk^2],nk,nk);
+kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
+E1 = [eye(nspred); zeros(kp-nspred,nspred)];
+H = E1;
+hxx = dr.ghxx(nstatic+[1:nspred],:);
+[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
+k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)';
+[B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC);
+mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
+RHS = RHS + jacobia(:,k2)*guu(k2a,:)+B1;
+
+% LHS
+LHS = LHS + jacobia(:,k2)*(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)*H O2(k2a,:)]);
+
+RHS = RHS*M_.Sigma_e(:);
+dr.fuu = RHS;
+%RHS = -RHS-dr.fbias;
+RHS = -RHS;
+dr.ghs2 = LHS\RHS;
+
+% deterministic exogenous variables
+if M_.exo_det_nbr > 0
+end
diff --git a/matlab/dyn_waitbar.m b/matlab/dyn_waitbar.m
index f9665c950982b6cc879fc9af6c4489c5120be9b7..5b315a5d1d99a71aada401fa7737ffeaa606d224 100644
--- a/matlab/dyn_waitbar.m
+++ b/matlab/dyn_waitbar.m
@@ -45,7 +45,7 @@ end
if ~whoiam
if isoctave || options_.console_mode
-
+
if init,
diary off;
running_text = varargin{1};
diff --git a/matlab/dynare.m b/matlab/dynare.m
index 3ab08aef485d2c5e24db3be395a02e7c024bd848..38dbe1b4c12b40f0157ed249619131093531fa85 100644
--- a/matlab/dynare.m
+++ b/matlab/dynare.m
@@ -111,7 +111,7 @@ if isempty(strfind(fname,'.'))
% Checking file extension
else
if dot_location~=length(fname)-3 ... %if the file name has fewer than 4 characters and there is a period
- || ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.MOD') ...
+ || ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.MOD') ...
&& ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),'.DYN')
error('DYNARE: argument must be a filename with .mod or .dyn extension and must not include any other periods')
end;
@@ -176,11 +176,11 @@ else
end
if isempty(strfind(arch, '64'))
- arch_ext = '32';
- disp('Using 32-bit preprocessor');
+ arch_ext = '32';
+ disp('Using 32-bit preprocessor');
else
- arch_ext = '64';
- disp('Using 64-bit preprocessor');
+ arch_ext = '64';
+ disp('Using 64-bit preprocessor');
end
command = ['"' dynareroot 'preprocessor' arch_ext filesep 'dynare_m" ' fname] ;
diff --git a/matlab/dynare_config.m b/matlab/dynare_config.m
index 5b2f34b9e59307f2b1c2e023941b16d6e3d71ff6..342a03c4b7726f4acf3f99e827c7c17886a47800 100644
--- a/matlab/dynare_config.m
+++ b/matlab/dynare_config.m
@@ -115,7 +115,7 @@ end
% nanmean is in Octave Forge Statistics package and in MATLAB Statistics
% toolbox
if (isoctave && ~user_has_octave_forge_package('statistics')) ...
- || (~isoctave && ~user_has_matlab_license('statistics_toolbox'))
+ || (~isoctave && ~user_has_matlab_license('statistics_toolbox'))
p{end+1} = '/missing/nanmean';
end
diff --git a/matlab/dynare_estimation.m b/matlab/dynare_estimation.m
index 5d140bd189888125d94a25089c280ae21c75bedb..a476a8072c9f4ba1f5ba833e35ef28d7251a68ef 100644
--- a/matlab/dynare_estimation.m
+++ b/matlab/dynare_estimation.m
@@ -33,7 +33,7 @@ global options_ oo_ M_ dataset_ dataset_info
oo_recursive_={};
mode_file0 = options_.mode_file; % store mode_file set by the user
-% Test if the order of approximation is nonzero (the preprocessor tests if order is non negative).
+ % Test if the order of approximation is nonzero (the preprocessor tests if order is non negative).
if isequal(options_.order,0)
error('Estimation:: The order of the Taylor approximation cannot be 0!')
end
@@ -55,7 +55,7 @@ nfirstobs = length(first_obs);
if nnobs~=1 && nfirstobs~=1
error('You cannot simultaneously do rolling window and recursive estimation')
end
-
+
horizon = options_.forecast;
if nargin<2 || ~exist('dname','var') || isempty(dname)
@@ -142,7 +142,7 @@ if nnobs > 1 && horizon > 0
gend = dataset_.nobs;
time_offset=min(3,gend-1); %for observables, plot 3 previous periods unless data is shorter
k = time_offset+min(nobs(end)-nobs(1)+horizon, ...
- size(dataset_.data,1)-nobs(1));
+ size(dataset_.data,1)-nobs(1));
data2 = dataset_info.rawdata(end-k+1:end,:);
[nbplt,nr,nc,lr,lc,nstar] = pltorg(nvar);
m = 1;
@@ -167,23 +167,23 @@ if nnobs > 1 && horizon > 0
for j=1:nnobs
if mh_replic > 0
oo_.RecursiveForecast.Mean.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.MeanForecast.Mean.(vname);
+ oo_recursive_{nobs(j)}.MeanForecast.Mean.(vname);
oo_.RecursiveForecast.HPDinf.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.MeanForecast.HPDinf.(vname);
+ oo_recursive_{nobs(j)}.MeanForecast.HPDinf.(vname);
oo_.RecursiveForecast.HPDsup.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.MeanForecast.HPDsup.(vname);
+ oo_recursive_{nobs(j)}.MeanForecast.HPDsup.(vname);
oo_.RecursiveForecast.HPDTotalinf.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.PointForecast.HPDinf.(vname);
+ oo_recursive_{nobs(j)}.PointForecast.HPDinf.(vname);
oo_.RecursiveForecast.HPDTotalsup.(vname)(j,:) = ...
- oo_recursive_{nobs(j)}.PointForecast.HPDsup.(vname);
+ oo_recursive_{nobs(j)}.PointForecast.HPDsup.(vname);
else
oo_.RecursiveForecast.Mean.(vname)(j,:) =...
- oo_recursive_{nobs(j)}.forecast.Mean.(vname);
+ oo_recursive_{nobs(j)}.forecast.Mean.(vname);
oo_.RecursiveForecast.HPDinf.(vname)(j,:) =...
- oo_recursive_{nobs(j)}.forecast.HPDinf.(vname);
+ oo_recursive_{nobs(j)}.forecast.HPDinf.(vname);
oo_.RecursiveForecast.HPDsup.(vname)(j,:) =...
- oo_recursive_{nobs(j)}.forecast.HPDsup.(vname);
- end
+ oo_recursive_{nobs(j)}.forecast.HPDsup.(vname);
+ end
x = nobs(1)+nobs(j)-nobs(1)+(1:horizon);
y = oo_.RecursiveForecast.Mean.(vname)(j,:);
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 0a27271ded23857c25bafe534256686b7c167f2f..83361af1164a3ee07e48ce0ee20cf3236a706c1e 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -221,7 +221,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
fprintf('\nFinal value of minus the log posterior (or likelihood):%f \n', fval);
if isnumeric(options_.mode_compute) && options_.mode_compute==5 && options_.analytic_derivation==-1 %reset options changed by newrat
- options_.analytic_derivation = options_analytic_derivation_old; %reset
+ options_.analytic_derivation = options_analytic_derivation_old; %reset
elseif isnumeric(options_.mode_compute) && options_.mode_compute==6 %save scaling factor
save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
options_.mh_jscale = Scale;
@@ -233,7 +233,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 2;
[junk1, junk2,junk3, junk4, hh] = feval(objective_function,xparam1, ...
- dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
+ dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
options_.analytic_derivation = ana_deriv_old;
elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1),
% with flag==0, we force to use the hessian from outer product gradient of optimizer 5
@@ -296,7 +296,7 @@ if ~options_.mh_posterior_mode_estimation && options_.cova_compute
params_at_bound=find(abs(xparam1-bounds.ub)<1.e-10 | abs(xparam1-bounds.lb)<1.e-10);
if ~isempty(params_at_bound)
for ii=1:length(params_at_bound)
- params_at_bound_name{ii,1}=get_the_name(params_at_bound(ii),0,M_,estim_params_,options_);
+ params_at_bound_name{ii,1}=get_the_name(params_at_bound(ii),0,M_,estim_params_,options_);
end
disp_string=[params_at_bound_name{1,:}];
for ii=2:size(params_at_bound_name,1)
@@ -385,36 +385,36 @@ if np > 0
end
switch options_.MCMC_jumping_covariance
- case 'hessian' %Baseline
- %do nothing and use hessian from mode_compute
- case 'prior_variance' %Use prior variance
- if any(isinf(bayestopt_.p2))
- error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
- else
- hh = diag(1./(bayestopt_.p2.^2));
- end
+ case 'hessian' %Baseline
+ %do nothing and use hessian from mode_compute
+ case 'prior_variance' %Use prior variance
+ if any(isinf(bayestopt_.p2))
+ error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
+ else
+ hh = diag(1./(bayestopt_.p2.^2));
+ end
+ hsd = sqrt(diag(hh));
+ invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
+ case 'identity_matrix' %Use identity
+ invhess = eye(nx);
+ otherwise %user specified matrix in file
+ try
+ load(options_.MCMC_jumping_covariance,'jumping_covariance')
+ hh=jumping_covariance;
+ catch
+ error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
+ end
+ [nrow, ncol]=size(hh);
+ if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
+ error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
+ end
+ try %check for positive definiteness
+ chol(hh);
hsd = sqrt(diag(hh));
invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
- case 'identity_matrix' %Use identity
- invhess = eye(nx);
- otherwise %user specified matrix in file
- try
- load(options_.MCMC_jumping_covariance,'jumping_covariance')
- hh=jumping_covariance;
- catch
- error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
- end
- [nrow, ncol]=size(hh);
- if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
- error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
- end
- try %check for positive definiteness
- chol(hh);
- hsd = sqrt(diag(hh));
- invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
- catch
- error(['Specified jumping_covariance is not positive definite'])
- end
+ catch
+ error(['Specified jumping_covariance is not positive definite'])
+ end
end
if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
@@ -483,9 +483,9 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
elseif options_.load_mh_file && options_.load_results_after_load_mh
%% load fields from previous MCMC run stored in results-file
field_names={'posterior_mode','posterior_std_at_mode',...% fields set by marginal_density
- 'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
- 'prior_density',...%fields set by PlotPosteriorDistributions
- };
+ 'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
+ 'prior_density',...%fields set by PlotPosteriorDistributions
+ };
for field_iter=1:size(field_names,2)
if isfield(oo_load_mh.oo_,field_names{1,field_iter})
oo_.(field_names{1,field_iter})=oo_load_mh.oo_.(field_names{1,field_iter});
@@ -692,76 +692,76 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
%% Historical and smoothed variabes
%%
if ~options_.nograph
- [nbplt,nr,nc,lr,lc,nstar] = pltorg(n_varobs);
- if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fidTeX = fopen([M_.fname '_HistoricalAndSmoothedVariables.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation_1.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
- fprintf(fidTeX,' \n');
- end
- for plt = 1:nbplt,
- fh = dyn_figure(options_.nodisplay,'Name','Historical and smoothed variables');
- NAMES = [];
- if options_.TeX, TeXNAMES = []; end
- nstar0=min(nstar,n_varobs-(plt-1)*nstar);
- if gend==1
- marker_string{1,1}='-ro';
- marker_string{2,1}='--ko';
- else
- marker_string{1,1}='-r';
- marker_string{2,1}='--k';
+ [nbplt,nr,nc,lr,lc,nstar] = pltorg(n_varobs);
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fidTeX = fopen([M_.fname '_HistoricalAndSmoothedVariables.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation_1.m (Dynare).\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
+ fprintf(fidTeX,' \n');
end
- for i=1:nstar0,
- k = (plt-1)*nstar+i;
- subplot(nr,nc,i);
- plot(1:gend,yf(k,:),marker_string{1,1},'linewidth',1)
- hold on
- plot(1:gend,rawdata(:,k),marker_string{2,1},'linewidth',1)
- hold off
- name = options_.varobs{k};
- if isempty(NAMES)
- NAMES = name;
+ for plt = 1:nbplt,
+ fh = dyn_figure(options_.nodisplay,'Name','Historical and smoothed variables');
+ NAMES = [];
+ if options_.TeX, TeXNAMES = []; end
+ nstar0=min(nstar,n_varobs-(plt-1)*nstar);
+ if gend==1
+ marker_string{1,1}='-ro';
+ marker_string{2,1}='--ko';
else
- NAMES = char(NAMES,name);
- end
- if ~isempty(options_.XTick)
- set(gca,'XTick',options_.XTick)
- set(gca,'XTickLabel',options_.XTickLabel)
- end
- if gend>1
- xlim([1 gend])
+ marker_string{1,1}='-r';
+ marker_string{2,1}='--k';
end
- if options_.TeX
- idx = strmatch(options_.varobs{k},M_.endo_names,'exact');
- texname = M_.endo_names_tex(idx,:);
- if isempty(TeXNAMES)
- TeXNAMES = ['$ ' deblank(texname) ' $'];
+ for i=1:nstar0,
+ k = (plt-1)*nstar+i;
+ subplot(nr,nc,i);
+ plot(1:gend,yf(k,:),marker_string{1,1},'linewidth',1)
+ hold on
+ plot(1:gend,rawdata(:,k),marker_string{2,1},'linewidth',1)
+ hold off
+ name = options_.varobs{k};
+ if isempty(NAMES)
+ NAMES = name;
else
- TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
+ NAMES = char(NAMES,name);
+ end
+ if ~isempty(options_.XTick)
+ set(gca,'XTick',options_.XTick)
+ set(gca,'XTickLabel',options_.XTickLabel)
+ end
+ if gend>1
+ xlim([1 gend])
+ end
+ if options_.TeX
+ idx = strmatch(options_.varobs{k},M_.endo_names,'exact');
+ texname = M_.endo_names_tex(idx,:);
+ if isempty(TeXNAMES)
+ TeXNAMES = ['$ ' deblank(texname) ' $'];
+ else
+ TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
+ end
+ end
+ title(name,'Interpreter','none')
+ end
+ dyn_saveas(fh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)],options_.nodisplay,options_.graph_format);
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ for jj = 1:nstar0,
+ fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_HistoricalAndSmoothedVariables%s}\n',options_.figures.textwidth*min(i/nc,1),M_.fname,int2str(plt));
+ fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
+ fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(plt));
+ fprintf(fidTeX,'\\end{figure}\n');
+ fprintf(fidTeX,'\n');
end
- title(name,'Interpreter','none')
end
- dyn_saveas(fh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- for jj = 1:nstar0,
- fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
- end
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_HistoricalAndSmoothedVariables%s}\n',options_.figures.textwidth*min(i/nc,1),M_.fname,int2str(plt));
- fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
- fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(plt));
- fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
+ fprintf(fidTeX,'%% End of TeX file.\n');
+ fclose(fidTeX);
end
end
- if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fprintf(fidTeX,'\n');
- fprintf(fidTeX,'%% End of TeX file.\n');
- fclose(fidTeX);
- end
- end
end
if options_.forecast > 0 && options_.mh_replic == 0 && ~options_.load_mh_file
diff --git a/matlab/dynare_estimation_init.m b/matlab/dynare_estimation_init.m
index b8e8b4ff6d525f2b272f971d2dab29bad13b9080..6e52a2d538d509bd8f1562d6b6fa661b5eebf33c 100644
--- a/matlab/dynare_estimation_init.m
+++ b/matlab/dynare_estimation_init.m
@@ -14,7 +14,7 @@ function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,
% estim_params_: structure storing information about estimated
% parameters
% bayestopt_: structure storing information about priors
-
+
% OUTPUTS
% dataset_: the dataset after required transformation
% dataset_info: Various informations about the dataset (descriptive statistics and missing observations).
@@ -107,10 +107,10 @@ end
if options_.fast_kalman_filter
if ~ismember(options_.kalman_algo, [0,1,3])
error(['estimation option conflict: fast_kalman_filter is only available ' ...
- 'with kalman_algo = 0, 1 or 3'])
+ 'with kalman_algo = 0, 1 or 3'])
elseif options_.block
error(['estimation option conflict: fast_kalman_filter is not available ' ...
- 'with block'])
+ 'with block'])
end
end
@@ -322,8 +322,8 @@ if ~isempty(estim_params_) && ~(all(strcmp(fieldnames(estim_params_),'full_calib
bounds.lb = max(bounds.lb,lb);
bounds.ub = min(bounds.ub,ub);
else % estimated parameters but no declared priors
- % No priors are declared so Dynare will estimate the model by
- % maximum likelihood with inequality constraints for the parameters.
+ % No priors are declared so Dynare will estimate the model by
+ % maximum likelihood with inequality constraints for the parameters.
options_.mh_replic = 0;% No metropolis.
bounds.lb = lb;
bounds.ub = ub;
@@ -551,7 +551,7 @@ ncx = estim_params_.ncx;
nvn = estim_params_.nvn;
ncn = estim_params_.ncn;
if estim_params_.np
- M.params(estim_params_.param_vals(:,1)) = xparam1(nvx+ncx+nvn+ncn+1:end);
+ M.params(estim_params_.param_vals(:,1)) = xparam1(nvx+ncx+nvn+ncn+1:end);
end
[oo_.steady_state, params,info] = evaluate_steady_state(oo_.steady_state,M,options_,oo_,steadystate_check_flag);
diff --git a/matlab/dynare_identification.m b/matlab/dynare_identification.m
index c84faf2c67a57d4dd42d9f3d60f34430d01de45d..a7d005b38aa1101723ee798442883d5f81afe6d7 100644
--- a/matlab/dynare_identification.m
+++ b/matlab/dynare_identification.m
@@ -250,18 +250,18 @@ if iload <=0,
[I,J]=find(M_.lead_lag_incidence');
if prior_exist,
-% if exist([fname_,'_mean.mat'],'file'),
-% % disp('Testing posterior mean')
-% load([fname_,'_mean'],'xparam1')
-% pmean = xparam1';
-% clear xparam1
-% end
-% if exist([fname_,'_mode.mat'],'file'),
-% % disp('Testing posterior mode')
-% load([fname_,'_mode'],'xparam1')
-% pmode = xparam1';
-% clear xparam1
-% end
+ % if exist([fname_,'_mean.mat'],'file'),
+ % % disp('Testing posterior mean')
+ % load([fname_,'_mean'],'xparam1')
+ % pmean = xparam1';
+ % clear xparam1
+ % end
+ % if exist([fname_,'_mode.mat'],'file'),
+ % % disp('Testing posterior mode')
+ % load([fname_,'_mode'],'xparam1')
+ % pmode = xparam1';
+ % clear xparam1
+ % end
params = set_prior(estim_params_,M_,options_)';
if all(bayestopt_.pshape == 0)
parameters = 'ML_Starting_value';
@@ -269,38 +269,38 @@ if iload <=0,
disp('Testing ML Starting value')
else
switch parameters
- case 'calibration'
- parameters_TeX = 'Calibration';
- disp('Testing calibration')
- params(1,:) = get_all_parameters(estim_params_,M_);;
- case 'posterior_mode'
- parameters_TeX = 'Posterior mode';
- disp('Testing posterior mode')
- params(1,:) = get_posterior_parameters('mode');
- case 'posterior_mean'
- parameters_TeX = 'Posterior mean';
- disp('Testing posterior mean')
- params(1,:) = get_posterior_parameters('mean');
- case 'posterior_median'
- parameters_TeX = 'Posterior median';
- disp('Testing posterior median')
- params(1,:) = get_posterior_parameters('median');
- case 'prior_mode'
- parameters_TeX = 'Prior mode';
- disp('Testing prior mode')
- params(1,:) = bayestopt_.p5(:);
- case 'prior_mean'
- parameters_TeX = 'Prior mean';
- disp('Testing prior mean')
- params(1,:) = bayestopt_.p1;
- otherwise
- disp('The option parameter_set has to be equal to:')
- disp(' ''posterior_mode'', ')
- disp(' ''posterior_mean'', ')
- disp(' ''posterior_median'', ')
- disp(' ''prior_mode'' or')
- disp(' ''prior_mean''.')
- error;
+ case 'calibration'
+ parameters_TeX = 'Calibration';
+ disp('Testing calibration')
+ params(1,:) = get_all_parameters(estim_params_,M_);;
+ case 'posterior_mode'
+ parameters_TeX = 'Posterior mode';
+ disp('Testing posterior mode')
+ params(1,:) = get_posterior_parameters('mode');
+ case 'posterior_mean'
+ parameters_TeX = 'Posterior mean';
+ disp('Testing posterior mean')
+ params(1,:) = get_posterior_parameters('mean');
+ case 'posterior_median'
+ parameters_TeX = 'Posterior median';
+ disp('Testing posterior median')
+ params(1,:) = get_posterior_parameters('median');
+ case 'prior_mode'
+ parameters_TeX = 'Prior mode';
+ disp('Testing prior mode')
+ params(1,:) = bayestopt_.p5(:);
+ case 'prior_mean'
+ parameters_TeX = 'Prior mean';
+ disp('Testing prior mean')
+ params(1,:) = bayestopt_.p1;
+ otherwise
+ disp('The option parameter_set has to be equal to:')
+ disp(' ''posterior_mode'', ')
+ disp(' ''posterior_mean'', ')
+ disp(' ''posterior_median'', ')
+ disp(' ''prior_mode'' or')
+ disp(' ''prior_mean''.')
+ error;
end
end
else
@@ -318,31 +318,31 @@ if iload <=0,
disp(['The model does not solve for ', parameters, ' with error code info = ', int2str(info(1))]),
skipline()
if info(1)==1,
- disp('info==1 %! The model doesn''t determine the current variables uniquely.')
+ disp('info==1 %! The model doesn''t determine the current variables uniquely.')
elseif info(1)==2,
- disp('info==2 %! MJDGGES returned an error code.')
+ disp('info==2 %! MJDGGES returned an error code.')
elseif info(1)==3,
- disp('info==3 %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium. ')
+ disp('info==3 %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium. ')
elseif info(1)==4,
- disp('info==4 %! Blanchard & Kahn conditions are not satisfied: indeterminacy. ')
+ disp('info==4 %! Blanchard & Kahn conditions are not satisfied: indeterminacy. ')
elseif info(1)==5,
- disp('info==5 %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. ')
+ disp('info==5 %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. ')
elseif info(1)==6,
- disp('info==6 %! The jacobian evaluated at the deterministic steady state is complex.')
+ disp('info==6 %! The jacobian evaluated at the deterministic steady state is complex.')
elseif info(1)==19,
- disp('info==19 %! The steadystate routine thrown an exception (inconsistent deep parameters). ')
+ disp('info==19 %! The steadystate routine thrown an exception (inconsistent deep parameters). ')
elseif info(1)==20,
- disp('info==20 %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). ')
+ disp('info==20 %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). ')
elseif info(1)==21,
- disp('info==21 %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.')
+ disp('info==21 %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.')
elseif info(1)==22,
- disp('info==22 %! The steady has NaNs. ')
+ disp('info==22 %! The steady has NaNs. ')
elseif info(1)==23,
- disp('info==23 %! M_.params has been updated in the steadystate routine and has complex valued scalars. ')
+ disp('info==23 %! M_.params has been updated in the steadystate routine and has complex valued scalars. ')
elseif info(1)==24,
- disp('info==24 %! M_.params has been updated in the steadystate routine and has some NaNs. ')
+ disp('info==24 %! M_.params has been updated in the steadystate routine and has some NaNs. ')
elseif info(1)==30,
- disp('info==30 %! Ergodic variance can''t be computed. ')
+ disp('info==30 %! Ergodic variance can''t be computed. ')
end
disp('----------- ')
skipline()
@@ -372,12 +372,12 @@ if iload <=0,
end
end
idehess_point.params=params;
-% siH = idemodel_point.siH;
-% siJ = idemoments_point.siJ;
-% siLRE = idelre_point.siLRE;
-% normH = max(abs(siH)')';
-% normJ = max(abs(siJ)')';
-% normLRE = max(abs(siLRE)')';
+ % siH = idemodel_point.siH;
+ % siJ = idemoments_point.siJ;
+ % siLRE = idelre_point.siLRE;
+ % normH = max(abs(siH)')';
+ % normJ = max(abs(siJ)')';
+ % normLRE = max(abs(siLRE)')';
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_point', 'idemoments_point','idemodel_point', 'idelre_point','store_options_ident')
save([IdentifDirectoryName '/' M_.fname '_' parameters '_identif.mat'], 'idehess_point', 'idemoments_point','idemodel_point', 'idelre_point','store_options_ident')
disp_identification(params, idemodel_point, idemoments_point, name, advanced);
@@ -485,11 +485,11 @@ if iload <=0,
end
if SampleSize > 1,
-% if isoctave || options_.console_mode,
-% console_waitbar(0,iteration/SampleSize);
-% else
- dyn_waitbar(iteration/SampleSize,h,['MC identification checks ',int2str(iteration),'/',int2str(SampleSize)])
-% end
+ % if isoctave || options_.console_mode,
+ % console_waitbar(0,iteration/SampleSize);
+ % else
+ dyn_waitbar(iteration/SampleSize,h,['MC identification checks ',int2str(iteration),'/',int2str(SampleSize)])
+ % end
end
end
@@ -522,7 +522,7 @@ if iload <=0,
idemodel.siHnorm = siHnorm;
idelre.siLREnorm = siLREnorm;
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'pdraws', 'idemodel', 'idemoments', 'idelre', ... %'indJJ', 'indH', 'indLRE', ...
- 'TAU', 'GAM', 'LRE','-append')
+ 'TAU', 'GAM', 'LRE','-append')
else
siJnorm = idemoments_point.siJnorm;
siHnorm = idemodel_point.siHnorm;
@@ -531,7 +531,7 @@ if iload <=0,
else
load([IdentifDirectoryName '/' M_.fname '_identif'])
-% identFiles = dir([IdentifDirectoryName '/' M_.fname '_identif_*']);
+ % identFiles = dir([IdentifDirectoryName '/' M_.fname '_identif_*']);
parameters = store_options_ident.parameter_set;
options_ident.parameter_set = parameters;
options_ident.prior_mc=size(pdraws,1);
diff --git a/matlab/dynare_sensitivity.m b/matlab/dynare_sensitivity.m
index 62f688ec7b4b23edd77db43f21c2b709fca3ddb7..b661dd32e5bb38e258b74b844765ab730c041082 100644
--- a/matlab/dynare_sensitivity.m
+++ b/matlab/dynare_sensitivity.m
@@ -24,7 +24,7 @@ function x0=dynare_sensitivity(options_gsa)
global M_ options_ oo_ bayestopt_ estim_params_
if options_.dsge_var
- error('Identification does not support DSGE-VARs at the current stage')
+ error('Identification does not support DSGE-VARs at the current stage')
end
fname_ = M_.fname;
@@ -43,7 +43,7 @@ end
if isfield(options_gsa,'morris') && options_gsa.morris==1,
if isfield(options_gsa,'identification') && options_gsa.identification==0,
-% options_gsa.redform=1;
+ % options_gsa.redform=1;
end
if isfield(options_gsa,'ppost') && options_gsa.ppost,
error('sensitivity:: Morris is incompatible with posterior sampling')
@@ -214,7 +214,7 @@ if options_gsa.morris>2,
disp('the option is reset at morris = 1 .')
options_gsa.morris=1;
end
-
+
if options_gsa.morris==1,
if ~options_gsa.identification,
options_gsa.redform=1;
@@ -232,12 +232,12 @@ if options_gsa.morris==1,
options_gsa.alpha2_stab=1;
options_gsa.pvalue_ks=0;
options_gsa.pvalue_corr=0;
-% if options_gsa.morris==3,
-% options_gsa = set_default_option(options_gsa,'Nsam',256);
-% OutputDirectoryName = CheckPath('gsa/identif',M_.dname);
-% else
- OutputDirectoryName = CheckPath('gsa/screen',M_.dname);
-% end
+ % if options_gsa.morris==3,
+ % options_gsa = set_default_option(options_gsa,'Nsam',256);
+ % OutputDirectoryName = CheckPath('gsa/identif',M_.dname);
+ % else
+ OutputDirectoryName = CheckPath('gsa/screen',M_.dname);
+ % end
else
OutputDirectoryName = CheckPath('gsa',M_.dname);
end
@@ -295,7 +295,7 @@ if options_gsa.identification,
end
if options_gsa.redform && ~isempty(options_gsa.namendo),% ...
- % && ~options_gsa.ppost,
+ % && ~options_gsa.ppost,
if options_gsa.ppost,
filnam = dir([M_.dname filesep 'metropolis' filesep '*param_irf*.mat']);
lpmat=[];
@@ -329,13 +329,13 @@ if options_gsa.redform && ~isempty(options_gsa.namendo),% ...
if strmatch(':',options_gsa.namlagendo,'exact'),
options_gsa.namlagendo=M_.endo_names(1:M_.orig_endo_nbr,:);
end
-% options_.opt_gsa = options_gsa;
+ % options_.opt_gsa = options_gsa;
if options_gsa.morris==1,
redform_screen(OutputDirectoryName,options_gsa);
else
% check existence of the SS_ANOVA toolbox
if isempty(options_gsa.threshold_redform) && ...
- ~(exist('gsa_sdp','file')==6 || exist('gsa_sdp','file')==2),
+ ~(exist('gsa_sdp','file')==6 || exist('gsa_sdp','file')==2),
fprintf('\nThe "SS-ANOVA-R: MATLAB Toolbox for the estimation of Smoothing Spline ANOVA models with Recursive algorithms" is missing.\n')
fprintf('To obtain it, go to:\n\n')
fprintf('http://ipsc.jrc.ec.europa.eu/?id=790 \n\n')
@@ -369,54 +369,54 @@ if options_gsa.rmse,
end
end
if isempty(a),
- if options_gsa.lik_only,
- options_.smoother=0;
- options_.filter_step_ahead=[];
- options_.forecast=0;
- options_.filtered_vars=0;
- end
-% dynare_MC([],OutputDirectoryName,data,rawdata,data_info);
- if options_gsa.pprior
- TmpDirectoryName = ([M_.dname filesep 'gsa' filesep 'prior']);
- else
- TmpDirectoryName = ([M_.dname filesep 'gsa' filesep 'mc']);
- end
- if exist(TmpDirectoryName,'dir');
- mydelete([M_.fname '_filter_step_ahead*.mat'],[TmpDirectoryName filesep]);
- mydelete([M_.fname '_inno*.mat'],[TmpDirectoryName filesep]);
- mydelete([M_.fname '_smooth*.mat'],[TmpDirectoryName filesep]);
- mydelete([M_.fname '_update*.mat'],[TmpDirectoryName filesep]);
- filparam = dir([TmpDirectoryName filesep M_.fname '_param*.mat']);
- for j=1:length(filparam),
- if isempty(strmatch([M_.fname '_param_irf'],filparam(j).name))
- delete([TmpDirectoryName filesep filparam(j).name]);
+ if options_gsa.lik_only,
+ options_.smoother=0;
+ options_.filter_step_ahead=[];
+ options_.forecast=0;
+ options_.filtered_vars=0;
+ end
+ % dynare_MC([],OutputDirectoryName,data,rawdata,data_info);
+ if options_gsa.pprior
+ TmpDirectoryName = ([M_.dname filesep 'gsa' filesep 'prior']);
+ else
+ TmpDirectoryName = ([M_.dname filesep 'gsa' filesep 'mc']);
+ end
+ if exist(TmpDirectoryName,'dir');
+ mydelete([M_.fname '_filter_step_ahead*.mat'],[TmpDirectoryName filesep]);
+ mydelete([M_.fname '_inno*.mat'],[TmpDirectoryName filesep]);
+ mydelete([M_.fname '_smooth*.mat'],[TmpDirectoryName filesep]);
+ mydelete([M_.fname '_update*.mat'],[TmpDirectoryName filesep]);
+ filparam = dir([TmpDirectoryName filesep M_.fname '_param*.mat']);
+ for j=1:length(filparam),
+ if isempty(strmatch([M_.fname '_param_irf'],filparam(j).name))
+ delete([TmpDirectoryName filesep filparam(j).name]);
+ end
end
+
end
-
- end
- prior_posterior_statistics('gsa',dataset_, dataset_info);
- if options_.bayesian_irf
- PosteriorIRF('gsa');
- end
- options_gsa.load_rmse=0;
- % else
- % if options_gsa.load_rmse==0,
- % disp('You already saved a MC filter/smoother analysis ')
- % disp('Do you want to overwrite ?')
- % pause;
- % if options_gsa.pprior
- % delete([OutputDirectoryName,'/',fname_,'_prior_*.mat'])
- % else
- % delete([OutputDirectoryName,'/',fname_,'_mc_*.mat'])
- % end
- % dynare_MC([],OutputDirectoryName);
- % options_gsa.load_rmse=0;
- % end
+ prior_posterior_statistics('gsa',dataset_, dataset_info);
+ if options_.bayesian_irf
+ PosteriorIRF('gsa');
+ end
+ options_gsa.load_rmse=0;
+ % else
+ % if options_gsa.load_rmse==0,
+ % disp('You already saved a MC filter/smoother analysis ')
+ % disp('Do you want to overwrite ?')
+ % pause;
+ % if options_gsa.pprior
+ % delete([OutputDirectoryName,'/',fname_,'_prior_*.mat'])
+ % else
+ % delete([OutputDirectoryName,'/',fname_,'_mc_*.mat'])
+ % end
+ % dynare_MC([],OutputDirectoryName);
+ % options_gsa.load_rmse=0;
+ % end
end
end
clear a;
-% filt_mc_(OutputDirectoryName,data_info);
+ % filt_mc_(OutputDirectoryName,data_info);
filt_mc_(OutputDirectoryName,options_gsa,dataset_,dataset_info);
end
options_.opt_gsa = options_gsa;
diff --git a/matlab/dynare_solve.m b/matlab/dynare_solve.m
index 36981b949318f55f5c56810ea083a54c6a37649b..8c13f524e41e4fd897846a3409a78122785ca5ec 100644
--- a/matlab/dynare_solve.m
+++ b/matlab/dynare_solve.m
@@ -209,13 +209,13 @@ if options.solve_algo == 0
info = 1;
end
elseif options.solve_algo == 1
- [x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag,options.gstep, ...
+ [x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag,options.gstep, ...
tolf,options.solve_tolx, ...
maxit,options.debug,varargin{:});
elseif options.solve_algo == 9
- [x,info]=trust_region(func,x,1:nn,1:nn,jacobian_flag,options.gstep, ...
- tolf,options.solve_tolx, ...
- maxit,options.debug,varargin{:});
+ [x,info]=trust_region(func,x,1:nn,1:nn,jacobian_flag,options.gstep, ...
+ tolf,options.solve_tolx, ...
+ maxit,options.debug,varargin{:});
elseif options.solve_algo == 2 || options.solve_algo == 4
if options.solve_algo == 2
diff --git a/matlab/dynare_solve_block_or_bytecode.m b/matlab/dynare_solve_block_or_bytecode.m
index 1032d0921ecdc0246beb172fc23dbecdeadac6f9..37ba7a8ea41dda13bf3d8dce9a2ec0b0e6f7a31f 100644
--- a/matlab/dynare_solve_block_or_bytecode.m
+++ b/matlab/dynare_solve_block_or_bytecode.m
@@ -22,7 +22,7 @@ if options.block && ~options.bytecode
for b = 1:length(M.block_structure_stat.block)
ss = x;
if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
- M.block_structure_stat.block(b).Simulation_Type ~= 2
+ M.block_structure_stat.block(b).Simulation_Type ~= 2
if options.solve_algo <= 4
[y, check] = dynare_solve('block_mfs_steadystate', ...
ss(M.block_structure_stat.block(b).variable), ...
@@ -60,7 +60,7 @@ elseif options.bytecode
elseif options.block
for b = 1:length(M.block_structure_stat.block)
if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
- M.block_structure_stat.block(b).Simulation_Type ~= 2
+ M.block_structure_stat.block(b).Simulation_Type ~= 2
[y, check] = dynare_solve('block_bytecode_mfs_steadystate', ...
x(M.block_structure_stat ...
.block(b).variable), ...
diff --git a/matlab/dyntable.m b/matlab/dyntable.m
index 23d56feff1a507e8ae93286bd5504a78e50a61b1..f67789bc3b88733e291f75fe4cab8f0be7477218 100644
--- a/matlab/dyntable.m
+++ b/matlab/dyntable.m
@@ -35,7 +35,7 @@ end
%% get width of label column
if ~isempty(label_width)
label_width = max(size(deblank(char(headers(1,:),labels)),2)+2, ...
- label_width);
+ label_width);
else %use default length
label_width = max(size(deblank(char(headers(1,:),labels)),2))+2;
end
diff --git a/matlab/endogenous_prior.m b/matlab/endogenous_prior.m
index c2930abad8a9b82e316df4ce2c4e26483bb0c85f..7e879df60901a10e7d2b424c561a43832a76eed3 100644
--- a/matlab/endogenous_prior.m
+++ b/matlab/endogenous_prior.m
@@ -75,7 +75,7 @@ end
% Finally, we have the sampling uncertainty measure Shat:
Shat=C0 +(1-1/(2+1))*(C1+C1')...
- +(1-2/(2+1))*(C2+C2');
+ +(1-2/(2+1))*(C2+C2');
% Model variances below:
mf=BayesInfo.mf1;
diff --git a/matlab/endogenous_prior_restrictions.m b/matlab/endogenous_prior_restrictions.m
index 617116a890dbca4e70feb197227384e9d02f5861..5d480fd9e6b8f0552ec9fd8a5ad0a1f831303681 100644
--- a/matlab/endogenous_prior_restrictions.m
+++ b/matlab/endogenous_prior_restrictions.m
@@ -1,178 +1,178 @@
-function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults);
-% Check for prior (sign) restrictions on irf's and theoretical moments
-%
-% INPUTS
-% T [double] n*n state space matrix
-% R [double] n*k matrix of shocks
-% Model [structure]
-% DynareOptions [structure]
-% DynareResults [structure]
-
-% OUTPUTS
-% info [double] check if prior restrictions are matched by the
-% model and related info
-% info_irf [double] array of test checks for all individual irf restrictions
-% info_moment [double] array of test checks for all individual moment restrictions
-%
-
-% Copyright (C) 2013 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-info=[0 0];
-info_irf=[];
-info_moment=[];
-data_irf=[];
-data_moment=[];
-
-endo_prior_restrictions.irf= DynareOptions.endogenous_prior_restrictions.irf;
-endo_prior_restrictions.moment= DynareOptions.endogenous_prior_restrictions.moment;
-
-if ~isempty(endo_prior_restrictions.irf),
- data_irf=cell(size(endo_prior_restrictions.irf,1),1);
- if DynareOptions.order>1,
- error('The algorithm for prior (sign) restrictions on irf''s is currently restricted to first-order decision rules')
- return
- end
- varlist=Model.endo_names(DynareResults.dr.order_var,:);
- if isempty(T),
- [T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
- else % check if T and R are given in the restricted form!!!
- if size(T,1)<size(varlist,1),
- varlist=varlist(DynareResults.dr.restrict_var_list,:);
- end
- % check if endo_prior_restrictions.irf{:,1} variables are in varlist
- varlistok=1;
- for j=1:size(endo_prior_restrictions.irf,1)
- if isempty(strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact'))
- varlistok=0;
- end
- end
- if ~varlistok
- varlist=Model.endo_names(DynareResults.dr.order_var,:);
- [T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
- end
- end
- NT=1;
- for j=1:size(endo_prior_restrictions.irf,1),
- NT=max(NT,max(endo_prior_restrictions.irf{j,3}));
- end
- info_irf=ones(size(endo_prior_restrictions.irf,1),2);
- for t=1:NT,
- if ~DynareOptions.relative_irf,
- RR = T^(t-1)*R*diag(sqrt(diag(Model.Sigma_e)));
- else
- RR = T^(t-1)*R*100;
- end
- for j=1:size(endo_prior_restrictions.irf,1),
- if endo_prior_restrictions.irf{j,3}~=t,
- continue,
- end
- iendo=strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact');
- iexo=strmatch(endo_prior_restrictions.irf{j,2},Model.exo_names,'exact');
- data_irf{j}=[data_irf{j}; [t RR(iendo,iexo)]];
- if (RR(iendo,iexo)>endo_prior_restrictions.irf{j,4}(1)) && (RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(2)),
- info_irf(j,:)=info_irf(j,:).*[0, 0];
- else
- if RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(1),
- delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(1))^2;
- else
- delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(2))^2;
- end
- info_irf(j,:)=info_irf(j,:).*[49, delt];
- end
- end
- end
- if any(info_irf),
- info=[49,sum(info_irf(:,2))];
- end
-
-end
-
-if ~isempty(endo_prior_restrictions.moment),
-
- if DynareOptions.order>1,
- error('The algorithm for prior (sign) restrictions on moments is currently restricted to first-order decision rules')
- return
- end
-
- data_moment=cell(size(endo_prior_restrictions.moment,1),1);
- var_list_=endo_prior_restrictions.moment{1,1};
- for j=1:size(endo_prior_restrictions.moment,1),
- tmp=endo_prior_restrictions.moment{j,1};
- if ~ismember(tmp,cellstr(var_list_)),
- var_list_ = char(var_list_, tmp);
- end
- tmp=endo_prior_restrictions.moment{j,2};
- if ~ismember(tmp,cellstr(var_list_)),
- var_list_ = char(var_list_, tmp);
- end
- end
- NTmax=0;
- NTmin=0;
- for j=1:size(endo_prior_restrictions.moment,1),
- NTmax=max(NTmax,max(endo_prior_restrictions.moment{j,3}));
- NTmin=min(NTmin,min(endo_prior_restrictions.moment{j,3}));
- end
- info_moment=ones(size(endo_prior_restrictions.moment,1),2);
- nvar = size(var_list_,1);
- ivar=zeros(nvar,1);
- for i=1:nvar
- i_tmp = strmatch(var_list_(i,:),Model.endo_names,'exact');
- if isempty(i_tmp)
- error (['One of the variable specified does not exist']) ;
- else
- ivar(i) = i_tmp;
- end
- end
- DynareOptions.ar = max(abs(NTmin),NTmax);
- [gamma_y,stationary_vars] = th_autocovariances(DynareResults.dr, ivar, Model, DynareOptions,1);
- for t=NTmin:NTmax,
- RR = gamma_y{abs(t)+1};
- if t==0,
- RR = RR./(sqrt(diag(RR))*sqrt(diag(RR))')-eye(nvar)+diag(diag(gamma_y{t+1})); % becomes correlation
- end
- for j=1:size(endo_prior_restrictions.moment,1),
- if endo_prior_restrictions.moment{j,3}~=t,
- continue,
- end
- iendo1 = strmatch(endo_prior_restrictions.moment{j,1},var_list_,'exact');
- iendo2 = strmatch(endo_prior_restrictions.moment{j,2},var_list_,'exact');
- if t>0
- tmp0 = iendo1;
- iendo1=iendo2;
- iendo2=tmp0;
- end
- data_moment{j}=[data_moment{j}; [t RR(iendo1,iendo2)]];
- if (RR(iendo1,iendo2)>endo_prior_restrictions.moment{j,4}(1)) && (RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(2)),
- info_moment(j,:)=info_moment(j,:).*[0, 0];
- else
- if RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(1),
- delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(1))^2;
- else
- delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(2))^2;
- end
- info_moment(j,:)=info_moment(j,:).*[49, delt];
- end
- end
- end
- if any(info_moment),
- info=[49, info(2) + sum(info_moment(:,2))];
- end
-end
-return
-
-
+function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,Model,DynareOptions,DynareResults);
+% Check for prior (sign) restrictions on irf's and theoretical moments
+%
+% INPUTS
+% T [double] n*n state space matrix
+% R [double] n*k matrix of shocks
+% Model [structure]
+% DynareOptions [structure]
+% DynareResults [structure]
+
+% OUTPUTS
+% info [double] check if prior restrictions are matched by the
+% model and related info
+% info_irf [double] array of test checks for all individual irf restrictions
+% info_moment [double] array of test checks for all individual moment restrictions
+%
+
+% Copyright (C) 2013 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+info=[0 0];
+info_irf=[];
+info_moment=[];
+data_irf=[];
+data_moment=[];
+
+endo_prior_restrictions.irf= DynareOptions.endogenous_prior_restrictions.irf;
+endo_prior_restrictions.moment= DynareOptions.endogenous_prior_restrictions.moment;
+
+if ~isempty(endo_prior_restrictions.irf),
+ data_irf=cell(size(endo_prior_restrictions.irf,1),1);
+ if DynareOptions.order>1,
+ error('The algorithm for prior (sign) restrictions on irf''s is currently restricted to first-order decision rules')
+ return
+ end
+ varlist=Model.endo_names(DynareResults.dr.order_var,:);
+ if isempty(T),
+ [T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
+ else % check if T and R are given in the restricted form!!!
+ if size(T,1)<size(varlist,1),
+ varlist=varlist(DynareResults.dr.restrict_var_list,:);
+ end
+ % check if endo_prior_restrictions.irf{:,1} variables are in varlist
+ varlistok=1;
+ for j=1:size(endo_prior_restrictions.irf,1)
+ if isempty(strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact'))
+ varlistok=0;
+ end
+ end
+ if ~varlistok
+ varlist=Model.endo_names(DynareResults.dr.order_var,:);
+ [T,R,SteadyState,infox,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
+ end
+ end
+ NT=1;
+ for j=1:size(endo_prior_restrictions.irf,1),
+ NT=max(NT,max(endo_prior_restrictions.irf{j,3}));
+ end
+ info_irf=ones(size(endo_prior_restrictions.irf,1),2);
+ for t=1:NT,
+ if ~DynareOptions.relative_irf,
+ RR = T^(t-1)*R*diag(sqrt(diag(Model.Sigma_e)));
+ else
+ RR = T^(t-1)*R*100;
+ end
+ for j=1:size(endo_prior_restrictions.irf,1),
+ if endo_prior_restrictions.irf{j,3}~=t,
+ continue,
+ end
+ iendo=strmatch(endo_prior_restrictions.irf{j,1},varlist,'exact');
+ iexo=strmatch(endo_prior_restrictions.irf{j,2},Model.exo_names,'exact');
+ data_irf{j}=[data_irf{j}; [t RR(iendo,iexo)]];
+ if (RR(iendo,iexo)>endo_prior_restrictions.irf{j,4}(1)) && (RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(2)),
+ info_irf(j,:)=info_irf(j,:).*[0, 0];
+ else
+ if RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(1),
+ delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(1))^2;
+ else
+ delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(2))^2;
+ end
+ info_irf(j,:)=info_irf(j,:).*[49, delt];
+ end
+ end
+ end
+ if any(info_irf),
+ info=[49,sum(info_irf(:,2))];
+ end
+
+end
+
+if ~isempty(endo_prior_restrictions.moment),
+
+ if DynareOptions.order>1,
+ error('The algorithm for prior (sign) restrictions on moments is currently restricted to first-order decision rules')
+ return
+ end
+
+ data_moment=cell(size(endo_prior_restrictions.moment,1),1);
+ var_list_=endo_prior_restrictions.moment{1,1};
+ for j=1:size(endo_prior_restrictions.moment,1),
+ tmp=endo_prior_restrictions.moment{j,1};
+ if ~ismember(tmp,cellstr(var_list_)),
+ var_list_ = char(var_list_, tmp);
+ end
+ tmp=endo_prior_restrictions.moment{j,2};
+ if ~ismember(tmp,cellstr(var_list_)),
+ var_list_ = char(var_list_, tmp);
+ end
+ end
+ NTmax=0;
+ NTmin=0;
+ for j=1:size(endo_prior_restrictions.moment,1),
+ NTmax=max(NTmax,max(endo_prior_restrictions.moment{j,3}));
+ NTmin=min(NTmin,min(endo_prior_restrictions.moment{j,3}));
+ end
+ info_moment=ones(size(endo_prior_restrictions.moment,1),2);
+ nvar = size(var_list_,1);
+ ivar=zeros(nvar,1);
+ for i=1:nvar
+ i_tmp = strmatch(var_list_(i,:),Model.endo_names,'exact');
+ if isempty(i_tmp)
+ error (['One of the variable specified does not exist']) ;
+ else
+ ivar(i) = i_tmp;
+ end
+ end
+ DynareOptions.ar = max(abs(NTmin),NTmax);
+ [gamma_y,stationary_vars] = th_autocovariances(DynareResults.dr, ivar, Model, DynareOptions,1);
+ for t=NTmin:NTmax,
+ RR = gamma_y{abs(t)+1};
+ if t==0,
+ RR = RR./(sqrt(diag(RR))*sqrt(diag(RR))')-eye(nvar)+diag(diag(gamma_y{t+1})); % becomes correlation
+ end
+ for j=1:size(endo_prior_restrictions.moment,1),
+ if endo_prior_restrictions.moment{j,3}~=t,
+ continue,
+ end
+ iendo1 = strmatch(endo_prior_restrictions.moment{j,1},var_list_,'exact');
+ iendo2 = strmatch(endo_prior_restrictions.moment{j,2},var_list_,'exact');
+ if t>0
+ tmp0 = iendo1;
+ iendo1=iendo2;
+ iendo2=tmp0;
+ end
+ data_moment{j}=[data_moment{j}; [t RR(iendo1,iendo2)]];
+ if (RR(iendo1,iendo2)>endo_prior_restrictions.moment{j,4}(1)) && (RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(2)),
+ info_moment(j,:)=info_moment(j,:).*[0, 0];
+ else
+ if RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(1),
+ delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(1))^2;
+ else
+ delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(2))^2;
+ end
+ info_moment(j,:)=info_moment(j,:).*[49, delt];
+ end
+ end
+ end
+ if any(info_moment),
+ info=[49, info(2) + sum(info_moment(:,2))];
+ end
+end
+return
+
+
diff --git a/matlab/ep/ep_problem_2.m b/matlab/ep/ep_problem_2.m
index d31f0e7aa1da97e66a04521493fe6548661a3bd8..1b4e0d9a79903cc12a23672d31cacc9aba07571c 100644
--- a/matlab/ep/ep_problem_2.m
+++ b/matlab/ep/ep_problem_2.m
@@ -180,7 +180,7 @@ for j=1:world_nbr
nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
else
d1 = dynamic_model(Y(i_rows_y,j),x,params, ...
- steady_state,i+1);
+ steady_state,i+1);
end
res(:,i,j) = d1(eq_index);
i_rows_y = i_rows_y + ny;
diff --git a/matlab/ep/extended_path_core.m b/matlab/ep/extended_path_core.m
index 33c42b619c5ecc25506af06cc47e01740db7c66c..8a56646434479be248fdbb8548ab7c59bbcc8d85 100644
--- a/matlab/ep/extended_path_core.m
+++ b/matlab/ep/extended_path_core.m
@@ -1,8 +1,8 @@
function [y, info_convergence, endogenousvariablespaths] = extended_path_core(periods,endo_nbr,exo_nbr,positive_var_indx, ...
- exo_simul,init,initial_conditions,...
- steady_state, ...
- debug,bytecode_flag,order,M,pfm,algo,solve_algo,stack_solve_algo,...
- olmmcp,options,oo,initialguess)
+ exo_simul,init,initial_conditions,...
+ steady_state, ...
+ debug,bytecode_flag,order,M,pfm,algo,solve_algo,stack_solve_algo,...
+ olmmcp,options,oo,initialguess)
% Copyright (C) 2016 Dynare Team
%
diff --git a/matlab/ep/extended_path_homotopy.m b/matlab/ep/extended_path_homotopy.m
index 463003d2bfc5b76559106b46cf97a57c3742a952..47259765c035c04dc99ff0fbf1f7132a17b12d97 100644
--- a/matlab/ep/extended_path_homotopy.m
+++ b/matlab/ep/extended_path_homotopy.m
@@ -17,7 +17,7 @@ function [info_convergence, endo_simul] = extended_path_homotopy(endo_simul, exo
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
endo_simul0 = endo_simul;
if ismember(method, [1, 2])
noconvergence = true;
diff --git a/matlab/ep/setup_integration_nodes.m b/matlab/ep/setup_integration_nodes.m
index 85d1be960c4bc5bdf93b0aa8862ea0c3b04234bc..2577d593271b9067c6537b552bbf7abd59e266e8 100644
--- a/matlab/ep/setup_integration_nodes.m
+++ b/matlab/ep/setup_integration_nodes.m
@@ -1,39 +1,39 @@
function [nodes,weights,nnodes] = setup_integration_nodes(EpOptions,pfm)
- if EpOptions.stochastic.order
- % Compute weights and nodes for the stochastic version of the extended path.
- switch EpOptions.IntegrationAlgorithm
- case 'Tensor-Gaussian-Quadrature'
- % Get the nodes and weights from a univariate Gauss-Hermite quadrature.
- [nodes0,weights0] = gauss_hermite_weights_and_nodes(EpOptions.stochastic.quadrature.nodes);
- % Replicate the univariate nodes for each innovation and dates, and, if needed, correlate them.
- nodes0 = repmat(nodes0,1,pfm.number_of_shocks*pfm.stochastic_order)*kron(eye(pfm.stochastic_order),pfm.Omega);
- % Put the nodes and weights in cells
- for i=1:pfm.number_of_shocks
- rr(i) = {nodes0(:,i)};
- ww(i) = {weights0};
- end
- % Build the tensorial grid
- nodes = cartesian_product_of_sets(rr{:});
- weights = prod(cartesian_product_of_sets(ww{:}),2);
- nnodes = length(weights);
- case 'Stroud-Cubature-3'
- [nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,3,'Stroud')
- nodes = kron(eye(pfm.stochastic_order),transpose(pfm.Omega))*nodes;
- weights = weights;
- nnodes = length(weights);
- case 'Stroud-Cubature-5'
- [nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,5,'Stroud')
- nodes = kron(eye(pfm.stochastic_order),transpose(pfm.Omega))*nodes;
- weights = weights;
- nnodes = length(weights);
- case 'Unscented'
- p = pfm.number_of_shocks;
- k = EpOptions.ut.k;
- C = sqrt(pfm.number_of_shocks + k)*pfm.Omega';
- nodes = [zeros(1,p); -C; C];
- weights = [k/(p+k); (1/(2*(p+k)))*ones(2*p,1)];
- nnodes = 2*p+1;
- otherwise
- error('Stochastic extended path:: Unknown integration algorithm!')
+if EpOptions.stochastic.order
+ % Compute weights and nodes for the stochastic version of the extended path.
+ switch EpOptions.IntegrationAlgorithm
+ case 'Tensor-Gaussian-Quadrature'
+ % Get the nodes and weights from a univariate Gauss-Hermite quadrature.
+ [nodes0,weights0] = gauss_hermite_weights_and_nodes(EpOptions.stochastic.quadrature.nodes);
+ % Replicate the univariate nodes for each innovation and dates, and, if needed, correlate them.
+ nodes0 = repmat(nodes0,1,pfm.number_of_shocks*pfm.stochastic_order)*kron(eye(pfm.stochastic_order),pfm.Omega);
+ % Put the nodes and weights in cells
+ for i=1:pfm.number_of_shocks
+ rr(i) = {nodes0(:,i)};
+ ww(i) = {weights0};
end
+ % Build the tensorial grid
+ nodes = cartesian_product_of_sets(rr{:});
+ weights = prod(cartesian_product_of_sets(ww{:}),2);
+ nnodes = length(weights);
+ case 'Stroud-Cubature-3'
+ [nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,3,'Stroud')
+ nodes = kron(eye(pfm.stochastic_order),transpose(pfm.Omega))*nodes;
+ weights = weights;
+ nnodes = length(weights);
+ case 'Stroud-Cubature-5'
+ [nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,5,'Stroud')
+ nodes = kron(eye(pfm.stochastic_order),transpose(pfm.Omega))*nodes;
+ weights = weights;
+ nnodes = length(weights);
+ case 'Unscented'
+ p = pfm.number_of_shocks;
+ k = EpOptions.ut.k;
+ C = sqrt(pfm.number_of_shocks + k)*pfm.Omega';
+ nodes = [zeros(1,p); -C; C];
+ weights = [k/(p+k); (1/(2*(p+k)))*ones(2*p,1)];
+ nnodes = 2*p+1;
+ otherwise
+ error('Stochastic extended path:: Unknown integration algorithm!')
end
+end
diff --git a/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m b/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
index 286bd16acb4a11bcb52d2bea6a39ab84906d98b2..6ec1dbe9e5858ccb0fff3e73ea1c33ad9d929657 100644
--- a/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
+++ b/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
@@ -16,7 +16,7 @@ function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,Dynar
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
pfm.lead_lag_incidence = DynareModel.lead_lag_incidence;
pfm.ny = DynareModel.endo_nbr;
pfm.Sigma = DynareModel.Sigma_e;
diff --git a/matlab/ep/solve_stochastic_perfect_foresight_model.m b/matlab/ep/solve_stochastic_perfect_foresight_model.m
index ab13e3043ac6c89cd30f80aff549f6fb7f3b57b6..12373d15d9d66f2c37caac128e91948fe90a4ada 100644
--- a/matlab/ep/solve_stochastic_perfect_foresight_model.m
+++ b/matlab/ep/solve_stochastic_perfect_foresight_model.m
@@ -17,219 +17,219 @@ function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model(endo_s
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- flag = 0;
- err = 0;
- stop = 0;
+flag = 0;
+err = 0;
+stop = 0;
- params = pfm.params;
- steady_state = pfm.steady_state;
- ny = pfm.ny;
- periods = pfm.periods;
- dynamic_model = pfm.dynamic_model;
- lead_lag_incidence = pfm.lead_lag_incidence;
- nyp = pfm.nyp;
- nyf = pfm.nyf;
- i_cols_1 = pfm.i_cols_1;
- i_cols_A1 = pfm.i_cols_A1;
- i_cols_j = pfm.i_cols_j;
- i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
+params = pfm.params;
+steady_state = pfm.steady_state;
+ny = pfm.ny;
+periods = pfm.periods;
+dynamic_model = pfm.dynamic_model;
+lead_lag_incidence = pfm.lead_lag_incidence;
+nyp = pfm.nyp;
+nyf = pfm.nyf;
+i_cols_1 = pfm.i_cols_1;
+i_cols_A1 = pfm.i_cols_A1;
+i_cols_j = pfm.i_cols_j;
+i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
- maxit = pfm.maxit_;
- tolerance = pfm.tolerance;
- verbose = pfm.verbose;
+maxit = pfm.maxit_;
+tolerance = pfm.tolerance;
+verbose = pfm.verbose;
- number_of_shocks = size(exo_simul,2);
+number_of_shocks = size(exo_simul,2);
- [nodes,weights] = gauss_hermite_weights_and_nodes(nnodes);
+[nodes,weights] = gauss_hermite_weights_and_nodes(nnodes);
- if number_of_shocks>1
- nodes = repmat(nodes,1,number_of_shocks)*chol(pfm.Sigma);
- % to be fixed for Sigma ~= I
- for i=1:number_of_shocks
- rr(i) = {nodes(:,i)};
- ww(i) = {weights};
- end
- nodes = cartesian_product_of_sets(rr{:});
- weights = prod(cartesian_product_of_sets(ww{:}),2);
- nnodes = nnodes^number_of_shocks;
- else
- nodes = nodes*sqrt(pfm.Sigma);
+if number_of_shocks>1
+ nodes = repmat(nodes,1,number_of_shocks)*chol(pfm.Sigma);
+ % to be fixed for Sigma ~= I
+ for i=1:number_of_shocks
+ rr(i) = {nodes(:,i)};
+ ww(i) = {weights};
end
+ nodes = cartesian_product_of_sets(rr{:});
+ weights = prod(cartesian_product_of_sets(ww{:}),2);
+ nnodes = nnodes^number_of_shocks;
+else
+ nodes = nodes*sqrt(pfm.Sigma);
+end
- innovations = zeros(periods+2,number_of_shocks);
+innovations = zeros(periods+2,number_of_shocks);
- if verbose
- disp ([' -----------------------------------------------------']);
- disp (['MODEL SIMULATION :']);
- fprintf('\n');
- end
+if verbose
+ disp ([' -----------------------------------------------------']);
+ disp (['MODEL SIMULATION :']);
+ fprintf('\n');
+end
- z = endo_simul(find(lead_lag_incidence'));
- [d1,jacobian] = dynamic_model(z,exo_simul,params,steady_state,2);
+z = endo_simul(find(lead_lag_incidence'));
+[d1,jacobian] = dynamic_model(z,exo_simul,params,steady_state,2);
- % Each column of Y represents a different world
- % The upper right cells are unused
- % The first row block is ny x 1
- % The second row block is ny x nnodes
- % The third row block is ny x nnodes^2
- % and so on until size ny x nnodes^order
- world_nbr = nnodes^order;
- Y = repmat(endo_simul(:),1,world_nbr);
+% Each column of Y represents a different world
+% The upper right cells are unused
+% The first row block is ny x 1
+% The second row block is ny x nnodes
+% The third row block is ny x nnodes^2
+% and so on until size ny x nnodes^order
+world_nbr = nnodes^order;
+Y = repmat(endo_simul(:),1,world_nbr);
- % The columns of A map the elements of Y such that
- % each block of Y with ny rows are unfolded column wise
- dimension = ny*(sum(nnodes.^(0:order-1),2)+(periods-order)*world_nbr);
- if order == 0
- i_upd_r = (1:ny*periods);
- i_upd_y = i_upd_r + ny;
- else
- i_upd_r = zeros(dimension,1);
- i_upd_y = i_upd_r;
- i_upd_r(1:ny) = (1:ny);
- i_upd_y(1:ny) = ny+(1:ny);
- i1 = ny+1;
- i2 = 2*ny;
- n1 = ny+1;
- n2 = 2*ny;
- for i=2:periods
- k = n1:n2;
- for j=1:nnodes^min(i-1,order)
- i_upd_r(i1:i2) = (n1:n2)+(j-1)*ny*periods;
- i_upd_y(i1:i2) = (n1:n2)+ny+(j-1)*ny*(periods+2);
- i1 = i2+1;
- i2 = i2+ny;
- end
- n1 = n2+1;
- n2 = n2+ny;
+% The columns of A map the elements of Y such that
+% each block of Y with ny rows are unfolded column wise
+dimension = ny*(sum(nnodes.^(0:order-1),2)+(periods-order)*world_nbr);
+if order == 0
+ i_upd_r = (1:ny*periods);
+ i_upd_y = i_upd_r + ny;
+else
+ i_upd_r = zeros(dimension,1);
+ i_upd_y = i_upd_r;
+ i_upd_r(1:ny) = (1:ny);
+ i_upd_y(1:ny) = ny+(1:ny);
+ i1 = ny+1;
+ i2 = 2*ny;
+ n1 = ny+1;
+ n2 = 2*ny;
+ for i=2:periods
+ k = n1:n2;
+ for j=1:nnodes^min(i-1,order)
+ i_upd_r(i1:i2) = (n1:n2)+(j-1)*ny*periods;
+ i_upd_y(i1:i2) = (n1:n2)+ny+(j-1)*ny*(periods+2);
+ i1 = i2+1;
+ i2 = i2+ny;
end
+ n1 = n2+1;
+ n2 = n2+ny;
end
- icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ...
- find(lead_lag_incidence(3,:))+2*world_nbr*ny]';
- h1 = clock;
- for iter = 1:maxit
- h2 = clock;
- A1 = sparse([],[],[],ny*(sum(nnodes.^(0:order-1),2)+1),dimension,(order+1)*world_nbr*nnz(jacobian));
- res = zeros(ny,periods,world_nbr);
- i_rows = 1:ny;
- i_cols = find(lead_lag_incidence');
- i_cols_p = i_cols(1:nyp);
- i_cols_s = i_cols(nyp+(1:ny));
- i_cols_f = i_cols(nyp+ny+(1:nyf));
- i_cols_A = i_cols;
- i_cols_Ap = i_cols_p;
- i_cols_As = i_cols_s;
- i_cols_Af = i_cols_f - ny;
- for i = 1:order+1
- i_w_p = 1;
- for j = 1:nnodes^(i-1)
- innovation = exo_simul;
- if i > 1
- innovation(i+1,:) = nodes(mod(j-1,nnodes)+1,:);
- end
- if i <= order
- for k=1:nnodes
- y = [Y(i_cols_p,i_w_p);
- Y(i_cols_s,j);
- Y(i_cols_f,(j-1)*nnodes+k)];
- [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1);
- if i == 1
- % in first period we don't keep track of
- % predetermined variables
- i_cols_A = [i_cols_As - ny; i_cols_Af];
- A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1);
- else
- i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
- A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j);
- end
- res(:,i,j) = res(:,i,j)+weights(k)*d1;
- i_cols_Af = i_cols_Af + ny;
- end
- else
+end
+icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ...
+ find(lead_lag_incidence(3,:))+2*world_nbr*ny]';
+h1 = clock;
+for iter = 1:maxit
+ h2 = clock;
+ A1 = sparse([],[],[],ny*(sum(nnodes.^(0:order-1),2)+1),dimension,(order+1)*world_nbr*nnz(jacobian));
+ res = zeros(ny,periods,world_nbr);
+ i_rows = 1:ny;
+ i_cols = find(lead_lag_incidence');
+ i_cols_p = i_cols(1:nyp);
+ i_cols_s = i_cols(nyp+(1:ny));
+ i_cols_f = i_cols(nyp+ny+(1:nyf));
+ i_cols_A = i_cols;
+ i_cols_Ap = i_cols_p;
+ i_cols_As = i_cols_s;
+ i_cols_Af = i_cols_f - ny;
+ for i = 1:order+1
+ i_w_p = 1;
+ for j = 1:nnodes^(i-1)
+ innovation = exo_simul;
+ if i > 1
+ innovation(i+1,:) = nodes(mod(j-1,nnodes)+1,:);
+ end
+ if i <= order
+ for k=1:nnodes
y = [Y(i_cols_p,i_w_p);
Y(i_cols_s,j);
- Y(i_cols_f,j)];
+ Y(i_cols_f,(j-1)*nnodes+k)];
[d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1);
if i == 1
% in first period we don't keep track of
% predetermined variables
i_cols_A = [i_cols_As - ny; i_cols_Af];
- A1(i_rows,i_cols_A) = jacobian(:,i_cols_1);
+ A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1);
else
i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
- A1(i_rows,i_cols_A) = jacobian(:,i_cols_j);
+ A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j);
end
- res(:,i,j) = d1;
+ res(:,i,j) = res(:,i,j)+weights(k)*d1;
i_cols_Af = i_cols_Af + ny;
end
- i_rows = i_rows + ny;
- if mod(j,nnodes) == 0
- i_w_p = i_w_p + 1;
- end
- if i > 1
- if mod(j,nnodes) == 0
- i_cols_Ap = i_cols_Ap + ny;
- end
- i_cols_As = i_cols_As + ny;
- end
- end
- i_cols_p = i_cols_p + ny;
- i_cols_s = i_cols_s + ny;
- i_cols_f = i_cols_f + ny;
- end
- nzA = cell(periods,world_nbr);
- for j=1:world_nbr
- i_rows_y = find(lead_lag_incidence')+(order+1)*ny;
- offset_c = ny*(sum(nnodes.^(0:order-1),2)+j-1);
- offset_r = (j-1)*ny;
- for i=order+2:periods
- [d1,jacobian] = dynamic_model(Y(i_rows_y,j), ...
- exo_simul,params, ...
- steady_state,i+1);
- if i == periods
- [ir,ic,v] = find(jacobian(:,i_cols_T));
+ else
+ y = [Y(i_cols_p,i_w_p);
+ Y(i_cols_s,j);
+ Y(i_cols_f,j)];
+ [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1);
+ if i == 1
+ % in first period we don't keep track of
+ % predetermined variables
+ i_cols_A = [i_cols_As - ny; i_cols_Af];
+ A1(i_rows,i_cols_A) = jacobian(:,i_cols_1);
else
- [ir,ic,v] = find(jacobian(:,i_cols_j));
+ i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
+ A1(i_rows,i_cols_A) = jacobian(:,i_cols_j);
end
- nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
res(:,i,j) = d1;
- i_rows_y = i_rows_y + ny;
- offset_c = offset_c + world_nbr*ny;
- offset_r = offset_r + world_nbr*ny;
+ i_cols_Af = i_cols_Af + ny;
+ end
+ i_rows = i_rows + ny;
+ if mod(j,nnodes) == 0
+ i_w_p = i_w_p + 1;
+ end
+ if i > 1
+ if mod(j,nnodes) == 0
+ i_cols_Ap = i_cols_Ap + ny;
+ end
+ i_cols_As = i_cols_As + ny;
end
end
- err = max(abs(res(i_upd_r)));
- if err < tolerance
- stop = 1;
- if verbose
- fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
- fprintf('\n') ;
- disp([' Convergency obtained.']) ;
- fprintf('\n') ;
+ i_cols_p = i_cols_p + ny;
+ i_cols_s = i_cols_s + ny;
+ i_cols_f = i_cols_f + ny;
+ end
+ nzA = cell(periods,world_nbr);
+ for j=1:world_nbr
+ i_rows_y = find(lead_lag_incidence')+(order+1)*ny;
+ offset_c = ny*(sum(nnodes.^(0:order-1),2)+j-1);
+ offset_r = (j-1)*ny;
+ for i=order+2:periods
+ [d1,jacobian] = dynamic_model(Y(i_rows_y,j), ...
+ exo_simul,params, ...
+ steady_state,i+1);
+ if i == periods
+ [ir,ic,v] = find(jacobian(:,i_cols_T));
+ else
+ [ir,ic,v] = find(jacobian(:,i_cols_j));
end
- flag = 0;% Convergency obtained.
- endo_simul = reshape(Y(:,1),ny,periods+2);%Y(ny+(1:ny),1);
- % figure;plot(Y(16:ny:(periods+2)*ny,:))
- % pause
- break
+ nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
+ res(:,i,j) = d1;
+ i_rows_y = i_rows_y + ny;
+ offset_c = offset_c + world_nbr*ny;
+ offset_r = offset_r + world_nbr*ny;
end
- A2 = [nzA{:}]';
- A = [A1; sparse(A2(:,1),A2(:,2),A2(:,3),ny*(periods-order-1)*world_nbr,dimension)];
- dy = -A\res(i_upd_r);
- Y(i_upd_y) = Y(i_upd_y) + dy;
end
-
- if ~stop
+ err = max(abs(res(i_upd_r)));
+ if err < tolerance
+ stop = 1;
if verbose
fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
fprintf('\n') ;
- disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
+ disp([' Convergency obtained.']) ;
fprintf('\n') ;
end
- flag = 1;% more iterations are needed.
- endo_simul = 1;
+ flag = 0;% Convergency obtained.
+ endo_simul = reshape(Y(:,1),ny,periods+2);%Y(ny+(1:ny),1);
+ % figure;plot(Y(16:ny:(periods+2)*ny,:))
+ % pause
+ break
end
+ A2 = [nzA{:}]';
+ A = [A1; sparse(A2(:,1),A2(:,2),A2(:,3),ny*(periods-order-1)*world_nbr,dimension)];
+ dy = -A\res(i_upd_r);
+ Y(i_upd_y) = Y(i_upd_y) + dy;
+end
+
+if ~stop
if verbose
- disp (['-----------------------------------------------------']) ;
+ fprintf('\n') ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ fprintf('\n') ;
+ disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
+ fprintf('\n') ;
end
+ flag = 1;% more iterations are needed.
+ endo_simul = 1;
+end
+if verbose
+ disp (['-----------------------------------------------------']) ;
+end
diff --git a/matlab/ep/stroud_judd_7.5.8.m b/matlab/ep/stroud_judd_7.5.8.m
index 40c9bc08db172e2a6d371d468f099ffa1817e73d..57d3f8d62fe278bac2d253f1d1b2753027b97a8c 100644
--- a/matlab/ep/stroud_judd_7.5.8.m
+++ b/matlab/ep/stroud_judd_7.5.8.m
@@ -1,9 +1,9 @@
-function [X,w]=stroud_judd_7.5.8(d)
-
- E = eye(d);
- X = cell(2*d,1);
- m = 1;
- for i=1:d
- X{m} = E(:,i);
- m = m+1;
- X{m} = -E(:,i);
+function [X,w]=stroud_judd_7.5.8.5.8(d)
+
+E = eye(d);
+X = cell(2*d,1);
+m = 1;
+for i=1:d
+ X{m} = E(:,i);
+ m = m+1;
+ X{m} = -E(:,i);
diff --git a/matlab/evaluate_max_dynamic_residual.m b/matlab/evaluate_max_dynamic_residual.m
index 41aa4ec249c2fae2dcfb9721832e2aee20bcedef..ac4dbb32a0c5f08e4efeae0e24d737d9b4aa587b 100644
--- a/matlab/evaluate_max_dynamic_residual.m
+++ b/matlab/evaluate_max_dynamic_residual.m
@@ -16,7 +16,7 @@ function err = evaluate_max_dynamic_residual(model_dynamic, Y, exogenous_variabl
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
i_rows = 1:ny;
i_cols = find(lead_lag_incidence');
diff --git a/matlab/evaluate_planner_objective.m b/matlab/evaluate_planner_objective.m
index 40f1865903608528ecad2f69ded09cda08f74866..599fab1c04e13f315d974bb1c7404792271fe6e5 100644
--- a/matlab/evaluate_planner_objective.m
+++ b/matlab/evaluate_planner_objective.m
@@ -40,7 +40,7 @@ if nspred > 180
return
end
beta = get_optimal_policy_discount_factor(M.params,M.param_names);
-
+
Gy = dr.ghx(nstatic+(1:nspred),:);
Gu = dr.ghu(nstatic+(1:nspred),:);
gy(dr.order_var,:) = dr.ghx;
@@ -113,7 +113,7 @@ if ~options.noprint
disp('Approximated value of planner objective function')
if options.ramsey_policy
disp([' - with initial Lagrange multipliers set to 0: ' ...
- num2str(planner_objective_value(2)) ])
+ num2str(planner_objective_value(2)) ])
disp([' - with initial Lagrange multipliers set to steady state: ' ...
num2str(planner_objective_value(1)) ])
elseif options.discretionary_policy
diff --git a/matlab/evaluate_smoother.m b/matlab/evaluate_smoother.m
index fb3eaad7addb18272e853f35eea137faca78326b..09fb958be799a9d1a538f85b07527e3715bae3f3 100644
--- a/matlab/evaluate_smoother.m
+++ b/matlab/evaluate_smoother.m
@@ -106,7 +106,7 @@ end
[oo_]=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty);
if nargout>4
- Smoothed_variables_declaration_order_deviation_form=atT(oo_.dr.inv_order_var(bayestopt_.smoother_var_list),:);
+ Smoothed_variables_declaration_order_deviation_form=atT(oo_.dr.inv_order_var(bayestopt_.smoother_var_list),:);
end
%reset qz_criterium
diff --git a/matlab/evaluate_static_model.m b/matlab/evaluate_static_model.m
index 92251796423a0c79c6224b65efd0752acb313f13..873dabe2b43d6b001e16f3ed1b51230786e8c009 100644
--- a/matlab/evaluate_static_model.m
+++ b/matlab/evaluate_static_model.m
@@ -37,29 +37,29 @@ function [residuals,check1,jacob] = evaluate_static_model(ys,exo_ss,params,M,opt
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- check1 = 0;
- if options.bytecode
- [check1, residuals] = bytecode('evaluate','static',ys,...
- exo_ss, params, ys, 1);
- mexErrCheck('bytecode', check1);
- else
- fh_static = str2func([M.fname '_static']);
- if options.block
- residuals = zeros(M.endo_nbr,1);
- for b = 1:length(M.block_structure_stat.block)
- mfsb = M.block_structure_stat.block(b).variable;
- % blocks that can be directly evaluated (mfsb is empty)
- % have zero residuals by construction
- if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
- M.block_structure_stat.block(b).Simulation_Type ~= 2
- residuals(mfsb) = feval(fh_static,b,ys,exo_ss,params);
- else
- %need to evaluate the recursive blocks to compute the
- %temporary terms
- feval(fh_static,b,ys,exo_ss,params);
- end
+check1 = 0;
+if options.bytecode
+ [check1, residuals] = bytecode('evaluate','static',ys,...
+ exo_ss, params, ys, 1);
+ mexErrCheck('bytecode', check1);
+else
+ fh_static = str2func([M.fname '_static']);
+ if options.block
+ residuals = zeros(M.endo_nbr,1);
+ for b = 1:length(M.block_structure_stat.block)
+ mfsb = M.block_structure_stat.block(b).variable;
+ % blocks that can be directly evaluated (mfsb is empty)
+ % have zero residuals by construction
+ if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
+ M.block_structure_stat.block(b).Simulation_Type ~= 2
+ residuals(mfsb) = feval(fh_static,b,ys,exo_ss,params);
+ else
+ %need to evaluate the recursive blocks to compute the
+ %temporary terms
+ feval(fh_static,b,ys,exo_ss,params);
end
- else
- residuals = feval(fh_static,ys,exo_ss,params);
end
+ else
+ residuals = feval(fh_static,ys,exo_ss,params);
end
+end
diff --git a/matlab/evaluate_steady_state.m b/matlab/evaluate_steady_state.m
index 053d62255efbe8a915a6a5a1f5a05990f6361541..fd101f32113560a7504861bcb06bda35c5e6088f 100644
--- a/matlab/evaluate_steady_state.m
+++ b/matlab/evaluate_steady_state.m
@@ -39,120 +39,36 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- info = 0;
- check = 0;
+info = 0;
+check = 0;
- steadystate_flag = options.steadystate_flag;
- params = M.params;
- exo_ss = [oo.exo_steady_state; oo.exo_det_steady_state];
+steadystate_flag = options.steadystate_flag;
+params = M.params;
+exo_ss = [oo.exo_steady_state; oo.exo_det_steady_state];
- if length(M.aux_vars) > 0
- h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
- if ~steadystate_flag
- ys_init = h_set_auxiliary_variables(ys_init,exo_ss,M.params);
- end
+if length(M.aux_vars) > 0
+ h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
+ if ~steadystate_flag
+ ys_init = h_set_auxiliary_variables(ys_init,exo_ss,M.params);
end
+end
- if options.ramsey_policy
- if steadystate_flag
- % explicit steady state file
- [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M, ...
- options,steadystate_check_flag);
- %test whether it solves model conditional on the instruments
- resids = evaluate_static_model(ys,exo_ss,params,M,options);
- n_multipliers=M.ramsey_eq_nbr;
- nan_indices=find(isnan(resids(n_multipliers+1:end)));
-
- if ~isempty(nan_indices)
- if options.debug
- fprintf('\nevaluate_steady_state: The steady state file computation for the Ramsey problem resulted in NaNs.\n')
- fprintf('evaluate_steady_state: The steady state was computed conditional on the following initial instrument values: \n')
- for ii = 1:size(options.instruments,1);
- fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
- fprintf('\t Equation(s): ')
- for ii=1:length(nan_indices)
- fprintf('%d, ',nan_indices(ii));
- end
- skipline();
- fprintf('evaluate_steady_state: If those initial values are not admissable, change them using an initval-block.\n')
- skipline(2);
- end
- info(1) = 84;
- info(2) = resids'*resids;
- return;
- end
-
- if any(imag(ys(n_multipliers+1:end)))
- if options.debug
- fprintf('\nevaluate_steady_state: The steady state file computation for the Ramsey problem resulted in complex numbers.\n')
- fprintf('evaluate_steady_state: The steady state was computed conditional on the following initial instrument values: \n')
- for ii = 1:size(options.instruments,1);
- fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: If those initial values are not admissable, change them using an initval-block.\n')
- skipline(2);
- end
- info(1) = 86;
- info(2) = resids'*resids;
- return;
- end
-
- if max(abs(resids(n_multipliers+1:end))) > options.solve_tolf %does it solve for all variables except for the Lagrange multipliers
- if options.debug
- fprintf('\nevaluate_steady_state: The steady state file does not solve the steady state for the Ramsey problem.\n')
- fprintf('evaluate_steady_state: Conditional on the following instrument values: \n')
- for ii = 1:size(options.instruments,1);
- fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: the following equations have non-zero residuals: \n')
- for ii=n_multipliers+1:M.endo_nbr
- if abs(resids(ii)) > options.solve_tolf
- fprintf('\t Equation number %d: %f\n',ii-n_multipliers, resids(ii))
- end
- end
- skipline(2);
- end
- info(1) = 85;
- info(2) = resids'*resids;
- return;
- end
- end
- if options.debug
- infrow=find(isinf(ys_init));
- if ~isempty(infrow)
- fprintf('\nevaluate_steady_state: The initial values for the steady state of the following variables are Inf:\n');
- for iter=1:length(infrow)
- fprintf('%s\n',M.endo_names(infrow(iter),:));
- end
- end
- nanrow=find(isnan(ys_init));
- if ~isempty(nanrow)
- fprintf('\nevaluate_steady_state: The initial values for the steady state of the following variables are NaN:\n');
- for iter=1:length(nanrow)
- fprintf('%s\n',M.endo_names(nanrow(iter),:));
- end
- end
- end
- %either if no steady state file or steady state file without problems
- [ys,params,info] = dyn_ramsey_static(ys_init,M,options,oo);
- if info
- return;
- end
- %check whether steady state really solves the model
+if options.ramsey_policy
+ if steadystate_flag
+ % explicit steady state file
+ [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M, ...
+ options,steadystate_check_flag);
+ %test whether it solves model conditional on the instruments
resids = evaluate_static_model(ys,exo_ss,params,M,options);
-
n_multipliers=M.ramsey_eq_nbr;
- nan_indices_multiplier=find(isnan(resids(1:n_multipliers)));
nan_indices=find(isnan(resids(n_multipliers+1:end)));
if ~isempty(nan_indices)
if options.debug
- fprintf('\nevaluate_steady_state: The steady state computation for the Ramsey problem resulted in NaNs.\n')
- fprintf('evaluate_steady_state: The steady state computation resulted in the following instrument values: \n')
- for i = 1:size(options.instruments,1);
- fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
+ fprintf('\nevaluate_steady_state: The steady state file computation for the Ramsey problem resulted in NaNs.\n')
+ fprintf('evaluate_steady_state: The steady state was computed conditional on the following initial instrument values: \n')
+ for ii = 1:size(options.instruments,1);
+ fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
end
fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
fprintf('\t Equation(s): ')
@@ -160,188 +76,272 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
fprintf('%d, ',nan_indices(ii));
end
skipline();
+ fprintf('evaluate_steady_state: If those initial values are not admissable, change them using an initval-block.\n')
+ skipline(2);
end
- info(1) = 82;
+ info(1) = 84;
+ info(2) = resids'*resids;
return;
end
-
- if ~isempty(nan_indices_multiplier)
+
+ if any(imag(ys(n_multipliers+1:end)))
if options.debug
- fprintf('\nevaluate_steady_state: The steady state computation for the Ramsey problem resulted in NaNs in the auxiliary equations.\n')
- fprintf('evaluate_steady_state: The steady state computation resulted in the following instrument values: \n')
- for i = 1:size(options.instruments,1);
- fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
- fprintf('\t Auxiliary equation(s): ')
- for ii=1:length(nan_indices_multiplier)
- fprintf('%d, ',nan_indices_multiplier(ii));
+ fprintf('\nevaluate_steady_state: The steady state file computation for the Ramsey problem resulted in complex numbers.\n')
+ fprintf('evaluate_steady_state: The steady state was computed conditional on the following initial instrument values: \n')
+ for ii = 1:size(options.instruments,1);
+ fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
end
- skipline();
+ fprintf('evaluate_steady_state: If those initial values are not admissable, change them using an initval-block.\n')
+ skipline(2);
end
- info(1) = 83;
+ info(1) = 86;
+ info(2) = resids'*resids;
return;
end
- if max(abs(resids)) > options.solve_tolf %does it solve for all variables including the auxiliary ones
+ if max(abs(resids(n_multipliers+1:end))) > options.solve_tolf %does it solve for all variables except for the Lagrange multipliers
if options.debug
- fprintf('\nevaluate_steady_state: The steady state for the Ramsey problem could not be computed.\n')
- fprintf('evaluate_steady_state: The steady state computation stopped with the following instrument values:: \n')
- for i = 1:size(options.instruments,1);
- fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
- end
- fprintf('evaluate_steady_state: The following equations have non-zero residuals: \n')
- for ii=1:n_multipliers
- if abs(resids(ii)) > options.solve_tolf/100
- fprintf('\t Auxiliary Ramsey equation number %d: %f\n',ii, resids(ii))
- end
+ fprintf('\nevaluate_steady_state: The steady state file does not solve the steady state for the Ramsey problem.\n')
+ fprintf('evaluate_steady_state: Conditional on the following instrument values: \n')
+ for ii = 1:size(options.instruments,1);
+ fprintf('\t %s \t %f \n',options.instruments(ii,:),ys_init(strmatch(options.instruments(ii,:),M.endo_names,'exact')))
end
+ fprintf('evaluate_steady_state: the following equations have non-zero residuals: \n')
for ii=n_multipliers+1:M.endo_nbr
- if abs(resids(ii)) > options.solve_tolf/100
+ if abs(resids(ii)) > options.solve_tolf
fprintf('\t Equation number %d: %f\n',ii-n_multipliers, resids(ii))
end
end
skipline(2);
end
- info(1) = 81;
+ info(1) = 85;
info(2) = resids'*resids;
return;
end
- elseif steadystate_flag
- % explicit steady state file
- [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M, ...
- options,steadystate_check_flag);
- if size(ys,2)>size(ys,1)
- error('STEADY: steady_state-file must return a column vector, not a row vector.')
- end
- if info(1)
- return;
+ end
+ if options.debug
+ infrow=find(isinf(ys_init));
+ if ~isempty(infrow)
+ fprintf('\nevaluate_steady_state: The initial values for the steady state of the following variables are Inf:\n');
+ for iter=1:length(infrow)
+ fprintf('%s\n',M.endo_names(infrow(iter),:));
+ end
end
- elseif (options.bytecode == 0 && options.block == 0)
- if options.linear == 0
- % non linear model
- static_model = str2func([M.fname '_static']);
- [ys,check] = dynare_solve(@static_problem,...
- ys_init,...
- options, exo_ss, params,...
- M.endo_nbr,...
- static_model);
- if check && options.debug
- [ys,check,fvec,fjac] = dynare_solve(@static_problem,...
- ys_init,...
- options, exo_ss, params,...
- M.endo_nbr,...
- static_model);
- [infrow,infcol]=find(isinf(fjac) | isnan(fjac));
- if ~isempty(infrow)
- fprintf('\nSTEADY: The Jacobian at the initial values contains Inf or NaN. The problem arises from: \n')
- display_problematic_vars_Jacobian(infrow,infcol,M,ys_init,'static','STEADY: ')
- end
- problematic_equation = find(~isfinite(fvec));
- if ~isempty(problematic_equation)
- fprintf('\nSTEADY: numerical initial values or parameters incompatible with the following equations\n')
- disp(problematic_equation')
- fprintf('Please check for example\n')
- fprintf(' i) if all parameters occurring in these equations are defined\n')
- fprintf(' ii) that no division by an endogenous variable initialized to 0 occurs\n')
- end
+ nanrow=find(isnan(ys_init));
+ if ~isempty(nanrow)
+ fprintf('\nevaluate_steady_state: The initial values for the steady state of the following variables are NaN:\n');
+ for iter=1:length(nanrow)
+ fprintf('%s\n',M.endo_names(nanrow(iter),:));
end
- else
- % linear model
- fh_static = str2func([M.fname '_static']);
- [fvec,jacob] = fh_static(ys_init,exo_ss, ...
- params);
+ end
+ end
+ %either if no steady state file or steady state file without problems
+ [ys,params,info] = dyn_ramsey_static(ys_init,M,options,oo);
+ if info
+ return;
+ end
+ %check whether steady state really solves the model
+ resids = evaluate_static_model(ys,exo_ss,params,M,options);
- ii = find(~isfinite(fvec));
- if ~isempty(ii)
- ys=fvec;
- check=1;
- disp(['STEADY: numerical initial values or parameters incompatible with the following' ...
- ' equations'])
- disp(ii')
- disp('Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
- elseif isempty(ii) && max(abs(fvec)) > 1e-12
- ys = ys_init-jacob\fvec;
- resid = evaluate_static_model(ys,exo_ss,params,M,options);
- if max(abs(resid)) > 1e-6
- check=1;
- fprintf('STEADY: No steady state for your model could be found\n')
- fprintf('STEADY: Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
- end
+ n_multipliers=M.ramsey_eq_nbr;
+ nan_indices_multiplier=find(isnan(resids(1:n_multipliers)));
+ nan_indices=find(isnan(resids(n_multipliers+1:end)));
- else
- ys = ys_init;
+ if ~isempty(nan_indices)
+ if options.debug
+ fprintf('\nevaluate_steady_state: The steady state computation for the Ramsey problem resulted in NaNs.\n')
+ fprintf('evaluate_steady_state: The steady state computation resulted in the following instrument values: \n')
+ for i = 1:size(options.instruments,1);
+ fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
end
- if options.debug
- if any(any(isinf(jacob) | isnan(jacob)))
- [infrow,infcol]=find(isinf(jacob) | isnan(jacob));
- fprintf('\nSTEADY: The Jacobian contains Inf or NaN. The problem arises from: \n\n')
- for ii=1:length(infrow)
- fprintf('STEADY: Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',infrow(ii),deblank(M.endo_names(infcol(ii),:)),deblank(M.endo_names(infcol(ii),:)),ys_init(infcol(ii)))
- end
- fprintf('Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
- end
+ fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
+ fprintf('\t Equation(s): ')
+ for ii=1:length(nan_indices)
+ fprintf('%d, ',nan_indices(ii));
end
+ skipline();
end
- else
- % block or bytecode
- [ys,check] = dynare_solve_block_or_bytecode(ys_init,exo_ss, params, ...
- options, M);
+ info(1) = 82;
+ return;
end
- if check
- info(1)= 20;
- %make sure ys contains auxiliary variables in case of problem with dynare_solve
- if length(M.aux_vars) > 0 && ~steadystate_flag
- ys = h_set_auxiliary_variables(ys,exo_ss,M.params);
+ if ~isempty(nan_indices_multiplier)
+ if options.debug
+ fprintf('\nevaluate_steady_state: The steady state computation for the Ramsey problem resulted in NaNs in the auxiliary equations.\n')
+ fprintf('evaluate_steady_state: The steady state computation resulted in the following instrument values: \n')
+ for i = 1:size(options.instruments,1);
+ fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
+ end
+ fprintf('evaluate_steady_state: The problem occured in the following equations: \n')
+ fprintf('\t Auxiliary equation(s): ')
+ for ii=1:length(nan_indices_multiplier)
+ fprintf('%d, ',nan_indices_multiplier(ii));
+ end
+ skipline();
end
- resid = evaluate_static_model(ys,exo_ss,params,M,options);
- info(2) = resid'*resid ;
- if isnan(info(2))
- info(1)=22;
+ info(1) = 83;
+ return;
+ end
+
+ if max(abs(resids)) > options.solve_tolf %does it solve for all variables including the auxiliary ones
+ if options.debug
+ fprintf('\nevaluate_steady_state: The steady state for the Ramsey problem could not be computed.\n')
+ fprintf('evaluate_steady_state: The steady state computation stopped with the following instrument values:: \n')
+ for i = 1:size(options.instruments,1);
+ fprintf('\t %s \t %f \n',options.instruments(i,:),ys(strmatch(options.instruments(i,:),M.endo_names,'exact')))
+ end
+ fprintf('evaluate_steady_state: The following equations have non-zero residuals: \n')
+ for ii=1:n_multipliers
+ if abs(resids(ii)) > options.solve_tolf/100
+ fprintf('\t Auxiliary Ramsey equation number %d: %f\n',ii, resids(ii))
+ end
+ end
+ for ii=n_multipliers+1:M.endo_nbr
+ if abs(resids(ii)) > options.solve_tolf/100
+ fprintf('\t Equation number %d: %f\n',ii-n_multipliers, resids(ii))
+ end
+ end
+ skipline(2);
end
- return
+ info(1) = 81;
+ info(2) = resids'*resids;
+ return;
+ end
+elseif steadystate_flag
+ % explicit steady state file
+ [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M, ...
+ options,steadystate_check_flag);
+ if size(ys,2)>size(ys,1)
+ error('STEADY: steady_state-file must return a column vector, not a row vector.')
+ end
+ if info(1)
+ return;
end
+elseif (options.bytecode == 0 && options.block == 0)
+ if options.linear == 0
+ % non linear model
+ static_model = str2func([M.fname '_static']);
+ [ys,check] = dynare_solve(@static_problem,...
+ ys_init,...
+ options, exo_ss, params,...
+ M.endo_nbr,...
+ static_model);
+ if check && options.debug
+ [ys,check,fvec,fjac] = dynare_solve(@static_problem,...
+ ys_init,...
+ options, exo_ss, params,...
+ M.endo_nbr,...
+ static_model);
+ [infrow,infcol]=find(isinf(fjac) | isnan(fjac));
+ if ~isempty(infrow)
+ fprintf('\nSTEADY: The Jacobian at the initial values contains Inf or NaN. The problem arises from: \n')
+ display_problematic_vars_Jacobian(infrow,infcol,M,ys_init,'static','STEADY: ')
+ end
+ problematic_equation = find(~isfinite(fvec));
+ if ~isempty(problematic_equation)
+ fprintf('\nSTEADY: numerical initial values or parameters incompatible with the following equations\n')
+ disp(problematic_equation')
+ fprintf('Please check for example\n')
+ fprintf(' i) if all parameters occurring in these equations are defined\n')
+ fprintf(' ii) that no division by an endogenous variable initialized to 0 occurs\n')
+ end
+ end
+ else
+ % linear model
+ fh_static = str2func([M.fname '_static']);
+ [fvec,jacob] = fh_static(ys_init,exo_ss, ...
+ params);
+
+ ii = find(~isfinite(fvec));
+ if ~isempty(ii)
+ ys=fvec;
+ check=1;
+ disp(['STEADY: numerical initial values or parameters incompatible with the following' ...
+ ' equations'])
+ disp(ii')
+ disp('Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
+ elseif isempty(ii) && max(abs(fvec)) > 1e-12
+ ys = ys_init-jacob\fvec;
+ resid = evaluate_static_model(ys,exo_ss,params,M,options);
+ if max(abs(resid)) > 1e-6
+ check=1;
+ fprintf('STEADY: No steady state for your model could be found\n')
+ fprintf('STEADY: Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
+ end
- % If some equations are tagged [static] or [dynamic], verify consistency
- if M.static_and_dynamic_models_differ
- % Evaluate residual of *dynamic* model using the steady state
- % computed on the *static* one
- z = repmat(ys,1,M.maximum_lead + M.maximum_lag + 1);
- zx = repmat([exo_ss'], M.maximum_lead + M.maximum_lag + 1, 1);
- if options.bytecode
- [chck, r, junk]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1);
- mexErrCheck('bytecode', chck);
- elseif options.block
- [r, oo.dr] = feval([M.fname '_dynamic'], z', zx, M.params, ys, M.maximum_lag+1, oo.dr);
else
- iyv = M.lead_lag_incidence';
- iyr0 = find(iyv(:));
- xys = z(iyr0);
- r = feval([M.fname '_dynamic'], z(iyr0), zx, M.params, ys, M.maximum_lag + 1);
+ ys = ys_init;
end
-
- % Fail if residual greater than tolerance
- if max(abs(r)) > options.solve_tolf
- info(1) = 25;
- return
+ if options.debug
+ if any(any(isinf(jacob) | isnan(jacob)))
+ [infrow,infcol]=find(isinf(jacob) | isnan(jacob));
+ fprintf('\nSTEADY: The Jacobian contains Inf or NaN. The problem arises from: \n\n')
+ for ii=1:length(infrow)
+ fprintf('STEADY: Derivative of Equation %d with respect to Variable %s (initial value of %s: %g) \n',infrow(ii),deblank(M.endo_names(infcol(ii),:)),deblank(M.endo_names(infcol(ii),:)),ys_init(infcol(ii)))
+ end
+ fprintf('Check whether your model is truly linear. Put "resid(1);" before "steady;" to see the problematic equations.\n')
+ end
end
end
+else
+ % block or bytecode
+ [ys,check] = dynare_solve_block_or_bytecode(ys_init,exo_ss, params, ...
+ options, M);
+end
- if ~isreal(ys)
- info(1) = 21;
- info(2) = sum(imag(ys).^2);
- ys = real(ys);
- return
+if check
+ info(1)= 20;
+ %make sure ys contains auxiliary variables in case of problem with dynare_solve
+ if length(M.aux_vars) > 0 && ~steadystate_flag
+ ys = h_set_auxiliary_variables(ys,exo_ss,M.params);
+ end
+ resid = evaluate_static_model(ys,exo_ss,params,M,options);
+ info(2) = resid'*resid ;
+ if isnan(info(2))
+ info(1)=22;
end
+ return
+end
- if ~isempty(find(isnan(ys)))
- info(1) = 22;
- info(2) = NaN;
+% If some equations are tagged [static] or [dynamic], verify consistency
+if M.static_and_dynamic_models_differ
+ % Evaluate residual of *dynamic* model using the steady state
+ % computed on the *static* one
+ z = repmat(ys,1,M.maximum_lead + M.maximum_lag + 1);
+ zx = repmat([exo_ss'], M.maximum_lead + M.maximum_lag + 1, 1);
+ if options.bytecode
+ [chck, r, junk]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1);
+ mexErrCheck('bytecode', chck);
+ elseif options.block
+ [r, oo.dr] = feval([M.fname '_dynamic'], z', zx, M.params, ys, M.maximum_lag+1, oo.dr);
+ else
+ iyv = M.lead_lag_incidence';
+ iyr0 = find(iyv(:));
+ xys = z(iyr0);
+ r = feval([M.fname '_dynamic'], z(iyr0), zx, M.params, ys, M.maximum_lag + 1);
+ end
+
+ % Fail if residual greater than tolerance
+ if max(abs(r)) > options.solve_tolf
+ info(1) = 25;
return
end
+end
+
+if ~isreal(ys)
+ info(1) = 21;
+ info(2) = sum(imag(ys).^2);
+ ys = real(ys);
+ return
+end
+
+if ~isempty(find(isnan(ys)))
+ info(1) = 22;
+ info(2) = NaN;
+ return
+end
function [resids,jac] = static_problem(y,x,params,nvar,fh_static_model)
- [r,j] = fh_static_model(y,x,params);
- resids = r(1:nvar);
- jac = j(1:nvar,1:nvar);
+[r,j] = fh_static_model(y,x,params);
+resids = r(1:nvar);
+jac = j(1:nvar,1:nvar);
diff --git a/matlab/evaluate_steady_state_file.m b/matlab/evaluate_steady_state_file.m
index 8f67a5b87de4cee0f022ccd38a08bb6d8828ab7c..2e3afceb9efe0405d58b978d6b046c4ce7f1999a 100644
--- a/matlab/evaluate_steady_state_file.m
+++ b/matlab/evaluate_steady_state_file.m
@@ -36,125 +36,125 @@ function [ys,params,info] = evaluate_steady_state_file(ys_init,exo_ss,M,options,
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- debug = false;
+debug = false;
- ys = [];
- params = M.params;
- info = 0;
+ys = [];
+params = M.params;
+info = 0;
- fname = M.fname;
+fname = M.fname;
- if options.steadystate_flag == 1
- % old format
- assignin('base','tmp_00_',params);
- evalin('base','M_.params=tmp_00_; clear(''tmp_00_'')');
- h_steadystate = str2func([fname '_steadystate']);
- [ys,check] = h_steadystate(ys_init, exo_ss);
- params1 = evalin('base','M_.params');
- else % steadystate_flag == 2
- % new format
- h_steadystate = str2func([fname '_steadystate2']);
- [ys,params1,check] = h_steadystate(ys_init, exo_ss, params);
- end
-
- if check
- info(1) = 19;
- info(2) = NaN;
- return
- end
-
- if M.param_nbr > 0
- updated_params_flag = max(abs(params1-params)) > 1e-12 ...
- || ~isequal(isnan(params1),isnan(params)); %checks whether numbers or NaN changed
- else
- updated_params_flag = 0;
- end
+if options.steadystate_flag == 1
+ % old format
+ assignin('base','tmp_00_',params);
+ evalin('base','M_.params=tmp_00_; clear(''tmp_00_'')');
+ h_steadystate = str2func([fname '_steadystate']);
+ [ys,check] = h_steadystate(ys_init, exo_ss);
+ params1 = evalin('base','M_.params');
+else % steadystate_flag == 2
+ % new format
+ h_steadystate = str2func([fname '_steadystate2']);
+ [ys,params1,check] = h_steadystate(ys_init, exo_ss, params);
+end
- h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
- if isnan(updated_params_flag) || (updated_params_flag && any(isnan(params(~isnan(params))-params1(~isnan(params))))) %checks if new NaNs were added
- info(1) = 24;
- info(2) = NaN;
- ys = h_set_auxiliary_variables(ys,exo_ss,params);
- return
- end
+if check
+ info(1) = 19;
+ info(2) = NaN;
+ return
+end
- if updated_params_flag && ~isreal(params1)
- info(1) = 23;
- info(2) = sum(imag(params).^2);
- ys = h_set_auxiliary_variables(ys,exo_ss,params);
- return
+if M.param_nbr > 0
+ updated_params_flag = max(abs(params1-params)) > 1e-12 ...
+ || ~isequal(isnan(params1),isnan(params)); %checks whether numbers or NaN changed
+else
+ updated_params_flag = 0;
+end
+
+h_set_auxiliary_variables = str2func([M.fname '_set_auxiliary_variables']);
+if isnan(updated_params_flag) || (updated_params_flag && any(isnan(params(~isnan(params))-params1(~isnan(params))))) %checks if new NaNs were added
+ info(1) = 24;
+ info(2) = NaN;
+ ys = h_set_auxiliary_variables(ys,exo_ss,params);
+ return
+end
+
+if updated_params_flag && ~isreal(params1)
+ info(1) = 23;
+ info(2) = sum(imag(params).^2);
+ ys = h_set_auxiliary_variables(ys,exo_ss,params);
+ return
+end
+
+if updated_params_flag
+ params = params1;
+end
+
+% adding values for auxiliary variables
+if length(M.aux_vars) > 0 && ~options.ramsey_policy
+ if debug
+ ys0 = ys;
end
-
- if updated_params_flag
- params = params1;
+ ys = h_set_auxiliary_variables(ys,exo_ss,params);
+ if debug
+ ys1 = ys;
end
-
- % adding values for auxiliary variables
- if length(M.aux_vars) > 0 && ~options.ramsey_policy
- if debug
- ys0 = ys;
- end
+ ys = h_set_auxiliary_variables(ys,exo_ss,params);
+ if debug
+ ys2 = ys;
+ end
+ if debug
ys = h_set_auxiliary_variables(ys,exo_ss,params);
- if debug
- ys1 = ys;
+ ys3 = ys;
+ idx = find(abs(ys0-ys1)>0);
+ if ~isempty(idx)
+ M.endo_names(idx,:)
+ else
+ disp('1-invariant')
end
- ys = h_set_auxiliary_variables(ys,exo_ss,params);
- if debug
- ys2 = ys;
+ idx = find(abs(ys2-ys1)>0);
+ if ~isempty(idx)
+ M.endo_names(idx,:)
+ else
+ disp('2-invariant')
end
- if debug
- ys = h_set_auxiliary_variables(ys,exo_ss,params);
- ys3 = ys;
- idx = find(abs(ys0-ys1)>0);
- if ~isempty(idx)
- M.endo_names(idx,:)
- else
- disp('1-invariant')
- end
- idx = find(abs(ys2-ys1)>0);
- if ~isempty(idx)
- M.endo_names(idx,:)
- else
- disp('2-invariant')
- end
- idx = find(abs(ys3-ys3)>0);
- if ~isempty(idx)
- M.endo_names(idx,:)
- else
- disp('3-invariant')
- end
- pause
+ idx = find(abs(ys3-ys3)>0);
+ if ~isempty(idx)
+ M.endo_names(idx,:)
+ else
+ disp('3-invariant')
end
+ pause
end
+end
- check1 = 0;
- if steady_state_checkflag
- % Check whether the steady state obtained from the _steadystate file is a steady state.
- [residuals, check] = evaluate_static_model(ys, exo_ss, params, M, options);
- if check
- info(1) = 19;
- info(2) = check; % to be improved
- return;
- end
- if max(abs(residuals)) > options.dynatol.f
- info(1) = 19;
- info(2) = residuals'*residuals;
- return
- end
- if any(isnan(residuals))
- info(1) = 22;
- return
- end
- elseif ~isempty(options.steadystate_partial)
- ssvar = options.steadystate_partial.ssvar;
- nov = length(ssvar);
- indv = zeros(nov,1);
- for i = 1:nov
- indv(i) = strmatch(ssvar(i),M.endo_names,'exact');
- end
- [ys,check] = dynare_solve('restricted_steadystate',...
- ys(indv),...
- options, exo_ss,indv);
+check1 = 0;
+if steady_state_checkflag
+ % Check whether the steady state obtained from the _steadystate file is a steady state.
+ [residuals, check] = evaluate_static_model(ys, exo_ss, params, M, options);
+ if check
+ info(1) = 19;
+ info(2) = check; % to be improved
+ return;
end
+ if max(abs(residuals)) > options.dynatol.f
+ info(1) = 19;
+ info(2) = residuals'*residuals;
+ return
+ end
+ if any(isnan(residuals))
+ info(1) = 22;
+ return
+ end
+elseif ~isempty(options.steadystate_partial)
+ ssvar = options.steadystate_partial.ssvar;
+ nov = length(ssvar);
+ indv = zeros(nov,1);
+ for i = 1:nov
+ indv(i) = strmatch(ssvar(i),M.endo_names,'exact');
+ end
+ [ys,check] = dynare_solve('restricted_steadystate',...
+ ys(indv),...
+ options, exo_ss,indv);
+end
+
-
diff --git a/matlab/flip_plan.m b/matlab/flip_plan.m
index a5a0952a9fd8cc4ef1ee97f43dbfe75853b7237b..15fd3aa6884b321276a98e1146cee7e49c0a75b2 100644
--- a/matlab/flip_plan.m
+++ b/matlab/flip_plan.m
@@ -31,71 +31,71 @@ function plan = flip_plan(plan, exogenous, endogenous, expectation_type, date, v
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
- error(['in flip_plan the fourth argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
- end
- exogenous = strtrim(exogenous);
- ix = find(strcmp(exogenous, plan.endo_names));
- if isempty(ix)
- error(['in flip_plan the second argument ' exogenous ' is not an endogenous variable']);
- end;
- endogenous = strtrim(endogenous);
- iy = find(strcmp(endogenous, plan.exo_names));
- if isempty(iy)
- error(['in flip_plan the third argument ' endogenous ' is not an exogenous variable']);
- end;
- sdate = length(date);
- if sdate > 1
- if date(1) < plan.date(1) || date(end) > plan.date(end)
- error(['in flip_plan the fifth argument (date=' date ') must lay inside the plan.date ' plan.date]);
- end
- else
- if date < plan.date(1) || date > plan.date(end)
- error(['in flip_plan the fifth argument (date=' date ') must lay iside the plan.date ' plan.date]);
- end
- end
- if ~isempty(plan.shock_vars_)
- common_var = find(iy == plan.shock_vars_);
- if ~isempty(common_var)
- common_date = intersect(date, plan.shock_date_{common_var});
- if ~isempty(common_date)
- if common_date.length > 1
- the_dates = [cell2mat(strings(common_date(1))) ':' cell2mat(strings(common_date(end)))];
- else
- the_dates = cell2mat(strings(common_date));
- end
- error(['Impossible case: ' plan.exo_names{plan.shock_vars_(common_var)} ' is used both as a shock and as an endogenous variable to control the path of ' plan.endo_names{ix} ' at the dates ' the_dates]);
- end
- end
- end
- i_ix = find(ix == plan.constrained_vars_);
- if isempty(i_ix)
+if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
+ error(['in flip_plan the fourth argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
+end
+exogenous = strtrim(exogenous);
+ix = find(strcmp(exogenous, plan.endo_names));
+if isempty(ix)
+ error(['in flip_plan the second argument ' exogenous ' is not an endogenous variable']);
+end;
+endogenous = strtrim(endogenous);
+iy = find(strcmp(endogenous, plan.exo_names));
+if isempty(iy)
+ error(['in flip_plan the third argument ' endogenous ' is not an exogenous variable']);
+end;
+sdate = length(date);
+if sdate > 1
+ if date(1) < plan.date(1) || date(end) > plan.date(end)
+ error(['in flip_plan the fifth argument (date=' date ') must lay inside the plan.date ' plan.date]);
+ end
+else
+ if date < plan.date(1) || date > plan.date(end)
+ error(['in flip_plan the fifth argument (date=' date ') must lay iside the plan.date ' plan.date]);
+ end
+end
+if ~isempty(plan.shock_vars_)
+ common_var = find(iy == plan.shock_vars_);
+ if ~isempty(common_var)
+ common_date = intersect(date, plan.shock_date_{common_var});
+ if ~isempty(common_date)
+ if common_date.length > 1
+ the_dates = [cell2mat(strings(common_date(1))) ':' cell2mat(strings(common_date(end)))];
+ else
+ the_dates = cell2mat(strings(common_date));
+ end
+ error(['Impossible case: ' plan.exo_names{plan.shock_vars_(common_var)} ' is used both as a shock and as an endogenous variable to control the path of ' plan.endo_names{ix} ' at the dates ' the_dates]);
+ end
+ end
+end
+i_ix = find(ix == plan.constrained_vars_);
+if isempty(i_ix)
if isempty(plan.constrained_vars_)
- plan.constrained_vars_ = ix;
- plan.options_cond_fcst_.controlled_varexo = iy;
- if strcmp(expectation_type, 'perfect_foresight')
- plan.constrained_perfect_foresight_ = 1;
- else
- plan.constrained_perfect_foresight_ = 0;
- end
+ plan.constrained_vars_ = ix;
+ plan.options_cond_fcst_.controlled_varexo = iy;
+ if strcmp(expectation_type, 'perfect_foresight')
+ plan.constrained_perfect_foresight_ = 1;
+ else
+ plan.constrained_perfect_foresight_ = 0;
+ end
else
- plan.constrained_vars_ = [plan.constrained_vars_ ; ix];
- plan.options_cond_fcst_.controlled_varexo = [plan.options_cond_fcst_.controlled_varexo ; iy];
- if strcmp(expectation_type, 'perfect_foresight')
- plan.constrained_perfect_foresight_ = [plan.constrained_perfect_foresight_ ; 1];
- else
- plan.constrained_perfect_foresight_ = [plan.constrained_perfect_foresight_ ; 0];
- end
+ plan.constrained_vars_ = [plan.constrained_vars_ ; ix];
+ plan.options_cond_fcst_.controlled_varexo = [plan.options_cond_fcst_.controlled_varexo ; iy];
+ if strcmp(expectation_type, 'perfect_foresight')
+ plan.constrained_perfect_foresight_ = [plan.constrained_perfect_foresight_ ; 1];
+ else
+ plan.constrained_perfect_foresight_ = [plan.constrained_perfect_foresight_ ; 0];
+ end
end
plan.constrained_date_{length(plan.constrained_date_) + 1} = date;
plan.constrained_str_date_{length(plan.constrained_str_date_) + 1} = strings(date);
plan.constrained_int_date_{length(plan.constrained_int_date_) + 1} = date - plan.date(1) + 1;
plan.constrained_paths_{length(plan.constrained_paths_) + 1} = value;
- elseif plan.options_cond_fcst_.controlled_varexo(i_ix) == iy % same exogenous and endogenous hard tune
- [plan.constrained_str_date_{i_ix}, i1, i2] = union(strings(date), plan.constrained_str_date_{i_ix});
- plan.constrained_date_{i_ix} = [date(i1) plan.constrained_date_{i_ix}(i2)];
- plan.constrained_int_date_{i_ix} = [date(i1) - plan.date(1) + 1; plan.constrained_int_date_{i_ix}(i2)];
- plan.constrained_paths_{i_ix} = [value(i1)'; plan.constrained_paths_{i_ix}(i2)];
- else
+elseif plan.options_cond_fcst_.controlled_varexo(i_ix) == iy % same exogenous and endogenous hard tune
+[plan.constrained_str_date_{i_ix}, i1, i2] = union(strings(date), plan.constrained_str_date_{i_ix});
+plan.constrained_date_{i_ix} = [date(i1) plan.constrained_date_{i_ix}(i2)];
+plan.constrained_int_date_{i_ix} = [date(i1) - plan.date(1) + 1; plan.constrained_int_date_{i_ix}(i2)];
+plan.constrained_paths_{i_ix} = [value(i1)'; plan.constrained_paths_{i_ix}(i2)];
+else
error(['impossible case you have two conditional forecasts:\n - one involving ' plan.endo_names{plan.options_cond_fcst_.controlled_varexo(i_ix),:} ' as control and ' plan_exo_names{plan.constrained_vars_(ix_)} ' as constrined endogenous\n - the other involving ' plan.endo_names{plan.options_cond_fcst_.controlled_varexo(iy),:} ' as control and ' plan_exo_names{plan.constrained_vars_(ix)} ' as constrined endogenous\n']);
- end
+end
diff --git a/matlab/gensylv_fp.m b/matlab/gensylv_fp.m
index 2e5c167b5ecf2e4fd9d876be43fc6e2b02612c5b..35ca3352aebe49739c285ea3b2d0342a4bc1481e 100644
--- a/matlab/gensylv_fp.m
+++ b/matlab/gensylv_fp.m
@@ -62,7 +62,7 @@ while it_fp < maxit_fp && evol > tol;
Z_old = Z;
Z = - (B * X * C + D);
evol = max(sum(abs(Z - Z_old))); %norm_1
- %evol = max(sum(abs(Z - Z_old)')); %norm_inf
+ %evol = max(sum(abs(Z - Z_old)')); %norm_inf
it_fp = it_fp + 1;
end;
%fprintf('sylvester it_fp=%d evol=%g | ',it_fp,evol);
diff --git a/matlab/getH.m b/matlab/getH.m
index 0db11d8e33ac31260a2cf2bd937573e732171e67..7a3b8d791e2feee2c38109ed64e31e34d6c36f66 100644
--- a/matlab/getH.m
+++ b/matlab/getH.m
@@ -96,7 +96,7 @@ if kronflag==-1, % perturbation
end
if nargout>5,
H2 = hessian_sparse('thet2tau',[sqrt(diag(M_.Sigma_e(indexo,indexo))); M_.params(indx)], ...
- options_.gstep,estim_params_,M_, oo_, indx,indexo,0,[],[],[],iv);
+ options_.gstep,estim_params_,M_, oo_, indx,indexo,0,[],[],[],iv);
H2ss = zeros(m1,tot_param_nbr,tot_param_nbr);
iax=find(triu(rand(tot_param_nbr,tot_param_nbr)));
H2 = H2(:,iax);
@@ -109,23 +109,23 @@ if kronflag==-1, % perturbation
d2A(:,:) = H2(m1+1:m1+m1*m1,:);
d2Om(:,:) = H2(m1+m1*m1+1:end,:);
clear H2
-% tmp0=zeros(m1,m1);
-% tmp0(iv,iv)=1;
-% iax=find(tmp0);
-% d2A=d2a(iax,:);
-% iax=find(dyn_vech(tmp0));
-% d2Om=d2om(iax,:);
+ % tmp0=zeros(m1,m1);
+ % tmp0(iv,iv)=1;
+ % iax=find(tmp0);
+ % d2A=d2a(iax,:);
+ % iax=find(dyn_vech(tmp0));
+ % d2Om=d2om(iax,:);
end
-% assignin('base','M_', M_);
-% assignin('base','oo_', oo_);
+ % assignin('base','M_', M_);
+ % assignin('base','oo_', oo_);
return
end
if kronflag==-2,
if nargout>5,
[residual, g1, g2 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1);
+ M_.params, oo_.dr.ys, 1);
g22 = hessian_sparse('thet2tau',[M_.params(indx)],options_.gstep,estim_params_,M_, oo_, indx,[],-1);
H2ss=full(g22(1:M_.endo_nbr,:));
H2ss = reshape(H2ss,[M_.endo_nbr param_nbr param_nbr]);
@@ -145,7 +145,7 @@ if kronflag==-2,
clear gx22;
else
[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1);
+ M_.params, oo_.dr.ys, 1);
end
gp = fjaco('thet2tau',[M_.params(indx)],estim_params_,M_, oo_, indx,[],-1);
Hss=gp(1:M_.endo_nbr,:);
@@ -153,134 +153,134 @@ if kronflag==-2,
gp = reshape(gp,[size(g1) param_nbr]);
else
-% yy0=[];
-% for j=1:size(M_.lead_lag_incidence,1);
-% yy0 = [ yy0; oo_.dr.ys(find(M_.lead_lag_incidence(j,:)))];
-% end
-dyssdtheta=zeros(length(oo_.dr.ys),M_.param_nbr);
-d2yssdtheta=zeros(length(oo_.dr.ys),M_.param_nbr,M_.param_nbr);
-[residual, gg1] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
-df = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), ...
- M_.params);
-dyssdtheta = -gg1\df;
-if nargout>5,
- [residual, gg1, gg2] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
- [residual, g1, g2, g3] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1);
- [nr, nc]=size(gg2);
-
- [df, gpx, d2f] = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, oo_.exo_steady_state', ...
- M_.params);%, oo_.dr.ys, 1, dyssdtheta*0, d2yssdtheta);
- d2f = get_all_resid_2nd_derivs(d2f,length(oo_.dr.ys),M_.param_nbr);
-
- if isempty(find(gg2)),
- for j=1:M_.param_nbr,
- d2yssdtheta(:,:,j) = -gg1\d2f(:,:,j);
- end
- else
- gam = d2f*0;
- for j=1:nr,
- tmp1 = (squeeze(gpx(j,:,:))'*dyssdtheta);
- gam(j,:,:)=transpose(reshape(gg2(j,:),[nr nr])*dyssdtheta)*dyssdtheta ...
- + tmp1 + tmp1';
- end
- for j=1:M_.param_nbr,
- d2yssdtheta(:,:,j) = -gg1\(d2f(:,:,j)+gam(:,:,j));
-% d2yssdtheta(:,:,j) = -gg1\(d2f(:,:,j)+gam(:,:,j)+ squeeze(gpx(:,:,j))*dyssdtheta);
+ % yy0=[];
+ % for j=1:size(M_.lead_lag_incidence,1);
+ % yy0 = [ yy0; oo_.dr.ys(find(M_.lead_lag_incidence(j,:)))];
+ % end
+ dyssdtheta=zeros(length(oo_.dr.ys),M_.param_nbr);
+ d2yssdtheta=zeros(length(oo_.dr.ys),M_.param_nbr,M_.param_nbr);
+ [residual, gg1] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
+ df = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), ...
+ M_.params);
+ dyssdtheta = -gg1\df;
+ if nargout>5,
+ [residual, gg1, gg2] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
+ [residual, g1, g2, g3] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
+ M_.params, oo_.dr.ys, 1);
+ [nr, nc]=size(gg2);
+
+ [df, gpx, d2f] = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, oo_.exo_steady_state', ...
+ M_.params);%, oo_.dr.ys, 1, dyssdtheta*0, d2yssdtheta);
+ d2f = get_all_resid_2nd_derivs(d2f,length(oo_.dr.ys),M_.param_nbr);
+
+ if isempty(find(gg2)),
+ for j=1:M_.param_nbr,
+ d2yssdtheta(:,:,j) = -gg1\d2f(:,:,j);
+ end
+ else
+ gam = d2f*0;
+ for j=1:nr,
+ tmp1 = (squeeze(gpx(j,:,:))'*dyssdtheta);
+ gam(j,:,:)=transpose(reshape(gg2(j,:),[nr nr])*dyssdtheta)*dyssdtheta ...
+ + tmp1 + tmp1';
+ end
+ for j=1:M_.param_nbr,
+ d2yssdtheta(:,:,j) = -gg1\(d2f(:,:,j)+gam(:,:,j));
+ % d2yssdtheta(:,:,j) = -gg1\(d2f(:,:,j)+gam(:,:,j)+ squeeze(gpx(:,:,j))*dyssdtheta);
+ end
+ clear tmp1 gpx gam,
end
- clear tmp1 gpx gam,
end
-end
-if any(any(isnan(dyssdtheta))),
- [U,T] = schur(gg1);
- qz_criterium=options_.qz_criterium;
- e1 = abs(ordeig(T)) < qz_criterium-1;
- k = sum(e1); % Number non stationary variables.
-% n = length(e1)-k; % Number of stationary variables.
- [U,T] = ordschur(U,T,e1);
- T = T(k+1:end,k+1:end);
- dyssdtheta = -U(:,k+1:end)*(T\U(:,k+1:end)')*df;
- if nargout>5,
- for j=1:length(indx),
- d2yssdtheta(:,:,j) = -U(:,k+1:end)*(T\U(:,k+1:end)')*d2f(:,:,j);
+ if any(any(isnan(dyssdtheta))),
+ [U,T] = schur(gg1);
+ qz_criterium=options_.qz_criterium;
+ e1 = abs(ordeig(T)) < qz_criterium-1;
+ k = sum(e1); % Number non stationary variables.
+ % n = length(e1)-k; % Number of stationary variables.
+ [U,T] = ordschur(U,T,e1);
+ T = T(k+1:end,k+1:end);
+ dyssdtheta = -U(:,k+1:end)*(T\U(:,k+1:end)')*df;
+ if nargout>5,
+ for j=1:length(indx),
+ d2yssdtheta(:,:,j) = -U(:,k+1:end)*(T\U(:,k+1:end)')*d2f(:,:,j);
+ end
end
end
-end
-if nargout>5,
- [df, gp, d2f, gpp, hp] = feval([M_.fname,'_params_derivs'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1, dyssdtheta, d2yssdtheta);
- H2ss = d2yssdtheta(oo_.dr.order_var,indx,indx);
-% nelem=size(g1,2);
-% g22 = get_all_2nd_derivs(gpp,m,nelem,M_.param_nbr);
-% g22 = g22(:,:,indx,indx);
-else
- [df, gp] = feval([M_.fname,'_params_derivs'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
- M_.params, oo_.dr.ys, 1, dyssdtheta,d2yssdtheta);
- [residual, g1, g2 ] = feval([M_.fname,'_dynamic'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
- M_.params, oo_.dr.ys, 1);
-end
+ if nargout>5,
+ [df, gp, d2f, gpp, hp] = feval([M_.fname,'_params_derivs'],yy0, oo_.exo_steady_state', ...
+ M_.params, oo_.dr.ys, 1, dyssdtheta, d2yssdtheta);
+ H2ss = d2yssdtheta(oo_.dr.order_var,indx,indx);
+ % nelem=size(g1,2);
+ % g22 = get_all_2nd_derivs(gpp,m,nelem,M_.param_nbr);
+ % g22 = g22(:,:,indx,indx);
+ else
+ [df, gp] = feval([M_.fname,'_params_derivs'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
+ M_.params, oo_.dr.ys, 1, dyssdtheta,d2yssdtheta);
+ [residual, g1, g2 ] = feval([M_.fname,'_dynamic'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
+ M_.params, oo_.dr.ys, 1);
+ end
-[nr, nc]=size(g2);
-nc = sqrt(nc);
-Hss = dyssdtheta(oo_.dr.order_var,indx);
-dyssdtheta = dyssdtheta(I,:);
-ns = max(max(M_.lead_lag_incidence)); % retrieve the number of states excluding columns for shocks
-gp2 = gp*0;
-for j=1:nr,
- [II JJ]=ind2sub([nc nc],find(g2(j,:)));
- for i=1:nc,
- is = find(II==i);
- is = is(find(JJ(is)<=ns));
- if ~isempty(is),
- g20=full(g2(j,find(g2(j,:))));
- gp2(j,i,:)=g20(is)*dyssdtheta(JJ(is),:);
+ [nr, nc]=size(g2);
+ nc = sqrt(nc);
+ Hss = dyssdtheta(oo_.dr.order_var,indx);
+ dyssdtheta = dyssdtheta(I,:);
+ ns = max(max(M_.lead_lag_incidence)); % retrieve the number of states excluding columns for shocks
+ gp2 = gp*0;
+ for j=1:nr,
+ [II JJ]=ind2sub([nc nc],find(g2(j,:)));
+ for i=1:nc,
+ is = find(II==i);
+ is = is(find(JJ(is)<=ns));
+ if ~isempty(is),
+ g20=full(g2(j,find(g2(j,:))));
+ gp2(j,i,:)=g20(is)*dyssdtheta(JJ(is),:);
+ end
end
end
-end
-gp = gp+gp2;
-gp = gp(:,:,indx);
+ gp = gp+gp2;
+ gp = gp(:,:,indx);
-if nargout>5,
-% h22 = get_all_hess_derivs(hp,nr,nc,M_.param_nbr);
- g22 = gpp;
- gp22 = sparse(nr*nc,param_nbr*param_nbr);
- tmp1 = reshape(g3,[nr*nc*nc nc]);
- tmp2=sparse(size(tmp1,1),M_.param_nbr);
-% tmp2=tmp1*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
- tmpa=[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
- tmpa=sparse(tmpa);
- for j=1:M_.param_nbr,
- tmp2(:,j)=tmp1*tmpa(:,j);
- end
-% tmp2=sparse(tmp2);
-% [i1 i2]=ind2sub([nc M_.param_nbr],[1:nc*M_.param_nbr]');
+ if nargout>5,
+ % h22 = get_all_hess_derivs(hp,nr,nc,M_.param_nbr);
+ g22 = gpp;
+ gp22 = sparse(nr*nc,param_nbr*param_nbr);
+ tmp1 = reshape(g3,[nr*nc*nc nc]);
+ tmp2=sparse(size(tmp1,1),M_.param_nbr);
+ % tmp2=tmp1*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ tmpa=[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ tmpa=sparse(tmpa);
+ for j=1:M_.param_nbr,
+ tmp2(:,j)=tmp1*tmpa(:,j);
+ end
+ % tmp2=sparse(tmp2);
+ % [i1 i2]=ind2sub([nc M_.param_nbr],[1:nc*M_.param_nbr]');
- for j=1:nr,
- tmp0=reshape(g2(j,:),[nc nc]);
- tmp0 = tmp0(:,1:ns)*reshape(d2yssdtheta(I,:,:),[ns,M_.param_nbr*M_.param_nbr]);
- for i=1:nc,
- indo = sub2ind([nr nc nc], ones(nc,1)*j ,ones(nc,1)*i, (1:nc)');
- tmpx = (tmp2(indo,:))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
-% gp22(j,i,:,:)=squeeze(tmp1(j,i,:,:))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
- tmpu = (get_hess_deriv(hp,j,i,nc,M_.param_nbr))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
- tmpy = tmpx+tmpu+tmpu'+reshape(tmp0(i,:,:),[M_.param_nbr M_.param_nbr]);
- tmpy = tmpy + get_2nd_deriv_mat(gpp,j,i,M_.param_nbr);
- tmpy = tmpy(indx,indx);
- if any(any(tmpy)),
- ina = find(triu(tmpy));
- gp22(sub2ind([nr nc],j,i),ina)=transpose(tmpy(ina));
-% gp22(j,i,:,:)= reshape(tmpy,[1 1 M_.param_nbr M_.param_nbr]);
+ for j=1:nr,
+ tmp0=reshape(g2(j,:),[nc nc]);
+ tmp0 = tmp0(:,1:ns)*reshape(d2yssdtheta(I,:,:),[ns,M_.param_nbr*M_.param_nbr]);
+ for i=1:nc,
+ indo = sub2ind([nr nc nc], ones(nc,1)*j ,ones(nc,1)*i, (1:nc)');
+ tmpx = (tmp2(indo,:))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ % gp22(j,i,:,:)=squeeze(tmp1(j,i,:,:))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ tmpu = (get_hess_deriv(hp,j,i,nc,M_.param_nbr))'*[dyssdtheta; zeros(nc-ns,M_.param_nbr)];
+ tmpy = tmpx+tmpu+tmpu'+reshape(tmp0(i,:,:),[M_.param_nbr M_.param_nbr]);
+ tmpy = tmpy + get_2nd_deriv_mat(gpp,j,i,M_.param_nbr);
+ tmpy = tmpy(indx,indx);
+ if any(any(tmpy)),
+ ina = find(triu(tmpy));
+ gp22(sub2ind([nr nc],j,i),ina)=transpose(tmpy(ina));
+ % gp22(j,i,:,:)= reshape(tmpy,[1 1 M_.param_nbr M_.param_nbr]);
+ end
end
+ % gp22(j,:,:,:)=gp22(j,:,:,:)+reshape(tmp0(:,1:ns)*d2yssdtheta(I,:,:),[1 nc M_.param_nbr M_.param_nbr]);
end
-% gp22(j,:,:,:)=gp22(j,:,:,:)+reshape(tmp0(:,1:ns)*d2yssdtheta(I,:,:),[1 nc M_.param_nbr M_.param_nbr]);
- end
-% g22 = g22+gp22;
-% g22 = g22(:,:,indx,indx);
- clear tmp0 tmp1 tmp2 tmpu tmpx tmpy,
+ % g22 = g22+gp22;
+ % g22 = g22(:,:,indx,indx);
+ clear tmp0 tmp1 tmp2 tmpu tmpx tmpy,
inx=find(gp22);
gx22=zeros(length(inx),5);
for j=1:length(inx),
@@ -292,7 +292,7 @@ if nargout>5,
g22 = gx22;
clear gx22 gp22;
-end
+ end
end
@@ -309,7 +309,7 @@ if nargout > 5,
inxinx = find(nonzeros(k11')==tmp(j,2));
d2a(j,2) = inxinx;
end
-% d2a = g22(:,nonzeros(k11'),:,:);
+ % d2a = g22(:,nonzeros(k11'),:,:);
end
kstate = oo_.dr.kstate;
@@ -474,13 +474,13 @@ else % generalized sylvester equation
for j=1:length(indexo)
dSig(indexo(j),indexo(j),j) = 2*sqrt(M_.Sigma_e(indexo(j),indexo(j)));
y = B*dSig(:,:,j)*B';
-% y = y(nauxe+1:end,nauxe+1:end);
-% H(:,j) = [zeros((m-nauxe)^2,1); dyn_vech(y)];
+ % y = y(nauxe+1:end,nauxe+1:end);
+ % H(:,j) = [zeros((m-nauxe)^2,1); dyn_vech(y)];
H(:,j) = [zeros(m1^2,1); dyn_vech(y(iv,iv))];
if nargout>1,
dOm(:,:,j) = y(iv,iv);
end
-% dSig(indexo(j),indexo(j)) = 0;
+ % dSig(indexo(j),indexo(j)) = 0;
end
end
for j=1:param_nbr,
@@ -490,8 +490,8 @@ else % generalized sylvester equation
dB(:,:,j) = y;
end
y = y*M_.Sigma_e*B'+B*M_.Sigma_e*y';
-% x = x(nauxe+1:end,nauxe+1:end);
-% y = y(nauxe+1:end,nauxe+1:end);
+ % x = x(nauxe+1:end,nauxe+1:end);
+ % y = y(nauxe+1:end,nauxe+1:end);
if nargout>1,
dA(:,:,j+length(indexo)) = x(iv,iv);
dOm(:,:,j+length(indexo)) = y(iv,iv);
@@ -532,7 +532,7 @@ if nargout > 5,
cumjcount=0;
jinx = [];
x2x=sparse(m*m,param_nbr_2);
-% x2x=[];
+ % x2x=[];
for i=1:param_nbr,
for j=1:i,
elem1 = (get_2nd_deriv(D2g0,m,m,j,i)-get_2nd_deriv(D2g1,m,m,j,i)*A);
@@ -549,7 +549,7 @@ if nargout > 5,
if (j*i)==param_nbr^2,
d = d(:,:,1:jcount);
end
-% d(find(abs(d)<1.e-12))=0;
+ % d(find(abs(d)<1.e-12))=0;
xx2=sylvester3(a,b,c,d);
flag=1;
icount=0;
@@ -557,12 +557,12 @@ if nargout > 5,
[xx2, flag]=sylvester3a(xx2,a,b,c,d);
icount = icount + 1;
end
-% inx = find(abs(xx2)>1.e-12);
-% xx2(find(abs(xx2)<1.e-12))=0;
+ % inx = find(abs(xx2)>1.e-12);
+ % xx2(find(abs(xx2)<1.e-12))=0;
x2x(:,cumjcount+1:cumjcount+jcount)=reshape(xx2,[m*m jcount]);
cumjcount=cumjcount+jcount;
-% [i1 i2 i3]=ind2sub(size(xx2),inx);
-% x2x = [x2x; [i1 i2 jinx(i3,:) xx2(inx)]];
+ % [i1 i2 i3]=ind2sub(size(xx2),inx);
+ % x2x = [x2x; [i1 i2 jinx(i3,:) xx2(inx)]];
jcount = 0;
jinx = [];
end
@@ -590,7 +590,7 @@ if nargout > 5,
if i==j,
d2Sig(indexo(j),indexo(j),j) = 2;
y = B*d2Sig(:,:,j)*B';
-% y(abs(y)<1.e-8)=0;
+ % y(abs(y)<1.e-8)=0;
d2Om_tmp(:,jcount) = dyn_vech(y(iv,iv));
end
else
@@ -598,12 +598,12 @@ if nargout > 5,
iind = i-offset;
if i<=offset,
y = dB(:,:,jind)*dSig(:,:,i)*B'+B*dSig(:,:,i)*dB(:,:,jind)';
-% y(abs(y)<1.e-8)=0;
+ % y(abs(y)<1.e-8)=0;
d2Om_tmp(:,jcount) = dyn_vech(y(iv,iv));
else
icount=icount+1;
x = reshape(x2x(:,icount),[m m]);
-% x = get_2nd_deriv(x2x,m,m,iind,jind);%xx2(:,:,jcount);
+ % x = get_2nd_deriv(x2x,m,m,iind,jind);%xx2(:,:,jcount);
elem1 = (get_2nd_deriv(D2g0,m,m,iind,jind)-get_2nd_deriv(D2g1,m,m,iind,jind)*A);
elem1 = elem1 -( Dg1(:,:,jind)*xx(:,:,iind) + Dg1(:,:,iind)*xx(:,:,jind) );
elemj0 = Dg0(:,:,jind)-Dg1(:,:,jind)*A-GAM1*xx(:,:,jind);
@@ -614,9 +614,9 @@ if nargout > 5,
% d2B(:,:,i+length(indexo),j+length(indexo)) = y;
y = y*M_.Sigma_e*B'+B*M_.Sigma_e*y'+ ...
dB(:,:,jind)*M_.Sigma_e*dB(:,:,iind)'+dB(:,:,iind)*M_.Sigma_e*dB(:,:,jind)';
-% x(abs(x)<1.e-8)=0;
+ % x(abs(x)<1.e-8)=0;
d2A_tmp(:,jcount) = vec(x(iv,iv));
-% y(abs(y)<1.e-8)=0;
+ % y(abs(y)<1.e-8)=0;
d2Om_tmp(:,jcount) = dyn_vech(y(iv,iv));
end
end
@@ -674,23 +674,23 @@ end
if fsparse,
g22=sparse(m*n,npar*npar);
else
-g22=zeros(m,n,npar,npar);
+ g22=zeros(m,n,npar,npar);
end
% c=ones(npar,npar);
% c=triu(c);
% ic=find(c);
for is=1:length(gpp),
-% d=zeros(npar,npar);
-% d(gpp(is,3),gpp(is,4))=1;
-% indx = find(ic==find(d));
+ % d=zeros(npar,npar);
+ % d(gpp(is,3),gpp(is,4))=1;
+ % indx = find(ic==find(d));
if fsparse,
g22(sub2ind([m,n],gpp(is,1),gpp(is,2)),sub2ind([npar,npar],gpp(is,3),gpp(is,4)))=gpp(is,5);
g22(sub2ind([m,n],gpp(is,1),gpp(is,2)),sub2ind([npar,npar],gpp(is,4),gpp(is,3)))=gpp(is,5);
else
- g22(gpp(is,1),gpp(is,2),gpp(is,3),gpp(is,4))=gpp(is,5);
- g22(gpp(is,1),gpp(is,2),gpp(is,4),gpp(is,3))=gpp(is,5);
-end
+ g22(gpp(is,1),gpp(is,2),gpp(is,3),gpp(is,4))=gpp(is,5);
+ g22(gpp(is,1),gpp(is,2),gpp(is,4),gpp(is,3))=gpp(is,5);
+ end
end
return
@@ -703,9 +703,9 @@ r22=zeros(m,npar,npar);
% ic=find(c);
for is=1:length(rpp),
-% d=zeros(npar,npar);
-% d(rpp(is,2),rpp(is,3))=1;
-% indx = find(ic==find(d));
+ % d=zeros(npar,npar);
+ % d(rpp(is,2),rpp(is,3))=1;
+ % indx = find(ic==find(d));
r22(rpp(is,1),rpp(is,2),rpp(is,3))=rpp(is,4);
r22(rpp(is,1),rpp(is,3),rpp(is,2))=rpp(is,4);
end
diff --git a/matlab/getJJ.m b/matlab/getJJ.m
index b7686a03599f8161d1171efec9792ac2130f6157..c3b1f1c1e881379ec7c26d412b4c383c21bf9edd 100644
--- a/matlab/getJJ.m
+++ b/matlab/getJJ.m
@@ -55,7 +55,7 @@ function [JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo_,opt
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin<8 || isempty(indx)
-% indx = [1:M_.param_nbr];
+ % indx = [1:M_.param_nbr];
end,
if nargin<9 || isempty(indexo)
indexo = [];
diff --git a/matlab/get_Hessian.m b/matlab/get_Hessian.m
index a42b3c81250b89ad85163dcfa46eff7f40094fe3..bf423e07c6598f2dc8f1efc671588d150e3a5742 100644
--- a/matlab/get_Hessian.m
+++ b/matlab/get_Hessian.m
@@ -28,142 +28,142 @@ function [Hess] = get_Hessian(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- k = size(DT,3); % number of structural parameters
- smpl = size(Y,2); % Sample size.
- pp = size(Y,1); % Maximum number of observed variables.
- mm = size(T,2); % Number of state variables.
- a = zeros(mm,1); % State vector.
- Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
- t = 0; % Initialization of the time index.
- oldK = 0;
- notsteady = 1; % Steady state flag.
- F_singular = 1;
-
- Hess = zeros(k,k); % Initialization of the Hessian
- Da = zeros(mm,k); % State vector.
- Dv = zeros(length(mf),k);
- D2a = zeros(mm,k,k); % State vector.
- D2v = zeros(length(mf),k,k);
-
- C = zeros(length(mf),mm);
- for ii=1:length(mf); C(ii,mf(ii))=1;end % SELECTION MATRIX IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
- dC = zeros(length(mf),mm,k);
- d2C = zeros(length(mf),mm,k,k);
-
- s = zeros(pp,1); % CONSTANT TERM IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
- ds = zeros(pp,1,k);
- d2s = zeros(pp,1,k,k);
-
+k = size(DT,3); % number of structural parameters
+smpl = size(Y,2); % Sample size.
+pp = size(Y,1); % Maximum number of observed variables.
+mm = size(T,2); % Number of state variables.
+a = zeros(mm,1); % State vector.
+Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
+t = 0; % Initialization of the time index.
+oldK = 0;
+notsteady = 1; % Steady state flag.
+F_singular = 1;
+
+Hess = zeros(k,k); % Initialization of the Hessian
+Da = zeros(mm,k); % State vector.
+Dv = zeros(length(mf),k);
+D2a = zeros(mm,k,k); % State vector.
+D2v = zeros(length(mf),k,k);
+
+C = zeros(length(mf),mm);
+for ii=1:length(mf); C(ii,mf(ii))=1;end % SELECTION MATRIX IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
+dC = zeros(length(mf),mm,k);
+d2C = zeros(length(mf),mm,k,k);
+
+s = zeros(pp,1); % CONSTANT TERM IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
+ds = zeros(pp,1,k);
+d2s = zeros(pp,1,k,k);
+
% for ii = 1:k
% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
% end
-
- while notsteady & t<smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- F = P(mf,mf) + H;
- if rcond(F) < kalman_tol
- if ~all(abs(F(:))<kalman_tol)
- return
- else
- a = T*a;
- P = T*P*transpose(T)+Om;
- end
+
+while notsteady & t<smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
+ F = P(mf,mf) + H;
+ if rcond(F) < kalman_tol
+ if ~all(abs(F(:))<kalman_tol)
+ return
else
- F_singular = 0;
- iF = inv(F);
- K = P(:,mf)*iF;
-
- [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
- [D2K,D2F,D2P1] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,mf,iF,K,DK);
- tmp = (a+K*v);
-
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii) - DYss(mf,ii);
- % dai = da(:,:,ii);
- dKi = DK(:,:,ii);
- diFi = -iF*DF(:,:,ii)*iF;
- dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
+ a = T*a;
+ P = T*P*transpose(T)+Om;
+ end
+ else
+ F_singular = 0;
+ iF = inv(F);
+ K = P(:,mf)*iF;
+
+ [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
+ [D2K,D2F,D2P1] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,mf,iF,K,DK);
+ tmp = (a+K*v);
+
+ for ii = 1:k
+ Dv(:,ii) = -Da(mf,ii) - DYss(mf,ii);
+ % dai = da(:,:,ii);
+ dKi = DK(:,:,ii);
+ diFi = -iF*DF(:,:,ii)*iF;
+ dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
-
- for jj = 1:ii
- dFj = DF(:,:,jj);
- diFj = -iF*DF(:,:,jj)*iF;
- dKj = DK(:,:,jj);
- d2Kij = D2K(:,:,jj,ii);
- d2Fij = D2F(:,:,jj,ii);
- d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
- dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
-
- d2vij = -D2Yss(mf,jj,ii) - D2a(mf,jj,ii);
- d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
- D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
-
- Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
+
+ for jj = 1:ii
+ dFj = DF(:,:,jj);
+ diFj = -iF*DF(:,:,jj)*iF;
+ dKj = DK(:,:,jj);
+ d2Kij = D2K(:,:,jj,ii);
+ d2Fij = D2F(:,:,jj,ii);
+ d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+ dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
+
+ d2vij = -D2Yss(mf,jj,ii) - D2a(mf,jj,ii);
+ d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
+ D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
+
+ Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
+ end
+ Da(:,ii) = DT(:,:,ii)*tmp + T*dtmpi;
end
- Da(:,ii) = DT(:,:,ii)*tmp + T*dtmpi;
- end
-% vecDPmf = reshape(DP(mf,mf,:),[],k);
-% iPmf = inv(P(mf,mf));
- if t>=start
- Hess = Hess + Hesst;
- end
- a = T*(a+K*v);
- P = T*(P-K*P(mf,:))*transpose(T)+Om;
- DP = DP1;
- D2P = D2P1;
+ % vecDPmf = reshape(DP(mf,mf,:),[],k);
+ % iPmf = inv(P(mf,mf));
+ if t>=start
+ Hess = Hess + Hesst;
end
- notsteady = max(max(abs(K-oldK))) > riccati_tol;
- oldK = K;
+ a = T*(a+K*v);
+ P = T*(P-K*P(mf,:))*transpose(T)+Om;
+ DP = DP1;
+ D2P = D2P1;
end
+ notsteady = max(max(abs(K-oldK))) > riccati_tol;
+ oldK = K;
+end
- if F_singular
- error('The variance of the forecast error remains singular until the end of the sample')
- end
+if F_singular
+ error('The variance of the forecast error remains singular until the end of the sample')
+end
-
- if t < smpl
- t0 = t+1;
- while t < smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- tmp = (a+K*v);
- for ii = 1:k,
- Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
- dKi = DK(:,:,ii);
- diFi = -iF*DF(:,:,ii)*iF;
- dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
+
+if t < smpl
+ t0 = t+1;
+ while t < smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
+ tmp = (a+K*v);
+ for ii = 1:k,
+ Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
+ dKi = DK(:,:,ii);
+ diFi = -iF*DF(:,:,ii)*iF;
+ dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
+
+ for jj = 1:ii,
+ dFj = DF(:,:,jj);
+ diFj = -iF*DF(:,:,jj)*iF;
+ dKj = DK(:,:,jj);
+ d2Kij = D2K(:,:,jj,ii);
+ d2Fij = D2F(:,:,jj,ii);
+ d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+ dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
- for jj = 1:ii,
- dFj = DF(:,:,jj);
- diFj = -iF*DF(:,:,jj)*iF;
- dKj = DK(:,:,jj);
- d2Kij = D2K(:,:,jj,ii);
- d2Fij = D2F(:,:,jj,ii);
- d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
- dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
-
- d2vij = -D2Yss(mf,jj,ii) - D2a(mf,jj,ii);
- d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
- D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
-
- Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
- end
- Da(:,ii) = DT(:,:,ii)*tmp + T*dtmpi;
- end
- if t>=start
- Hess = Hess + Hesst;
+ d2vij = -D2Yss(mf,jj,ii) - D2a(mf,jj,ii);
+ d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
+ D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
+
+ Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
end
- a = T*(a+K*v);
+ Da(:,ii) = DT(:,:,ii)*tmp + T*dtmpi;
+ end
+ if t>=start
+ Hess = Hess + Hesst;
end
-% Hess = Hess + .5*(smpl+t0-1)*(vecDPmf' * kron(iPmf,iPmf) * vecDPmf);
- % for ii = 1:k;
- % for jj = 1:ii
- % H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
- % end
- % end
+ a = T*(a+K*v);
end
-
+ % Hess = Hess + .5*(smpl+t0-1)*(vecDPmf' * kron(iPmf,iPmf) * vecDPmf);
+ % for ii = 1:k;
+ % for jj = 1:ii
+ % H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
+ % end
+ % end
+end
+
Hess = Hess + tril(Hess,-1)';
Hess = -Hess/2;
@@ -178,8 +178,8 @@ Hesst_ij = trace(diSi*dSj + iS*d2Sij) + e'*d2iSij*e + 2*(dei'*diSj*e + dei'*iS*d
function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
- k = size(DT,3);
- tmp = P-K*P(mf,:);
+k = size(DT,3);
+tmp = P-K*P(mf,:);
for ii = 1:k
DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
@@ -194,9 +194,9 @@ end
function [d2K,d2S,d2P1] = computeD2Kalman(A,dA,d2A,d2Om,P0,dP0,d2P0,DH,mf,iF,K0,dK0);
% computes the second derivatives of the Kalman matrices
% note: A=T in main func.
-
- k = size(dA,3);
- tmp = P0-K0*P0(mf,:);
+
+k = size(dA,3);
+tmp = P0-K0*P0(mf,:);
[ns,no] = size(K0);
% CPC = C*P0*C'; CPC = .5*(CPC+CPC');iF = inv(CPC);
@@ -225,34 +225,33 @@ for ii = 1:k
d2Aij = d2A(:,:,jj,ii);
d2Pij = d2P0(:,:,jj,ii);
d2Omij = d2Om(:,:,jj,ii);
-
- % second order
-
- d2Fij = d2Pij(mf,mf) ;
-
-% d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
- d2APC = d2Pij(:,mf);
-
- d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-
- d2Kij= d2Pij(:,mf)*iF + P0(:,mf)*d2iF + dP0(:,mf,jj)*diFi + dP0(:,mf,ii)*diFj;
- d2KCP = d2Kij*P0(mf,:) + K0*d2Pij(mf,:) + dKi*dP0(mf,:,jj) + dKj*dP0(mf,:,ii) ;
-
- dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*P0(mf,:) - K0*dP0(mf,:,ii);
- dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*P0(mf,:) - K0*dP0(mf,:,jj);
- d2tmp = d2Pij - d2KCP;
-
- d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
-
- d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
- d2P1(:,:,ii,jj) = d2AtmpA + d2Omij; %#ok<*NASGU>
- d2S(:,:,ii,jj) = d2Fij;
-% d2iS(:,:,ii,jj) = d2iF;
+ % second order
+
+ d2Fij = d2Pij(mf,mf) ;
+
+ % d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
+ d2APC = d2Pij(:,mf);
+
+ d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+
+ d2Kij= d2Pij(:,mf)*iF + P0(:,mf)*d2iF + dP0(:,mf,jj)*diFi + dP0(:,mf,ii)*diFj;
+
+ d2KCP = d2Kij*P0(mf,:) + K0*d2Pij(mf,:) + dKi*dP0(mf,:,jj) + dKj*dP0(mf,:,ii) ;
+
+ dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*P0(mf,:) - K0*dP0(mf,:,ii);
+ dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*P0(mf,:) - K0*dP0(mf,:,jj);
+ d2tmp = d2Pij - d2KCP;
+
+ d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
+
+ d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
+ d2P1(:,:,ii,jj) = d2AtmpA + d2Omij; %#ok<*NASGU>
+ d2S(:,:,ii,jj) = d2Fij;
+ % d2iS(:,:,ii,jj) = d2iF;
end
end
% end of computeD2Kalman
-
\ No newline at end of file
diff --git a/matlab/get_all_parameters.m b/matlab/get_all_parameters.m
index f924157705de918ae8917e2329c003a2eb8fd061..30d325f68b1e521b8e1016f08a33a99e34689de0 100644
--- a/matlab/get_all_parameters.m
+++ b/matlab/get_all_parameters.m
@@ -6,7 +6,7 @@ function xparam1=get_all_parameters(estim_params_,M_)
% parameter values
%
% INPUTS
-% estim_params_: Dynare structure describing the estimated parameters.
+% estim_params_: Dynare structure describing the estimated parameters.
% M_: Dynare structure describing the model.
%
% OUTPUTS
@@ -100,5 +100,5 @@ offset = nvx+ncx+nvn+ncn;
% structural parameters
if np
- xparam1(offset+1:end)=M_.params(estim_params_.param_vals(:,1));
+ xparam1(offset+1:end)=M_.params(estim_params_.param_vals(:,1));
end
\ No newline at end of file
diff --git a/matlab/get_dynare_random_generator_state.m b/matlab/get_dynare_random_generator_state.m
index 513919c220ff7de3f5a3d364cefd6637381d08e0..4525bc20d925bf62b7fe4e49af3540539c3684fa 100644
--- a/matlab/get_dynare_random_generator_state.m
+++ b/matlab/get_dynare_random_generator_state.m
@@ -23,22 +23,22 @@ function [state_u,state_n] = get_dynare_random_generator_state()
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- matlab_random_streams = ~(isoctave || matlab_ver_less_than('7.7'));
+matlab_random_streams = ~(isoctave || matlab_ver_less_than('7.7'));
- if matlab_random_streams% Use new matlab interface.
- if matlab_ver_less_than('7.12')
- s = RandStream.getDefaultStream();
- else
- s = RandStream.getGlobalStream();
- end
- if isequal(s.Type,'legacy')
- state_u = rand('state');
- state_n = randn('state');
- else
- state_u = s.State;
- state_n = state_u;
- end
- else% Use old matlab interface.
+if matlab_random_streams% Use new matlab interface.
+ if matlab_ver_less_than('7.12')
+ s = RandStream.getDefaultStream();
+ else
+ s = RandStream.getGlobalStream();
+ end
+ if isequal(s.Type,'legacy')
state_u = rand('state');
state_n = randn('state');
- end
\ No newline at end of file
+ else
+ state_u = s.State;
+ state_n = state_u;
+ end
+else% Use old matlab interface.
+ state_u = rand('state');
+ state_n = randn('state');
+end
\ No newline at end of file
diff --git a/matlab/get_file_extension.m b/matlab/get_file_extension.m
index d922b36ac50150c4a8e925ad4446df03ad166ded..3951e8c3cb25b4e7a62d205968d797c9b930c8df 100644
--- a/matlab/get_file_extension.m
+++ b/matlab/get_file_extension.m
@@ -10,7 +10,7 @@ function ext = get_file_extension(file)
%
% REMARKS
% If the provided file name has no extension, the routine will return an empty array.
-
+
% Copyright (C) 2013-2015 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/get_optimal_policy_discount_factor.m b/matlab/get_optimal_policy_discount_factor.m
index 67931b8ab1eb51fd7552c4288acd3b9c2023d657..fd471a01bb87858c47a3af0721aaec31ab902a51 100644
--- a/matlab/get_optimal_policy_discount_factor.m
+++ b/matlab/get_optimal_policy_discount_factor.m
@@ -30,4 +30,4 @@ function discount_factor=get_optimal_policy_discount_factor(params,param_names)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- discount_factor = params(find(strcmp('optimal_policy_discount_factor',cellstr(param_names))));
+discount_factor = params(find(strcmp('optimal_policy_discount_factor',cellstr(param_names))));
diff --git a/matlab/get_subsamples_range_indx.m b/matlab/get_subsamples_range_indx.m
index 1d114c6869d38b01258bf262d60567f7055b53dd..cb9b971f5dfd5d59461be04f7ec247005b421901 100644
--- a/matlab/get_subsamples_range_indx.m
+++ b/matlab/get_subsamples_range_indx.m
@@ -39,5 +39,5 @@ range_indx = find(strcmp(range_label, estimation_info.subsamples(subsamples_indx
if size(range_indx,2) ~= 1
error(['Error: Index not found in estimation_info.subsamples(' ...
- num2str(subsamples_indx) ').range_index for label ' range_label]);
+ num2str(subsamples_indx) ').range_index for label ' range_label]);
end
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index cbeff6085ecd02c19f6722a16bd5edb5f0f80c08..168fe8d5d9332ed6ba83283038ca2f887171c57b 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -471,7 +471,7 @@ options_.sub_draws = [];
options_.gradient_method = 2; %used by csminwel and newrat
options_.gradient_epsilon = 1e-6; %used by csminwel and newrat
options_.posterior_sampler_options.sampling_opt = []; %extended set of options for individual posterior samplers
-% Random Walk Metropolis-Hastings
+ % Random Walk Metropolis-Hastings
options_.posterior_sampler_options.posterior_sampling_method = 'random_walk_metropolis_hastings';
options_.posterior_sampler_options.rwmh.proposal_distribution = 'rand_multivariate_normal';
options_.posterior_sampler_options.rwmh.student_degrees_of_freedom = 3;
@@ -525,8 +525,8 @@ options_.estimation.moments_posterior_density.indicator = 1;
options_.estimation.moments_posterior_density.gridpoints = 2^9;
options_.estimation.moments_posterior_density.bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
options_.estimation.moments_posterior_density.kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton.
-% Misc
-% options_.conf_sig = 0.6;
+ % Misc
+ % options_.conf_sig = 0.6;
oo_.exo_simul = [];
oo_.endo_simul = [];
ys0_ = [];
diff --git a/matlab/graph_decomp.m b/matlab/graph_decomp.m
index 6fae28f3bb24cc32fd0423d97ceddf8ebef48782..341658ff7a7883675c08050476238e78ee11dabd 100644
--- a/matlab/graph_decomp.m
+++ b/matlab/graph_decomp.m
@@ -131,8 +131,8 @@ for j=1:nvar
fhandle = dyn_figure(DynareOptions.plot_shock_decomp.nodisplay,'Name',[preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': ' deblank(endo_names(i_var(j),:)) '.'], 'PaperPositionMode', 'auto','PaperOrientation','landscape','renderermode','auto');
set(fhandle,'position' ,[50 50 1500 750])
ax=axes('Position',[0.1 0.1 0.6 0.8],'box','on');
-% plot(ax,x(2:end),z1(end,:),'k-','LineWidth',2)
-% axis(ax,[xmin xmax ymin ymax]);
+ % plot(ax,x(2:end),z1(end,:),'k-','LineWidth',2)
+ % axis(ax,[xmin xmax ymin ymax]);
hold on;
for i=1:gend
i_1 = i-1;
@@ -184,7 +184,7 @@ for j=1:nvar
height = 1/comp_nbr;
for i=comp_nbr:-1:1
-% for i=1:comp_nbr
+ % for i=1:comp_nbr
hl = fill([0 0 0.2 0.2],[y1 y1+0.7*height y1+0.7*height y1],i);
hold on
ht = text(0.3,y1+0.3*height,labels(i,:),'Interpreter','none');
diff --git a/matlab/graph_decomp_detail.m b/matlab/graph_decomp_detail.m
index 077653ea6d0ec1b148453caac10b92b510d83375..771d2d9225585bd19496bb91268835dd61b2a113 100644
--- a/matlab/graph_decomp_detail.m
+++ b/matlab/graph_decomp_detail.m
@@ -59,7 +59,7 @@ fig_name_long = opts_decomp.fig_name;
% fig_name = ['_' fig_name];
if screen_shocks
-% fig_name1 = [fig_name1 '_screen'];
+ % fig_name1 = [fig_name1 '_screen'];
fig_name_long = [fig_name_long ' SCREEN'];
end
@@ -151,89 +151,89 @@ for j=1:nvar
continue
end
for jf = 1:nfigs
- fhandle = dyn_figure(DynareOptions.plot_shock_decomp.nodisplay,'Name',[preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': ' deblank(endo_names(i_var(j),:)) ' (detail).'],'position',[200 100 650 850], 'PaperPositionMode', 'auto','PaperOrientation','portrait','renderermode','auto');
- a0=zeros(1,4);
- a0(3)=inf;
- a0(4)=-inf;
- for ic=1+nrow*ncol*(jf-1):min(nrow*ncol*jf,comp_nbr),
- i = ic-nrow*ncol*(jf-1);
- zz = z1(ic,:);
- zz(2,:)=z1(end,:)-zz;
- ipos=zz>0;
- ineg=zz<0;
- hax = subplot(nrow,ncol,i); set(gca,'box','on')
- hbar = bar(x(2:end),(zz.*ipos)','stacked');
- colormap([0.15 0.15 0.15;0.85 0.85 0.85]),
- set(hbar,'edgecolor','flat');
- hold on,
- hbar = bar(x(2:end),(zz.*ineg)','stacked');
- colormap([0.15 0.15 0.15;0.85 0.85 0.85]),
- set(hbar,'edgecolor','flat');
- title(deblank(labels(ic,:)),'Interpreter','none'),
- axis tight;
- a=axis;
- set(gca,'Xtick',xind_tick)
- set(gca,'xlim',[xmin xmax])
- a0(3)=min(a(3),a0(3));
- a0(4)=max(a(4),a0(4));
- set(gca,'ylim',a0(3:4))
- hold on, h1=plot(x(2:end),z1(end,:),'k-','LineWidth',2);
- if interactive & (~isoctave & use_shock_groups)
- mydata.fig_name = DynareOptions.plot_shock_decomp.fig_name(2:end);
- mydata.use_shock_groups = DynareOptions.plot_shock_decomp.use_shock_groups;
- mydata.shock_group = shock_groups.(shock_ind{ic});
- mydata.shock_decomp = DynareOptions.plot_shock_decomp;
- if ~isempty(mydata.shock_group.shocks{1})
- c = uicontextmenu;
- hax.UIContextMenu=c;
- browse_menu = uimenu(c,'Label','Browse group');
- expand_menu = uimenu(c,'Label','Expand group','Callback',['expand_group(''' mydata.use_shock_groups ''',''' deblank(mydata.shock_decomp.orig_varlist(j,:)) ''',' int2str(ic) ')']);
- set(expand_menu,'UserData',mydata,'Tag',['group' int2str(ic)]);
- for jmember = mydata.shock_group.shocks
- uimenu('parent',browse_menu,'Label',char(jmember))
+ fhandle = dyn_figure(DynareOptions.plot_shock_decomp.nodisplay,'Name',[preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': ' deblank(endo_names(i_var(j),:)) ' (detail).'],'position',[200 100 650 850], 'PaperPositionMode', 'auto','PaperOrientation','portrait','renderermode','auto');
+ a0=zeros(1,4);
+ a0(3)=inf;
+ a0(4)=-inf;
+ for ic=1+nrow*ncol*(jf-1):min(nrow*ncol*jf,comp_nbr),
+ i = ic-nrow*ncol*(jf-1);
+ zz = z1(ic,:);
+ zz(2,:)=z1(end,:)-zz;
+ ipos=zz>0;
+ ineg=zz<0;
+ hax = subplot(nrow,ncol,i); set(gca,'box','on')
+ hbar = bar(x(2:end),(zz.*ipos)','stacked');
+ colormap([0.15 0.15 0.15;0.85 0.85 0.85]),
+ set(hbar,'edgecolor','flat');
+ hold on,
+ hbar = bar(x(2:end),(zz.*ineg)','stacked');
+ colormap([0.15 0.15 0.15;0.85 0.85 0.85]),
+ set(hbar,'edgecolor','flat');
+ title(deblank(labels(ic,:)),'Interpreter','none'),
+ axis tight;
+ a=axis;
+ set(gca,'Xtick',xind_tick)
+ set(gca,'xlim',[xmin xmax])
+ a0(3)=min(a(3),a0(3));
+ a0(4)=max(a(4),a0(4));
+ set(gca,'ylim',a0(3:4))
+ hold on, h1=plot(x(2:end),z1(end,:),'k-','LineWidth',2);
+ if interactive & (~isoctave & use_shock_groups)
+ mydata.fig_name = DynareOptions.plot_shock_decomp.fig_name(2:end);
+ mydata.use_shock_groups = DynareOptions.plot_shock_decomp.use_shock_groups;
+ mydata.shock_group = shock_groups.(shock_ind{ic});
+ mydata.shock_decomp = DynareOptions.plot_shock_decomp;
+ if ~isempty(mydata.shock_group.shocks{1})
+ c = uicontextmenu;
+ hax.UIContextMenu=c;
+ browse_menu = uimenu(c,'Label','Browse group');
+ expand_menu = uimenu(c,'Label','Expand group','Callback',['expand_group(''' mydata.use_shock_groups ''',''' deblank(mydata.shock_decomp.orig_varlist(j,:)) ''',' int2str(ic) ')']);
+ set(expand_menu,'UserData',mydata,'Tag',['group' int2str(ic)]);
+ for jmember = mydata.shock_group.shocks
+ uimenu('parent',browse_menu,'Label',char(jmember))
+ end
end
end
end
- end
- for isub=1:i,
- subplot(nrow,ncol,isub),
- set(gca,'ylim',a0(3:4))
- end
-
-% make legend
- axes('Position',[0.1 0.01 0.8 0.02],'units','normalized');
- axis([0 1 0 1]);
- axis off;
- hold on;
- x1 = 0;
- width = 1/2;
- mylabels = {'Individual contrib.','Residual contrib.'};
+ for isub=1:i,
+ subplot(nrow,ncol,isub),
+ set(gca,'ylim',a0(3:4))
+ end
+
+ % make legend
+ axes('Position',[0.1 0.01 0.8 0.02],'units','normalized');
+ axis([0 1 0 1]);
+ axis off;
+ hold on;
+ x1 = 0;
+ width = 1/2;
+ mylabels = {'Individual contrib.','Residual contrib.'};
- for i=1:2
-% for i=1:comp_nbr
- hl = fill([x1 x1 x1+0.3*width x1+0.3*width],[0 1 1 0],i);
- hold on
- ht = text(x1+0.4*width,0.3,mylabels{i},'Interpreter','none');
- hold on
- x1 = x1 + width;
- end
-
-
- if nfigs>1,
- suffix = ['_detail_' int2str(jf)];
- else
- suffix = ['_detail'];
- end
- dyn_saveas(fhandle,[GraphDirectoryName, filesep, DynareModel.fname,'_shock_decomposition_',deblank(endo_names(i_var(j),:)),fig_mode1,fig_name suffix],DynareOptions.plot_shock_decomp.nodisplay,DynareOptions.plot_shock_decomp.graph_format);
- if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.plot_shock_decomp.graph_format)))
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s/graphs/%s_shock_decomposition_%s}\n',DynareModel.fname,DynareModel.fname,[deblank(endo_names(i_var(j),:)) fig_mode1 fig_name suffix]);
- fprintf(fidTeX,'\\label{Fig:shock_decomp_detail:%s}\n',[fig_mode deblank(endo_names(i_var(j),:)) fig_name suffix]);
- fprintf(fidTeX,['\\caption{' preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': $ %s $ (detail).}\n'],deblank(DynareModel.endo_names_tex(i_var(j),:)));
- fprintf(fidTeX,'\\end{figure}\n');
- fprintf(fidTeX,' \n');
- end
+ for i=1:2
+ % for i=1:comp_nbr
+ hl = fill([x1 x1 x1+0.3*width x1+0.3*width],[0 1 1 0],i);
+ hold on
+ ht = text(x1+0.4*width,0.3,mylabels{i},'Interpreter','none');
+ hold on
+ x1 = x1 + width;
+ end
+
+
+ if nfigs>1,
+ suffix = ['_detail_' int2str(jf)];
+ else
+ suffix = ['_detail'];
+ end
+ dyn_saveas(fhandle,[GraphDirectoryName, filesep, DynareModel.fname,'_shock_decomposition_',deblank(endo_names(i_var(j),:)),fig_mode1,fig_name suffix],DynareOptions.plot_shock_decomp.nodisplay,DynareOptions.plot_shock_decomp.graph_format);
+ if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.plot_shock_decomp.graph_format)))
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s/graphs/%s_shock_decomposition_%s}\n',DynareModel.fname,DynareModel.fname,[deblank(endo_names(i_var(j),:)) fig_mode1 fig_name suffix]);
+ fprintf(fidTeX,'\\label{Fig:shock_decomp_detail:%s}\n',[fig_mode deblank(endo_names(i_var(j),:)) fig_name suffix]);
+ fprintf(fidTeX,['\\caption{' preamble_txt fig_name_long strrep(fig_mode1, '_', ' ') ': $ %s $ (detail).}\n'],deblank(DynareModel.endo_names_tex(i_var(j),:)));
+ fprintf(fidTeX,'\\end{figure}\n');
+ fprintf(fidTeX,' \n');
+ end
end
end
diff --git a/matlab/gsa/Morris_Measure_Groups.m b/matlab/gsa/Morris_Measure_Groups.m
index dfd37bdd12f3be7948ba3d8dbb5a5e98363a6baa..cc5921206e7716215bfd46be7eb3a6eb8278400a 100644
--- a/matlab/gsa/Morris_Measure_Groups.m
+++ b/matlab/gsa/Morris_Measure_Groups.m
@@ -45,9 +45,9 @@ function [SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p,
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin==0,
- skipline()
- disp('[SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group);')
- return
+ skipline()
+ disp('[SAmeas, OutMatrix] = Morris_Measure_Groups(NumFact, Sample, Output, p, Group);')
+ return
end
OutMatrix=[];
@@ -72,10 +72,10 @@ r=size(Sample,1)/(sizea+1); % Number of trajectories
for k=1:size(Output,2)
OutValues=Output(:,k);
-
+
% For each r trajectory
for i=1:r
-
+
% For each step j in the trajectory
% Read the orientation matrix fact for the r-th sampling
% Read the corresponding output values
@@ -87,12 +87,12 @@ for k=1:size(Output,2)
% For each point of the fixed trajectory compute the values of the Morris function. The function
% is partitioned in four parts, from order zero to order 4th.
for j=1:sizea % For each point in the trajectory i.e for each factor
- % matrix of factor which changes
+ % matrix of factor which changes
if NumGroups ~ 0;
AuxFind (:,1) = A(:,j);
-% AuxFind(find(A(:,j)),1)=1;
-% Pippo = sum((Group - repmat(AuxFind,1,NumGroups)),1);
-% Change_factor(j,i) = find(Pippo==0);
+ % AuxFind(find(A(:,j)),1)=1;
+ % Pippo = sum((Group - repmat(AuxFind,1,NumGroups)),1);
+ % Change_factor(j,i) = find(Pippo==0);
Change_factor = find(abs(AuxFind)>1e-010);
% If we deal with groups we can only estimate the new mu*
% measure since factors in the same groups can move in
@@ -113,31 +113,31 @@ for k=1:size(Output,2)
end
end
end %for j=1:sizea
-
+
end %for i=1:r
-
+
if NumGroups ~ 0
SAmeas = SAmeas';
end
% Compute Mu AbsMu and StDev
if any(any(isnan(SAmeas)))
- for j=1:NumFact,
- SAm = SAmeas(j,:);
- SAm = SAm(find(~isnan(SAm)));
- rr=length(SAm);
- AbsMu(j,1) = sum(abs(SAm),2)/rr;
- if NumGroups == 0
- Mu(j,1) = sum(SAm,2)/rr;
- StDev(j,1) = sum((SAm - repmat(Mu(j),1,rr)).^2/(rr*(rr-1)),2).^0.5;
- end
- end
+ for j=1:NumFact,
+ SAm = SAmeas(j,:);
+ SAm = SAm(find(~isnan(SAm)));
+ rr=length(SAm);
+ AbsMu(j,1) = sum(abs(SAm),2)/rr;
+ if NumGroups == 0
+ Mu(j,1) = sum(SAm,2)/rr;
+ StDev(j,1) = sum((SAm - repmat(Mu(j),1,rr)).^2/(rr*(rr-1)),2).^0.5;
+ end
+ end
else
- AbsMu = sum(abs(SAmeas),2)/r;
- if NumGroups == 0
- Mu = sum(SAmeas,2)/r;
- StDev = sum((SAmeas - repmat(Mu,1,r)).^2/(r*(r-1)),2).^0.5;
- end
+ AbsMu = sum(abs(SAmeas),2)/r;
+ if NumGroups == 0
+ Mu = sum(SAmeas,2)/r;
+ StDev = sum((SAmeas - repmat(Mu,1,r)).^2/(r*(r-1)),2).^0.5;
+ end
end
% Define the output Matrix - if we have groups we cannot define the old
diff --git a/matlab/gsa/Sampling_Function_2.m b/matlab/gsa/Sampling_Function_2.m
index 60255cafbb89b381041bd8eafb13758168155bf1..a7260d25251b2c998f40a60608e988d207211a8a 100644
--- a/matlab/gsa/Sampling_Function_2.m
+++ b/matlab/gsa/Sampling_Function_2.m
@@ -1,10 +1,10 @@
function [Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
%[Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
-% Inputs: k (1,1) := number of factors examined or number of groups examined.
+% Inputs: k (1,1) := number of factors examined or number of groups examined.
% In case the groups are chosen the number of factors is stores in NumFact and
% sizea becomes the number of created groups.
% NumFact (1,1) := number of factors examined in the case when groups are chosen
-% r (1,1) := sample size
+% r (1,1) := sample size
% p (1,1) := number of intervals considered in [0, 1]
% UB(sizea,1) := Upper Bound for each factor
% LB(sizea,1) := Lower Bound for each factor
@@ -13,18 +13,18 @@ function [Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
% are set to 1 in correspondence of the factors that belong to the fixed group. All
% the other elements are zero.
% Local Variables:
-% sizeb (1,1) := sizea+1
+% sizeb (1,1) := sizea+1
% sizec (1,1) := 1
% randmult (sizea,1) := vector of random +1 and -1
% perm_e(1,sizea) := vector of sizea random permutated indeces
% fact(sizea) := vector containing the factor varied within each traj
-% DDo(sizea,sizea) := D* in Morris, 1991
-% A(sizeb,sizea) := Jk+1,k in Morris, 1991
-% B(sizeb,sizea) := B in Morris, 1991
-% Po(sizea,sizea) := P* in Morris, 1991
+% DDo(sizea,sizea) := D* in Morris, 1991
+% A(sizeb,sizea) := Jk+1,k in Morris, 1991
+% B(sizeb,sizea) := B in Morris, 1991
+% Po(sizea,sizea) := P* in Morris, 1991
% Bo(sizeb,sizea) := B* in Morris, 1991
-% Ao(sizeb,sizec) := Jk+1,1 in Morris, 1991
-% xo(sizec,sizea) := x* in Morris, 1991 (starting point for the trajectory)
+% Ao(sizeb,sizec) := Jk+1,1 in Morris, 1991
+% xo(sizec,sizea) := x* in Morris, 1991 (starting point for the trajectory)
% In(sizeb,sizea) := for each loop orientation matrix. It corresponds to a trajectory
% of k step in the parameter space and it provides a single elementary
% effect per factor
@@ -34,7 +34,7 @@ function [Outmatrix, OutFact] = Sampling_Function_2(p, k, r, UB, LB, GroupMat)
% AuxMat(sizeb,sizea) := Delta*0.5*((2*B - A) * DD0 + A) in Morris, 1991. The AuxMat is used as in Morris design
% for single factor analysis, while it constitutes an intermediate step for the group analysis.
%
-% Output: Outmatrix(sizeb*r, sizea) := for the entire sample size computed In(i,j) matrices
+% Output: Outmatrix(sizeb*r, sizea) := for the entire sample size computed In(i,j) matrices
% OutFact(sizea*r,1) := for the entire sample size computed Fact(i,1) vectors
%
% Note: B0 is constructed as in Morris design when groups are not considered. When groups are considered the routine
@@ -110,7 +110,7 @@ for i=1:r
% Construct B (lower triangular)
B = ones(sizeb,sizea);
for j = 1:sizea
- B(1:j,j)=0;
+ B(1:j,j)=0;
end
% Construct A0, A
diff --git a/matlab/gsa/filt_mc_.m b/matlab/gsa/filt_mc_.m
index b6c4994d8d9b8babefb519fd3e88c0cddb44b981..f9510c8afaad4fa7f70dfb95b5adf4c71e0f83d1 100644
--- a/matlab/gsa/filt_mc_.m
+++ b/matlab/gsa/filt_mc_.m
@@ -58,33 +58,33 @@ disp('Starting sensitivity analysis')
disp('for the fit of EACH observed series ...')
skipline()
if ~options_.nograph,
-disp('Deleting old SA figures...')
-a=dir([OutDir,filesep,'*.*']);
-tmp1='0';
-if options_.opt_gsa.ppost,
- tmp=['_rmse_post'];
-else
- if options_.opt_gsa.pprior
- tmp=['_rmse_prior'];
+ disp('Deleting old SA figures...')
+ a=dir([OutDir,filesep,'*.*']);
+ tmp1='0';
+ if options_.opt_gsa.ppost,
+ tmp=['_rmse_post'];
else
- tmp=['_rmse_mc'];
- end
- if options_gsa_.lik_only,
- tmp1 = [tmp,'_post_SA'];
- tmp = [tmp,'_lik_SA'];
+ if options_.opt_gsa.pprior
+ tmp=['_rmse_prior'];
+ else
+ tmp=['_rmse_mc'];
+ end
+ if options_gsa_.lik_only,
+ tmp1 = [tmp,'_post_SA'];
+ tmp = [tmp,'_lik_SA'];
+ end
end
-end
-for j=1:length(a),
- if strmatch([fname_,tmp],a(j).name),
- disp(a(j).name)
- delete([OutDir,filesep,a(j).name])
- end,
- if strmatch([fname_,tmp1],a(j).name),
- disp(a(j).name)
- delete([OutDir,filesep,a(j).name])
- end,
-end
-disp('done !')
+ for j=1:length(a),
+ if strmatch([fname_,tmp],a(j).name),
+ disp(a(j).name)
+ delete([OutDir,filesep,a(j).name])
+ end,
+ if strmatch([fname_,tmp1],a(j).name),
+ disp(a(j).name)
+ delete([OutDir,filesep,a(j).name])
+ end,
+ end
+ disp('done !')
end
nshock=estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_.ncn;
@@ -425,19 +425,19 @@ else
if options_.opt_gsa.ppost
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_post_lnprior',int2str(ifig)],options_.nodisplay,options_.graph_format);
if options_.TeX
- create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_post_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_post_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
+ create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_post_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_post_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
end
else
if options_.opt_gsa.pprior
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_prior_lnprior',int2str(ifig) ],options_.nodisplay,options_.graph_format);
- if options_.TeX
- create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_prior_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_prior_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
- end
+ if options_.TeX
+ create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_prior_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_prior_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
+ end
else
dyn_saveas(hh,[OutDir filesep fname_ '_rmse_mc_lnprior',int2str(ifig) ],options_.nodisplay,options_.graph_format);
- if options_.TeX
- create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_mc_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_mc_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
- end
+ if options_.TeX
+ create_TeX_loader(options_,[OutDir '/' fname_ '_rmse_mc_lnprior',int2str(ifig)],ifig,[temp_name,' ',int2str(ifig)],'rmse_mc_lnprior',options_.figures.textwidth*min((i-9*(ifig-1))/3,1))
+ end
end
end
end
@@ -604,10 +604,10 @@ else
data_mat=NaN(size(vvarvecm,1),5);
for j=1:size(vvarvecm,1),
data_mat(j,:)=[min(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
- max(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
- min(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
- max(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
- rmse_txt(j)];
+ max(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
+ min(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
+ max(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
+ rmse_txt(j)];
end
%get formatting for additional header line
val_width=15;
@@ -678,10 +678,10 @@ else
data_mat=NaN(size(vvarvecm,1),5);
for j=1:size(vvarvecm,1),
data_mat(j,:)=[min(r2_MC(ixx(1:nfilt0(j),j),j)) ...
- max(r2_MC(ixx(1:nfilt0(j),j),j)) ...
- min(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
- max(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
- r2_txt(j)];
+ max(r2_MC(ixx(1:nfilt0(j),j),j)) ...
+ min(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
+ max(r2_MC(ixx(nfilt0(j)+1:end,j),j)) ...
+ r2_txt(j)];
end
%get formatting for additional header line
val_width=15;
@@ -924,64 +924,64 @@ else
end
end
-% for j=1:size(SP,2),
-% nsx(j)=length(find(SP(:,j)));
-% end
+ % for j=1:size(SP,2),
+ % nsx(j)=length(find(SP(:,j)));
+ % end
-% skipline(2)
-% disp('Sensitivity table (significance and direction):')
-% vav=char(zeros(1, size(param_names,2)+3 ));
-% ibl = 12-size(vvarvecm,2);
-% for j=1:size(vvarvecm,1),
-% vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
-% end
-% disp(vav)
-% for j=1:npar+nshock, %estim_params_.np,
-% %disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])
-% disp([param_names(j,:),' ', sprintf('%12.3g',PP(j,:))])
-% disp([char(zeros(1, size(param_names,2)+3 )),sprintf(' (%6g)',SS(j,:))])
-% end
+ % skipline(2)
+ % disp('Sensitivity table (significance and direction):')
+ % vav=char(zeros(1, size(param_names,2)+3 ));
+ % ibl = 12-size(vvarvecm,2);
+ % for j=1:size(vvarvecm,1),
+ % vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
+ % end
+ % disp(vav)
+ % for j=1:npar+nshock, %estim_params_.np,
+ % %disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])
+ % disp([param_names(j,:),' ', sprintf('%12.3g',PP(j,:))])
+ % disp([char(zeros(1, size(param_names,2)+3 )),sprintf(' (%6g)',SS(j,:))])
+ % end
-% skipline()
-% disp('Starting bivariate analysis:')
-%
-% for i=1:size(vvarvecm,1)
-% if options_.opt_gsa.ppost
-% fnam = ['rmse_post_',deblank(vvarvecm(i,:))];
-% else
-% if options_.opt_gsa.pprior
-% fnam = ['rmse_prior_',deblank(vvarvecm(i,:))];
-% else
-% fnam = ['rmse_mc_',deblank(vvarvecm(i,:))];
-% end
-% end
-% stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,pvalue,fnam, OutDir,[],[temp_name ' observed variable ' deblank(vvarvecm(i,:))]);
-%
-% % [pc,latent,explained] = pcacov(c0);
-% % %figure, bar([explained cumsum(explained)])
-% % ifig=0;
-% % j2=0;
-% % for j=1:npar+nshock,
-% % i2=find(abs(pc(:,j))>alphaPC);
-% % if ~isempty(i2),
-% % j2=j2+1;
-% % if mod(j2,12)==1,
-% % ifig=ifig+1;
-% % figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),
-% % end
-% % subplot(3,4,j2-(ifig-1)*12)
-% % bar(pc(i2,j)),
-% % set(gca,'xticklabel',bayestopt_.name(i2)),
-% % set(gca,'xtick',[1:length(i2)])
-% % title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])
-% % end
-% % if (mod(j2,12)==0 | j==(npar+nshock)) & j2,
-% % saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])
-% % end
-% % end
-% % close all
-% end
+ % skipline()
+ % disp('Starting bivariate analysis:')
+ %
+ % for i=1:size(vvarvecm,1)
+ % if options_.opt_gsa.ppost
+ % fnam = ['rmse_post_',deblank(vvarvecm(i,:))];
+ % else
+ % if options_.opt_gsa.pprior
+ % fnam = ['rmse_prior_',deblank(vvarvecm(i,:))];
+ % else
+ % fnam = ['rmse_mc_',deblank(vvarvecm(i,:))];
+ % end
+ % end
+ % stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,pvalue,fnam, OutDir,[],[temp_name ' observed variable ' deblank(vvarvecm(i,:))]);
+ %
+ % % [pc,latent,explained] = pcacov(c0);
+ % % %figure, bar([explained cumsum(explained)])
+ % % ifig=0;
+ % % j2=0;
+ % % for j=1:npar+nshock,
+ % % i2=find(abs(pc(:,j))>alphaPC);
+ % % if ~isempty(i2),
+ % % j2=j2+1;
+ % % if mod(j2,12)==1,
+ % % ifig=ifig+1;
+ % % figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),
+ % % end
+ % % subplot(3,4,j2-(ifig-1)*12)
+ % % bar(pc(i2,j)),
+ % % set(gca,'xticklabel',bayestopt_.name(i2)),
+ % % set(gca,'xtick',[1:length(i2)])
+ % % title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])
+ % % end
+ % % if (mod(j2,12)==0 | j==(npar+nshock)) & j2,
+ % % saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])
+ % % end
+ % % end
+ % % close all
+ % end
end
diff --git a/matlab/gsa/ghx2transition.m b/matlab/gsa/ghx2transition.m
index 072d8fe3623df4732bc80f0e721f47b7c4da2e47..80b1fe040e4cef114dd8a0fb58a2d5414c1149ed 100644
--- a/matlab/gsa/ghx2transition.m
+++ b/matlab/gsa/ghx2transition.m
@@ -25,10 +25,10 @@ function [A,B] = ghx2transition(mm,iv,ic,aux)
global oo_ M_
- [nr1, nc1] = size(mm);
- ghx = mm(:, [1:(nc1-M_.exo_nbr)]);
- ghu = mm(:, [(nc1-M_.exo_nbr+1):end] );
- if nargin == 1
+[nr1, nc1] = size(mm);
+ghx = mm(:, [1:(nc1-M_.exo_nbr)]);
+ghu = mm(:, [(nc1-M_.exo_nbr+1):end] );
+if nargin == 1
oo_.dr.ghx = ghx;
oo_.dr.ghu = ghu;
endo_nbr = M_.endo_nbr;
@@ -40,18 +40,18 @@ global oo_ M_
k = find(aux(:,2) > nspred);
aux(:,2) = aux(:,2) + nstatic;
aux(k,2) = aux(k,2) + M_.nfwrd;
- end
- n_iv = length(iv);
- n_ir1 = size(aux,1);
- nr = n_iv + n_ir1;
-
- A = zeros(nr,nr);
- B = zeros(nr,M_.exo_nbr);
-
- i_n_iv = 1:n_iv;
- A(i_n_iv,ic) = ghx(iv,:);
- if n_ir1 > 0
+end
+n_iv = length(iv);
+n_ir1 = size(aux,1);
+nr = n_iv + n_ir1;
+
+A = zeros(nr,nr);
+B = zeros(nr,M_.exo_nbr);
+
+i_n_iv = 1:n_iv;
+A(i_n_iv,ic) = ghx(iv,:);
+if n_ir1 > 0
A(n_iv+1:end,:) = sparse(aux(:,1),aux(:,2),ones(n_ir1,1),n_ir1,nr);
- end
-
- B(i_n_iv,:) = ghu(iv,:);
+end
+
+B(i_n_iv,:) = ghu(iv,:);
diff --git a/matlab/gsa/gsa_plotmatrix.m b/matlab/gsa/gsa_plotmatrix.m
index 9c9aee99fc6a06a3a3f232c744f52b2666745759..b2d9a027dfb8e2d183c8d444cd67e41fbf0dd8e9 100644
--- a/matlab/gsa/gsa_plotmatrix.m
+++ b/matlab/gsa/gsa_plotmatrix.m
@@ -38,31 +38,31 @@ end
iexplosive = iunstable(~ismember(iunstable,[iindeterm;iwrong]));
switch type
- case 'all'
- x=[lpmat0 lpmat];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'stable'
- x=[lpmat0(istable,:) lpmat(istable,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'nosolution'
- x=[lpmat0(iunstable,:) lpmat(iunstable,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'unstable'
- x=[lpmat0(iexplosive,:) lpmat(iexplosive,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'indeterm'
- x=[lpmat0(iindeterm,:) lpmat(iindeterm,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
- case 'wrong'
- x=[lpmat0(iwrong,:) lpmat(iwrong,:)];
- NumberOfDraws=size(x,1);
- B=NumberOfDraws;
-
+ case 'all'
+ x=[lpmat0 lpmat];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'stable'
+ x=[lpmat0(istable,:) lpmat(istable,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'nosolution'
+ x=[lpmat0(iunstable,:) lpmat(iunstable,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'unstable'
+ x=[lpmat0(iexplosive,:) lpmat(iexplosive,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'indeterm'
+ x=[lpmat0(iindeterm,:) lpmat(iindeterm,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+ case 'wrong'
+ x=[lpmat0(iwrong,:) lpmat(iwrong,:)];
+ NumberOfDraws=size(x,1);
+ B=NumberOfDraws;
+
end
if isempty(x),
diff --git a/matlab/gsa/gsa_skewness.m b/matlab/gsa/gsa_skewness.m
index b5af1c6ce28a7d600e89bda63fb4c2fbba4e00ba..45dfb390141104a8ee6a516e7a835a7785b56c95 100644
--- a/matlab/gsa/gsa_skewness.m
+++ b/matlab/gsa/gsa_skewness.m
@@ -24,6 +24,6 @@ function s=gsa_skewness(y),
% y=stand_(y);
% s=mean(y.^3);
- m2=mean((y-mean(y)).^2);
- m3=mean((y-mean(y)).^3);
- s=m3/m2^1.5;
\ No newline at end of file
+m2=mean((y-mean(y)).^2);
+m3=mean((y-mean(y)).^3);
+s=m3/m2^1.5;
\ No newline at end of file
diff --git a/matlab/gsa/gsa_speed.m b/matlab/gsa/gsa_speed.m
index 9fce2e94b0ebf4a9a5e85d3b9d03f65fb253b6d7..7ed7e594730df0f214f51676f13d59d30b76378c 100644
--- a/matlab/gsa/gsa_speed.m
+++ b/matlab/gsa/gsa_speed.m
@@ -34,24 +34,24 @@ disp('Computing speed of adjustement ...')
h = dyn_waitbar(0,'Speed of adjustement...');
for i=1:nrun,
- irf=zeros(nvar,nshock);
- a=squeeze(A(:,:,i));
- b=squeeze(B(:,:,i));
- IFF=inv(eye(nstate)-a)*b;
- iff(:,:,i)=IFF(mf,:);
- IF=IFF-b;
-
- t=0;
- while any(any(irf<0.5))
- t=t+1;
- IFT=((eye(nstate)-a^(t+1))*inv(eye(nstate)-a))*b-b;
- irf=IFT(mf,:)./(IF(mf,:)+eps);
- irf = irf.*(abs(IF(mf,:))>1.e-7)+(abs(IF(mf,:))<=1.e-7);
- %irf=ft(mf,:);
- tt=(irf>0.5).*t;
- tadj(:,:,i)=((tt-tadj(:,:,i))==tt).*tt+tadj(:,:,i);
- end
- dyn_waitbar(i/nrun,h)
+ irf=zeros(nvar,nshock);
+ a=squeeze(A(:,:,i));
+ b=squeeze(B(:,:,i));
+ IFF=inv(eye(nstate)-a)*b;
+ iff(:,:,i)=IFF(mf,:);
+ IF=IFF-b;
+
+ t=0;
+ while any(any(irf<0.5))
+ t=t+1;
+ IFT=((eye(nstate)-a^(t+1))*inv(eye(nstate)-a))*b-b;
+ irf=IFT(mf,:)./(IF(mf,:)+eps);
+ irf = irf.*(abs(IF(mf,:))>1.e-7)+(abs(IF(mf,:))<=1.e-7);
+ %irf=ft(mf,:);
+ tt=(irf>0.5).*t;
+ tadj(:,:,i)=((tt-tadj(:,:,i))==tt).*tt+tadj(:,:,i);
+ end
+ dyn_waitbar(i/nrun,h)
end
skipline()
disp('.. done !')
diff --git a/matlab/gsa/log_trans_.m b/matlab/gsa/log_trans_.m
index d1a028e65f60fbe3e29dcecb2474cf44e2188fbf..e051a0c8840f1aab888525dcbb72fe4f8939c79a 100644
--- a/matlab/gsa/log_trans_.m
+++ b/matlab/gsa/log_trans_.m
@@ -29,55 +29,55 @@ if nargin==4,
end
if nargin==1,
- xdir0='';
+ xdir0='';
end
f=@(lam,y)gsa_skewness(log(y+lam));
isig=1;
if ~(max(y0)<0 | min(y0)>0)
- if gsa_skewness(y0)<0,
- isig=-1;
- y0=-y0;
- end
- n=hist(y0,10);
- if n(1)>20*n(end),
- try lam=fzero(f,[-min(y0)+10*eps -min(y0)+abs(median(y0))],[],y0);
- catch
- yl(1)=f(-min(y0)+10*eps,y0);
- yl(2)=f(-min(y0)+abs(median(y0)),y0);
- if abs(yl(1))<abs(yl(2))
- lam=-min(y0)+eps;
- else
- lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
- end
+ if gsa_skewness(y0)<0,
+ isig=-1;
+ y0=-y0;
end
- yy = log(y0+lam);
- xdir=[xdir0,'_logskew'];
- else
- isig=0;
- lam=0;
- yy = log(y0.^2);
- xdir=[xdir0,'_logsquared'];
- end
-else
- if max(y0)<0
- isig=-1;
- y0=-y0;
- %yy=log(-y0);
- xdir=[xdir0,'_minuslog'];
- elseif min(y0)>0
- %yy=log(y0);
- xdir=[xdir0,'_log'];
- end
- try lam=fzero(f,[-min(y0)+10*eps -min(y0)+median(y0)],[],y0);
- catch
- yl(1)=f(-min(y0)+10*eps,y0);
- yl(2)=f(-min(y0)+abs(median(y0)),y0);
- if abs(yl(1))<abs(yl(2))
- lam=-min(y0)+eps;
+ n=hist(y0,10);
+ if n(1)>20*n(end),
+ try lam=fzero(f,[-min(y0)+10*eps -min(y0)+abs(median(y0))],[],y0);
+ catch
+ yl(1)=f(-min(y0)+10*eps,y0);
+ yl(2)=f(-min(y0)+abs(median(y0)),y0);
+ if abs(yl(1))<abs(yl(2))
+ lam=-min(y0)+eps;
+ else
+ lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
+ end
+ end
+ yy = log(y0+lam);
+ xdir=[xdir0,'_logskew'];
else
- lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
+ isig=0;
+ lam=0;
+ yy = log(y0.^2);
+ xdir=[xdir0,'_logsquared'];
+ end
+else
+ if max(y0)<0
+ isig=-1;
+ y0=-y0;
+ %yy=log(-y0);
+ xdir=[xdir0,'_minuslog'];
+ elseif min(y0)>0
+ %yy=log(y0);
+ xdir=[xdir0,'_log'];
+ end
+ try lam=fzero(f,[-min(y0)+10*eps -min(y0)+median(y0)],[],y0);
+ catch
+ yl(1)=f(-min(y0)+10*eps,y0);
+ yl(2)=f(-min(y0)+abs(median(y0)),y0);
+ if abs(yl(1))<abs(yl(2))
+ lam=-min(y0)+eps;
+ else
+ lam = -min(y0)+abs(median(y0)); %abs(100*(1+min(y0)));
+ end
end
- end
- lam = max(lam,0);
- yy = log(y0+lam);
+ lam = max(lam,0);
+ yy = log(y0+lam);
end
diff --git a/matlab/gsa/map_calibration.m b/matlab/gsa/map_calibration.m
index 712d4572fea0803bb596cc6f7b8f6106b70be3fe..20b8084a457da00d1e7909f25bc038065aac3f0a 100644
--- a/matlab/gsa/map_calibration.m
+++ b/matlab/gsa/map_calibration.m
@@ -165,7 +165,7 @@ if ~isempty(indx_irf),
end
disp('done !')
skipline()
-
+
% For single legend search which has maximum nbr of restrictions
all_irf_couples = cellstr([char(endo_prior_restrictions.irf(:,1)) char(endo_prior_restrictions.irf(:,2))]);
irf_couples = unique(all_irf_couples);
diff --git a/matlab/gsa/map_ident_.m b/matlab/gsa/map_ident_.m
index 94e69f7fd845df539f69dc611387931057d8b376..a93ef006f0cd528b7df7fe229b5c38ac89089da8 100644
--- a/matlab/gsa/map_ident_.m
+++ b/matlab/gsa/map_ident_.m
@@ -32,23 +32,23 @@ itrans = opt_gsa.trans_ident;
np = estim_params_.np;
if opt_gsa.load_ident_files,
- gsa_flag=0;
+ gsa_flag=0;
else
- gsa_flag=-2;
+ gsa_flag=-2;
end
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
- if opt_gsa.pprior,
+if opt_gsa.pprior,
-filetoload=[OutputDirectoryName '/' fname_ '_prior'];
- else
-filetoload=[OutputDirectoryName '/' fname_ '_mc'];
- end
+ filetoload=[OutputDirectoryName '/' fname_ '_prior'];
+else
+ filetoload=[OutputDirectoryName '/' fname_ '_mc'];
+end
load(filetoload,'lpmat','lpmat0','istable','T','yys','nspred','nboth','nfwrd')
if ~isempty(lpmat0),
- lpmatx=lpmat0(istable,:);
+ lpmatx=lpmat0(istable,:);
else
- lpmatx=[];
+ lpmatx=[];
end
Nsam = size(lpmat,1);
nshock = size(lpmat0,2);
@@ -57,139 +57,139 @@ npT = np+nshock;
fname_ = M_.fname;
if opt_gsa.load_ident_files==0,
- % th moments
-% options_.ar = min(3,options_.ar);
+ % th moments
+ % options_.ar = min(3,options_.ar);
- mss = yys(bayestopt_.mfys,:);
- mss = teff(mss(:,istable),Nsam,istable);
- yys = teff(yys(oo_.dr.order_var,istable),Nsam,istable);
- if exist('T'),
- [vdec, cc, ac] = mc_moments(T, lpmatx, oo_.dr);
- else
- return,
- end
+ mss = yys(bayestopt_.mfys,:);
+ mss = teff(mss(:,istable),Nsam,istable);
+ yys = teff(yys(oo_.dr.order_var,istable),Nsam,istable);
+ if exist('T'),
+ [vdec, cc, ac] = mc_moments(T, lpmatx, oo_.dr);
+ else
+ return,
+ end
- if opt_gsa.morris==2,
- pdraws = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
-% [pdraws, TAU, GAM] = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
- if ~isempty(pdraws) && max(max(abs(pdraws-[lpmatx lpmat(istable,:)])))==0,
- disp(['Sample check OK ', num2str(max(max(abs(pdraws-[lpmatx lpmat(istable,:)]))))]),
- clear pdraws;
+ if opt_gsa.morris==2,
+ pdraws = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
+ % [pdraws, TAU, GAM] = dynare_identification(options_.options_ident,[lpmatx lpmat(istable,:)]);
+ if ~isempty(pdraws) && max(max(abs(pdraws-[lpmatx lpmat(istable,:)])))==0,
+ disp(['Sample check OK ', num2str(max(max(abs(pdraws-[lpmatx lpmat(istable,:)]))))]),
+ clear pdraws;
+ end
+ % for j=1:length(istable), gas(:,j)=[vech(cc(:,:,j)); vec(ac(:,:,j))]; end
+ % if ~isempty(mss),
+ % gas = [mss(istable,:)'; gas];
+ % end
+ % if max(max(abs(GAM-gas)))<=1.e-8,
+ % disp(['Moments check OK ',num2str(max(max(abs(GAM-gas))))]),
+ clear GAM gas
+ % end
end
-% for j=1:length(istable), gas(:,j)=[vech(cc(:,:,j)); vec(ac(:,:,j))]; end
-% if ~isempty(mss),
-% gas = [mss(istable,:)'; gas];
-% end
-% if max(max(abs(GAM-gas)))<=1.e-8,
-% disp(['Moments check OK ',num2str(max(max(abs(GAM-gas))))]),
- clear GAM gas
-% end
- end
- if opt_gsa.morris~=1 & M_.exo_nbr>1,
- ifig=0;
- for j=1:M_.exo_nbr,
- if mod(j,6)==1
- hh=dyn_figure(options_.nodisplay,'name',['Variance decomposition shocks']);
- ifig=ifig+1;
- iplo=0;
- end
- iplo=iplo+1;
- subplot(2,3,iplo)
- myboxplot(squeeze(vdec(:,j,:))',[],'.',[],10)
- % boxplot(squeeze(vdec(:,j,:))','whis',10,'symbol','.r')
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:size(options_.varobs,1)])
- set(gca,'xlim',[0.5 size(options_.varobs,1)+0.5])
- set(gca,'ylim',[-2 102])
- for ip=1:size(options_.varobs,1),
- text(ip,-4,deblank(options_.varobs(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- ylabel(' ')
- title(M_.exo_names(j,:),'interpreter','none')
- if mod(j,6)==0 | j==M_.exo_nbr,
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],ifig,['Variance decomposition shocks'],'vdec_exo',options_.figures.textwidth*min(iplo/3,1))
- end
+ if opt_gsa.morris~=1 & M_.exo_nbr>1,
+ ifig=0;
+ for j=1:M_.exo_nbr,
+ if mod(j,6)==1
+ hh=dyn_figure(options_.nodisplay,'name',['Variance decomposition shocks']);
+ ifig=ifig+1;
+ iplo=0;
+ end
+ iplo=iplo+1;
+ subplot(2,3,iplo)
+ myboxplot(squeeze(vdec(:,j,:))',[],'.',[],10)
+ % boxplot(squeeze(vdec(:,j,:))','whis',10,'symbol','.r')
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:size(options_.varobs,1)])
+ set(gca,'xlim',[0.5 size(options_.varobs,1)+0.5])
+ set(gca,'ylim',[-2 102])
+ for ip=1:size(options_.varobs,1),
+ text(ip,-4,deblank(options_.varobs(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ ylabel(' ')
+ title(M_.exo_names(j,:),'interpreter','none')
+ if mod(j,6)==0 | j==M_.exo_nbr,
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],ifig,['Variance decomposition shocks'],'vdec_exo',options_.figures.textwidth*min(iplo/3,1))
+ end
+ end
+ end
+ for j=1:size(cc,1)
+ cc(j,j,:)=stand_(squeeze(log(cc(j,j,:))))./2;
end
- end
- for j=1:size(cc,1)
- cc(j,j,:)=stand_(squeeze(log(cc(j,j,:))))./2;
- end
- [vdec, j0, ir_vdec, ic_vdec] = teff(vdec,Nsam,istable);
- [cc, j0, ir_cc, ic_cc] = teff(cc,Nsam,istable);
- [ac, j0, ir_ac, ic_ac] = teff(ac,Nsam,istable);
+ [vdec, j0, ir_vdec, ic_vdec] = teff(vdec,Nsam,istable);
+ [cc, j0, ir_cc, ic_cc] = teff(cc,Nsam,istable);
+ [ac, j0, ir_ac, ic_ac] = teff(ac,Nsam,istable);
- [nr1, nc1, nnn] = size(T);
+ [nr1, nc1, nnn] = size(T);
endo_nbr = M_.endo_nbr;
nstatic = M_.nstatic;
nspred = M_.nspred;
iv = (1:endo_nbr)';
ic = [ nstatic+(1:nspred) endo_nbr+(1:size(oo_.dr.ghx,2)-nspred) ]';
-
+
dr.ghx = T(:, [1:(nc1-M_.exo_nbr)],1);
dr.ghu = T(:, [(nc1-M_.exo_nbr+1):end], 1);
[Aa,Bb] = kalman_transition_matrix(dr,iv,ic,M_.exo_nbr);
-% bayestopt_.restrict_var_list, ...
-% bayestopt_.restrict_columns, ...
-% bayestopt_.restrict_aux, M_.exo_nbr);
- A = zeros(size(Aa,1),size(Aa,2)+size(Aa,1),length(istable));
- % Sig(estim_params_.var_exo(:,1))=lpmatx(1,:).^2;
- if ~isempty(lpmatx),
- set_shocks_param(lpmatx(1,:));
- end
- A(:,:,1)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
- for j=2:length(istable),
- dr.ghx = T(:, [1:(nc1-M_.exo_nbr)],j);
- dr.ghu = T(:, [(nc1-M_.exo_nbr+1):end], j);
- [Aa,Bb] = kalman_transition_matrix(dr, iv, ic, M_.exo_nbr);
-% bayestopt_.restrict_var_list, ...
-% bayestopt_.restrict_columns, ...
-% bayestopt_.restrict_aux, M_.exo_nbr);
+ % bayestopt_.restrict_var_list, ...
+ % bayestopt_.restrict_columns, ...
+ % bayestopt_.restrict_aux, M_.exo_nbr);
+ A = zeros(size(Aa,1),size(Aa,2)+size(Aa,1),length(istable));
+ % Sig(estim_params_.var_exo(:,1))=lpmatx(1,:).^2;
if ~isempty(lpmatx),
- set_shocks_param(lpmatx(j,:));
+ set_shocks_param(lpmatx(1,:));
end
- A(:,:,j)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
- end
- clear T;
- clear lpmatx;
+ A(:,:,1)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
+ for j=2:length(istable),
+ dr.ghx = T(:, [1:(nc1-M_.exo_nbr)],j);
+ dr.ghu = T(:, [(nc1-M_.exo_nbr+1):end], j);
+ [Aa,Bb] = kalman_transition_matrix(dr, iv, ic, M_.exo_nbr);
+ % bayestopt_.restrict_var_list, ...
+ % bayestopt_.restrict_columns, ...
+ % bayestopt_.restrict_aux, M_.exo_nbr);
+ if ~isempty(lpmatx),
+ set_shocks_param(lpmatx(j,:));
+ end
+ A(:,:,j)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
+ end
+ clear T;
+ clear lpmatx;
- [nr,nc,nn]=size(A);
- io=bayestopt_.mf2;
- % T1=A(io,1:nr,:);
- % ino=find(~ismember([1:nr],io));
- % T2=A(ino,1:nr,:);
- R=A(:,nr+1:nc,:);
-% [tadj, iff] = gsa_speed(A(1:nr,1:nr,:),R,io,0.5);
-% [tadj, j0, ir_tadj, ic_tadj] = teff(tadj,Nsam,istable);
-% [iff, j0, ir_if, ic_if] = teff(iff,Nsam,istable);
+ [nr,nc,nn]=size(A);
+ io=bayestopt_.mf2;
+ % T1=A(io,1:nr,:);
+ % ino=find(~ismember([1:nr],io));
+ % T2=A(ino,1:nr,:);
+ R=A(:,nr+1:nc,:);
+ % [tadj, iff] = gsa_speed(A(1:nr,1:nr,:),R,io,0.5);
+ % [tadj, j0, ir_tadj, ic_tadj] = teff(tadj,Nsam,istable);
+ % [iff, j0, ir_if, ic_if] = teff(iff,Nsam,istable);
- [yt, j0]=teff(A,Nsam,istable);
- yt = [yys yt];
- if opt_gsa.morris==2,
-% iii=find(std(yt(istable,:))>1.e-8);
-% if max(max(abs(TAU-yt(istable,iii)')))<= 1.e-8,
-% err = max(max(abs(TAU-yt(istable,iii)')));
-% disp(['Model check OK ',num2str(err)]),
- clear TAU A
-% end
- else
- clear A,
- end
- % [yt1, j01]=teff(T1,Nsam,istable);
- % [yt2, j02]=teff(T2,Nsam,istable);
- % [ytr, j0r]=teff(R,Nsam,istable);
- %
- % yt=[yt1 yt2 ytr];
- save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
+ [yt, j0]=teff(A,Nsam,istable);
+ yt = [yys yt];
+ if opt_gsa.morris==2,
+ % iii=find(std(yt(istable,:))>1.e-8);
+ % if max(max(abs(TAU-yt(istable,iii)')))<= 1.e-8,
+ % err = max(max(abs(TAU-yt(istable,iii)')));
+ % disp(['Model check OK ',num2str(err)]),
+ clear TAU A
+ % end
+ else
+ clear A,
+ end
+ % [yt1, j01]=teff(T1,Nsam,istable);
+ % [yt2, j02]=teff(T2,Nsam,istable);
+ % [ytr, j0r]=teff(R,Nsam,istable);
+ %
+ % yt=[yt1 yt2 ytr];
+ save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
else
- if opt_gsa.morris==2,
-% [pdraws, TAU, GAM] = dynare_identification([1:npT]); %,[lpmatx lpmat(istable,:)]);
-% [pdraws, TAU, GAM] = dynare_identification(options_.options_ident);
- pdraws = dynare_identification(options_.options_ident);
- end
- load([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
+ if opt_gsa.morris==2,
+ % [pdraws, TAU, GAM] = dynare_identification([1:npT]); %,[lpmatx lpmat(istable,:)]);
+ % [pdraws, TAU, GAM] = dynare_identification(options_.options_ident);
+ pdraws = dynare_identification(options_.options_ident);
+ end
+ load([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
end
% for j=1:nr,
@@ -206,1353 +206,1353 @@ end
% yt = yt(:,j0);
if opt_gsa.morris==1,
- %OutputDir = CheckPath('gsa/screen');
- if ~isempty(vdec),
- if opt_gsa.load_ident_files==0,
- SAMorris = [];
- for i=1:size(vdec,2),
- [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], vdec(:,i),nliv);
- end
- SAvdec = squeeze(SAMorris(:,1,:))';
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAvdec','vdec','ir_vdec','ic_vdec')
- else
- load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAvdec','vdec','ir_vdec','ic_vdec')
- end
-
- hh = dyn_figure(options_.nodisplay,'name','Screening identification: variance decomposition');
-% boxplot(SAvdec,'whis',10,'symbol','r.')
- myboxplot(SAvdec,[],'.',[],10)
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
- set(gca,'xlim',[0.5 npT+0.5])
- ydum = get(gca,'ylim');
- set(gca,'ylim',[0 ydum(2)])
- set(gca,'position',[0.13 0.2 0.775 0.7])
- for ip=1:npT,
- text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- title('Elementary effects variance decomposition')
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_vdec'],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_vdec'],1,'Screening identification: variance decomposition','morris_vdec',1)
- else
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'vdec')
-
- end
+ %OutputDir = CheckPath('gsa/screen');
+ if ~isempty(vdec),
+ if opt_gsa.load_ident_files==0,
+ SAMorris = [];
+ for i=1:size(vdec,2),
+ [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], vdec(:,i),nliv);
+ end
+ SAvdec = squeeze(SAMorris(:,1,:))';
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAvdec','vdec','ir_vdec','ic_vdec')
+ else
+ load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAvdec','vdec','ir_vdec','ic_vdec')
+ end
+
+ hh = dyn_figure(options_.nodisplay,'name','Screening identification: variance decomposition');
+ % boxplot(SAvdec,'whis',10,'symbol','r.')
+ myboxplot(SAvdec,[],'.',[],10)
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ set(gca,'xlim',[0.5 npT+0.5])
+ ydum = get(gca,'ylim');
+ set(gca,'ylim',[0 ydum(2)])
+ set(gca,'position',[0.13 0.2 0.775 0.7])
+ for ip=1:npT,
+ text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ title('Elementary effects variance decomposition')
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_vdec'],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_vdec'],1,'Screening identification: variance decomposition','morris_vdec',1)
+ else
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'vdec')
+
+ end
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET variance decomposition observed variables']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_vdec==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAvdec(iv,:),[],'.',[],3)
-% else
-% plot(SAvdec(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_vdec_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_vdec_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_vdec_varobs_',int2str(ifig)]);
-% close(gcf)
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['EET variance decomposition shocks']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_vdec==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAvdec(iv,:),[],'.',[],3)
-% else
-% plot(SAvdec(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr,
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_vdec_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_vdec_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_vdec_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET variance decomposition observed variables']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_vdec==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAvdec(iv,:),[],'.',[],3)
+ % else
+ % plot(SAvdec(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_vdec_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_vdec_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_vdec_varobs_',int2str(ifig)]);
+ % close(gcf)
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['EET variance decomposition shocks']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_vdec==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAvdec(iv,:),[],'.',[],3)
+ % else
+ % plot(SAvdec(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr,
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_vdec_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_vdec_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_vdec_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
- if opt_gsa.load_ident_files==0,
- SAMorris = [];
- ccac = [mss cc ac];
- for i=1:size(ccac,2),
- [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ccac(:,i)],nliv);
- end
- SAcc = squeeze(SAMorris(:,1,:))';
- SAcc = SAcc./(max(SAcc')'*ones(1,npT));
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAcc','cc','ir_cc','ic_cc','-append')
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'ac','ir_ac','ic_ac','-append')
- else
- load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAcc','cc','ir_cc','ic_cc')
- load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'ac','ir_ac','ic_ac')
- end
-
- hh=dyn_figure(options_.nodisplay,'name','Screening identification: theoretical moments');
-% boxplot(SAcc,'whis',10,'symbol','r.')
- myboxplot(SAcc,[],'.',[],10)
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
- set(gca,'xlim',[0.5 npT+0.5])
- ydum = get(gca,'ylim');
- set(gca,'ylim',[0 1])
- set(gca,'position',[0.13 0.2 0.775 0.7])
- for ip=1:npT,
- text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- title('Elementary effects in the moments')
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_moments'],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_moments'],1,'Screening identification: theoretical moments','morris_moments',1)
+ if opt_gsa.load_ident_files==0,
+ SAMorris = [];
+ ccac = [mss cc ac];
+ for i=1:size(ccac,2),
+ [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ccac(:,i)],nliv);
+ end
+ SAcc = squeeze(SAMorris(:,1,:))';
+ SAcc = SAcc./(max(SAcc')'*ones(1,npT));
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAcc','cc','ir_cc','ic_cc','-append')
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'ac','ir_ac','ic_ac','-append')
+ else
+ load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAcc','cc','ir_cc','ic_cc')
+ load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'ac','ir_ac','ic_ac')
+ end
+
+ hh=dyn_figure(options_.nodisplay,'name','Screening identification: theoretical moments');
+ % boxplot(SAcc,'whis',10,'symbol','r.')
+ myboxplot(SAcc,[],'.',[],10)
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ set(gca,'xlim',[0.5 npT+0.5])
+ ydum = get(gca,'ylim');
+ set(gca,'ylim',[0 1])
+ set(gca,'position',[0.13 0.2 0.775 0.7])
+ for ip=1:npT,
+ text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ title('Elementary effects in the moments')
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_moments'],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_moments'],1,'Screening identification: theoretical moments','morris_moments',1)
-% close(gcf),
+ % close(gcf),
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% MORRIS FOR DERIVATIVES
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ % MORRIS FOR DERIVATIVES
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% if opt_gsa.load_ident_files==0,
-% for j=1:npT,
-% SAMorris = [];
-% ddd=NaN(size(lpmat,1),size(JJ,1));
-% ddd(istable,:) = squeeze(JJ(:,j,:))';
-% for i=1:size(ddd,2),
-% [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ddd(:,i)],nliv);
-% end
-% SAddd(:,:,j) = squeeze(SAMorris(:,1,:))';
-% SAddd(:,:,j) = SAddd(:,:,j)./(max(SAddd(:,:,j)')'*ones(1,npT));
-% sad(:,j) = median(SAddd(find(~isnan(squeeze(SAddd(:,1,j)))),:,j))';
-% end
-% save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAddd','sad','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAddd','sad')
-% end
-% figure,
-% contourf(sad,10), colorbar
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'yticklabel',' ','fontsize',10,'ytick',[1:npT])
-% for ip=1:npT,
-% text(ip,0.9,['D(',bayestopt_.name{ip},')'],'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% text(0.9,ip,[bayestopt_.name{ip}],'rotation',0,'HorizontalAlignment','right','interpreter','none')
-% end
-% [m,im]=max(sad);
-% iii = find((im-[1:npT])==0);
-% disp('Most identified params')
-% disp(bayestopt_.name(iii))
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% END OF MORRIS FOR DERIVATIVES
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET cross-correlations']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_cc==j);
-% iv = [iv; find(ic_cc==j)];
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAcc(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAcc(iv,:),[],'.',[],3)
-% else
-% plot(SAcc(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_cc_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_cc_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_cc_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ % if opt_gsa.load_ident_files==0,
+ % for j=1:npT,
+ % SAMorris = [];
+ % ddd=NaN(size(lpmat,1),size(JJ,1));
+ % ddd(istable,:) = squeeze(JJ(:,j,:))';
+ % for i=1:size(ddd,2),
+ % [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ddd(:,i)],nliv);
+ % end
+ % SAddd(:,:,j) = squeeze(SAMorris(:,1,:))';
+ % SAddd(:,:,j) = SAddd(:,:,j)./(max(SAddd(:,:,j)')'*ones(1,npT));
+ % sad(:,j) = median(SAddd(find(~isnan(squeeze(SAddd(:,1,j)))),:,j))';
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAddd','sad','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAddd','sad')
+ % end
+ % figure,
+ % contourf(sad,10), colorbar
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'yticklabel',' ','fontsize',10,'ytick',[1:npT])
+ % for ip=1:npT,
+ % text(ip,0.9,['D(',bayestopt_.name{ip},')'],'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % text(0.9,ip,[bayestopt_.name{ip}],'rotation',0,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % [m,im]=max(sad);
+ % iii = find((im-[1:npT])==0);
+ % disp('Most identified params')
+ % disp(bayestopt_.name(iii))
+
+
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ % END OF MORRIS FOR DERIVATIVES
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET cross-correlations']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_cc==j);
+ % iv = [iv; find(ic_cc==j)];
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAcc(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAcc(iv,:),[],'.',[],3)
+ % else
+ % plot(SAcc(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_cc_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_cc_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_cc_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
-% if opt_gsa.load_ident_files==0,
-% SAMorris = [];
-% for i=1:size(ac,2),
-% [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], ac(:,i),nliv);
-% end
-% %end
-% SAac = squeeze(SAMorris(:,1,:))';
-% save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAac','ac','ir_ac','ic_ac','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAac','ac','ir_ac','ic_ac')
-% end
-% figure,
-% % boxplot(SAac,'whis',10,'symbol','r.')
-% myboxplot(SAac,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title('EET All auto-correlations')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_ac'])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_ac']);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_ac']);
-% close(gcf),
+ % if opt_gsa.load_ident_files==0,
+ % SAMorris = [];
+ % for i=1:size(ac,2),
+ % [SAmeas, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], ac(:,i),nliv);
+ % end
+ % %end
+ % SAac = squeeze(SAMorris(:,1,:))';
+ % save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAac','ac','ir_ac','ic_ac','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAac','ac','ir_ac','ic_ac')
+ % end
+ % figure,
+ % % boxplot(SAac,'whis',10,'symbol','r.')
+ % myboxplot(SAac,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title('EET All auto-correlations')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_ac'])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_ac']);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_ac']);
+ % close(gcf),
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET auto-correlations']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_ac==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAac(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAac(iv,:),[],'.',[],3)
-% else
-% plot(SAac(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_ac_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_ac_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_ac_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET auto-correlations']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_ac==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAac(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAac(iv,:),[],'.',[],3)
+ % else
+ % plot(SAac(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_ac_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_ac_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_ac_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
-% if opt_gsa.load_ident_files==0,
-% js=0;
-% %for j=1:size(tadj,1),
-% SAMorris = [];
-% for i=1:size(tadj,2),
-% js=js+1;
-% [SAmeas, SAMorris(:,:,js)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], tadj(:,i),nliv);
-% end
-% %end
-% SAM = squeeze(SAMorris(nshock+1:end,1,:));
-% for j=1:js,
-% SAtadj(:,j)=SAM(:,j)./(max(SAM(:,j))+eps);
-% end
-% SAtadj = SAtadj';
-% save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAtadj','tadj','ir_tadj','ic_tadj','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAtadj','tadj','ir_tadj','ic_tadj')
-% end
-% if opt_gsa.load_ident_files==0,
-% js=0;
-% SAMorris = [];
-% for i=1:size(iff,2),
-% js=js+1;
-% [SAmeas, SAMorriss(:,:,js)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], iff(:,i),nliv);
-% end
-% SAM = squeeze(SAMorriss(nshock+1:end,1,:));
-% for j=1:js,
-% SAIF(:,j)=SAM(:,j)./(max(SAM(:,j))+eps);
-% end
-% SAIF = SAIF';
-% save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAIF','iff','ir_if','ic_if','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAIF','iff','ir_if','ic_if')
-% end
-% figure,
-% %bar(SAtadj),
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAtadj,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% set(gca,'ylim',[0 1])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title('All half-life')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj'])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj']);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj']);
-% close(gcf),
+ % if opt_gsa.load_ident_files==0,
+ % js=0;
+ % %for j=1:size(tadj,1),
+ % SAMorris = [];
+ % for i=1:size(tadj,2),
+ % js=js+1;
+ % [SAmeas, SAMorris(:,:,js)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], tadj(:,i),nliv);
+ % end
+ % %end
+ % SAM = squeeze(SAMorris(nshock+1:end,1,:));
+ % for j=1:js,
+ % SAtadj(:,j)=SAM(:,j)./(max(SAM(:,j))+eps);
+ % end
+ % SAtadj = SAtadj';
+ % save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAtadj','tadj','ir_tadj','ic_tadj','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAtadj','tadj','ir_tadj','ic_tadj')
+ % end
+ % if opt_gsa.load_ident_files==0,
+ % js=0;
+ % SAMorris = [];
+ % for i=1:size(iff,2),
+ % js=js+1;
+ % [SAmeas, SAMorriss(:,:,js)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], iff(:,i),nliv);
+ % end
+ % SAM = squeeze(SAMorriss(nshock+1:end,1,:));
+ % for j=1:js,
+ % SAIF(:,j)=SAM(:,j)./(max(SAM(:,j))+eps);
+ % end
+ % SAIF = SAIF';
+ % save([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAIF','iff','ir_if','ic_if','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAIF','iff','ir_if','ic_if')
+ % end
+ % figure,
+ % %bar(SAtadj),
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAtadj,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % set(gca,'ylim',[0 1])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title('All half-life')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj'])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj']);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj']);
+ % close(gcf),
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET speed of adjustment observed variables']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_tadj==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAtadj(iv,:),[],'.',[],3)
-% else
-% plot(SAtadj(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET speed of adjustment observed variables']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_tadj==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAtadj(iv,:),[],'.',[],3)
+ % else
+ % plot(SAtadj(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['EET speed of adjustment shocks']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_tadj==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAtadj(iv,:),[],'.',[],3)
-% else
-% plot(SAtadj(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr,
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['EET speed of adjustment shocks']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_tadj==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAtadj(iv,:),[],'.',[],3)
+ % else
+ % plot(SAtadj(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr,
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_tadj_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_tadj_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_tadj_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
-% figure,
-% %bar(SAIF),
-% % boxplot(SAIF,'whis',10,'symbol','r.')
-% myboxplot(SAIF,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% set(gca,'ylim',[0 1])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% ylabel('Elementary Effects')
-% title('Steady state gains (impact factors)')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain'])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain']);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain']);
-% close(gcf),
- %figure, bar(SAIF'), title('All Gain Relationships')
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['EET steady state gain observed series']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_if==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAIF(iv,:),'whis',10,'symbol','r.');
-% myboxplot(SAIF(iv,:),[],'.',[],10)
-% else
-% plot(SAIF(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['EET steady state gain shocks']);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_if==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAIF(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAIF(iv,:),[],'.',[],3)
-% else
-% plot(SAIF(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr,
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
+ % figure,
+ % %bar(SAIF),
+ % % boxplot(SAIF,'whis',10,'symbol','r.')
+ % myboxplot(SAIF,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % set(gca,'ylim',[0 1])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % ylabel('Elementary Effects')
+ % title('Steady state gains (impact factors)')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain'])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain']);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain']);
+ % close(gcf),
+ %figure, bar(SAIF'), title('All Gain Relationships')
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['EET steady state gain observed series']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_if==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAIF(iv,:),'whis',10,'symbol','r.');
+ % myboxplot(SAIF(iv,:),[],'.',[],10)
+ % else
+ % plot(SAIF(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['EET steady state gain shocks']);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_if==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAIF(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAIF(iv,:),[],'.',[],3)
+ % else
+ % plot(SAIF(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr,
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_gain_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_gain_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_gain_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
- if opt_gsa.load_ident_files==0,
- SAMorris = [];
- for j=1:j0,
- [SAmeas, SAMorris(:,:,j)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], yt(:,j),nliv);
- end
+ if opt_gsa.load_ident_files==0,
+ SAMorris = [];
+ for j=1:j0,
+ [SAmeas, SAMorris(:,:,j)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], yt(:,j),nliv);
+ end
-% SAM = squeeze(SAMorris(nshock+1:end,1,:));
- SAM = squeeze(SAMorris(1:end,1,:));
- for j=1:j0
- SAnorm(:,j)=SAM(:,j)./max(SAM(:,j));
- irex(j)=length(find(SAnorm(:,j)>0.01));
- end
- [dum, irel]=sort(irex);
+ % SAM = squeeze(SAMorris(nshock+1:end,1,:));
+ SAM = squeeze(SAMorris(1:end,1,:));
+ for j=1:j0
+ SAnorm(:,j)=SAM(:,j)./max(SAM(:,j));
+ irex(j)=length(find(SAnorm(:,j)>0.01));
+ end
+ [dum, irel]=sort(irex);
-% SAMmu = squeeze(SAMorris(nshock+1:end,2,:));
- SAMmu = squeeze(SAMorris(1:end,2,:));
- for j=1:j0
- SAmunorm(:,j)=SAMmu(:,j)./max(SAM(:,j)); % normalised w.r.t. mu*
- end
-% SAMsig = squeeze(SAMorris(nshock+1:end,3,:));
- SAMsig = squeeze(SAMorris(1:end,3,:));
- for j=1:j0
- SAsignorm(:,j)=SAMsig(:,j)./max(SAMsig(:,j));
- end
- save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAnorm','SAmunorm','SAsignorm','-append')
- else
- load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAnorm','SAmunorm','SAsignorm')
- end
- hh=dyn_figure(options_.nodisplay,'name','Screening identification: model'); %bar(SAnorm(:,irel))
-% boxplot(SAnorm','whis',10,'symbol','r.')
- myboxplot(SAnorm',[],'.',[],10)
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
- set(gca,'xlim',[0.5 npT+0.5])
- set(gca,'ylim',[0 1])
- set(gca,'position',[0.13 0.2 0.775 0.7])
- xlabel(' ')
- for ip=1:npT,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- title('Elementary effects in the model')
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_par'],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_par'],1,'Screening identification: model','morris_par',1)
+ % SAMmu = squeeze(SAMorris(nshock+1:end,2,:));
+ SAMmu = squeeze(SAMorris(1:end,2,:));
+ for j=1:j0
+ SAmunorm(:,j)=SAMmu(:,j)./max(SAM(:,j)); % normalised w.r.t. mu*
+ end
+ % SAMsig = squeeze(SAMorris(nshock+1:end,3,:));
+ SAMsig = squeeze(SAMorris(1:end,3,:));
+ for j=1:j0
+ SAsignorm(:,j)=SAMsig(:,j)./max(SAMsig(:,j));
+ end
+ save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAnorm','SAmunorm','SAsignorm','-append')
+ else
+ load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAnorm','SAmunorm','SAsignorm')
+ end
+ hh=dyn_figure(options_.nodisplay,'name','Screening identification: model'); %bar(SAnorm(:,irel))
+ % boxplot(SAnorm','whis',10,'symbol','r.')
+ myboxplot(SAnorm',[],'.',[],10)
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ set(gca,'xlim',[0.5 npT+0.5])
+ set(gca,'ylim',[0 1])
+ set(gca,'position',[0.13 0.2 0.775 0.7])
+ xlabel(' ')
+ for ip=1:npT,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ title('Elementary effects in the model')
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morris_par'],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_par'],1,'Screening identification: model','morris_par',1)
-% hh=dyn_figure(options_.nodisplay); %bar(SAmunorm(:,irel))
-% % boxplot(SAmunorm','whis',10,'symbol','r.')
-% myboxplot(SAmunorm',[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% set(gca,'ylim',[-1 1])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% xlabel(' ')
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title('\mu in the model')
-% dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morrismu_par'],options_.nodisplay,options_.graph_format);
-%
-% hh=dyn_figure(options_.nodisplay); %bar(SAsignorm(:,irel))
-% % boxplot(SAsignorm','whis',10,'symbol','r.')
-% myboxplot(SAsignorm',[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% set(gca,'ylim',[0 1])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% xlabel(' ')
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title('\sigma in the model')
-% dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morrissig_par'],options_.nodisplay,options_.graph_format);
+ % hh=dyn_figure(options_.nodisplay); %bar(SAmunorm(:,irel))
+ % % boxplot(SAmunorm','whis',10,'symbol','r.')
+ % myboxplot(SAmunorm',[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % set(gca,'ylim',[-1 1])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % xlabel(' ')
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title('\mu in the model')
+ % dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morrismu_par'],options_.nodisplay,options_.graph_format);
+ %
+ % hh=dyn_figure(options_.nodisplay); %bar(SAsignorm(:,irel))
+ % % boxplot(SAsignorm','whis',10,'symbol','r.')
+ % myboxplot(SAsignorm',[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % set(gca,'ylim',[0 1])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % xlabel(' ')
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title('\sigma in the model')
+ % dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_morrissig_par'],options_.nodisplay,options_.graph_format);
- % figure, bar(SAnorm(:,irel)')
- % set(gca,'xtick',[1:j0])
- % set(gca,'xlim',[0.5 j0+0.5])
- % title('Elementary effects relationships')
- % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_redform'])
- % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_redform']);
- % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_redform']);
+ % figure, bar(SAnorm(:,irel)')
+ % set(gca,'xtick',[1:j0])
+ % set(gca,'xlim',[0.5 j0+0.5])
+ % title('Elementary effects relationships')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_morris_redform'])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_morris_redform']);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_morris_redform']);
elseif opt_gsa.morris==3,
return
- np=estim_params_.np;
- na=(4*np+1)*opt_gsa.Nsam;
- for j=1:j0,
- [idex(j,:), yd(j,:)] = spop_ide(lpmat, yt(:,j), opt_gsa.Nsam, 5-1);
- end
- iok=find(~isnan(yt(1:opt_gsa.Nsam,1)));
- yr=NaN*ones(size(lpmat,1),j0);
- for j=1:j0,
- ys(j,:)=yd(j,:)./max(yd(j,:));
- [dum, is]=sort(yt(iok,j));
- yr(iok(is),j)=[1:length(iok)]'./length(iok);
- yr(istable(length(iok)+1:end),j) = interp1(yt(iok,j),yr(iok,j),yt(istable(length(iok)+1:end),j),'','extrap');
- ineg=find(yr(:,j)<0);
- if any(ineg),
- [dum, is]=sort(yr(ineg,j));
- yr(ineg(is),j)=-[length(ineg):-1:1]./length(iok);
-
+ np=estim_params_.np;
+ na=(4*np+1)*opt_gsa.Nsam;
+ for j=1:j0,
+ [idex(j,:), yd(j,:)] = spop_ide(lpmat, yt(:,j), opt_gsa.Nsam, 5-1);
end
- [idex_r(j,:), yd_r(j,:)] = spop_ide(lpmat, yr(:,j), opt_gsa.Nsam, 5-1);
- ys_r(j,:)=yd_r(j,:)./max(yd_r(j,:));
+ iok=find(~isnan(yt(1:opt_gsa.Nsam,1)));
+ yr=NaN*ones(size(lpmat,1),j0);
+ for j=1:j0,
+ ys(j,:)=yd(j,:)./max(yd(j,:));
+ [dum, is]=sort(yt(iok,j));
+ yr(iok(is),j)=[1:length(iok)]'./length(iok);
+ yr(istable(length(iok)+1:end),j) = interp1(yt(iok,j),yr(iok,j),yt(istable(length(iok)+1:end),j),'','extrap');
+ ineg=find(yr(:,j)<0);
+ if any(ineg),
+ [dum, is]=sort(yr(ineg,j));
+ yr(ineg(is),j)=-[length(ineg):-1:1]./length(iok);
+
+ end
+ [idex_r(j,:), yd_r(j,:)] = spop_ide(lpmat, yr(:,j), opt_gsa.Nsam, 5-1);
+ ys_r(j,:)=yd_r(j,:)./max(yd_r(j,:));
- end,
- figure, bar((idex.*ys)./opt_gsa.Nsam), title('Relationships')
- figure, bar((idex.*ys)'./opt_gsa.Nsam), title('Parameters')
- figure, bar((idex_r.*ys_r)./opt_gsa.Nsam), title('Relationships rank')
- figure, bar((idex_r.*ys_r)'./opt_gsa.Nsam), title('Parameters rank')
- [v0,d0]=eig(corrcoef(yt(iok,:)));
- ee=diag(d0);
- ee=ee([end:-1:1])./j0;
- i0=length(find(ee>0.01));
- v0=v0(:,[end:-1:1]);
- for j=1:i0,
- [idex_pc(j,:), yd_pc(j,:)] = spop_ide(lpmat, yt*v0(:,j), opt_gsa.Nsam, 5-1);
- end
- for j=1:i0,
- ys_pc(j,:)=yd_pc(j,:)./max(yd_pc(j,:));
- end,
- figure, bar((idex_pc.*ys_pc)./opt_gsa.Nsam), title('Relationships PCA')
- figure, bar((idex_pc.*ys_pc)'./opt_gsa.Nsam), title('Parameters PCA')
+ end,
+ figure, bar((idex.*ys)./opt_gsa.Nsam), title('Relationships')
+ figure, bar((idex.*ys)'./opt_gsa.Nsam), title('Parameters')
+ figure, bar((idex_r.*ys_r)./opt_gsa.Nsam), title('Relationships rank')
+ figure, bar((idex_r.*ys_r)'./opt_gsa.Nsam), title('Parameters rank')
+ [v0,d0]=eig(corrcoef(yt(iok,:)));
+ ee=diag(d0);
+ ee=ee([end:-1:1])./j0;
+ i0=length(find(ee>0.01));
+ v0=v0(:,[end:-1:1]);
+ for j=1:i0,
+ [idex_pc(j,:), yd_pc(j,:)] = spop_ide(lpmat, yt*v0(:,j), opt_gsa.Nsam, 5-1);
+ end
+ for j=1:i0,
+ ys_pc(j,:)=yd_pc(j,:)./max(yd_pc(j,:));
+ end,
+ figure, bar((idex_pc.*ys_pc)./opt_gsa.Nsam), title('Relationships PCA')
+ figure, bar((idex_pc.*ys_pc)'./opt_gsa.Nsam), title('Parameters PCA')
- [vr,dr]=eig(corrcoef(yr(iok,:)));
- er=diag(dr);
- er=er([end:-1:1])./j0;
- ir0=length(find(er>0.01));
- vr=vr(:,[end:-1:1]);
- for j=1:ir0,
- [idex_pcr(j,:), yd_pcr(j,:)] = spop_ide(lpmat, yr*vr(:,j), opt_gsa.Nsam, 5-1);
- end
- for j=1:ir0,
- ys_pcr(j,:)=yd_pcr(j,:)./max(yd_pcr(j,:));
- end,
- figure, bar((idex_pcr.*ys_pcr)./opt_gsa.Nsam), title('Relationships rank PCA')
- figure, bar((idex_pcr.*ys_pcr)'./opt_gsa.Nsam), title('Parameters rank PCA')
-
+ [vr,dr]=eig(corrcoef(yr(iok,:)));
+ er=diag(dr);
+ er=er([end:-1:1])./j0;
+ ir0=length(find(er>0.01));
+ vr=vr(:,[end:-1:1]);
+ for j=1:ir0,
+ [idex_pcr(j,:), yd_pcr(j,:)] = spop_ide(lpmat, yr*vr(:,j), opt_gsa.Nsam, 5-1);
+ end
+ for j=1:ir0,
+ ys_pcr(j,:)=yd_pcr(j,:)./max(yd_pcr(j,:));
+ end,
+ figure, bar((idex_pcr.*ys_pcr)./opt_gsa.Nsam), title('Relationships rank PCA')
+ figure, bar((idex_pcr.*ys_pcr)'./opt_gsa.Nsam), title('Parameters rank PCA')
+
elseif opt_gsa.morris==2, % ISKREV staff
- return,
+ return,
-
+
else, % main effects analysis
-
- if itrans==0,
- fsuffix = '';
- elseif itrans==1,
- fsuffix = '_log';
- else
- fsuffix = '_rank';
- end
-
- imap=[1:npT];
-
- if isempty(lpmat0),
- x0=lpmat(istable,:);
- else
-
- x0=[lpmat0(istable,:), lpmat(istable,:)];
- end
- nrun=length(istable);
- nest=min(250,nrun);
- nfit=min(1000,nrun);
-
-% opt_gsa.load_ident_files=0;
-
-% if opt_gsa.load_ident_files==0,
-% try
-% EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAvdec','vdec','ir_vdec','ic_vdec');
-% catch
-% EET=[];
-% end
-% SAvdec=zeros(size(vdec,2),npT);
-%
-% for j=1:size(vdec,2),
-% if itrans==0,
-% y0 = vdec(istable,j);
-% elseif itrans==1,
-% y0 = log_trans_(vdec(istable,j));
-% else
-% y0 = trank(vdec(istable,j));
-% end
-% if ~isempty(EET),
-% % imap=find(EET.SAvdec(j,:));
-% % [dum, isort]=sort(-EET.SAvdec(j,:));
-% imap=find(EET.SAvdec(j,:) >= (0.1.*max(EET.SAvdec(j,:))) );
-% end
-% gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
-% 2, [],[],[],0,[OutputDirectoryName,'/map_vdec',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_vdec',fsuffix,int2str(j)], pnames);
-% end
-%
-% SAvdec(j,imap)=gsa_(j).si;
-% imap_vdec{j}=imap;
-% end
-% save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_vdec','SAvdec','vdec','ir_vdec','ic_vdec','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_vdec','SAvdec','vdec','ir_vdec','ic_vdec')
-% end
-% figure,
-% % boxplot(SAvdec,'whis',10,'symbol','r.')
-% myboxplot(SAvdec,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects variance decomposition ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix]);
-% close(gcf),
-%
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects observed variance decomposition ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_vdec==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAvdec(iv,:),'whis',10,'symbol','r.');
-% myboxplot(SAvdec(iv,:),[],'.',[],10)
-% else
-% plot(SAvdec(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix,'_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['Main effects shocks variance decomposition ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_vdec==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAvdec(iv,:),[],'.',[],10)
-% else
-% plot(SAvdec(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',3,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% set(gca,'fontsize',10)
-% end
-% title(M_.exo_names(j,:),'interpreter','none','fontsize',10)
-% if mod(j,6)==0 | j==M_.exo_nbr
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix,'_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-
- if opt_gsa.load_ident_files==0,
- try
- EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAcc','ir_cc','ic_cc');
- catch
- EET=[];
- end
- ccac = stand_([mss cc ac]);
- [pcc, dd] = eig(cov(ccac(istable,:)));
- [latent, isort] = sort(-diag(dd));
- latent = -latent;
- figure, bar(latent)
- title('Eigenvalues in PCA')
- pcc=pcc(:,isort);
- ccac = ccac*pcc;
-% npca = min(40, max(find(cumsum(latent)./length(latent)<0.99))+1);
- npca = max(find(cumsum(latent)./length(latent)<0.99))+1;
- siPCA = (EET.SAcc'*abs(pcc'))';
-% siPCA = siPCA./(max(siPCA')'*ones(1,npT)).*(latent*ones(1,npT));
- siPCA = siPCA./(max(siPCA')'*ones(1,npT));
-% siPCA = sum(siPCA,1);
-% siPCA = siPCA./max(siPCA);
- SAcc=zeros(size(ccac,2),npT);
- for j=1:npca, %size(ccac,2),
+
if itrans==0,
- y0 = ccac(istable,j);
+ fsuffix = '';
elseif itrans==1,
- y0 = log_trans_(ccac(istable,j));
+ fsuffix = '_log';
else
- y0 = trank(ccac(istable,j));
- end
- if ~isempty(EET),
-% imap=find(EET.SAvdec(j,:));
-% [dum, isort]=sort(-EET.SAvdec(j,:));
- imap=find(siPCA(j,:) >= (0.1.*max(siPCA(j,:))) );
-% imap=find(EET.SAcc(j,:) >= (0.1.*max(EET.SAcc(j,:))) );
+ fsuffix = '_rank';
end
- gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
- 2, [],[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
-% end
- SAcc(j,imap)=gsa_(j).si;
- imap_cc{j}=imap;
+
+ imap=[1:npT];
- end
- save([OutputDirectoryName,'/map_cc',fsuffix,'.mat'],'gsa_')
- save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'imap_cc','SAcc','ccac','-append')
- else
- load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_cc','SAcc','ccac')
+ if isempty(lpmat0),
+ x0=lpmat(istable,:);
+ else
+
+ x0=[lpmat0(istable,:), lpmat(istable,:)];
+ end
+ nrun=length(istable);
+ nest=min(250,nrun);
+ nfit=min(1000,nrun);
- end
-% figure,
-% % boxplot(SAcc,'whis',10,'symbol','r.')
-% myboxplot(SAcc,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% ylabel(' ')
-% title(['Main effects moments''s PCA ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_cc',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
-% close(gcf),
+ % opt_gsa.load_ident_files=0;
+
+ % if opt_gsa.load_ident_files==0,
+ % try
+ % EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAvdec','vdec','ir_vdec','ic_vdec');
+ % catch
+ % EET=[];
+ % end
+ % SAvdec=zeros(size(vdec,2),npT);
+ %
+ % for j=1:size(vdec,2),
+ % if itrans==0,
+ % y0 = vdec(istable,j);
+ % elseif itrans==1,
+ % y0 = log_trans_(vdec(istable,j));
+ % else
+ % y0 = trank(vdec(istable,j));
+ % end
+ % if ~isempty(EET),
+ % % imap=find(EET.SAvdec(j,:));
+ % % [dum, isort]=sort(-EET.SAvdec(j,:));
+ % imap=find(EET.SAvdec(j,:) >= (0.1.*max(EET.SAvdec(j,:))) );
+ % end
+ % gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ % 2, [],[],[],0,[OutputDirectoryName,'/map_vdec',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_vdec',fsuffix,int2str(j)], pnames);
+ % end
+ %
+ % SAvdec(j,imap)=gsa_(j).si;
+ % imap_vdec{j}=imap;
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_vdec','SAvdec','vdec','ir_vdec','ic_vdec','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_vdec','SAvdec','vdec','ir_vdec','ic_vdec')
+ % end
+ % figure,
+ % % boxplot(SAvdec,'whis',10,'symbol','r.')
+ % myboxplot(SAvdec,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects variance decomposition ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix]);
+ % close(gcf),
+ %
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects observed variance decomposition ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_vdec==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAvdec(iv,:),'whis',10,'symbol','r.');
+ % myboxplot(SAvdec(iv,:),[],'.',[],10)
+ % else
+ % plot(SAvdec(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix,'_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['Main effects shocks variance decomposition ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_vdec==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAvdec(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAvdec(iv,:),[],'.',[],10)
+ % else
+ % plot(SAvdec(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',3,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % set(gca,'fontsize',10)
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none','fontsize',10)
+ % if mod(j,6)==0 | j==M_.exo_nbr
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_vdec',fsuffix,'_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_vdec',fsuffix,'_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects cross-covariances ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_cc==j);
-% iv = [iv; find(ic_cc==j)];
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAcc(iv,:),'whis',10,'symbol','r.');
-% myboxplot(SAcc(iv,:),[],'.',[],10)
-% else
-% plot(SAcc(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% set(gca,'fontsize',10)
-% end
-% title(options_.varobs(j,:),'interpreter','none','fontsize',10)
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_cc',fsuffix,'_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_cc',fsuffix,'_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_cc',fsuffix,'_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% if opt_gsa.load_ident_files==0,
-% try
-% EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAac','ir_ac','ic_ac');
-% catch
-% EET=[];
-% end
-% SAac=zeros(size(ac,2),npT);
-% for j=1:size(ac,2),
-% if itrans==0,
-% y0 = ac(istable,j);
-% elseif itrans==1,
-% y0 = log_trans_(ac(istable,j));
-% else
-% y0 = trank(ac(istable,j));
-% end
-% if ~isempty(EET),
-% imap=find(EET.SAac(j,:) >= (0.1.*max(EET.SAac(j,:))) );
-% end
-% % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
-% % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
-% gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
-% 2, [],[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
-% end
-% SAac(j,imap)=gsa_(j).si;
-% imap_ac{j}=imap;
-%
-% end
-% save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_ac','SAac','ac','ir_ac','ic_ac','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_ac','SAac','ac','ir_ac','ic_ac')
-% end
-%
-% figure,
-% % boxplot(SAac,'whis',10,'symbol','r.')
-% myboxplot(SAac,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects 1 lag auto-covariances ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ac',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix]);
-% close(gcf),
-%
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects auto-covariances ',fsuffix]);
-% ifig=ifig+1;
-% iplo = 0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_ac==j);
-% %iv = [iv; find(ic_ac==j)];
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAac(iv,:),'whis',10,'symbol','r.');
-% myboxplot(SAac(iv,:),[],'.',[],10)
-% else
-% plot(SAac(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:npT,
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% set(gca,'fontsize',10)
-% end
-% title(options_.varobs(j,:),'interpreter','none','fontsize',10)
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ac',fsuffix,'_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix,'_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix,'_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-
-% x0=x0(:,nshock+1:end);
- imap=[1:npT];
+ if opt_gsa.load_ident_files==0,
+ try
+ EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAcc','ir_cc','ic_cc');
+ catch
+ EET=[];
+ end
+ ccac = stand_([mss cc ac]);
+ [pcc, dd] = eig(cov(ccac(istable,:)));
+ [latent, isort] = sort(-diag(dd));
+ latent = -latent;
+ figure, bar(latent)
+ title('Eigenvalues in PCA')
+ pcc=pcc(:,isort);
+ ccac = ccac*pcc;
+ % npca = min(40, max(find(cumsum(latent)./length(latent)<0.99))+1);
+ npca = max(find(cumsum(latent)./length(latent)<0.99))+1;
+ siPCA = (EET.SAcc'*abs(pcc'))';
+ % siPCA = siPCA./(max(siPCA')'*ones(1,npT)).*(latent*ones(1,npT));
+ siPCA = siPCA./(max(siPCA')'*ones(1,npT));
+ % siPCA = sum(siPCA,1);
+ % siPCA = siPCA./max(siPCA);
+ SAcc=zeros(size(ccac,2),npT);
+ for j=1:npca, %size(ccac,2),
+ if itrans==0,
+ y0 = ccac(istable,j);
+ elseif itrans==1,
+ y0 = log_trans_(ccac(istable,j));
+ else
+ y0 = trank(ccac(istable,j));
+ end
+ if ~isempty(EET),
+ % imap=find(EET.SAvdec(j,:));
+ % [dum, isort]=sort(-EET.SAvdec(j,:));
+ imap=find(siPCA(j,:) >= (0.1.*max(siPCA(j,:))) );
+ % imap=find(EET.SAcc(j,:) >= (0.1.*max(EET.SAcc(j,:))) );
+ end
+ gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ 2, [],[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
+ % end
+ SAcc(j,imap)=gsa_(j).si;
+ imap_cc{j}=imap;
-% if opt_gsa.load_ident_files==0,
-% try
-% EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAtadj','ir_tadj','ic_tadj');
-% ny=size(EET.SAtadj,1);
-% catch
-% EET=[];
-% end
-% SAtadj=zeros(size(tadj,2),np);
-% for j=1:size(tadj,2),
-% if itrans==0,
-% y0 = tadj(istable,j);
-% elseif itrans==1,
-% y0 = log_trans_(tadj(istable,j));
-% else
-% y0 = trank(tadj(istable,j));
-% end
-% if ~isempty(EET),
-% if size(tadj,2)~=ny,
-% jj=find(EET.ir_tadj==ir_tadj(j));
-% jj=jj(find(EET.ic_tadj(jj)==ic_tadj(j)));
-% if ~isempty(jj),
-% imap=find(EET.SAtadj(jj,:) >= (0.1.*max(EET.SAtadj(jj,:))) );
-% else
-% imap=[1:np];
-% end
-% else
-% imap=find(EET.SAtadj(j,:) >= (0.1.*max(EET.SAtadj(j,:))) );
-% end
-% end
-% % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
-% % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
-% gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
-% 2, [],[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
-% end
-% SAtadj(j,imap)=gsa_(j).si;
-% imap_tadj{j}=imap;
-%
-% end
-% save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_tadj','SAtadj','tadj','ir_tadj','ic_tadj','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_tadj','SAtadj','tadj','ir_tadj','ic_tadj')
-% end
-%
-% figure,
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAtadj,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects speed of adjustment ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix]);
-% close(gcf),
-%
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects observed speed adjustment ',fsuffix]);
-% ifig=ifig+1;
-% iplo = 0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_tadj==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAtadj(iv,:),[],'.',[],10)
-% else
-% plot(SAtadj(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix,'_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['Main effects shocks speed of adjustment ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_tadj==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAtadj(iv,:),[],'.',[],10)
-% else
-% plot(SAtadj(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr,
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix,'_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-%
-% if opt_gsa.load_ident_files==0,
-% try
-% EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAIF','ir_if','ic_if');
-% catch
-% EET=[];
-% end
-% SAif=zeros(size(iff,2),np);
-% for j=1:size(iff,2),
-% if itrans==0,
-% y0 = iff(istable,j);
-% elseif itrans==1,
-% y0 = log_trans_(iff(istable,j));
-% else
-% y0 = trank(iff(istable,j));
-% end
-% if ~isempty(EET),
-% imap=find(EET.SAIF(j,:) >= (0.1.*max(EET.SAIF(j,:))) );
-% end
-% % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
-% % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
-% gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
-% 2, [],[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
-% if nfit>nest,
-% gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
-% -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
-% end
-% SAif(j,imap)=gsa_(j).si;
-% imap_if{j}=imap;
-%
-% end
-% save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_if','SAif','iff','ir_if','ic_if','-append')
-% else
-% load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_if','SAif','iff','ir_if','ic_if')
-% end
-%
-% figure,
-% % boxplot(SAif,'whis',10,'symbol','r.')
-% myboxplot(SAif,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects impact factors ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix]);
-% close(gcf),
-%
-% ifig = 0;
-% for j=1:size(options_.varobs,1)
-% if mod(j,6)==1
-% figure('name',['Main effects observed impact factors ',fsuffix]);
-% ifig=ifig+1;
-% iplo = 0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ir_if==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAif(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAif(iv,:),[],'.',[],10)
-% else
-% plot(SAif(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(options_.varobs(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==size(options_.varobs,1)
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix,'_varobs_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_varobs_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_varobs_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-%
-% ifig = 0;
-% for j=1:M_.exo_nbr,
-% if mod(j,6)==1
-% figure('name',['Main effects shocks impact factors ',fsuffix]);
-% ifig=ifig+1;
-% iplo=0;
-% end
-% iplo=iplo+1;
-% subplot(3,2,iplo)
-% iv = find(ic_if==j);
-% if ~isempty(iv)
-% if length(iv)>1
-% % boxplot(SAif(iv,:),'whis',3,'symbol','r.');
-% myboxplot(SAif(iv,:),[],'.',[],10)
-% else
-% plot(SAif(iv,:),'r.');
-% end
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% end
-% title(M_.exo_names(j,:),'interpreter','none')
-% if mod(j,6)==0 | j==M_.exo_nbr
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix,'_exo_',int2str(ifig)])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_exo_',int2str(ifig)]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_exo_',int2str(ifig)]);
-% close(gcf),
-% end
-% end
-% SAmom = [SAvdec' SAcc' SAac']';
-% SAdyn = [SAtadj' SAif']';
-% SAall = [SAmom(:,nshock+1:end)' SAdyn']';
-%
-% figure,
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAmom,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
-% set(gca,'xlim',[0.5 npT+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:npT,
-% % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects theoretical moments ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_moments',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
-% % close(gcf),
-%
-% figure,
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAdyn,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects short-long term dynamics ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_dynamics',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_dynamics',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_dynamics',fsuffix]);
-% % close(gcf),
-%
-% figure,
-% % boxplot(SAtadj,'whis',10,'symbol','r.')
-% myboxplot(SAall,[],'.',[],10)
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
-% set(gca,'xlim',[0.5 np+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:np,
-% text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Main effects all ',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ALL',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ALL',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ALL',fsuffix]);
-% % close(gcf),
+ end
+ save([OutputDirectoryName,'/map_cc',fsuffix,'.mat'],'gsa_')
+ save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'imap_cc','SAcc','ccac','-append')
+ else
+ load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_cc','SAcc','ccac')
+
+ end
+ % figure,
+ % % boxplot(SAcc,'whis',10,'symbol','r.')
+ % myboxplot(SAcc,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % ylabel(' ')
+ % title(['Main effects moments''s PCA ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_cc',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
+ % close(gcf),
-% for j=1:size(SAall,1),
-% SAallN(j,:)=SAall(j,:)./max(SAall(j,:));
-% end
-% SAmean=mean(SAallN);
-% for j=1:size(SAmom,1),
-% SAmomN(j,:)=SAmom(j,1:nshock)./max(SAmom(j,1:nshock));
-% end
-% SAmomN(find(isnan(SAmomN)))=0;
-% SAmeanexo=mean(SAmomN(:,1:nshock));
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects cross-covariances ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_cc==j);
+ % iv = [iv; find(ic_cc==j)];
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAcc(iv,:),'whis',10,'symbol','r.');
+ % myboxplot(SAcc(iv,:),[],'.',[],10)
+ % else
+ % plot(SAcc(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % set(gca,'fontsize',10)
+ % end
+ % title(options_.varobs(j,:),'interpreter','none','fontsize',10)
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_cc',fsuffix,'_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_cc',fsuffix,'_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_cc',fsuffix,'_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % if opt_gsa.load_ident_files==0,
+ % try
+ % EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAac','ir_ac','ic_ac');
+ % catch
+ % EET=[];
+ % end
+ % SAac=zeros(size(ac,2),npT);
+ % for j=1:size(ac,2),
+ % if itrans==0,
+ % y0 = ac(istable,j);
+ % elseif itrans==1,
+ % y0 = log_trans_(ac(istable,j));
+ % else
+ % y0 = trank(ac(istable,j));
+ % end
+ % if ~isempty(EET),
+ % imap=find(EET.SAac(j,:) >= (0.1.*max(EET.SAac(j,:))) );
+ % end
+ % % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
+ % % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
+ % gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ % 2, [],[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_ac',fsuffix,int2str(j)], pnames);
+ % end
+ % SAac(j,imap)=gsa_(j).si;
+ % imap_ac{j}=imap;
+ %
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_ac','SAac','ac','ir_ac','ic_ac','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_ac','SAac','ac','ir_ac','ic_ac')
+ % end
+ %
+ % figure,
+ % % boxplot(SAac,'whis',10,'symbol','r.')
+ % myboxplot(SAac,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects 1 lag auto-covariances ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ac',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix]);
+ % close(gcf),
+ %
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects auto-covariances ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo = 0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_ac==j);
+ % %iv = [iv; find(ic_ac==j)];
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAac(iv,:),'whis',10,'symbol','r.');
+ % myboxplot(SAac(iv,:),[],'.',[],10)
+ % else
+ % plot(SAac(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:npT,
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % set(gca,'fontsize',10)
+ % end
+ % title(options_.varobs(j,:),'interpreter','none','fontsize',10)
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ac',fsuffix,'_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix,'_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ac',fsuffix,'_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+
+ % x0=x0(:,nshock+1:end);
+ imap=[1:npT];
-% figure, bar(latent'*SAcc),
- hh=dyn_figure(options_.nodisplay,'Name',['Identifiability indices in the ',fsuffix,' moments.']);
- bar(sum(SAcc)),
- set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
- set(gca,'xlim',[0.5 npT+0.5])
- ydum = get(gca,'ylim');
- set(gca,'ylim',[0 ydum(2)])
- set(gca,'position',[0.13 0.2 0.775 0.7])
- for ip=1:npT,
- text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % if opt_gsa.load_ident_files==0,
+ % try
+ % EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAtadj','ir_tadj','ic_tadj');
+ % ny=size(EET.SAtadj,1);
+ % catch
+ % EET=[];
+ % end
+ % SAtadj=zeros(size(tadj,2),np);
+ % for j=1:size(tadj,2),
+ % if itrans==0,
+ % y0 = tadj(istable,j);
+ % elseif itrans==1,
+ % y0 = log_trans_(tadj(istable,j));
+ % else
+ % y0 = trank(tadj(istable,j));
+ % end
+ % if ~isempty(EET),
+ % if size(tadj,2)~=ny,
+ % jj=find(EET.ir_tadj==ir_tadj(j));
+ % jj=jj(find(EET.ic_tadj(jj)==ic_tadj(j)));
+ % if ~isempty(jj),
+ % imap=find(EET.SAtadj(jj,:) >= (0.1.*max(EET.SAtadj(jj,:))) );
+ % else
+ % imap=[1:np];
+ % end
+ % else
+ % imap=find(EET.SAtadj(j,:) >= (0.1.*max(EET.SAtadj(j,:))) );
+ % end
+ % end
+ % % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
+ % % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
+ % gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ % 2, [],[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_tadj',fsuffix,int2str(j)], pnames);
+ % end
+ % SAtadj(j,imap)=gsa_(j).si;
+ % imap_tadj{j}=imap;
+ %
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_tadj','SAtadj','tadj','ir_tadj','ic_tadj','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_tadj','SAtadj','tadj','ir_tadj','ic_tadj')
+ % end
+ %
+ % figure,
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAtadj,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects speed of adjustment ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix]);
+ % close(gcf),
+ %
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects observed speed adjustment ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo = 0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_tadj==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAtadj(iv,:),[],'.',[],10)
+ % else
+ % plot(SAtadj(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix,'_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['Main effects shocks speed of adjustment ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_tadj==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAtadj(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAtadj(iv,:),[],'.',[],10)
+ % else
+ % plot(SAtadj(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr,
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_tadj',fsuffix,'_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_tadj',fsuffix,'_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ %
+ % if opt_gsa.load_ident_files==0,
+ % try
+ % EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAIF','ir_if','ic_if');
+ % catch
+ % EET=[];
+ % end
+ % SAif=zeros(size(iff,2),np);
+ % for j=1:size(iff,2),
+ % if itrans==0,
+ % y0 = iff(istable,j);
+ % elseif itrans==1,
+ % y0 = log_trans_(iff(istable,j));
+ % else
+ % y0 = trank(iff(istable,j));
+ % end
+ % if ~isempty(EET),
+ % imap=find(EET.SAIF(j,:) >= (0.1.*max(EET.SAIF(j,:))) );
+ % end
+ % % gsa_(j) = gsa_sdp_dyn( y0, lpmat(istable,:), ...
+ % % gsa_flag, [],[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
+ % gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+ % 2, [],[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
+ % if nfit>nest,
+ % gsa_(j) = gsa_sdp(y0(1:nfit), x0(1:nfit,imap), ...
+ % -2, gsa_(j).nvr*nest^3/nfit^3,[],[],0,[OutputDirectoryName,'/map_if',fsuffix,int2str(j)], pnames);
+ % end
+ % SAif(j,imap)=gsa_(j).si;
+ % imap_if{j}=imap;
+ %
+ % end
+ % save([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_if','SAif','iff','ir_if','ic_if','-append')
+ % else
+ % load([OutputDirectoryName,'/',fname_,'_main_eff'],'imap_if','SAif','iff','ir_if','ic_if')
+ % end
+ %
+ % figure,
+ % % boxplot(SAif,'whis',10,'symbol','r.')
+ % myboxplot(SAif,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects impact factors ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix]);
+ % close(gcf),
+ %
+ % ifig = 0;
+ % for j=1:size(options_.varobs,1)
+ % if mod(j,6)==1
+ % figure('name',['Main effects observed impact factors ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo = 0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ir_if==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAif(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAif(iv,:),[],'.',[],10)
+ % else
+ % plot(SAif(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(options_.varobs(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==size(options_.varobs,1)
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix,'_varobs_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_varobs_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_varobs_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ %
+ % ifig = 0;
+ % for j=1:M_.exo_nbr,
+ % if mod(j,6)==1
+ % figure('name',['Main effects shocks impact factors ',fsuffix]);
+ % ifig=ifig+1;
+ % iplo=0;
+ % end
+ % iplo=iplo+1;
+ % subplot(3,2,iplo)
+ % iv = find(ic_if==j);
+ % if ~isempty(iv)
+ % if length(iv)>1
+ % % boxplot(SAif(iv,:),'whis',3,'symbol','r.');
+ % myboxplot(SAif(iv,:),[],'.',[],10)
+ % else
+ % plot(SAif(iv,:),'r.');
+ % end
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % end
+ % title(M_.exo_names(j,:),'interpreter','none')
+ % if mod(j,6)==0 | j==M_.exo_nbr
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_if',fsuffix,'_exo_',int2str(ifig)])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_exo_',int2str(ifig)]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_if',fsuffix,'_exo_',int2str(ifig)]);
+ % close(gcf),
+ % end
+ % end
+ % SAmom = [SAvdec' SAcc' SAac']';
+ % SAdyn = [SAtadj' SAif']';
+ % SAall = [SAmom(:,nshock+1:end)' SAdyn']';
+ %
+ % figure,
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAmom,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ % set(gca,'xlim',[0.5 npT+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:npT,
+ % % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
% text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- xlabel(' ')
- title(['Identifiability indices in the ',fsuffix,' moments.'],'interpreter','none')
- dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],1,['Identifiability indices in the ',fsuffix,' moments.'],['ident_ALL',fsuffix]',1)
+ % end
+ % xlabel(' ')
+ % title(['Main effects theoretical moments ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_moments',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_moments',fsuffix]);
+ % % close(gcf),
+ %
+ % figure,
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAdyn,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects short-long term dynamics ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_dynamics',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_dynamics',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_dynamics',fsuffix]);
+ % % close(gcf),
+ %
+ % figure,
+ % % boxplot(SAtadj,'whis',10,'symbol','r.')
+ % myboxplot(SAall,[],'.',[],10)
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
+ % set(gca,'xlim',[0.5 np+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:np,
+ % text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Main effects all ',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_map_ALL',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_map_ALL',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_map_ALL',fsuffix]);
+ % % close(gcf),
-% figure, bar(SAmeanexo),
-% set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:nshock])
-% set(gca,'xlim',[0.5 nshock+0.5])
-% ydum = get(gca,'ylim');
-% set(gca,'ylim',[0 ydum(2)])
-% set(gca,'position',[0.13 0.2 0.775 0.7])
-% for ip=1:nshock,
-% % text(ip,-0.02*(ydum(2)),deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
-% end
-% xlabel(' ')
-% title(['Identifiability indices for shocks',fsuffix],'interpreter','none')
-% saveas(gcf,[OutputDirectoryName,'/',fname_,'_ident_SHOCKS',fsuffix])
-% eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_ident_SHOCKS',fsuffix]);
-% eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_ident_SHOCKS',fsuffix]);
+ % for j=1:size(SAall,1),
+ % SAallN(j,:)=SAall(j,:)./max(SAall(j,:));
+ % end
+ % SAmean=mean(SAallN);
+ % for j=1:size(SAmom,1),
+ % SAmomN(j,:)=SAmom(j,1:nshock)./max(SAmom(j,1:nshock));
+ % end
+ % SAmomN(find(isnan(SAmomN)))=0;
+ % SAmeanexo=mean(SAmomN(:,1:nshock));
+
+ % figure, bar(latent'*SAcc),
+ hh=dyn_figure(options_.nodisplay,'Name',['Identifiability indices in the ',fsuffix,' moments.']);
+ bar(sum(SAcc)),
+ set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:npT])
+ set(gca,'xlim',[0.5 npT+0.5])
+ ydum = get(gca,'ylim');
+ set(gca,'ylim',[0 ydum(2)])
+ set(gca,'position',[0.13 0.2 0.775 0.7])
+ for ip=1:npT,
+ text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ xlabel(' ')
+ title(['Identifiability indices in the ',fsuffix,' moments.'],'interpreter','none')
+ dyn_saveas(hh,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],1,['Identifiability indices in the ',fsuffix,' moments.'],['ident_ALL',fsuffix]',1)
+
+ % figure, bar(SAmeanexo),
+ % set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:nshock])
+ % set(gca,'xlim',[0.5 nshock+0.5])
+ % ydum = get(gca,'ylim');
+ % set(gca,'ylim',[0 ydum(2)])
+ % set(gca,'position',[0.13 0.2 0.775 0.7])
+ % for ip=1:nshock,
+ % % text(ip,-0.02*(ydum(2)),deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ % end
+ % xlabel(' ')
+ % title(['Identifiability indices for shocks',fsuffix],'interpreter','none')
+ % saveas(gcf,[OutputDirectoryName,'/',fname_,'_ident_SHOCKS',fsuffix])
+ % eval(['print -depsc2 ' OutputDirectoryName '/' fname_ '_ident_SHOCKS',fsuffix]);
+ % eval(['print -dpdf ' OutputDirectoryName '/' fname_ '_ident_SHOCKS',fsuffix]);
end
return
diff --git a/matlab/gsa/mc_moments.m b/matlab/gsa/mc_moments.m
index b2ea935385a7fd79363496d8b5f6f6b9c7a15f56..ed492d7451196051e6895b3c2f6dea91297f2086 100644
--- a/matlab/gsa/mc_moments.m
+++ b/matlab/gsa/mc_moments.m
@@ -19,19 +19,19 @@ function [vdec, cc, ac] = mc_moments(mm, ss, dr)
global options_ M_ estim_params_ oo_
- [nr1, nc1, nsam] = size(mm);
- nobs=size(options_.varobs,2);
- disp('Computing theoretical moments ...')
- h = dyn_waitbar(0,'Theoretical moments ...');
- vdec = zeros(nobs,M_.exo_nbr,nsam);
- cc = zeros(nobs,nobs,nsam);
- ac = zeros(nobs,nobs*options_.ar,nsam);
-
- for j=1:nsam,
+[nr1, nc1, nsam] = size(mm);
+nobs=size(options_.varobs,2);
+disp('Computing theoretical moments ...')
+h = dyn_waitbar(0,'Theoretical moments ...');
+vdec = zeros(nobs,M_.exo_nbr,nsam);
+cc = zeros(nobs,nobs,nsam);
+ac = zeros(nobs,nobs*options_.ar,nsam);
+
+for j=1:nsam,
oo_.dr.ghx = mm(:, [1:(nc1-M_.exo_nbr)],j);
oo_.dr.ghu = mm(:, [(nc1-M_.exo_nbr+1):end], j);
if ~isempty(ss),
- set_shocks_param(ss(j,:));
+ set_shocks_param(ss(j,:));
end
[vdec(:,:,j), corr, autocorr, z, zz] = th_moments(oo_.dr,options_.varobs);
cc(:,:,j)=triu(corr);
@@ -41,7 +41,7 @@ global options_ M_ estim_params_ oo_
end
ac(:,:,j)=dum;
dyn_waitbar(j/nsam,h)
- end
- dyn_waitbar_close(h)
- skipline()
- disp('... done !')
+end
+dyn_waitbar_close(h)
+skipline()
+disp('... done !')
diff --git a/matlab/gsa/mcf_analysis.m b/matlab/gsa/mcf_analysis.m
index e15bdea5573a78ddd77747ac3256d4f1c7bcb371..2d61691851d19e59074f9ff1f68d36bb67f09d68 100644
--- a/matlab/gsa/mcf_analysis.m
+++ b/matlab/gsa/mcf_analysis.m
@@ -1,84 +1,84 @@
-function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
-%
-% Written by Marco Ratto
-% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
-%
-
-% Copyright (C) 2014 European Commission
-% Copyright (C) 2016 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-pvalue_ks = options_mcf.pvalue_ks;
-pvalue_corr = options_mcf.pvalue_corr;
-alpha2 = options_mcf.alpha2;
-param_names = options_mcf.param_names;
-
-if DynareOptions.TeX
- if ~isfield(options_mcf,'param_names_tex')
- param_names_tex = options_mcf.param_names;
- else
- param_names_tex = options_mcf.param_names_tex;
- end
-end
-amcf_name = options_mcf.amcf_name;
-amcf_title = options_mcf.amcf_title;
-beha_title = options_mcf.beha_title;
-nobeha_title = options_mcf.nobeha_title;
-title = options_mcf.title;
-fname_ = options_mcf.fname_;
-xparam1=[];
-if isfield(options_mcf,'xparam1'),
- xparam1=options_mcf.xparam1;
-end
-OutputDirectoryName = options_mcf.OutputDirectoryName;
-
-[proba, dproba] = stab_map_1(lpmat, ibeha, inobeha, [],0);
-indmcf=find(proba<pvalue_ks);
-[tmp,jtmp] = sort(proba(indmcf),2,'ascend');
-indmcf = indmcf(jtmp);
-if ~isempty(indmcf)
- skipline()
- headers=char('Parameter','d-stat','p-value');
- labels=char(param_names(indmcf,:));
- data_mat=[dproba(indmcf)' proba(indmcf)'];
- options_temp.noprint=0;
- dyntable(options_temp,['Smirnov statistics in driving ', title],headers,labels,data_mat,size(labels,2)+2,16,3);
- if DynareOptions.TeX
- labels_TeX=param_names_tex(indmcf,:);
- M_temp.dname=OutputDirectoryName ;
- M_temp.fname=fname_;
- dyn_latex_table(M_temp,options_temp,['Smirnov statistics in driving ', strrep(title,'_','\\_')],amcf_name,headers,labels_TeX,data_mat,size(labels,2)+2,16,6);
- end
-end
-
-
-if length(ibeha)>10 && length(inobeha)>10,
- indcorr1 = stab_map_2(lpmat(ibeha,:),alpha2, pvalue_corr, beha_title);
- indcorr2 = stab_map_2(lpmat(inobeha,:),alpha2, pvalue_corr, nobeha_title);
- indcorr = union(indcorr1(:), indcorr2(:));
- indcorr = indcorr(~ismember(indcorr(:),indmcf));
- indmcf = [indmcf(:); indcorr(:)];
-end
-if ~isempty(indmcf) && ~DynareOptions.nograph,
- skipline()
- xx=[];
- if ~ isempty(xparam1), xx=xparam1(indmcf); end
- scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names(indmcf,:), ...
- '.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,xx, DynareOptions, ...
- beha_title, nobeha_title)
-end
+function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
+%
+% Written by Marco Ratto
+% Joint Research Centre, The European Commission,
+% marco.ratto@ec.europa.eu
+%
+
+% Copyright (C) 2014 European Commission
+% Copyright (C) 2016 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+pvalue_ks = options_mcf.pvalue_ks;
+pvalue_corr = options_mcf.pvalue_corr;
+alpha2 = options_mcf.alpha2;
+param_names = options_mcf.param_names;
+
+if DynareOptions.TeX
+ if ~isfield(options_mcf,'param_names_tex')
+ param_names_tex = options_mcf.param_names;
+ else
+ param_names_tex = options_mcf.param_names_tex;
+ end
+end
+amcf_name = options_mcf.amcf_name;
+amcf_title = options_mcf.amcf_title;
+beha_title = options_mcf.beha_title;
+nobeha_title = options_mcf.nobeha_title;
+title = options_mcf.title;
+fname_ = options_mcf.fname_;
+xparam1=[];
+if isfield(options_mcf,'xparam1'),
+ xparam1=options_mcf.xparam1;
+end
+OutputDirectoryName = options_mcf.OutputDirectoryName;
+
+[proba, dproba] = stab_map_1(lpmat, ibeha, inobeha, [],0);
+indmcf=find(proba<pvalue_ks);
+[tmp,jtmp] = sort(proba(indmcf),2,'ascend');
+indmcf = indmcf(jtmp);
+if ~isempty(indmcf)
+ skipline()
+ headers=char('Parameter','d-stat','p-value');
+ labels=char(param_names(indmcf,:));
+ data_mat=[dproba(indmcf)' proba(indmcf)'];
+ options_temp.noprint=0;
+ dyntable(options_temp,['Smirnov statistics in driving ', title],headers,labels,data_mat,size(labels,2)+2,16,3);
+ if DynareOptions.TeX
+ labels_TeX=param_names_tex(indmcf,:);
+ M_temp.dname=OutputDirectoryName ;
+ M_temp.fname=fname_;
+ dyn_latex_table(M_temp,options_temp,['Smirnov statistics in driving ', strrep(title,'_','\\_')],amcf_name,headers,labels_TeX,data_mat,size(labels,2)+2,16,6);
+ end
+end
+
+
+if length(ibeha)>10 && length(inobeha)>10,
+ indcorr1 = stab_map_2(lpmat(ibeha,:),alpha2, pvalue_corr, beha_title);
+ indcorr2 = stab_map_2(lpmat(inobeha,:),alpha2, pvalue_corr, nobeha_title);
+ indcorr = union(indcorr1(:), indcorr2(:));
+ indcorr = indcorr(~ismember(indcorr(:),indmcf));
+ indmcf = [indmcf(:); indcorr(:)];
+end
+if ~isempty(indmcf) && ~DynareOptions.nograph,
+ skipline()
+ xx=[];
+ if ~ isempty(xparam1), xx=xparam1(indmcf); end
+ scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names(indmcf,:), ...
+ '.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,xx, DynareOptions, ...
+ beha_title, nobeha_title)
+end
diff --git a/matlab/gsa/myboxplot.m b/matlab/gsa/myboxplot.m
index b71ad169e3ac4ed3e66d90264fdfe5b7e799b437..054f66430634918c0fd5d368b7c821ac7145a862 100644
--- a/matlab/gsa/myboxplot.m
+++ b/matlab/gsa/myboxplot.m
@@ -36,10 +36,10 @@ a=1-notched;
% ## figure out how many data sets we have
if iscell(data),
- nc = length(data);
+ nc = length(data);
else
-% if isvector(data), data = data(:); end
- nc = size(data,2);
+ % if isvector(data), data = data(:); end
+ nc = size(data,2);
end
% ## compute statistics
@@ -63,60 +63,60 @@ for i=1:nc
else
col = data(:,i);
end
-% ## Skip missing data
-% % % % % % % col(isnan(col) | isna (col)) = [];
- col(isnan(col)) = [];
-
- % ## Remember the data length
- nd = length(col);
- box(i) = nd;
- if (nd > 1)
-% ## min,max and quartiles
-% s(1:5,i) = statistics(col)(1:5);
-s(1,i)=min(col);
-s(5,i)=max(col);
-s(2,i)=myprctilecol(col,25);
-s(3,i)=myprctilecol(col,50);
-s(4,i)=myprctilecol(col,75);
-
-
-
-
-
-
-
-
-% ## confidence interval for the median
- est = 1.57*(s(4,i)-s(2,i))/sqrt(nd);
- s(6,i) = max([s(3,i)-est, s(2,i)]);
- s(7,i) = min([s(3,i)+est, s(4,i)]);
-% ## whiskers out to the last point within the desired inter-quartile range
- IQR = maxwhisker*(s(4,i)-s(2,i));
- whisker_y(:,i) = [min(col(col >= s(2,i)-IQR)); s(2,i)];
- whisker_y(:,nc+i) = [max(col(col <= s(4,i)+IQR)); s(4,i)];
-% ## outliers beyond 1 and 2 inter-quartile ranges
- outliers = col((col < s(2,i)-IQR & col >= s(2,i)-2*IQR) | (col > s(4,i)+IQR & col <= s(4,i)+2*IQR));
- outliers2 = col(col < s(2,i)-2*IQR | col > s(4,i)+2*IQR);
- outliers_x = [outliers_x; i*ones(size(outliers))];
- outliers_y = [outliers_y; outliers];
- outliers2_x = [outliers2_x; i*ones(size(outliers2))];
- outliers2_y = [outliers2_y; outliers2];
- elseif (nd == 1)
-% ## all statistics collapse to the value of the point
- s(:,i) = col;
-% ## single point data sets are plotted as outliers.
- outliers_x = [outliers_x; i];
- outliers_y = [outliers_y; col];
- else
-% ## no statistics if no points
- s(:,i) = NaN;
- end
+ % ## Skip missing data
+ % % % % % % % col(isnan(col) | isna (col)) = [];
+ col(isnan(col)) = [];
+
+ % ## Remember the data length
+ nd = length(col);
+ box(i) = nd;
+ if (nd > 1)
+ % ## min,max and quartiles
+ % s(1:5,i) = statistics(col)(1:5);
+ s(1,i)=min(col);
+ s(5,i)=max(col);
+ s(2,i)=myprctilecol(col,25);
+ s(3,i)=myprctilecol(col,50);
+ s(4,i)=myprctilecol(col,75);
+
+
+
+
+
+
+
+
+ % ## confidence interval for the median
+ est = 1.57*(s(4,i)-s(2,i))/sqrt(nd);
+ s(6,i) = max([s(3,i)-est, s(2,i)]);
+ s(7,i) = min([s(3,i)+est, s(4,i)]);
+ % ## whiskers out to the last point within the desired inter-quartile range
+ IQR = maxwhisker*(s(4,i)-s(2,i));
+ whisker_y(:,i) = [min(col(col >= s(2,i)-IQR)); s(2,i)];
+ whisker_y(:,nc+i) = [max(col(col <= s(4,i)+IQR)); s(4,i)];
+ % ## outliers beyond 1 and 2 inter-quartile ranges
+ outliers = col((col < s(2,i)-IQR & col >= s(2,i)-2*IQR) | (col > s(4,i)+IQR & col <= s(4,i)+2*IQR));
+ outliers2 = col(col < s(2,i)-2*IQR | col > s(4,i)+2*IQR);
+ outliers_x = [outliers_x; i*ones(size(outliers))];
+ outliers_y = [outliers_y; outliers];
+ outliers2_x = [outliers2_x; i*ones(size(outliers2))];
+ outliers2_y = [outliers2_y; outliers2];
+ elseif (nd == 1)
+ % ## all statistics collapse to the value of the point
+ s(:,i) = col;
+ % ## single point data sets are plotted as outliers.
+ outliers_x = [outliers_x; i];
+ outliers_y = [outliers_y; col];
+ else
+ % ## no statistics if no points
+ s(:,i) = NaN;
+ end
end
% % % % if isempty(outliers2_y)
% % % % outliers2_y=
% ## Note which boxes don't have enough stats
chop = find(box <= 1);
-
+
% ## Draw a box around the quartiles, with width proportional to the number of
% ## items in the box. Draw notches if desired.
box = box*0.23/max(box);
@@ -154,25 +154,25 @@ MM=max(max(data));
if vertical
plot (quartile_x, quartile_y, 'b', ...
- whisker_x, whisker_y, 'b--', ...
- cap_x, cap_y, 'k', ...
- median_x, median_y, 'r', ...
- outliers_x, outliers_y, [symbol(1),'r'], ...
- outliers2_x, outliers2_y, [symbol(2),'r']);
- set(gca,'XTick',1:nc);
- set(gca, 'XLim', [0.5, nc+0.5]);
- set(gca, 'YLim', [mm-(MM-mm)*0.05-eps, MM+(MM-mm)*0.05+eps]);
+ whisker_x, whisker_y, 'b--', ...
+ cap_x, cap_y, 'k', ...
+ median_x, median_y, 'r', ...
+ outliers_x, outliers_y, [symbol(1),'r'], ...
+ outliers2_x, outliers2_y, [symbol(2),'r']);
+ set(gca,'XTick',1:nc);
+ set(gca, 'XLim', [0.5, nc+0.5]);
+ set(gca, 'YLim', [mm-(MM-mm)*0.05-eps, MM+(MM-mm)*0.05+eps]);
else
-% % % % % plot (quartile_y, quartile_x, "b;;",
-% % % % % whisker_y, whisker_x, "b;;",
-% % % % % cap_y, cap_x, "b;;",
-% % % % % median_y, median_x, "r;;",
-% % % % % outliers_y, outliers_x, [symbol(1),"r;;"],
-% % % % % outliers2_y, outliers2_x, [symbol(2),"r;;"]);
+ % % % % % plot (quartile_y, quartile_x, "b;;",
+ % % % % % whisker_y, whisker_x, "b;;",
+ % % % % % cap_y, cap_x, "b;;",
+ % % % % % median_y, median_x, "r;;",
+ % % % % % outliers_y, outliers_x, [symbol(1),"r;;"],
+ % % % % % outliers2_y, outliers2_x, [symbol(2),"r;;"]);
end
if nargout,
- sout=s;
+ sout=s;
end
% % % endfunction
\ No newline at end of file
diff --git a/matlab/gsa/myprctilecol.m b/matlab/gsa/myprctilecol.m
index a71d21687cb2701c6aafe907a2bf4f86ee8dd2a9..b6f8e266144996c99fec5d11cb833b7442af881a 100644
--- a/matlab/gsa/myprctilecol.m
+++ b/matlab/gsa/myprctilecol.m
@@ -27,10 +27,10 @@ xx = sort(x);
if m==1 | n==1
m = max(m,n);
- if m == 1,
- y = x*ones(length(p),1);
- return;
- end
+ if m == 1,
+ y = x*ones(length(p),1);
+ return;
+ end
n = 1;
q = 100*(0.5:m - 0.5)./m;
xx = [min(x); xx(:); max(x)];
diff --git a/matlab/gsa/pick.m b/matlab/gsa/pick.m
index 87838acc559ac61b4b8995ce944fdce87cd25ad8..9b6894c8dc684bf70e8432e7b92fbf017a6aee46 100644
--- a/matlab/gsa/pick.m
+++ b/matlab/gsa/pick.m
@@ -1,90 +1,90 @@
-function pick
-%
-% Copyright (C) 2001-2017 European Commission
-%
-% This file is part of GLUEWIN
-% GLUEWIN is a MATLAB code designed for analysing the output
-% of Monte Carlo runs when empirical observations of the model output are available
-% and implements the GSA-GLUE methodology by Ratto et al. [1], based on a combination
-% of GLUE (Generalised Likelihood Uncertainty Estimation) by K. Beven [2] and GSA
-% Global Sensitivity Analysis) [3].']
-% The program has been developed by M. Ratto, European Commission, Joint Research Centre,
-% Institute for the Protection and Security of The Citizen, Technological and Economic Risk Management,
-% Applied Statistics, as a deliverable of the IMPACT project
-% (EC Fifth Framework Programme, SCA Project, IST-1999-11313, DG-INFSO).
-%
-% The graphical layout of the code is inspired by the freeware GLUE package by K. Beven,
-% vailable at the Lancaster University web site on the page [4]:
-% http://www.es.lancs.ac.uk/hfdg/glue.html
-% to which the GLUEWIN code introduces several extensions and additional options.
-% Thanks are due to R. Girardi, A. Rossi, A. Saltelli, S. Tarantola and U. Callies for numerous
-% comments and suggestions.
-% For more information, please contact marco.ratto@ec.europa.eu
-%
-% Disclaimer: This software has been developed at the Joint Research Centre of European Commission
-% by officers in the course of their official duties. This software is not subject to copyright
-% protection and is in the public domain. It is an experimental system. The Joint Research Centre
-% of European Commission assumes no responsibility whatsoever for its use by other parties
-% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
-% characteristic. We would appreciate acknowledgement if the software is used.
-%
-% [1] Ratto, M., Tarantola, S., A. Saltelli, Sensitivity analysis in model calibration: GSA-GLUE approach.
-% 'Computer Physics Communications, 136, 2001, 212-224
-% [2] Beven K.J., Binley A., The Future of Distributed Models: Model Calibration and Uncertainty
-% 'Prediction, Hydrological Processes, 6, 279-298, 1992
-% [3] Saltelli, A., K. Chan, M. Scott, Editors, (2000), Sensitivity analysis, John Wiley & Sons
-% 'publishers, Probability and Statistics series.
-% [4] Beven K., GLUE for Windows User manual, 1998.
-
-
-
-pmenu=findobj(gcf,'type','uicontextmenu','Tag','Run viewer');
-button1=findobj(gcf,'type','uimenu','Tag','save params');
-button2=findobj(gcf,'type','uimenu','Tag','eval params');
-%button=get(pmenu,'children');
-gg=gco;
-ax0=gca;
-set(gg,'buttondownfcn',[]);
-c=get(gca,'currentpoint');
-x=c(1,1);
-y=c(1,2);
-X=get(gco,'xdata');
-Y=get(gco,'ydata');
-dx=get(gca,'xlim');
-dy=get(gca,'ylim');
-pos=get(gca,'position');
-scalex=dx(2)-dx(1);
-scaley=dy(2)-dy(1);
-if length(X)>1,
- K = dsearchn([(Y./scaley)' (X./scalex)'],[y/scaley x/scalex]);
-else
- az=get(gca,'children');
- T =get(az(end),'ydata');
- [dum K]=max(T);
-end
-
-KK=K;
-
-set(button1,'Label',['Save ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''save'')']);
-set(button2,'Label',['Eval ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''eval'')']);
-hh=findobj(gcf,'type','axes','Tag','scatter');
-for k=1:length(hh),
- axes(hh(k));
- dum=get(gca,'children');
- dumx=get(dum(end),'xdata');
- dumy=get(dum(end),'ydata');
- xmid=min(dumx) + 0.5*(max(dumx)-min(dumx));
- hold on
- plot(dumx(KK),dumy(KK),'or');
- if dumx(KK) < xmid,
- text(dumx(KK),dumy(KK),[' ',num2str(K)], ...
- 'FontWeight','Bold',...
- 'Color','r');
- else
- text(dumx(KK),dumy(KK),[num2str(K),' '], ...
- 'HorizontalAlignment','right', ...
- 'FontWeight','Bold',...
- 'Color','r');
- end
- hold off
+function pick
+%
+% Copyright (C) 2001-2017 European Commission
+%
+% This file is part of GLUEWIN
+% GLUEWIN is a MATLAB code designed for analysing the output
+% of Monte Carlo runs when empirical observations of the model output are available
+% and implements the GSA-GLUE methodology by Ratto et al. [1], based on a combination
+% of GLUE (Generalised Likelihood Uncertainty Estimation) by K. Beven [2] and GSA
+% Global Sensitivity Analysis) [3].']
+% The program has been developed by M. Ratto, European Commission, Joint Research Centre,
+% Institute for the Protection and Security of The Citizen, Technological and Economic Risk Management,
+% Applied Statistics, as a deliverable of the IMPACT project
+% (EC Fifth Framework Programme, SCA Project, IST-1999-11313, DG-INFSO).
+%
+% The graphical layout of the code is inspired by the freeware GLUE package by K. Beven,
+% vailable at the Lancaster University web site on the page [4]:
+% http://www.es.lancs.ac.uk/hfdg/glue.html
+% to which the GLUEWIN code introduces several extensions and additional options.
+% Thanks are due to R. Girardi, A. Rossi, A. Saltelli, S. Tarantola and U. Callies for numerous
+% comments and suggestions.
+% For more information, please contact marco.ratto@ec.europa.eu
+%
+% Disclaimer: This software has been developed at the Joint Research Centre of European Commission
+% by officers in the course of their official duties. This software is not subject to copyright
+% protection and is in the public domain. It is an experimental system. The Joint Research Centre
+% of European Commission assumes no responsibility whatsoever for its use by other parties
+% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
+% characteristic. We would appreciate acknowledgement if the software is used.
+%
+% [1] Ratto, M., Tarantola, S., A. Saltelli, Sensitivity analysis in model calibration: GSA-GLUE approach.
+% 'Computer Physics Communications, 136, 2001, 212-224
+% [2] Beven K.J., Binley A., The Future of Distributed Models: Model Calibration and Uncertainty
+% 'Prediction, Hydrological Processes, 6, 279-298, 1992
+% [3] Saltelli, A., K. Chan, M. Scott, Editors, (2000), Sensitivity analysis, John Wiley & Sons
+% 'publishers, Probability and Statistics series.
+% [4] Beven K., GLUE for Windows User manual, 1998.
+
+
+
+pmenu=findobj(gcf,'type','uicontextmenu','Tag','Run viewer');
+button1=findobj(gcf,'type','uimenu','Tag','save params');
+button2=findobj(gcf,'type','uimenu','Tag','eval params');
+%button=get(pmenu,'children');
+gg=gco;
+ax0=gca;
+set(gg,'buttondownfcn',[]);
+c=get(gca,'currentpoint');
+x=c(1,1);
+y=c(1,2);
+X=get(gco,'xdata');
+Y=get(gco,'ydata');
+dx=get(gca,'xlim');
+dy=get(gca,'ylim');
+pos=get(gca,'position');
+scalex=dx(2)-dx(1);
+scaley=dy(2)-dy(1);
+if length(X)>1,
+ K = dsearchn([(Y./scaley)' (X./scalex)'],[y/scaley x/scalex]);
+else
+ az=get(gca,'children');
+ T =get(az(end),'ydata');
+ [dum K]=max(T);
+end
+
+KK=K;
+
+set(button1,'Label',['Save ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''save'')']);
+set(button2,'Label',['Eval ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''eval'')']);
+hh=findobj(gcf,'type','axes','Tag','scatter');
+for k=1:length(hh),
+ axes(hh(k));
+ dum=get(gca,'children');
+ dumx=get(dum(end),'xdata');
+ dumy=get(dum(end),'ydata');
+ xmid=min(dumx) + 0.5*(max(dumx)-min(dumx));
+ hold on
+ plot(dumx(KK),dumy(KK),'or');
+ if dumx(KK) < xmid,
+ text(dumx(KK),dumy(KK),[' ',num2str(K)], ...
+ 'FontWeight','Bold',...
+ 'Color','r');
+ else
+ text(dumx(KK),dumy(KK),[num2str(K),' '], ...
+ 'HorizontalAlignment','right', ...
+ 'FontWeight','Bold',...
+ 'Color','r');
+ end
+ hold off
end
\ No newline at end of file
diff --git a/matlab/gsa/prior_draw_gsa.m b/matlab/gsa/prior_draw_gsa.m
index a54fda1b3b6d8b6d395caf0285e546e994f560fa..cdb795f0a8f82a6a0f4982586cfe16c010190234 100644
--- a/matlab/gsa/prior_draw_gsa.m
+++ b/matlab/gsa/prior_draw_gsa.m
@@ -38,7 +38,7 @@ function pdraw = prior_draw_gsa(init,rdraw)
global bayestopt_ options_ estim_params_ M_
persistent npar pshape p6 p7 p3 p4 lbcum ubcum
-
+
if init
pshape = bayestopt_.pshape;
p6 = bayestopt_.p6;
@@ -56,40 +56,40 @@ if init
bounds.lb = max(bounds.lb,lb);
bounds.ub = min(bounds.ub,ub);
else % estimated parameters but no declared priors
- % No priors are declared so Dynare will estimate the model by
- % maximum likelihood with inequality constraints for the parameters.
+ % No priors are declared so Dynare will estimate the model by
+ % maximum likelihood with inequality constraints for the parameters.
bounds.lb = lb;
bounds.ub = ub;
end
% set bounds for cumulative probabilities
for i = 1:npar
- switch pshape(i)
- case 5% Uniform prior.
- p4(i) = min(p4(i),bounds.ub(i));
- p3(i) = max(p3(i),bounds.lb(i));
- case 3% Gaussian prior.
- lbcum(i) = 0.5 * erfc(-(bounds.lb(i)-p6(i))/p7(i) ./ sqrt(2));
- ubcum(i) = 0.5 * erfc(-(bounds.ub(i)-p6(i))/p7(i) ./ sqrt(2));
- case 2% Gamma prior.
- lbcum(i) = gamcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
- ubcum(i) = gamcdf(bounds.ub(i)-p3(i),p6(i),p7(i));
- case 1% Beta distribution (TODO: generalized beta distribution)
- lbcum(i) = betainc((bounds.lb(i)-p3(i))./(p4(i)-p3(i)),p6(i),p7(i));
- ubcum(i) = betainc((bounds.ub(i)-p3(i))./(p4(i)-p3(i)),p6(i),p7(i));
- case 4% INV-GAMMA1 distribution
- % TO BE CHECKED
- lbcum(i) = gamcdf(1/(bounds.ub(i)-p3(i))^2,p7(i)/2,2/p6(i));
- ubcum(i) = gamcdf(1/(bounds.lb(i)-p3(i))^2,p7(i)/2,2/p6(i));
- case 6% INV-GAMMA2 distribution
- % TO BE CHECKED
- lbcum(i) = gamcdf(1/(bounds.ub(i)-p3(i)),p7(i)/2,2/p6(i));
- ubcum(i) = gamcdf(1/(bounds.lb(i)-p3(i)),p7(i)/2,2/p6(i));
- case 8
- lbcum(i) = weibcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
- ubcum(i) = weibcdf(bounds.ub(i)-p3(i),p6(i),p7(i));
- otherwise
- % Nothing to do here.
- end
+ switch pshape(i)
+ case 5% Uniform prior.
+ p4(i) = min(p4(i),bounds.ub(i));
+ p3(i) = max(p3(i),bounds.lb(i));
+ case 3% Gaussian prior.
+ lbcum(i) = 0.5 * erfc(-(bounds.lb(i)-p6(i))/p7(i) ./ sqrt(2));
+ ubcum(i) = 0.5 * erfc(-(bounds.ub(i)-p6(i))/p7(i) ./ sqrt(2));
+ case 2% Gamma prior.
+ lbcum(i) = gamcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
+ ubcum(i) = gamcdf(bounds.ub(i)-p3(i),p6(i),p7(i));
+ case 1% Beta distribution (TODO: generalized beta distribution)
+ lbcum(i) = betainc((bounds.lb(i)-p3(i))./(p4(i)-p3(i)),p6(i),p7(i));
+ ubcum(i) = betainc((bounds.ub(i)-p3(i))./(p4(i)-p3(i)),p6(i),p7(i));
+ case 4% INV-GAMMA1 distribution
+ % TO BE CHECKED
+ lbcum(i) = gamcdf(1/(bounds.ub(i)-p3(i))^2,p7(i)/2,2/p6(i));
+ ubcum(i) = gamcdf(1/(bounds.lb(i)-p3(i))^2,p7(i)/2,2/p6(i));
+ case 6% INV-GAMMA2 distribution
+ % TO BE CHECKED
+ lbcum(i) = gamcdf(1/(bounds.ub(i)-p3(i)),p7(i)/2,2/p6(i));
+ ubcum(i) = gamcdf(1/(bounds.lb(i)-p3(i)),p7(i)/2,2/p6(i));
+ case 8
+ lbcum(i) = weibcdf(bounds.lb(i)-p3(i),p6(i),p7(i));
+ ubcum(i) = weibcdf(bounds.ub(i)-p3(i),p6(i),p7(i));
+ otherwise
+ % Nothing to do here.
+ end
end
return
end
@@ -107,10 +107,10 @@ for i = 1:npar
case 1% Beta distribution (TODO: generalized beta distribution)
pdraw(:,i) = betainv(rdraw(:,i),p6(i),p7(i))*(p4(i)-p3(i))+p3(i);
case 4% INV-GAMMA1 distribution
- % TO BE CHECKED
+ % TO BE CHECKED
pdraw(:,i) = sqrt(1./gaminv(rdraw(:,i),p7(i)/2,2/p6(i)))+p3(i);
case 6% INV-GAMMA2 distribution
- % TO BE CHECKED
+ % TO BE CHECKED
pdraw(:,i) = 1./gaminv(rdraw(:,i),p7(i)/2,2/p6(i))+p3(i);
case 8
pdraw(:,i) = wblinv(rdraw(:,i),p6(i),p7(i))+p3(i);
@@ -119,4 +119,4 @@ for i = 1:npar
end
end
-
+
diff --git a/matlab/gsa/read_data.m b/matlab/gsa/read_data.m
index 85674bc5e28c88e749efc04a00cc59ebf71dc90a..bbb2d80a7a9645cd2aa25852965fb8218a90c92f 100644
--- a/matlab/gsa/read_data.m
+++ b/matlab/gsa/read_data.m
@@ -30,15 +30,15 @@ gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
if options_.loglinear == 1 & ~options_.logdata
- rawdata = log(rawdata);
+ rawdata = log(rawdata);
end
if options_.prefilter == 1
- data = transpose(rawdata-ones(gend,1)* mean(rawdata,1));
+ data = transpose(rawdata-ones(gend,1)* mean(rawdata,1));
else
- data = transpose(rawdata);
+ data = transpose(rawdata);
end
if ~isreal(rawdata)
- error(['There are complex values in the data. Probably a wrong' ...
- ' transformation'])
+ error(['There are complex values in the data. Probably a wrong' ...
+ ' transformation'])
end
diff --git a/matlab/gsa/redform_map.m b/matlab/gsa/redform_map.m
index c7b2573254a35b6d41a4d1aec329dfe5400932d6..5f2c712920e521467a791408b60847bbacb2e20b 100644
--- a/matlab/gsa/redform_map.m
+++ b/matlab/gsa/redform_map.m
@@ -429,7 +429,7 @@ end
if isempty(threshold) && ~options_.nograph,
if ilog==0,
hfig=dyn_figure(options_.nodisplay,'name','Reduced Form GSA'); %bar(si)
- % boxplot(si','whis',10,'symbol','r.')
+ % boxplot(si','whis',10,'symbol','r.')
myboxplot(si',[],'.',[],10)
xlabel(' ')
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
@@ -445,7 +445,7 @@ if isempty(threshold) && ~options_.nograph,
else
hfig=dyn_figure(options_.nodisplay,'name','Reduced Form GSA'); %bar(silog)
- % boxplot(silog','whis',10,'symbol','r.')
+ % boxplot(silog','whis',10,'symbol','r.')
myboxplot(silog',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',[1:np])
xlabel(' ')
@@ -494,15 +494,15 @@ if iload==0,
nest=min(250,nest);
nfit=min(1000,nrun);
% dotheplots = (nfit<=nest);
-% gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
+ % gsa_ = gsa_sdp(y0(1:nest), x0(1:nest,:), 2, [],[-1 -1 -1 -1 -1 0],[],0,[fname,'_est'], pnames);
[ys,is] = sort(y0);
istep = ceil(nrun/nest);
if istep>1,
- iest = is(floor(istep/2):istep:end);
- nest = length(iest);
- irest = is(setdiff([1:nrun],[floor(istep/2):istep:nrun]));
- istep = ceil(length(irest)/(nfit-nest));
- ifit = union(iest, irest(1:istep:end));
+ iest = is(floor(istep/2):istep:end);
+ nest = length(iest);
+ irest = is(setdiff([1:nrun],[floor(istep/2):istep:nrun]));
+ istep = ceil(length(irest)/(nfit-nest));
+ ifit = union(iest, irest(1:istep:end));
else
warning('the number of samples is too small for ANOVA estimation')
si=nan(np,1);
@@ -512,9 +512,9 @@ if iload==0,
ifit = union(ifit, irest(end));
end
nfit=length(ifit);
-% ifit = union(iest, irest(randperm(nrun-nest,nfit-nest)));
-% ifit = iest;
-% nfit=nest;
+ % ifit = union(iest, irest(randperm(nrun-nest,nfit-nest)));
+ % ifit = iest;
+ % nfit=nest;
ipred = setdiff([1:nrun],ifit);
if ilog,
@@ -544,61 +544,61 @@ if iload==0,
if ilog,
[gsa22, gsa1, gsax] = ss_anova_log(y1(iest), x0(iest,:), isig, lam, gsa0);
end
-% if (gsa1.out.bic-gsa0.out.bic) < 10,
-% y00=y0;
-% gsa00=gsa0;
-% gsa0=gsa1;
-% y0=y1;
-% ilog=1;
-% end
-if nfit>nest,
- % gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
- nvr = gsa0.nvr*nest^3/nfit^3;
- nvr(gsa0.stat<2) = gsa0.nvr(gsa0.stat<2)*nest^5/nfit^5;
- gsa_ = ss_anova(y0(ifit), x0(ifit,:), 1, 0, 2, nvr);
- if ilog
- gsa0 = gsa_;
- nvr1 = gsa1.nvr*nest^3/nfit^3;
- nvr1(gsa1.stat<2) = gsa1.nvr(gsa1.stat<2)*nest^5/nfit^5;
- nvrx = gsax.nvr*nest^3/nfit^3;
- nvrx(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
- [gsa22, gsa1, gsax] = ss_anova_log(y1(ifit), x0(ifit,:), isig, lam, gsa0, [nvr1' nvrx']);
-% gsa1 = ss_anova(y1(ifit), x0(ifit,:), 1, 0, 2, nvr);
-% gsa2=gsa1;
-% gsa2.y = gsa0.y;
-% gsa2.fit = (exp(gsa1.fit)-lam)*isig;
-% gsa2.f0 = mean(gsa2.fit);
-% gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
-% gsa2.out.bic = gsa2.out.bic-nest*log(gsa1.out.SSE)+nest*log(gsa2.out.SSE);
-% gsa2.r2 = 1-cov(gsa2.fit-gsa2.y)/cov(gsa2.y);
-% for j=1:np,
-% gsa2.fs(:,j) = exp(gsa1.fs(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
-% gsa2.f(:,j) = exp(gsa1.f(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
-% gsa2.si(j) = var(gsa2.f(:,j))/var(gsa2.y);
-% end
-% nvr = gsax.nvr*nest^3/nfit^3;
-% nvr(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
-% gsax = ss_anova([gsa2.y-gsa2.fit], x0(ifit,:), 1, 0, 2, nvr);
-% gsa22=gsa2;
-% gsa22.fit = gsa2.fit+gsax.fit;
-% gsa22.f0 = mean(gsa22.fit);
-% gsa22.out.SSE = sum((gsa22.fit-gsa22.y).^2);
-% gsa22.out.bic = nest*log(gsa22.out.SSE/nest) + (gsax.out.df+gsa2.out.df-1)*log(nest);
-% gsa22.r2 = 1-sum((gsa22.fit-gsa22.y).^2)/sum((gsa22.y-mean(gsa22.y)).^2);
-% for j=1:np,
-% gsa22.fs(:,j) = gsa2.fs(:,j)+gsax.fs(:,j);
-% gsa22.f(:,j) = gsa2.f(:,j)+gsax.f(:,j);
-% gsa22.si(j) = var(gsa22.f(:,j))/var(gsa22.y);
-% end
- gsa_ = gsa22;
- end
-else
- if ilog
- gsa_ = gsa22;
+ % if (gsa1.out.bic-gsa0.out.bic) < 10,
+ % y00=y0;
+ % gsa00=gsa0;
+ % gsa0=gsa1;
+ % y0=y1;
+ % ilog=1;
+ % end
+ if nfit>nest,
+ % gsa_ = gsa_sdp(y0(1:nfit), x0(1:nfit,:), -2, gsa_.nvr*nest^3/nfit^3,[-1 -1 -1 -1 -1 0],[],0,fname, pnames);
+ nvr = gsa0.nvr*nest^3/nfit^3;
+ nvr(gsa0.stat<2) = gsa0.nvr(gsa0.stat<2)*nest^5/nfit^5;
+ gsa_ = ss_anova(y0(ifit), x0(ifit,:), 1, 0, 2, nvr);
+ if ilog
+ gsa0 = gsa_;
+ nvr1 = gsa1.nvr*nest^3/nfit^3;
+ nvr1(gsa1.stat<2) = gsa1.nvr(gsa1.stat<2)*nest^5/nfit^5;
+ nvrx = gsax.nvr*nest^3/nfit^3;
+ nvrx(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
+ [gsa22, gsa1, gsax] = ss_anova_log(y1(ifit), x0(ifit,:), isig, lam, gsa0, [nvr1' nvrx']);
+ % gsa1 = ss_anova(y1(ifit), x0(ifit,:), 1, 0, 2, nvr);
+ % gsa2=gsa1;
+ % gsa2.y = gsa0.y;
+ % gsa2.fit = (exp(gsa1.fit)-lam)*isig;
+ % gsa2.f0 = mean(gsa2.fit);
+ % gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
+ % gsa2.out.bic = gsa2.out.bic-nest*log(gsa1.out.SSE)+nest*log(gsa2.out.SSE);
+ % gsa2.r2 = 1-cov(gsa2.fit-gsa2.y)/cov(gsa2.y);
+ % for j=1:np,
+ % gsa2.fs(:,j) = exp(gsa1.fs(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
+ % gsa2.f(:,j) = exp(gsa1.f(:,j)).*mean(exp(gsa1.fit-gsa1.f(:,j)))*isig-lam*isig-gsa2.f0;
+ % gsa2.si(j) = var(gsa2.f(:,j))/var(gsa2.y);
+ % end
+ % nvr = gsax.nvr*nest^3/nfit^3;
+ % nvr(gsax.stat<2) = gsax.nvr(gsax.stat<2)*nest^5/nfit^5;
+ % gsax = ss_anova([gsa2.y-gsa2.fit], x0(ifit,:), 1, 0, 2, nvr);
+ % gsa22=gsa2;
+ % gsa22.fit = gsa2.fit+gsax.fit;
+ % gsa22.f0 = mean(gsa22.fit);
+ % gsa22.out.SSE = sum((gsa22.fit-gsa22.y).^2);
+ % gsa22.out.bic = nest*log(gsa22.out.SSE/nest) + (gsax.out.df+gsa2.out.df-1)*log(nest);
+ % gsa22.r2 = 1-sum((gsa22.fit-gsa22.y).^2)/sum((gsa22.y-mean(gsa22.y)).^2);
+ % for j=1:np,
+ % gsa22.fs(:,j) = gsa2.fs(:,j)+gsax.fs(:,j);
+ % gsa22.f(:,j) = gsa2.f(:,j)+gsax.f(:,j);
+ % gsa22.si(j) = var(gsa22.f(:,j))/var(gsa22.y);
+ % end
+ gsa_ = gsa22;
+ end
else
- gsa_ = gsa0;
+ if ilog
+ gsa_ = gsa22;
+ else
+ gsa_ = gsa0;
+ end
end
-end
save([fname,'_map.mat'],'gsa_')
[sidum, iii]=sort(-gsa_.si);
gsa_.x0=x00(ifit,:);
@@ -613,13 +613,13 @@ end
subplot(223),
plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
r2 = gsa_.r2;
-% if ilog,
-% plot(y00(ifit),[log_trans_(gsa_.fit,'',isig,lam) y00(ifit)],'.'),
-% r2 = 1 - cov(log_trans_(gsa_.fit,'',isig,lam)-y00(ifit))/cov(y00(ifit));
-% else
-% plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
-% r2 = gsa_.r2;
-% end
+ % if ilog,
+ % plot(y00(ifit),[log_trans_(gsa_.fit,'',isig,lam) y00(ifit)],'.'),
+ % r2 = 1 - cov(log_trans_(gsa_.fit,'',isig,lam)-y00(ifit))/cov(y00(ifit));
+ % else
+ % plot(y0(ifit),[gsa_.fit y0(ifit)],'.'),
+ % r2 = gsa_.r2;
+ % end
title(['Learning sample fit - R2=' num2str(r2,2)],'interpreter','none')
if nfit<nrun,
if ilog,
@@ -643,7 +643,7 @@ end
end
else
% gsa_ = gsa_sdp_dyn(y0, x0, 0, [],[],[],0,fname, pnames);
-% gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
+ % gsa_ = gsa_sdp(y0, x0, 0, [],[],[],0,fname, pnames);
load([fname,'_map.mat'],'gsa_')
if ~options_.nograph,
yf = ss_anova_fcast(x0, gsa_);
@@ -727,7 +727,7 @@ function indmcf = redform_mcf(y0, x0, options_mcf, options_)
hfig=dyn_figure(options_.nodisplay,'name',options_mcf.amcf_title);
[post_mean, post_median, post_var, hpd_interval, post_deciles, ...
- density] = posterior_moments(y0,1,0.9);
+ density] = posterior_moments(y0,1,0.9);
post_deciles = [-inf; post_deciles; inf];
for jt=1:10,
@@ -763,16 +763,16 @@ aa(2)=0.02;
set(hleg,'Position',aa);
if ~isoctave
annotation('textbox', [0.25,0.01,0.5,0.05], ...
- 'String', options_mcf.title, ...
- 'Color','black',...
- 'FontWeight','bold',...
- 'interpreter','none',...
- 'horizontalalignment','center');
+ 'String', options_mcf.title, ...
+ 'Color','black',...
+ 'FontWeight','bold',...
+ 'interpreter','none',...
+ 'horizontalalignment','center');
end
dyn_saveas(hfig,[options_mcf.OutputDirectoryName filesep options_mcf.fname_,'_',options_mcf.amcf_name],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[options_mcf.OutputDirectoryName filesep options_mcf.fname_,'_',options_mcf.amcf_name],strrep(options_mcf.amcf_title,'_','\_'),[options_mcf.fname_,'_',options_mcf.amcf_name])
-
+
return
function []=create_TeX_loader(options_,figpath,caption,label_name,scale_factor)
diff --git a/matlab/gsa/redform_screen.m b/matlab/gsa/redform_screen.m
index 311c6b520b41a7a7adfab437403db882945cf4c4..fd09d9cace6032ef043da72f7b53a19a0bb1f636 100644
--- a/matlab/gsa/redform_screen.m
+++ b/matlab/gsa/redform_screen.m
@@ -40,7 +40,7 @@ nliv = options_gsa_.morris_nliv;
pnames = M_.param_names(estim_params_.param_vals(:,1),:);
if nargin==0,
- dirname='';
+ dirname='';
end
load([dirname,'/',M_.fname,'_prior'],'lpmat','lpmat0','istable','T');
@@ -54,92 +54,92 @@ nsok = length(find(M_.lead_lag_incidence(M_.maximum_lag,:)));
js=0;
for j=1:size(anamendo,1),
- namendo=deblank(anamendo(j,:));
- iendo=strmatch(namendo,M_.endo_names(oo_.dr.order_var,:),'exact');
-
- iplo=0;
- ifig=0;
- for jx=1:size(anamexo,1)
- namexo=deblank(anamexo(jx,:));
- iexo=strmatch(namexo,M_.exo_names,'exact');
-
- if ~isempty(iexo),
- y0=teff(T(iendo,iexo+nspred,:),kn,istable);
- if ~isempty(y0),
- if mod(iplo,9)==0,
- ifig=ifig+1;
- hh=dyn_figure(options_.nodisplay,'name',[namendo,' vs. shocks ',int2str(ifig)]);
- iplo=0;
+ namendo=deblank(anamendo(j,:));
+ iendo=strmatch(namendo,M_.endo_names(oo_.dr.order_var,:),'exact');
+
+ iplo=0;
+ ifig=0;
+ for jx=1:size(anamexo,1)
+ namexo=deblank(anamexo(jx,:));
+ iexo=strmatch(namexo,M_.exo_names,'exact');
+
+ if ~isempty(iexo),
+ y0=teff(T(iendo,iexo+nspred,:),kn,istable);
+ if ~isempty(y0),
+ if mod(iplo,9)==0,
+ ifig=ifig+1;
+ hh=dyn_figure(options_.nodisplay,'name',[namendo,' vs. shocks ',int2str(ifig)]);
+ iplo=0;
+ end
+ iplo=iplo+1;
+ js=js+1;
+ subplot(3,3,iplo),
+ [SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
+ SAM = squeeze(SAMorris(nshock+1:end,1));
+ SA(:,js)=SAM./(max(SAM)+eps);
+ [saso, iso] = sort(-SA(:,js));
+ bar(SA(iso(1:min(np,10)),js))
+ %set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
+ set(gca,'xticklabel',' ','fontsize',10)
+ set(gca,'xlim',[0.5 10.5])
+ for ip=1:min(np,10),
+ text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+ title([namendo,' vs. ',namexo],'interpreter','none')
+ if iplo==9,
+ dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],ifig,[namendo,' vs. shocks ',int2str(ifig)],[namendo,'_vs_shock'],1)
+ end
+
+ end
end
- iplo=iplo+1;
- js=js+1;
- subplot(3,3,iplo),
- [SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
- SAM = squeeze(SAMorris(nshock+1:end,1));
- SA(:,js)=SAM./(max(SAM)+eps);
- [saso, iso] = sort(-SA(:,js));
- bar(SA(iso(1:min(np,10)),js))
- %set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
- set(gca,'xticklabel',' ','fontsize',10)
- set(gca,'xlim',[0.5 10.5])
- for ip=1:min(np,10),
- text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
- title([namendo,' vs. ',namexo],'interpreter','none')
- if iplo==9,
- dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],ifig,[namendo,' vs. shocks ',int2str(ifig)],[namendo,'_vs_shock'],1)
- end
-
- end
end
- end
- if iplo<9 && iplo>0 && ifig,
- dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],ifig,[namendo,' vs. shocks ',int2str(ifig)],[namendo,'_vs_shock'],options_.figures.textwidth*min(iplo/3))
- end
-
- iplo=0;
- ifig=0;
- for je=1:size(anamlagendo,1)
- namlagendo=deblank(anamlagendo(je,:));
- ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(M_.nstatic+1:M_.nstatic+nsok),:),'exact');
-
- if ~isempty(ilagendo),
- y0=teff(T(iendo,ilagendo,:),kn,istable);
- if ~isempty(y0),
- if mod(iplo,9)==0,
- ifig=ifig+1;
- hh=dyn_figure(options_.nodisplay,'name',[namendo,' vs. lagged endogenous ',int2str(ifig)]);
- iplo=0;
- end
- iplo=iplo+1;
- js=js+1;
- subplot(3,3,iplo),
- [SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
- SAM = squeeze(SAMorris(nshock+1:end,1));
- SA(:,js)=SAM./(max(SAM)+eps);
- [saso, iso] = sort(-SA(:,js));
- bar(SA(iso(1:min(np,10)),js))
- %set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
- set(gca,'xticklabel',' ','fontsize',10)
- set(gca,'xlim',[0.5 10.5])
- for ip=1:min(np,10),
- text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
- end
+ if iplo<9 && iplo>0 && ifig,
+ dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_shocks_',num2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_shock_',num2str(ifig)],ifig,[namendo,' vs. shocks ',int2str(ifig)],[namendo,'_vs_shock'],options_.figures.textwidth*min(iplo/3))
+ end
- title([namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
- if iplo==9,
- dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],ifig,[namendo,' vs. lagged endogenous ',int2str(ifig)],[namendo,'_vs_lags'],1)
+ iplo=0;
+ ifig=0;
+ for je=1:size(anamlagendo,1)
+ namlagendo=deblank(anamlagendo(je,:));
+ ilagendo=strmatch(namlagendo,M_.endo_names(oo_.dr.order_var(M_.nstatic+1:M_.nstatic+nsok),:),'exact');
+
+ if ~isempty(ilagendo),
+ y0=teff(T(iendo,ilagendo,:),kn,istable);
+ if ~isempty(y0),
+ if mod(iplo,9)==0,
+ ifig=ifig+1;
+ hh=dyn_figure(options_.nodisplay,'name',[namendo,' vs. lagged endogenous ',int2str(ifig)]);
+ iplo=0;
+ end
+ iplo=iplo+1;
+ js=js+1;
+ subplot(3,3,iplo),
+ [SAmeas, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
+ SAM = squeeze(SAMorris(nshock+1:end,1));
+ SA(:,js)=SAM./(max(SAM)+eps);
+ [saso, iso] = sort(-SA(:,js));
+ bar(SA(iso(1:min(np,10)),js))
+ %set(gca,'xticklabel',pnames(iso(1:min(np,10)),:),'fontsize',8)
+ set(gca,'xticklabel',' ','fontsize',10)
+ set(gca,'xlim',[0.5 10.5])
+ for ip=1:min(np,10),
+ text(ip,-0.02,deblank(pnames(iso(ip),:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ end
+
+ title([namendo,' vs. ',namlagendo,'(-1)'],'interpreter','none')
+ if iplo==9,
+ dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],ifig,[namendo,' vs. lagged endogenous ',int2str(ifig)],[namendo,'_vs_lags'],1)
+ end
+ end
end
- end
end
- end
- if iplo<9 && iplo>0 && ifig,
- dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],options_.nodisplay,options_.graph_format);
- create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],ifig,[namendo,' vs. lagged endogenous ',int2str(ifig)],[namendo,'_vs_lags'],options_.figures.textwidth*min(iplo/3))
- end
+ if iplo<9 && iplo>0 && ifig,
+ dyn_saveas(hh,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],options_.nodisplay,options_.graph_format);
+ create_TeX_loader(options_,[dirname,'/',M_.fname,'_', namendo,'_vs_lags_',num2str(ifig)],ifig,[namendo,' vs. lagged endogenous ',int2str(ifig)],[namendo,'_vs_lags'],options_.figures.textwidth*min(iplo/3))
+ end
end
hh=dyn_figure(options_.nodisplay,'Name','Reduced form screening');
@@ -151,7 +151,7 @@ set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:np,
- text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
+ text(ip,-0.02,deblank(pnames(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
xlabel(' ')
ylabel('Elementary Effects')
diff --git a/matlab/gsa/scatter_callback.m b/matlab/gsa/scatter_callback.m
index 84b7d9ec0636ef8013f65106c9ebadbd2246e0a1..afba86225b18a2a1b48459c5ee36bd44f46549b1 100644
--- a/matlab/gsa/scatter_callback.m
+++ b/matlab/gsa/scatter_callback.m
@@ -32,11 +32,11 @@ x=x{2};
xparam1=x(K,:)';
switch type
- case 'save'
- save(['my_params_' int2str(K)],'xparam1')
-
- case 'eval'
- disp('Evaluating smoother ...')
- [oo_, M_]=evaluate_smoother(xparam1,M_.endo_names,M_,oo_,options_,bayestopt_,estim_params_);
- % [rmse, lnam, r2,vv] = plot_fit(obsname{:});
+ case 'save'
+ save(['my_params_' int2str(K)],'xparam1')
+
+ case 'eval'
+ disp('Evaluating smoother ...')
+ [oo_, M_]=evaluate_smoother(xparam1,M_.endo_names,M_,oo_,options_,bayestopt_,estim_params_);
+ % [rmse, lnam, r2,vv] = plot_fit(obsname{:});
end
diff --git a/matlab/gsa/scatter_mcf.m b/matlab/gsa/scatter_mcf.m
index fa422937507c17d58006546fa5aedfca9a862cc7..3718b23ed0d022e351ee195724a94b3cb0459ef1 100644
--- a/matlab/gsa/scatter_mcf.m
+++ b/matlab/gsa/scatter_mcf.m
@@ -1,183 +1,183 @@
-function scatter_mcf(X,Y,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, DynareOptions, beha_name, non_beha_name)
-%
-% Written by Marco Ratto
-% Joint Research Centre, The European Commission,
-% marco.ratto@ec.europa.eu
-
-% Copyright (C) 2014-2016 European Commission
-% Copyright (C) 2014-2017 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-% PURPOSE: Pairwise scatter plots of the columns of x and y after
-% Monte Carlo filtering
-%---------------------------------------------------
-% USAGE: scatter_mcf(x,y,vnames,pltsym,diagon)
-% or scatter_mcf(x,y) which relies on defaults
-% where:
-% x = an nxk matrix with columns containing behavioural sample
-% y = an mxk matrix with columns containing non-behavioural sample
-% vnames = a vector of variable names
-% (default = numeric labels 1,2,3 etc.)
-% pltsym = a plt symbol
-% (default = '.' for npts > 100, 'o' for npts < 100
-
-
-Z=[X;Y];
-[n,p] = size(X);
-% X = X - ones(n,1)*min(Z);
-% X = X ./ (ones(n,1)*max(Z));
-[n,p] = size(Y);
-% Y = Y - ones(n,1)*min(Z);
-% Y = Y ./ (ones(n,1)*max(Z));
-[n,p] = size(Z);
-clear Z;
-
-nflag = 0;
-if nargin >=3
- nflag = 1;
-end;
-
-if nargin<4 || isempty(plotsymbol)
- if n*p<100, plotsymbol = 'o';
- else plotsymbol = '.';
- end
-end
-
-if nargin<5
- fnam='';
-end
-if nargin<6,
- dirname='';
- nograph=1;
-else
- nograph=0;
-end
-if nargin<7,
- figtitle=fnam;
-end
-if nargin<8,
- xparam1=[];
-end
-if nargin<10,
- beha_name = 'BEHAVIOUR';
- non_beha_name = 'NON-BEHAVIOUR';
-end
-if nargin==10,
- non_beha_name = ['NON-' beha_name];
-end
-
-figtitle_tex=strrep(figtitle,'_','\_');
-
-fig_nam_=[fnam];
-if ~nograph,
- hh=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
-end
-
-bf = 0.1;
-ffs = 0.05/(p-1);
-ffl = (1-2*bf-0.05)/p;
-if p>1,
- fL = linspace(bf,1-bf+ffs,p+1);
-else
- fL = bf;
-end
-for i = 1:p
- for j = 1:p
- h = axes('position',[fL(i),fL(p+1-j),ffl,ffl]);
- if i==j
- h1=cumplot(X(:,j));
- % set(h1,'color',[0 0 1], 'linestyle','--','LineWidth',1.5)
- set(h1,'color',[0 0 1],'LineWidth',1.5)
- hold on,
- h2=cumplot(Y(:,j));
- set(h2,'color',[1 0 0],'LineWidth',1.5)
- if ~isempty(xparam1)
- hold on, plot(xparam1([j j]),[0 1],'k--')
- end
- if j<p
- set(gca,'XTickLabel',[],'XTick',[]);
- else
- grid off
- end
- set(gca,'YTickLabel',[],'YTick',[]);
- else
- if j>i
- plot(X(:,i),X(:,j),[plotsymbol,'b'])
- hold on,
- plot(Y(:,i),Y(:,j),[plotsymbol,'r'])
- else
- plot(Y(:,i),Y(:,j),[plotsymbol,'r'])
- hold on,
- plot(X(:,i),X(:,j),[plotsymbol,'b'])
- end
- if ~isempty(xparam1)
- hold on, plot(xparam1(i),xparam1(j),'s','MarkerFaceColor',[0 0.75 0],'MarkerEdgeColor',[0 0.75 0])
- end
- hold off;
- % axis([-0.1 1.1 -0.1 1.1])
- if i<p,
- set(gca,'YTickLabel',[],'YTick',[]);
- else
- set(gca,'yaxislocation','right');
- end
- if j<p
- set(gca,'XTickLabel',[],'XTick',[]);
- end
- end
- if nflag == 1
- set(gca,'fontsize',9);
- end;
- if i==1
- if nflag == 1
- ylabel(vnames(j,:),'Rotation',45,'interpreter','none', ...
- 'HorizontalAlignment','right','VerticalAlignment','middle');
- else
- ylabel([num2str(j),' '],'Rotation',90)
- end;
- end
- if j==1
- if nflag == 1
- title(vnames(i,:),'interpreter','none','Rotation',45, ...
- 'HorizontalAlignment','left','VerticalAlignment','bottom')
- else
- title(num2str(i))
- end;
- end
- drawnow
- end
-end
-if ~isoctave
- annotation('textbox', [0.1,0,0.35,0.05],'String', beha_name,'Color','Blue','horizontalalignment','center','interpreter','none');
- annotation('textbox', [0.55,0,0.35,0.05],'String', non_beha_name,'Color','Red','horizontalalignment','center','interpreter','none');
-end
-
-if ~nograph,
- dyn_saveas(hh,[dirname,filesep,fig_nam_],DynareOptions.nodisplay,DynareOptions.graph_format);
- if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
- fidTeX = fopen([dirname,'/',fig_nam_ '.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by scatter_mcf.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s}\n',strrep([dirname,'/',fig_nam_],'\','/'));
- fprintf(fidTeX,'\\caption{%s.}',figtitle_tex);
- fprintf(fidTeX,'\\label{Fig:%s}\n',fig_nam_);
- fprintf(fidTeX,'\\end{figure}\n\n');
- fprintf(fidTeX,'%% End Of TeX file. \n');
- fclose(fidTeX);
- end
+function scatter_mcf(X,Y,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, DynareOptions, beha_name, non_beha_name)
+%
+% Written by Marco Ratto
+% Joint Research Centre, The European Commission,
+% marco.ratto@ec.europa.eu
+
+% Copyright (C) 2014-2016 European Commission
+% Copyright (C) 2014-2017 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+% PURPOSE: Pairwise scatter plots of the columns of x and y after
+% Monte Carlo filtering
+%---------------------------------------------------
+% USAGE: scatter_mcf(x,y,vnames,pltsym,diagon)
+% or scatter_mcf(x,y) which relies on defaults
+% where:
+% x = an nxk matrix with columns containing behavioural sample
+% y = an mxk matrix with columns containing non-behavioural sample
+% vnames = a vector of variable names
+% (default = numeric labels 1,2,3 etc.)
+% pltsym = a plt symbol
+% (default = '.' for npts > 100, 'o' for npts < 100
+
+
+Z=[X;Y];
+[n,p] = size(X);
+% X = X - ones(n,1)*min(Z);
+% X = X ./ (ones(n,1)*max(Z));
+[n,p] = size(Y);
+% Y = Y - ones(n,1)*min(Z);
+% Y = Y ./ (ones(n,1)*max(Z));
+[n,p] = size(Z);
+clear Z;
+
+nflag = 0;
+if nargin >=3
+ nflag = 1;
+end;
+
+if nargin<4 || isempty(plotsymbol)
+ if n*p<100, plotsymbol = 'o';
+ else plotsymbol = '.';
+ end
+end
+
+if nargin<5
+ fnam='';
+end
+if nargin<6,
+ dirname='';
+ nograph=1;
+else
+ nograph=0;
+end
+if nargin<7,
+ figtitle=fnam;
+end
+if nargin<8,
+ xparam1=[];
+end
+if nargin<10,
+ beha_name = 'BEHAVIOUR';
+ non_beha_name = 'NON-BEHAVIOUR';
+end
+if nargin==10,
+ non_beha_name = ['NON-' beha_name];
+end
+
+figtitle_tex=strrep(figtitle,'_','\_');
+
+fig_nam_=[fnam];
+if ~nograph,
+ hh=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
+end
+
+bf = 0.1;
+ffs = 0.05/(p-1);
+ffl = (1-2*bf-0.05)/p;
+if p>1,
+ fL = linspace(bf,1-bf+ffs,p+1);
+else
+ fL = bf;
+end
+for i = 1:p
+ for j = 1:p
+ h = axes('position',[fL(i),fL(p+1-j),ffl,ffl]);
+ if i==j
+ h1=cumplot(X(:,j));
+ % set(h1,'color',[0 0 1], 'linestyle','--','LineWidth',1.5)
+ set(h1,'color',[0 0 1],'LineWidth',1.5)
+ hold on,
+ h2=cumplot(Y(:,j));
+ set(h2,'color',[1 0 0],'LineWidth',1.5)
+ if ~isempty(xparam1)
+ hold on, plot(xparam1([j j]),[0 1],'k--')
+ end
+ if j<p
+ set(gca,'XTickLabel',[],'XTick',[]);
+ else
+ grid off
+ end
+ set(gca,'YTickLabel',[],'YTick',[]);
+ else
+ if j>i
+ plot(X(:,i),X(:,j),[plotsymbol,'b'])
+ hold on,
+ plot(Y(:,i),Y(:,j),[plotsymbol,'r'])
+ else
+ plot(Y(:,i),Y(:,j),[plotsymbol,'r'])
+ hold on,
+ plot(X(:,i),X(:,j),[plotsymbol,'b'])
+ end
+ if ~isempty(xparam1)
+ hold on, plot(xparam1(i),xparam1(j),'s','MarkerFaceColor',[0 0.75 0],'MarkerEdgeColor',[0 0.75 0])
+ end
+ hold off;
+ % axis([-0.1 1.1 -0.1 1.1])
+ if i<p,
+ set(gca,'YTickLabel',[],'YTick',[]);
+ else
+ set(gca,'yaxislocation','right');
+ end
+ if j<p
+ set(gca,'XTickLabel',[],'XTick',[]);
+ end
+ end
+ if nflag == 1
+ set(gca,'fontsize',9);
+ end;
+ if i==1
+ if nflag == 1
+ ylabel(vnames(j,:),'Rotation',45,'interpreter','none', ...
+ 'HorizontalAlignment','right','VerticalAlignment','middle');
+ else
+ ylabel([num2str(j),' '],'Rotation',90)
+ end;
+ end
+ if j==1
+ if nflag == 1
+ title(vnames(i,:),'interpreter','none','Rotation',45, ...
+ 'HorizontalAlignment','left','VerticalAlignment','bottom')
+ else
+ title(num2str(i))
+ end;
+ end
+ drawnow
+ end
+end
+if ~isoctave
+ annotation('textbox', [0.1,0,0.35,0.05],'String', beha_name,'Color','Blue','horizontalalignment','center','interpreter','none');
+ annotation('textbox', [0.55,0,0.35,0.05],'String', non_beha_name,'Color','Red','horizontalalignment','center','interpreter','none');
+end
+
+if ~nograph,
+ dyn_saveas(hh,[dirname,filesep,fig_nam_],DynareOptions.nodisplay,DynareOptions.graph_format);
+ if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
+ fidTeX = fopen([dirname,'/',fig_nam_ '.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by scatter_mcf.m (Dynare).\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s}\n',strrep([dirname,'/',fig_nam_],'\','/'));
+ fprintf(fidTeX,'\\caption{%s.}',figtitle_tex);
+ fprintf(fidTeX,'\\label{Fig:%s}\n',fig_nam_);
+ fprintf(fidTeX,'\\end{figure}\n\n');
+ fprintf(fidTeX,'%% End Of TeX file. \n');
+ fclose(fidTeX);
+ end
end
\ No newline at end of file
diff --git a/matlab/gsa/scatter_plots.m b/matlab/gsa/scatter_plots.m
index e64195464cdcf3e5b8f872738d5233533c6b97d2..61e47bb5188ee4973215428660deb8e0d9fd73cb 100644
--- a/matlab/gsa/scatter_plots.m
+++ b/matlab/gsa/scatter_plots.m
@@ -56,25 +56,25 @@ if nargin<5 || isempty(fnam)
fnam='scatter_plot';
end
if nargin<6 || isempty(dirname),
- dirname='';
- nograph=1;
- DynareOptions.nodisplay=0;
+ dirname='';
+ nograph=1;
+ DynareOptions.nodisplay=0;
else
- nograph=0;
+ nograph=0;
end
if nargin<7 || isempty(figtitle),
- figtitle=fnam;
+ figtitle=fnam;
end
if nargin<8,
- xparam1=[];
+ xparam1=[];
end
figtitle_tex=strrep(figtitle,'_','\_');
fig_nam_=[fnam];
- hh=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
- set(hh,'userdata',{X,xp})
+hh=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
+set(hh,'userdata',{X,xp})
bf = 0.1;
ffs = 0.05/(p-1);
@@ -111,7 +111,7 @@ for i = 1:p
%%
if ~isoctave
- % Define a context menu; it is not attached to anything
+ % Define a context menu; it is not attached to anything
hcmenu = uicontextmenu('Callback','pick','Tag','Run viewer');
% Define callbacks for context menu
% items that change linestyle
@@ -150,7 +150,7 @@ for i = 1:p
if i==1
if nflag == 1
ylabel(vnames(j,:),'Rotation',45,'interpreter','none', ...
- 'HorizontalAlignment','right','VerticalAlignment','middle');
+ 'HorizontalAlignment','right','VerticalAlignment','middle');
else
ylabel([num2str(j),' '],'Rotation',90)
end;
@@ -158,7 +158,7 @@ for i = 1:p
if j==1
if nflag == 1
title(vnames(i,:),'interpreter','none','Rotation',45, ...
- 'HorizontalAlignment','left','VerticalAlignment','bottom')
+ 'HorizontalAlignment','left','VerticalAlignment','bottom')
else
title(num2str(i))
end;
diff --git a/matlab/gsa/set_shocks_param.m b/matlab/gsa/set_shocks_param.m
index 8902dfb102cd06f01a5830788cd4a25d0ecad8e4..bd22ccb5610b1984ab86977e409c1ac1da39aa4e 100644
--- a/matlab/gsa/set_shocks_param.m
+++ b/matlab/gsa/set_shocks_param.m
@@ -93,7 +93,7 @@ if isfield(estim_params_,'calibrated_covariances_ME')
H(estim_params_.calibrated_covariances_ME.position)=estim_params_.calibrated_covariances_ME.cov_value;
end
-
+
% updating matrices in M
if nvx || ncx
M_.Sigma_e = Sigma_e;
diff --git a/matlab/gsa/stab_map_.m b/matlab/gsa/stab_map_.m
index 78ab1a368c5de581f6ce0505a7834c9c5b10e34c..e6b22edacf3dfff43b5b30ecb2585ed972f03adc 100644
--- a/matlab/gsa/stab_map_.m
+++ b/matlab/gsa/stab_map_.m
@@ -99,8 +99,8 @@ if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
bounds.lb = max(bounds.lb,lb);
bounds.ub = min(bounds.ub,ub);
else % estimated parameters but no declared priors
- % No priors are declared so Dynare will estimate the model by
- % maximum likelihood with inequality constraints for the parameters.
+ % No priors are declared so Dynare will estimate the model by
+ % maximum likelihood with inequality constraints for the parameters.
bounds.lb = lb;
bounds.ub = ub;
if opt_gsa.prior_range==0
@@ -159,9 +159,9 @@ if fload==0,
Nsam=size(lpmat,1);
lpmat0 = lpmat(:,1:nshock);
lpmat = lpmat(:,nshock+1:end);
-% elseif opt_gsa.morris==3,
-% lpmat = prep_ide(Nsam,np,5);
-% Nsam=size(lpmat,1);
+ % elseif opt_gsa.morris==3,
+ % lpmat = prep_ide(Nsam,np,5);
+ % Nsam=size(lpmat,1);
else
if np<52 && ilptau>0,
[lpmat] = qmc_sequence(np, int64(1), 0, Nsam)';
@@ -180,14 +180,14 @@ if fload==0,
end
% try
dummy=prior_draw_gsa(1); %initialize persistent variables
- % catch
- % if pprior,
- % if opt_gsa.prior_range==0;
- % error('Some unknown prior is specified or ML estimation,: use prior_range=1 option!!');
- % end
- % end
- %
- % end
+ % catch
+ % if pprior,
+ % if opt_gsa.prior_range==0;
+ % error('Some unknown prior is specified or ML estimation,: use prior_range=1 option!!');
+ % end
+ % end
+ %
+ % end
if pprior,
for j=1:nshock,
if opt_gsa.morris~=1,
@@ -257,12 +257,12 @@ if fload==0,
if neighborhood_width>0 && isempty(options_.mode_file),
xparam1 = get_all_parameters(estim_params_,M_);
else
- eval(['load ' options_.mode_file '.mat;']);
+ eval(['load ' options_.mode_file '.mat;']);
end
if neighborhood_width>0,
for j=1:nshock,
if opt_gsa.morris ~= 1
- lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
+ lpmat0(:,j) = randperm(Nsam)'./(Nsam+1); %latin hypercube
end
ub=min([bounds.ub(j) xparam1(j)*(1+neighborhood_width)]);
lb=max([bounds.lb(j) xparam1(j)*(1-neighborhood_width)]);
@@ -335,7 +335,7 @@ if fload==0,
egg=zeros(length(dr_.eigval),Nsam);
end
if infox(j,1),
-% disp('no solution'),
+ % disp('no solution'),
if isfield(oo_.dr,'ghx'),
oo_.dr=rmfield(oo_.dr,'ghx');
end
@@ -457,23 +457,23 @@ if fload==0,
if pprior,
if ~prepSA
save([OutputDirectoryName filesep fname_ '_prior.mat'], ...
- 'bkpprior','lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
- 'egg','yys','nspred','nboth','nfwrd','infox')
+ 'bkpprior','lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
+ 'egg','yys','nspred','nboth','nfwrd','infox')
else
save([OutputDirectoryName filesep fname_ '_prior.mat'], ...
- 'bkpprior','lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
- 'egg','yys','T','nspred','nboth','nfwrd','infox')
+ 'bkpprior','lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
+ 'egg','yys','T','nspred','nboth','nfwrd','infox')
end
else
if ~prepSA
save([OutputDirectoryName filesep fname_ '_mc.mat'], ...
- 'lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
- 'egg','yys','nspred','nboth','nfwrd','infox')
+ 'lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
+ 'egg','yys','nspred','nboth','nfwrd','infox')
else
save([OutputDirectoryName filesep fname_ '_mc.mat'], ...
- 'lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
- 'egg','yys','T','nspred','nboth','nfwrd','infox')
+ 'lpmat','lpmat0','irestriction','iunstable','istable','iindeterm','iwrong','ixun', ...
+ 'egg','yys','T','nspred','nboth','nfwrd','infox')
end
end
else
@@ -601,7 +601,7 @@ if length(iunstable)>0 || length(iwrong)>0,
if length(iunstable)<Nsam || length(istable)>1
itot = [1:Nsam];
isolve = itot(find(~ismember(itot,iwrong))); % dynare could find a solution
- % Blanchard Kahn
+ % Blanchard Kahn
if neighborhood_width,
options_mcf.xparam1 = xparam1(nshock+1:end);
end
diff --git a/matlab/gsa/stab_map_1.m b/matlab/gsa/stab_map_1.m
index 8535a78ecdafd51e5a744a60e81368f1f399db44..6a23fe63dc2aec5a9c2311bf936c56f469c9d8d5 100644
--- a/matlab/gsa/stab_map_1.m
+++ b/matlab/gsa/stab_map_1.m
@@ -41,14 +41,14 @@ function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, i
global estim_params_ bayestopt_ M_ options_
if nargin<5,
- iplot=1;
+ iplot=1;
end
fname_ = M_.fname;
if nargin<7,
- dirname='';
+ dirname='';
end
if nargin<9,
- atitle=aname;
+ atitle=aname;
end
nshock = estim_params_.nvx;
@@ -60,20 +60,20 @@ npar=size(lpmat,2);
ishock= npar>estim_params_.np;
if nargin<6,
- ipar=[];
+ ipar=[];
end
if nargin<8 || isempty(pcrit),
- pcrit=1;
+ pcrit=1;
end
% Smirnov test for Blanchard;
for j=1:npar,
- [H,P,KSSTAT] = smirnov(lpmat(ibehaviour,j),lpmat(inonbehaviour,j));
- proba(j)=P;
- dproba(j)=KSSTAT;
+ [H,P,KSSTAT] = smirnov(lpmat(ibehaviour,j),lpmat(inonbehaviour,j));
+ proba(j)=P;
+ dproba(j)=KSSTAT;
end
if isempty(ipar),
-% ipar=find(dproba>dcrit);
+ % ipar=find(dproba>dcrit);
ipar=find(proba<pcrit);
end
nparplot=length(ipar);
diff --git a/matlab/gsa/stab_map_2.m b/matlab/gsa/stab_map_2.m
index beed786dc664f8465965d2ff27e6de72728555f1..80d140e15e9aebe7826c9d35b802a5308a065ecc 100644
--- a/matlab/gsa/stab_map_2.m
+++ b/matlab/gsa/stab_map_2.m
@@ -30,17 +30,17 @@ nsam=size(x,1);
ishock= npar>estim_params_.np;
nograph = options_.nograph;
if nargin<4,
- fnam='';
+ fnam='';
end
if nargin<5,
- dirname='';
- nograph=1;
+ dirname='';
+ nograph=1;
end
if nargin<6,
- xparam1=[];
+ xparam1=[];
end
if nargin<7,
- figtitle=fnam;
+ figtitle=fnam;
end
ys_ = oo_.dr.ys;
@@ -60,12 +60,12 @@ fig_nam_=strrep(fig_nam_,' ','_');
ifig=0;
j2=0;
if ishock==0
- npar=estim_params_.np;
- if ~isempty(xparam1),
- xparam1=xparam1(nshock+1:end);
- end
+ npar=estim_params_.np;
+ if ~isempty(xparam1),
+ xparam1=xparam1(nshock+1:end);
+ end
else
- npar=estim_params_.np+nshock;
+ npar=estim_params_.np+nshock;
end
skipline();
title_string=['Correlation analysis for ',fnam];
@@ -118,45 +118,45 @@ for j=1:npar,
if ~nograph,
if mod(j2,12)==1,
- ifig=ifig+1;
- hh=dyn_figure(options_.nodisplay,'name',[figtitle,' sample bivariate projection ', num2str(ifig)]);
- end
- subplot(3,4,j2-(ifig-1)*12)
- % bar(c0(i2,j)),
- % set(gca,'xticklabel',bayestopt_.name(i2)),
- % set(gca,'xtick',[1:length(i2)])
- %plot(stock_par(ixx(nfilt+1:end,i),j),stock_par(ixx(nfilt+1:end,i),i2(jx)),'.k')
- %hold on,
- plot(x(:,j),x(:,i2(jx)),'.')
- if ~isempty(xparam1)
- hold on, plot(xparam1(j),xparam1(i2(jx)),'ro')
- end
- % xlabel(deblank(estim_params_.param_names(j,:)),'interpreter','none'),
- % ylabel(deblank(estim_params_.param_names(i2(jx),:)),'interpreter','none'),
- if ishock,
- xlabel(bayestopt_.name{j},'interpreter','none'),
- ylabel(bayestopt_.name{i2(jx)},'interpreter','none'),
- else
- xlabel(bayestopt_.name{j+nshock},'interpreter','none'),
- ylabel(bayestopt_.name{i2(jx)+nshock},'interpreter','none'),
- end
- title(['cc = ',num2str(c0(i2(jx),j))])
- if (mod(j2,12)==0) && j2>0,
- dyn_saveas(hh,[dirname,filesep,fig_nam_,int2str(ifig)],options_.nodisplay,options_.graph_format);
- if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fidTeX = fopen([dirname,filesep,fig_nam_,int2str(ifig),'.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by stab_map_2.m (Dynare).\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s}\n',strrep([dirname,'/',fig_nam_,int2str(ifig)],'\','/'));
- fprintf(fidTeX,'\\caption{%s.}',[figtitle,' sample bivariate projection ', num2str(ifig)]);
- fprintf(fidTeX,'\\label{Fig:%s:%u}\n',fig_nam_,ifig);
- fprintf(fidTeX,'\\end{figure}\n\n');
- fprintf(fidTeX,'%% End Of TeX file. \n');
- fclose(fidTeX);
+ ifig=ifig+1;
+ hh=dyn_figure(options_.nodisplay,'name',[figtitle,' sample bivariate projection ', num2str(ifig)]);
+ end
+ subplot(3,4,j2-(ifig-1)*12)
+ % bar(c0(i2,j)),
+ % set(gca,'xticklabel',bayestopt_.name(i2)),
+ % set(gca,'xtick',[1:length(i2)])
+ %plot(stock_par(ixx(nfilt+1:end,i),j),stock_par(ixx(nfilt+1:end,i),i2(jx)),'.k')
+ %hold on,
+ plot(x(:,j),x(:,i2(jx)),'.')
+ if ~isempty(xparam1)
+ hold on, plot(xparam1(j),xparam1(i2(jx)),'ro')
+ end
+ % xlabel(deblank(estim_params_.param_names(j,:)),'interpreter','none'),
+ % ylabel(deblank(estim_params_.param_names(i2(jx),:)),'interpreter','none'),
+ if ishock,
+ xlabel(bayestopt_.name{j},'interpreter','none'),
+ ylabel(bayestopt_.name{i2(jx)},'interpreter','none'),
+ else
+ xlabel(bayestopt_.name{j+nshock},'interpreter','none'),
+ ylabel(bayestopt_.name{i2(jx)+nshock},'interpreter','none'),
+ end
+ title(['cc = ',num2str(c0(i2(jx),j))])
+ if (mod(j2,12)==0) && j2>0,
+ dyn_saveas(hh,[dirname,filesep,fig_nam_,int2str(ifig)],options_.nodisplay,options_.graph_format);
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fidTeX = fopen([dirname,filesep,fig_nam_,int2str(ifig),'.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by stab_map_2.m (Dynare).\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,'\\includegraphics[width=0.8\\textwidth]{%s}\n',strrep([dirname,'/',fig_nam_,int2str(ifig)],'\','/'));
+ fprintf(fidTeX,'\\caption{%s.}',[figtitle,' sample bivariate projection ', num2str(ifig)]);
+ fprintf(fidTeX,'\\label{Fig:%s:%u}\n',fig_nam_,ifig);
+ fprintf(fidTeX,'\\end{figure}\n\n');
+ fprintf(fidTeX,'%% End Of TeX file. \n');
+ fclose(fidTeX);
+ end
end
- end
end
end
diff --git a/matlab/gsa/stand_.m b/matlab/gsa/stand_.m
index c69ec4bf3d5c352aee5def5d708070a13e60dd04..f1547c3c6640ddef016ab68dd19eab57293e0b6b 100644
--- a/matlab/gsa/stand_.m
+++ b/matlab/gsa/stand_.m
@@ -35,8 +35,8 @@ if nargin==0,
end
for j=1:size(x,2);
-meany(j)=mean(x(find(~isnan(x(:,j))),j));
-stdy(j)=std(x(find(~isnan(x(:,j))),j));
+ meany(j)=mean(x(find(~isnan(x(:,j))),j));
+ stdy(j)=std(x(find(~isnan(x(:,j))),j));
y(:,j)=(x(:,j)-meany(j))./stdy(j);
end
% end of m-file
\ No newline at end of file
diff --git a/matlab/gsa/tcrit.m b/matlab/gsa/tcrit.m
index 291725aaddb6020683e484468e66390997aa1fda..560d90175cab22c285dfd6d4287f332b5ee48c7e 100644
--- a/matlab/gsa/tcrit.m
+++ b/matlab/gsa/tcrit.m
@@ -43,108 +43,108 @@ pval0=max(pval0,min(pval));
ncol=min(find(pval<=pval0))+1;
t_crit=[
- 1 3.078 6.314 12.706 31.821 63.657 318.313
- 2 1.886 2.920 4.303 6.965 9.925 22.327
- 3 1.638 2.353 3.182 4.541 5.841 10.215
- 4 1.533 2.132 2.776 3.747 4.604 7.173
- 5 1.476 2.015 2.571 3.365 4.032 5.893
- 6 1.440 1.943 2.447 3.143 3.707 5.208
- 7 1.415 1.895 2.365 2.998 3.499 4.782
- 8 1.397 1.860 2.306 2.896 3.355 4.499
- 9 1.383 1.833 2.262 2.821 3.250 4.296
- 10 1.372 1.812 2.228 2.764 3.169 4.143
- 11 1.363 1.796 2.201 2.718 3.106 4.024
- 12 1.356 1.782 2.179 2.681 3.055 3.929
- 13 1.350 1.771 2.160 2.650 3.012 3.852
- 14 1.345 1.761 2.145 2.624 2.977 3.787
- 15 1.341 1.753 2.131 2.602 2.947 3.733
- 16 1.337 1.746 2.120 2.583 2.921 3.686
- 17 1.333 1.740 2.110 2.567 2.898 3.646
- 18 1.330 1.734 2.101 2.552 2.878 3.610
- 19 1.328 1.729 2.093 2.539 2.861 3.579
- 20 1.325 1.725 2.086 2.528 2.845 3.552
- 21 1.323 1.721 2.080 2.518 2.831 3.527
- 22 1.321 1.717 2.074 2.508 2.819 3.505
- 23 1.319 1.714 2.069 2.500 2.807 3.485
- 24 1.318 1.711 2.064 2.492 2.797 3.467
- 25 1.316 1.708 2.060 2.485 2.787 3.450
- 26 1.315 1.706 2.056 2.479 2.779 3.435
- 27 1.314 1.703 2.052 2.473 2.771 3.421
- 28 1.313 1.701 2.048 2.467 2.763 3.408
- 29 1.311 1.699 2.045 2.462 2.756 3.396
- 30 1.310 1.697 2.042 2.457 2.750 3.385
- 31 1.309 1.696 2.040 2.453 2.744 3.375
- 32 1.309 1.694 2.037 2.449 2.738 3.365
- 33 1.308 1.692 2.035 2.445 2.733 3.356
- 34 1.307 1.691 2.032 2.441 2.728 3.348
- 35 1.306 1.690 2.030 2.438 2.724 3.340
- 36 1.306 1.688 2.028 2.434 2.719 3.333
- 37 1.305 1.687 2.026 2.431 2.715 3.326
- 38 1.304 1.686 2.024 2.429 2.712 3.319
- 39 1.304 1.685 2.023 2.426 2.708 3.313
- 40 1.303 1.684 2.021 2.423 2.704 3.307
- 41 1.303 1.683 2.020 2.421 2.701 3.301
- 42 1.302 1.682 2.018 2.418 2.698 3.296
- 43 1.302 1.681 2.017 2.416 2.695 3.291
- 44 1.301 1.680 2.015 2.414 2.692 3.286
- 45 1.301 1.679 2.014 2.412 2.690 3.281
- 46 1.300 1.679 2.013 2.410 2.687 3.277
- 47 1.300 1.678 2.012 2.408 2.685 3.273
- 48 1.299 1.677 2.011 2.407 2.682 3.269
- 49 1.299 1.677 2.010 2.405 2.680 3.265
- 50 1.299 1.676 2.009 2.403 2.678 3.261
- 51 1.298 1.675 2.008 2.402 2.676 3.258
- 52 1.298 1.675 2.007 2.400 2.674 3.255
- 53 1.298 1.674 2.006 2.399 2.672 3.251
- 54 1.297 1.674 2.005 2.397 2.670 3.248
- 55 1.297 1.673 2.004 2.396 2.668 3.245
- 56 1.297 1.673 2.003 2.395 2.667 3.242
- 57 1.297 1.672 2.002 2.394 2.665 3.239
- 58 1.296 1.672 2.002 2.392 2.663 3.237
- 59 1.296 1.671 2.001 2.391 2.662 3.234
- 60 1.296 1.671 2.000 2.390 2.660 3.232
- 61 1.296 1.670 2.000 2.389 2.659 3.229
- 62 1.295 1.670 1.999 2.388 2.657 3.227
- 63 1.295 1.669 1.998 2.387 2.656 3.225
- 64 1.295 1.669 1.998 2.386 2.655 3.223
- 65 1.295 1.669 1.997 2.385 2.654 3.220
- 66 1.295 1.668 1.997 2.384 2.652 3.218
- 67 1.294 1.668 1.996 2.383 2.651 3.216
- 68 1.294 1.668 1.995 2.382 2.650 3.214
- 69 1.294 1.667 1.995 2.382 2.649 3.213
- 70 1.294 1.667 1.994 2.381 2.648 3.211
- 71 1.294 1.667 1.994 2.380 2.647 3.209
- 72 1.293 1.666 1.993 2.379 2.646 3.207
- 73 1.293 1.666 1.993 2.379 2.645 3.206
- 74 1.293 1.666 1.993 2.378 2.644 3.204
- 75 1.293 1.665 1.992 2.377 2.643 3.202
- 76 1.293 1.665 1.992 2.376 2.642 3.201
- 77 1.293 1.665 1.991 2.376 2.641 3.199
- 78 1.292 1.665 1.991 2.375 2.640 3.198
- 79 1.292 1.664 1.990 2.374 2.640 3.197
- 80 1.292 1.664 1.990 2.374 2.639 3.195
- 81 1.292 1.664 1.990 2.373 2.638 3.194
- 82 1.292 1.664 1.989 2.373 2.637 3.193
- 83 1.292 1.663 1.989 2.372 2.636 3.191
- 84 1.292 1.663 1.989 2.372 2.636 3.190
- 85 1.292 1.663 1.988 2.371 2.635 3.189
- 86 1.291 1.663 1.988 2.370 2.634 3.188
- 87 1.291 1.663 1.988 2.370 2.634 3.187
- 88 1.291 1.662 1.987 2.369 2.633 3.185
- 89 1.291 1.662 1.987 2.369 2.632 3.184
- 90 1.291 1.662 1.987 2.368 2.632 3.183
- 91 1.291 1.662 1.986 2.368 2.631 3.182
- 92 1.291 1.662 1.986 2.368 2.630 3.181
- 93 1.291 1.661 1.986 2.367 2.630 3.180
- 94 1.291 1.661 1.986 2.367 2.629 3.179
- 95 1.291 1.661 1.985 2.366 2.629 3.178
- 96 1.290 1.661 1.985 2.366 2.628 3.177
- 97 1.290 1.661 1.985 2.365 2.627 3.176
- 98 1.290 1.661 1.984 2.365 2.627 3.175
- 99 1.290 1.660 1.984 2.365 2.626 3.175
-100 1.290 1.660 1.984 2.364 2.626 3.174
-inf 1.282 1.645 1.960 2.326 2.576 3.090
-];
+ 1 3.078 6.314 12.706 31.821 63.657 318.313
+ 2 1.886 2.920 4.303 6.965 9.925 22.327
+ 3 1.638 2.353 3.182 4.541 5.841 10.215
+ 4 1.533 2.132 2.776 3.747 4.604 7.173
+ 5 1.476 2.015 2.571 3.365 4.032 5.893
+ 6 1.440 1.943 2.447 3.143 3.707 5.208
+ 7 1.415 1.895 2.365 2.998 3.499 4.782
+ 8 1.397 1.860 2.306 2.896 3.355 4.499
+ 9 1.383 1.833 2.262 2.821 3.250 4.296
+ 10 1.372 1.812 2.228 2.764 3.169 4.143
+ 11 1.363 1.796 2.201 2.718 3.106 4.024
+ 12 1.356 1.782 2.179 2.681 3.055 3.929
+ 13 1.350 1.771 2.160 2.650 3.012 3.852
+ 14 1.345 1.761 2.145 2.624 2.977 3.787
+ 15 1.341 1.753 2.131 2.602 2.947 3.733
+ 16 1.337 1.746 2.120 2.583 2.921 3.686
+ 17 1.333 1.740 2.110 2.567 2.898 3.646
+ 18 1.330 1.734 2.101 2.552 2.878 3.610
+ 19 1.328 1.729 2.093 2.539 2.861 3.579
+ 20 1.325 1.725 2.086 2.528 2.845 3.552
+ 21 1.323 1.721 2.080 2.518 2.831 3.527
+ 22 1.321 1.717 2.074 2.508 2.819 3.505
+ 23 1.319 1.714 2.069 2.500 2.807 3.485
+ 24 1.318 1.711 2.064 2.492 2.797 3.467
+ 25 1.316 1.708 2.060 2.485 2.787 3.450
+ 26 1.315 1.706 2.056 2.479 2.779 3.435
+ 27 1.314 1.703 2.052 2.473 2.771 3.421
+ 28 1.313 1.701 2.048 2.467 2.763 3.408
+ 29 1.311 1.699 2.045 2.462 2.756 3.396
+ 30 1.310 1.697 2.042 2.457 2.750 3.385
+ 31 1.309 1.696 2.040 2.453 2.744 3.375
+ 32 1.309 1.694 2.037 2.449 2.738 3.365
+ 33 1.308 1.692 2.035 2.445 2.733 3.356
+ 34 1.307 1.691 2.032 2.441 2.728 3.348
+ 35 1.306 1.690 2.030 2.438 2.724 3.340
+ 36 1.306 1.688 2.028 2.434 2.719 3.333
+ 37 1.305 1.687 2.026 2.431 2.715 3.326
+ 38 1.304 1.686 2.024 2.429 2.712 3.319
+ 39 1.304 1.685 2.023 2.426 2.708 3.313
+ 40 1.303 1.684 2.021 2.423 2.704 3.307
+ 41 1.303 1.683 2.020 2.421 2.701 3.301
+ 42 1.302 1.682 2.018 2.418 2.698 3.296
+ 43 1.302 1.681 2.017 2.416 2.695 3.291
+ 44 1.301 1.680 2.015 2.414 2.692 3.286
+ 45 1.301 1.679 2.014 2.412 2.690 3.281
+ 46 1.300 1.679 2.013 2.410 2.687 3.277
+ 47 1.300 1.678 2.012 2.408 2.685 3.273
+ 48 1.299 1.677 2.011 2.407 2.682 3.269
+ 49 1.299 1.677 2.010 2.405 2.680 3.265
+ 50 1.299 1.676 2.009 2.403 2.678 3.261
+ 51 1.298 1.675 2.008 2.402 2.676 3.258
+ 52 1.298 1.675 2.007 2.400 2.674 3.255
+ 53 1.298 1.674 2.006 2.399 2.672 3.251
+ 54 1.297 1.674 2.005 2.397 2.670 3.248
+ 55 1.297 1.673 2.004 2.396 2.668 3.245
+ 56 1.297 1.673 2.003 2.395 2.667 3.242
+ 57 1.297 1.672 2.002 2.394 2.665 3.239
+ 58 1.296 1.672 2.002 2.392 2.663 3.237
+ 59 1.296 1.671 2.001 2.391 2.662 3.234
+ 60 1.296 1.671 2.000 2.390 2.660 3.232
+ 61 1.296 1.670 2.000 2.389 2.659 3.229
+ 62 1.295 1.670 1.999 2.388 2.657 3.227
+ 63 1.295 1.669 1.998 2.387 2.656 3.225
+ 64 1.295 1.669 1.998 2.386 2.655 3.223
+ 65 1.295 1.669 1.997 2.385 2.654 3.220
+ 66 1.295 1.668 1.997 2.384 2.652 3.218
+ 67 1.294 1.668 1.996 2.383 2.651 3.216
+ 68 1.294 1.668 1.995 2.382 2.650 3.214
+ 69 1.294 1.667 1.995 2.382 2.649 3.213
+ 70 1.294 1.667 1.994 2.381 2.648 3.211
+ 71 1.294 1.667 1.994 2.380 2.647 3.209
+ 72 1.293 1.666 1.993 2.379 2.646 3.207
+ 73 1.293 1.666 1.993 2.379 2.645 3.206
+ 74 1.293 1.666 1.993 2.378 2.644 3.204
+ 75 1.293 1.665 1.992 2.377 2.643 3.202
+ 76 1.293 1.665 1.992 2.376 2.642 3.201
+ 77 1.293 1.665 1.991 2.376 2.641 3.199
+ 78 1.292 1.665 1.991 2.375 2.640 3.198
+ 79 1.292 1.664 1.990 2.374 2.640 3.197
+ 80 1.292 1.664 1.990 2.374 2.639 3.195
+ 81 1.292 1.664 1.990 2.373 2.638 3.194
+ 82 1.292 1.664 1.989 2.373 2.637 3.193
+ 83 1.292 1.663 1.989 2.372 2.636 3.191
+ 84 1.292 1.663 1.989 2.372 2.636 3.190
+ 85 1.292 1.663 1.988 2.371 2.635 3.189
+ 86 1.291 1.663 1.988 2.370 2.634 3.188
+ 87 1.291 1.663 1.988 2.370 2.634 3.187
+ 88 1.291 1.662 1.987 2.369 2.633 3.185
+ 89 1.291 1.662 1.987 2.369 2.632 3.184
+ 90 1.291 1.662 1.987 2.368 2.632 3.183
+ 91 1.291 1.662 1.986 2.368 2.631 3.182
+ 92 1.291 1.662 1.986 2.368 2.630 3.181
+ 93 1.291 1.661 1.986 2.367 2.630 3.180
+ 94 1.291 1.661 1.986 2.367 2.629 3.179
+ 95 1.291 1.661 1.985 2.366 2.629 3.178
+ 96 1.290 1.661 1.985 2.366 2.628 3.177
+ 97 1.290 1.661 1.985 2.365 2.627 3.176
+ 98 1.290 1.661 1.984 2.365 2.627 3.175
+ 99 1.290 1.660 1.984 2.365 2.626 3.175
+ 100 1.290 1.660 1.984 2.364 2.626 3.174
+ inf 1.282 1.645 1.960 2.326 2.576 3.090
+ ];
if n<=100,
t_crit=t_crit(n,ncol);
diff --git a/matlab/gsa/teff.m b/matlab/gsa/teff.m
index 0956669a38577e9b778de1231ae2b014e07d9cdd..622abca94aae8a1bed64bd6d01a24b77b628e71b 100644
--- a/matlab/gsa/teff.m
+++ b/matlab/gsa/teff.m
@@ -28,32 +28,32 @@ function [yt, j0, ir, ic]=teff(T,Nsam,istable)
ndim = (length(size(T)));
if ndim==3,
-if nargin==1,
- Nsam=size(T,3);
- istable = [1:Nsam]';
-end
-tmax=max(T,[],3);
-tmin=min(T,[],3);
-[ir, ic]=(find( (tmax-tmin)>1.e-8));
-j0 = length(ir);
-yt=zeros(Nsam, j0);
+ if nargin==1,
+ Nsam=size(T,3);
+ istable = [1:Nsam]';
+ end
+ tmax=max(T,[],3);
+ tmin=min(T,[],3);
+ [ir, ic]=(find( (tmax-tmin)>1.e-8));
+ j0 = length(ir);
+ yt=zeros(Nsam, j0);
-for j=1:j0,
- y0=squeeze(T(ir(j),ic(j),:));
- %y1=ones(size(lpmat,1),1)*NaN;
- y1=ones(Nsam,1)*NaN;
- y1(istable,1)=y0;
- yt(:,j)=y1;
-end
+ for j=1:j0,
+ y0=squeeze(T(ir(j),ic(j),:));
+ %y1=ones(size(lpmat,1),1)*NaN;
+ y1=ones(Nsam,1)*NaN;
+ y1(istable,1)=y0;
+ yt(:,j)=y1;
+ end
else
-tmax=max(T,[],2);
-tmin=min(T,[],2);
-ir=(find( (tmax-tmin)>1.e-8));
-j0 = length(ir);
-yt=NaN(Nsam, j0);
-yt(istable,:)=T(ir,:)';
+ tmax=max(T,[],2);
+ tmin=min(T,[],2);
+ ir=(find( (tmax-tmin)>1.e-8));
+ j0 = length(ir);
+ yt=NaN(Nsam, j0);
+ yt(istable,:)=T(ir,:)';
-
+
end
%clear y0 y1;
diff --git a/matlab/gsa/trank.m b/matlab/gsa/trank.m
index d6ac2d2db07f2e5443ef0ca350b64b43502c97a9..a47c912622afac631c12b42fee60a2393920af09 100644
--- a/matlab/gsa/trank.m
+++ b/matlab/gsa/trank.m
@@ -29,6 +29,6 @@ function yr = trank(y);
[nr, nc] = size(y);
for j=1:nc,
- [dum, is]=sort(y(:,j));
- yr(is,j)=[1:nr]'./nr;
+ [dum, is]=sort(y(:,j));
+ yr(is,j)=[1:nr]'./nr;
end
diff --git a/matlab/hessian.m b/matlab/hessian.m
index 4faa4c50008dee3f7f754e18e37bc315b88c5920..cf2f549bd9af08a125fa6c7eaa799bb2563ffde7 100644
--- a/matlab/hessian.m
+++ b/matlab/hessian.m
@@ -90,7 +90,7 @@ for i=1:n
xh_1(i) = x(i)-h1(i);
xh_1(j) = x(j)-h_1(j);
hessian_mat(:,(i-1)*n+j) =-(-feval(func, xh1, varargin{:})-feval(func, xh_1, varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j)); %formula 25.3.27
- %reset grid points
+ %reset grid points
xh1(i) = x(i);
xh1(j) = x(j);
xh_1(i) = x(i);
diff --git a/matlab/hessian_sparse.m b/matlab/hessian_sparse.m
index eb447f8d34d9ac9c9237c021dba01762a708a911..d63534e79b353ef8cb30d998f82f68e98cf5f960 100644
--- a/matlab/hessian_sparse.m
+++ b/matlab/hessian_sparse.m
@@ -60,11 +60,11 @@ xh_1=xh1;
hessian_mat = sparse(size(f0,1),n*n);
for i=1:n
-% if i > 1
-% k=[i:n:n*(i-1)];
-% hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k);
-% hessian_mat(:,k)=0;
-% end
+ % if i > 1
+ % k=[i:n:n*(i-1)];
+ % hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k);
+ % hessian_mat(:,k)=0;
+ % end
hessian_mat(:,(i-1)*n+i)=(f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i));
temp=f1+f_1-f0*ones(1,n);
for j=1:i-1
diff --git a/matlab/ident_bruteforce.m b/matlab/ident_bruteforce.m
index 41ed952d71ce9ce83af4fdd6b860ec848ae9ec1d..ea68deaea1971430b9d1fb9b0f7c7536fbce7a2f 100644
--- a/matlab/ident_bruteforce.m
+++ b/matlab/ident_bruteforce.m
@@ -111,9 +111,9 @@ for ll = 1:n,
end
end
fprintf(fidTeX,'$%s$ & [%s] & %7.3f \\\\ \n',...
- pnames_TeX(i,:),...
- plist,...
- cosnJ(i,ll));
+ pnames_TeX(i,:),...
+ plist,...
+ cosnJ(i,ll));
end
fprintf(fidTeX,'\\bottomrule \n');
fprintf(fidTeX,'\\end{longtable}\n');
diff --git a/matlab/identification_analysis.m b/matlab/identification_analysis.m
index 88b60ca4d457900fb807c63e9e827c1751119842..f83bcf1a619b1c3484340247a90b12f7bf29d127 100644
--- a/matlab/identification_analysis.m
+++ b/matlab/identification_analysis.m
@@ -63,7 +63,7 @@ periods = options_ident.periods;
max_dim_cova_group = options_ident.max_dim_cova_group;
normalize_jacobians = options_ident.normalize_jacobians;
kron_flag = options_ident.analytic_derivation_mode;
-
+
[I,J]=find(M_.lead_lag_incidence');
ide_hess = struct();
@@ -78,8 +78,8 @@ if info(1)==0,
tau=[oo_.dr.ys(oo_.dr.order_var); vec(A); dyn_vech(B*M_.Sigma_e*B')];
yy0=oo_.dr.ys(I);
[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, ...
- repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), M_.params, ...
- oo_.dr.ys, 1);
+ repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), M_.params, ...
+ oo_.dr.ys, 1);
vg1 = [oo_.dr.ys(oo_.dr.order_var); vec(g1)];
[JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo0,options_,kron_flag,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
@@ -157,13 +157,13 @@ if info(1)==0,
derivatives_info.no_DLIK=1;
%bounds = prior_bounds(bayestopt_, options_.prior_trunc);
[fval,info,cost_flag,DLIK,AHess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
-% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
+ % fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = analytic_derivation;
AHess=-AHess;
if min(eig(AHess))<-1.e-10,
error('identification_analysis: Analytic Hessian is not positive semi-definite!')
end
-% chol(AHess);
+ % chol(AHess);
ide_hess.AHess= AHess;
deltaM = sqrt(diag(AHess));
iflag=any((deltaM.*deltaM)==0);
@@ -181,17 +181,17 @@ if info(1)==0,
cmm = siJ(:,ind1)*((AHess(ind1,ind1))\siJ(:,ind1)');
temp1=((AHess(ind1,ind1))\siH(:,ind1)');
diag_chh=sum(siH(:,ind1)'.*temp1)';
-% chh = siH(:,ind1)*((AHess(ind1,ind1))\siH(:,ind1)');
+ % chh = siH(:,ind1)*((AHess(ind1,ind1))\siH(:,ind1)');
ind1=ind1(ind1>offset);
clre = siLRE(:,ind1-offset)*((AHess(ind1,ind1))\siLRE(:,ind1-offset)');
rhoM=sqrt(1./diag(inv(tildaM(indok,indok))));
-% deltaM = deltaM.*abs(params');
+ % deltaM = deltaM.*abs(params');
flag_score=1;
catch,
-% replic = max([replic, nparam*(nparam+1)/2*10]);
+ % replic = max([replic, nparam*(nparam+1)/2*10]);
replic = max([replic, length(indJJ)*3]);
cmm = simulated_moment_uncertainty(indJJ, periods, replic,options_,M_,oo_);
-% [V,D,W]=eig(cmm);
+ % [V,D,W]=eig(cmm);
sd=sqrt(diag(cmm));
cc=cmm./(sd*sd');
if isoctave || matlab_ver_less_than('8.3')
@@ -206,21 +206,21 @@ if info(1)==0,
siTMP=siJ./repmat(sd,[1 nparam]);
MIM=(siTMP'*V(:,id))*(D(id,id)\(W(:,id)'*siTMP));
clear siTMP;
-% MIM=siJ(:,indok)'*(cmm\siJ(:,indok));
-% look for independent moments!
-% % % sd=sqrt(diag(cmm));
-% % % cc=cmm./(sd*sd');
-% % % ix=[];
-% % % for jc=1:length(cmm),
-% % % jcheck=find(abs(cc(:,jc))>(1-1.e-6));
-% % % ix=[ix; jcheck(jcheck>jc)];
-% % % end
-% % % iy=find(~ismember([1:length(cmm)],ix));
-% % % indJJ=indJJ(iy);
-% % % GAM=GAM(iy);
-% % % cmm=cmm(iy,iy);
-% % % siJ = (JJ(indJJ,:));
-% % % MIM=siJ'*(cmm\siJ);
+ % MIM=siJ(:,indok)'*(cmm\siJ(:,indok));
+ % look for independent moments!
+ % % % sd=sqrt(diag(cmm));
+ % % % cc=cmm./(sd*sd');
+ % % % ix=[];
+ % % % for jc=1:length(cmm),
+ % % % jcheck=find(abs(cc(:,jc))>(1-1.e-6));
+ % % % ix=[ix; jcheck(jcheck>jc)];
+ % % % end
+ % % % iy=find(~ismember([1:length(cmm)],ix));
+ % % % indJJ=indJJ(iy);
+ % % % GAM=GAM(iy);
+ % % % cmm=cmm(iy,iy);
+ % % % siJ = (JJ(indJJ,:));
+ % % % MIM=siJ'*(cmm\siJ);
ide_hess.AHess= MIM;
deltaM = sqrt(diag(MIM));
iflag=any((deltaM.*deltaM)==0);
@@ -231,12 +231,12 @@ if info(1)==0,
[ide_hess.cond, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification_checks(tildaM, 1);
end
indok = find(max(ide_hess.indno,[],1)==0);
-% rhoM=sqrt(1-1./diag(inv(tildaM)));
-% rhoM=(1-1./diag(inv(tildaM)));
+ % rhoM=sqrt(1-1./diag(inv(tildaM)));
+ % rhoM=(1-1./diag(inv(tildaM)));
ind1=find(ide_hess.ind0);
temp1=((MIM(ind1,ind1))\siH(:,ind1)');
diag_chh=sum(siH(:,ind1)'.*temp1)';
-% chh = siH(:,ind1)*((MIM(ind1,ind1))\siH(:,ind1)');
+ % chh = siH(:,ind1)*((MIM(ind1,ind1))\siH(:,ind1)');
ind1=ind1(ind1>offset);
clre = siLRE(:,ind1-offset)*((MIM(ind1,ind1))\siLRE(:,ind1-offset)');
if ~isempty(indok),
@@ -260,11 +260,11 @@ if info(1)==0,
end
% siJnorm = vnorm(siJ(inok,:)).*normaliz;
quant=[];
-% inok = find((abs(TAU)<1.e-8));
-% isok = find((abs(TAU)>=1.e-8));
-% quant(isok,:) = siH(isok,:)./repmat(TAU(isok,1),1,nparam);
-% quant(inok,:) = siH(inok,:)./repmat(mean(abs(TAU)),length(inok),nparam);
-% quant = siH./repmat(sqrt(diag(chh)),1,nparam);
+ % inok = find((abs(TAU)<1.e-8));
+ % isok = find((abs(TAU)>=1.e-8));
+ % quant(isok,:) = siH(isok,:)./repmat(TAU(isok,1),1,nparam);
+ % quant(inok,:) = siH(inok,:)./repmat(mean(abs(TAU)),length(inok),nparam);
+ % quant = siH./repmat(sqrt(diag(chh)),1,nparam);
iy = find(diag_chh);
indH=indH(iy);
siH=siH(iy,:);
@@ -280,10 +280,10 @@ if info(1)==0,
end
% siHnorm = vnorm(siH./repmat(TAU,1,nparam)).*normaliz;
quant=[];
-% inok = find((abs(LRE)<1.e-8));
-% isok = find((abs(LRE)>=1.e-8));
-% quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,1),1,np);
-% quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE)),length(inok),np);
+ % inok = find((abs(LRE)<1.e-8));
+ % isok = find((abs(LRE)>=1.e-8));
+ % quant(isok,:) = siLRE(isok,:)./repmat(LRE(isok,1),1,np);
+ % quant(inok,:) = siLRE(inok,:)./repmat(mean(abs(LRE)),length(inok),np);
diag_clre = diag(clre);
iy = find(diag_clre);
indLRE=indLRE(iy);
@@ -329,14 +329,14 @@ if info(1)==0,
ide_moments.GAM=GAM;
ide_model.TAU=TAU;
ide_lre.LRE=LRE;
-% [ide_checks.idemodel_Mco, ide_checks.idemoments_Mco, ide_checks.idelre_Mco, ...
-% ide_checks.idemodel_Pco, ide_checks.idemoments_Pco, ide_checks.idelre_Pco, ...
-% ide_checks.idemodel_cond, ide_checks.idemoments_cond, ide_checks.idelre_cond, ...
-% ide_checks.idemodel_ee, ide_checks.idemoments_ee, ide_checks.idelre_ee, ...
-% ide_checks.idemodel_ind, ide_checks.idemoments_ind, ...
-% ide_checks.idemodel_indno, ide_checks.idemoments_indno, ...
-% ide_checks.idemodel_ino, ide_checks.idemoments_ino] = ...
-% identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)));
+ % [ide_checks.idemodel_Mco, ide_checks.idemoments_Mco, ide_checks.idelre_Mco, ...
+ % ide_checks.idemodel_Pco, ide_checks.idemoments_Pco, ide_checks.idelre_Pco, ...
+ % ide_checks.idemodel_cond, ide_checks.idemoments_cond, ide_checks.idelre_cond, ...
+ % ide_checks.idemodel_ee, ide_checks.idemoments_ee, ide_checks.idelre_ee, ...
+ % ide_checks.idemodel_ind, ide_checks.idemoments_ind, ...
+ % ide_checks.idemodel_indno, ide_checks.idemoments_indno, ...
+ % ide_checks.idemodel_ino, ide_checks.idemoments_ino] = ...
+ % identification_checks(H(indH,:)./normH(:,ones(nparam,1)),JJ(indJJ,:)./normJ(:,ones(nparam,1)), gp(indLRE,:)./normLRE(:,ones(size(gp,2),1)));
[ide_moments.cond, ide_moments.ind0, ide_moments.indno, ide_moments.ino, ide_moments.Mco, ide_moments.Pco, ide_moments.jweak, ide_moments.jweak_pair] = ...
identification_checks(JJ(indJJ,:)./normJ, 0);
[ide_model.cond, ide_model.ind0, ide_model.indno, ide_model.ino, ide_model.Mco, ide_model.Pco, ide_model.jweak, ide_model.jweak_pair] = ...
diff --git a/matlab/identification_checks.m b/matlab/identification_checks.m
index 208d7ecd4efca23b10721d6d80428be7dbb5eeb8..735682ec7d37e396a47f80b4a319dd8929ea4a4d 100644
--- a/matlab/identification_checks.m
+++ b/matlab/identification_checks.m
@@ -70,13 +70,13 @@ if npar>0 && (rankJ<npar),
end
if icheck,
-JJ1 = JJ(:,ind1);
-[eu,ee2,ee1] = svd( JJ1, 0 );
-condJ= cond(JJ1);
-rankJ = rank(JJ);
-rankJJ = rankJ;
+ JJ1 = JJ(:,ind1);
+ [eu,ee2,ee1] = svd( JJ1, 0 );
+ condJ= cond(JJ1);
+ rankJ = rank(JJ);
+ rankJJ = rankJ;
end
-
+
% if hess_flag==0,
% rankJJ = rank(JJ'*JJ);
@@ -96,7 +96,7 @@ else
tildaJ = JJ(ind1,ind1)./((deltaJ)*(deltaJ'));
McoJ(ind1,1)=(1-1./diag(inv(tildaJ)));
rhoM=sqrt(1-McoJ);
-% PcoJ=inv(tildaJ);
+ % PcoJ=inv(tildaJ);
PcoJ=NaN(npar,npar);
PcoJ(ind1,ind1)=inv(JJ(ind1,ind1));
sd=sqrt(diag(PcoJ));
@@ -132,27 +132,27 @@ jweak=zeros(1,npar);
jweak_pair=zeros(npar,npar);
if hess_flag==0,
-PcoJ = NaN(npar,npar);
+ PcoJ = NaN(npar,npar);
-for ii = 1:size(JJ1,2);
- PcoJ(ind1(ii),ind1(ii)) = 1;
- for jj = ii+1:size(JJ1,2);
- PcoJ(ind1(ii),ind1(jj)) = cosn([JJ1(:,ii),JJ1(:,jj)]);
- PcoJ(ind1(jj),ind1(ii)) = PcoJ(ind1(ii),ind1(jj));
+ for ii = 1:size(JJ1,2);
+ PcoJ(ind1(ii),ind1(ii)) = 1;
+ for jj = ii+1:size(JJ1,2);
+ PcoJ(ind1(ii),ind1(jj)) = cosn([JJ1(:,ii),JJ1(:,jj)]);
+ PcoJ(ind1(jj),ind1(ii)) = PcoJ(ind1(ii),ind1(jj));
+ end
end
-end
-for j=1:npar,
- if McoJ(j)>(1-1.e-10),
- jweak(j)=1;
- [ipair, jpair] = find(PcoJ(j,j+1:end)>(1-1.e-10));
- for jx=1:length(jpair),
- jweak_pair(j, jpair(jx)+j)=1;
- jweak_pair(jpair(jx)+j, j)=1;
+ for j=1:npar,
+ if McoJ(j)>(1-1.e-10),
+ jweak(j)=1;
+ [ipair, jpair] = find(PcoJ(j,j+1:end)>(1-1.e-10));
+ for jx=1:length(jpair),
+ jweak_pair(j, jpair(jx)+j)=1;
+ jweak_pair(jpair(jx)+j, j)=1;
+ end
end
end
end
-end
jweak_pair=dyn_vech(jweak_pair)';
diff --git a/matlab/imcforecast.m b/matlab/imcforecast.m
index 8ddb3755db398faa69170f311ee713fdcc87063f..9f7bdc4805b2a26eff564f7dd88f164d94659d06 100644
--- a/matlab/imcforecast.m
+++ b/matlab/imcforecast.m
@@ -225,7 +225,7 @@ jdx = [];
for i = 1:n1
idx = [idx ; constrained_vars(i,:)];
-% idx = [idx ; oo_.dr.inv_order_var(constrained_vars(i,:))];
+ % idx = [idx ; oo_.dr.inv_order_var(constrained_vars(i,:))];
jdx = [jdx ; strmatch(deblank(options_cond_fcst.controlled_varexo(i,:)),M_.exo_names,'exact')];
end
mv = zeros(n1,NumberOfStates);
diff --git a/matlab/init_plan.m b/matlab/init_plan.m
index 0e9957f48f68c3936adf650911f59b7e81fbc6a4..24bd3ea6530bf28127286b5c7344659b0ffae195 100644
--- a/matlab/init_plan.m
+++ b/matlab/init_plan.m
@@ -25,28 +25,28 @@ function plan = init_plan(date)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- global M_
- plan = struct();
- plan.date = date;
- plan.date_str = strings(date);
- endo_names_length = size(M_.endo_names,2);
- plan.endo_names = deblank(mat2cell(M_.endo_names(1:M_.orig_endo_nbr,:),ones(1,M_.orig_endo_nbr),endo_names_length));
- exo_names_length = size(M_.exo_names,2);
- plan.exo_names = deblank(mat2cell(M_.exo_names(1:M_.exo_nbr,:),ones(1,M_.exo_nbr),exo_names_length));
- plan.constrained_vars_ = [];
- plan.constrained_paths_ = [];
- plan.constrained_date_ = [];
- plan.constrained_int_date_ = [];
- plan.constrained_str_date_ = [];
- plan.constrained_perfect_foresight_ = [];
- plan.shock_vars_ = [];
- plan.shock_paths_ = [];
- plan.shock_date_ = [];
- plan.shock_int_date_ = [];
- plan.shock_str_date_ = [];
- plan.shock_perfect_foresight_ = [];
- plan.options_cond_fcst_ = struct();
- plan.options_cond_fcst_.parameter_set = 'calibration';
- plan.options_cond_fcst_.simulation_type = 'deterministic';
- plan.options_cond_fcst_.controlled_varexo = [];
-
+global M_
+plan = struct();
+plan.date = date;
+plan.date_str = strings(date);
+endo_names_length = size(M_.endo_names,2);
+plan.endo_names = deblank(mat2cell(M_.endo_names(1:M_.orig_endo_nbr,:),ones(1,M_.orig_endo_nbr),endo_names_length));
+exo_names_length = size(M_.exo_names,2);
+plan.exo_names = deblank(mat2cell(M_.exo_names(1:M_.exo_nbr,:),ones(1,M_.exo_nbr),exo_names_length));
+plan.constrained_vars_ = [];
+plan.constrained_paths_ = [];
+plan.constrained_date_ = [];
+plan.constrained_int_date_ = [];
+plan.constrained_str_date_ = [];
+plan.constrained_perfect_foresight_ = [];
+plan.shock_vars_ = [];
+plan.shock_paths_ = [];
+plan.shock_date_ = [];
+plan.shock_int_date_ = [];
+plan.shock_str_date_ = [];
+plan.shock_perfect_foresight_ = [];
+plan.options_cond_fcst_ = struct();
+plan.options_cond_fcst_.parameter_set = 'calibration';
+plan.options_cond_fcst_.simulation_type = 'deterministic';
+plan.options_cond_fcst_.controlled_varexo = [];
+
diff --git a/matlab/initial_condition_decomposition.m b/matlab/initial_condition_decomposition.m
index 1eb2b795982cb21fce950259e1f1c62989990f01..f99bcd622e8be4d7b5e05bcad99d33ccdadc56a6 100644
--- a/matlab/initial_condition_decomposition.m
+++ b/matlab/initial_condition_decomposition.m
@@ -71,61 +71,61 @@ if isempty(parameter_set)
end
if ~isfield(oo_,'initval_decomposition')
-options_.selected_variables_only = 0; %make sure all variables are stored
-options_.plot_priors=0;
-[oo,junk1,junk2,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
+ options_.selected_variables_only = 0; %make sure all variables are stored
+ options_.plot_priors=0;
+ [oo,junk1,junk2,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
-% reduced form
-dr = oo.dr;
+ % reduced form
+ dr = oo.dr;
-% data reordering
-order_var = dr.order_var;
-inv_order_var = dr.inv_order_var;
+ % data reordering
+ order_var = dr.order_var;
+ inv_order_var = dr.inv_order_var;
-% coefficients
-A = dr.ghx;
-B = dr.ghu;
+ % coefficients
+ A = dr.ghx;
+ B = dr.ghu;
-% initialization
-gend = size(oo.SmoothedShocks.(deblank(M_.exo_names(1,:))),1); %+options_.forecast;
-z = zeros(endo_nbr,endo_nbr+2,gend);
-z(:,end,:) = Smoothed_Variables_deviation_from_mean;
+ % initialization
+ gend = size(oo.SmoothedShocks.(deblank(M_.exo_names(1,:))),1); %+options_.forecast;
+ z = zeros(endo_nbr,endo_nbr+2,gend);
+ z(:,end,:) = Smoothed_Variables_deviation_from_mean;
-for i=1:endo_nbr,
- z(i,i,1) = Smoothed_Variables_deviation_from_mean(i,1);
-end
+ for i=1:endo_nbr,
+ z(i,i,1) = Smoothed_Variables_deviation_from_mean(i,1);
+ end
-maximum_lag = M_.maximum_lag;
+ maximum_lag = M_.maximum_lag;
+
+ k2 = dr.kstate(find(dr.kstate(:,2) <= maximum_lag+1),[1 2]);
+ i_state = order_var(k2(:,1))+(min(i,maximum_lag)+1-k2(:,2))*M_.endo_nbr;
+ for i=1:gend
+ if i > 1 && i <= maximum_lag+1
+ lags = min(i-1,maximum_lag):-1:1;
+ end
+
+ if i > 1
+ tempx = permute(z(:,1:endo_nbr,lags),[1 3 2]);
+ m = min(i-1,maximum_lag);
+ tempx = [reshape(tempx,endo_nbr*m,endo_nbr); zeros(endo_nbr*(maximum_lag-i+1),endo_nbr)];
+ z(:,1:endo_nbr,i) = A(inv_order_var,:)*tempx(i_state,:);
+ lags = lags+1;
+ end
+ z(:,endo_nbr+1,i) = z(:,endo_nbr+2,i) - sum(z(:,1:endo_nbr,i),2);
-k2 = dr.kstate(find(dr.kstate(:,2) <= maximum_lag+1),[1 2]);
-i_state = order_var(k2(:,1))+(min(i,maximum_lag)+1-k2(:,2))*M_.endo_nbr;
-for i=1:gend
- if i > 1 && i <= maximum_lag+1
- lags = min(i-1,maximum_lag):-1:1;
- end
-
- if i > 1
- tempx = permute(z(:,1:endo_nbr,lags),[1 3 2]);
- m = min(i-1,maximum_lag);
- tempx = [reshape(tempx,endo_nbr*m,endo_nbr); zeros(endo_nbr*(maximum_lag-i+1),endo_nbr)];
- z(:,1:endo_nbr,i) = A(inv_order_var,:)*tempx(i_state,:);
- lags = lags+1;
end
- z(:,endo_nbr+1,i) = z(:,endo_nbr+2,i) - sum(z(:,1:endo_nbr,i),2);
-
-end
-oo_.initval_decomposition = z;
+ oo_.initval_decomposition = z;
end
% if ~options_.no_graph.shock_decomposition
- oo=oo_;
- oo.shock_decomposition = oo_.initval_decomposition;
- M_.exo_names = M_.endo_names;
- M_.exo_nbr = M_.endo_nbr;
- options_.plot_shock_decomp.screen_shocks=1;
- options_.plot_shock_decomp.use_shock_groups = '';
- options_.plot_shock_decomp.fig_names='initval';
- plot_shock_decomposition(M_,oo,options_,varlist);
+oo=oo_;
+oo.shock_decomposition = oo_.initval_decomposition;
+M_.exo_names = M_.endo_names;
+M_.exo_nbr = M_.endo_nbr;
+options_.plot_shock_decomp.screen_shocks=1;
+options_.plot_shock_decomp.use_shock_groups = '';
+options_.plot_shock_decomp.fig_names='initval';
+plot_shock_decomposition(M_,oo,options_,varlist);
% end
\ No newline at end of file
diff --git a/matlab/initial_estimation_checks.m b/matlab/initial_estimation_checks.m
index a13f88ee406f80592c9219b13221033ad5fc9d6b..7155552a6dbf84366b020037775f7533e0291cf1 100644
--- a/matlab/initial_estimation_checks.m
+++ b/matlab/initial_estimation_checks.m
@@ -69,7 +69,7 @@ if isfield(EstimatedParameters,'param_vals') && ~isempty(EstimatedParameters.par
[junk, new_steady_params_2] = evaluate_steady_state(DynareResults.steady_state,Model_par_varied,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
changed_par_indices=find((old_steady_params(EstimatedParameters.param_vals(:,1))-new_steady_params(EstimatedParameters.param_vals(:,1))) ...
- | (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
+ | (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
if ~isempty(changed_par_indices)
fprintf('\nThe steady state file internally changed the values of the following estimated parameters:\n')
@@ -171,8 +171,8 @@ if info(1) > 0
error('Your model has at least one unit root and you are using a nonlinear filter. Please set nonlinear_filter_initialization=3.')
end
else
- disp('Error in computing likelihood for initial parameter values')
- print_info(info, DynareOptions.noprint, DynareOptions)
+ disp('Error in computing likelihood for initial parameter values')
+ print_info(info, DynareOptions.noprint, DynareOptions)
end
end
diff --git a/matlab/initvalf.m b/matlab/initvalf.m
index 0493a2b48a8efdb378b478685083c6dacdbd02b8..28292ed61fac6a4444ac41dfe6f1cddbea62b9b7 100644
--- a/matlab/initvalf.m
+++ b/matlab/initvalf.m
@@ -58,15 +58,15 @@ if ~exist(fullname)
end
switch (extension)
- case '.m'
- eval(basename);
- case '.mat'
- load(basename);
- case { '.xls', '.xlsx' }
- [data_,names_v_]=xlsread(fullname); % Octave needs the extension explicitly
- series_=0;
- otherwise
- error(['Unsupported extension for datafile: ' extension])
+ case '.m'
+ eval(basename);
+ case '.mat'
+ load(basename);
+ case { '.xls', '.xlsx' }
+ [data_,names_v_]=xlsread(fullname); % Octave needs the extension explicitly
+ series_=0;
+ otherwise
+ error(['Unsupported extension for datafile: ' extension])
end
options_.initval_file = 1;
diff --git a/matlab/interpret_resol_info.m b/matlab/interpret_resol_info.m
index de16d47e093ffba38e9b35740a705bac521ff205..a80a23f5d4c12fa5ef08461ff99fbe7ad6cec3d6 100644
--- a/matlab/interpret_resol_info.m
+++ b/matlab/interpret_resol_info.m
@@ -1,5 +1,5 @@
function message = interpret_resol_info(info)
-
+
% Returns a message describing problem encountered during the resolution of
% a model.
%
@@ -8,7 +8,7 @@ function message = interpret_resol_info(info)
%
% OUTPUTS
% - message [string] Description of the issue preventing model's resolution.
-
+
% Copyright (C) 2001-2016 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/isolder.m b/matlab/isolder.m
index 091455ad0781022e52a9e31472e7fcc2d683e5c0..127393854e33e1feba530389111c22c59e263648 100644
--- a/matlab/isolder.m
+++ b/matlab/isolder.m
@@ -29,9 +29,9 @@ function b = isolder(f, F) % --*-- Unitary tests --*--
if nargin<2 || isempty(F)
F = pwd();
end
-
+
b = true;
-
+
files = dir(F);
tfile = dir(f);
tdate = tfile.datenum;
diff --git a/matlab/k_order_pert.m b/matlab/k_order_pert.m
index db1ef91f48587ba727b132f73f37c53128f49c8d..4c6917f5fb027f539cea2a363ccd93383b9c4a4a 100644
--- a/matlab/k_order_pert.m
+++ b/matlab/k_order_pert.m
@@ -28,26 +28,26 @@ exo_nbr = M.exo_nbr;
nspred = M.nspred;
if order>1 && options.loglinear
- error('The loglinear-option currently only works at order 1')
+ error('The loglinear-option currently only works at order 1')
end
if M.maximum_endo_lead == 0 && order>1
- error(['2nd and 3rd order approximation not implemented for purely ' ...
- 'backward models'])
+ error(['2nd and 3rd order approximation not implemented for purely ' ...
+ 'backward models'])
end
switch(order)
case 1
[err, g_1] = k_order_perturbation(dr,M,options);
if err
- info(1)=9;
- return;
+ info(1)=9;
+ return;
end
dr.g_1 = g_1;
case 2
[err, g_0, g_1, g_2] = k_order_perturbation(dr,M,options);
if err
- info(1)=9;
- return;
+ info(1)=9;
+ return;
end
dr.g_0 = g_0;
dr.g_1 = g_1;
@@ -57,15 +57,15 @@ switch(order)
[err, g_0, g_1, g_2, g_3, derivs] = k_order_perturbation(dr, ...
M,options);
if err
- info(1)=9;
- return;
+ info(1)=9;
+ return;
end
else
[err, g_0, g_1, g_2, g_3] = k_order_perturbation(dr, ...
M,options);
if err
- info(1)=9;
- return;
+ info(1)=9;
+ return;
end
end
dr.g_0 = g_0;
@@ -205,5 +205,5 @@ for i=1:n1
end
-
-
+
+
diff --git a/matlab/kalman/likelihood/computeDLIK.m b/matlab/kalman/likelihood/computeDLIK.m
index 1acb0796ff167c6112be36ad521eb0051a391635..eab34c3baf5de6ad9029094c2b705ce062234c8a 100644
--- a/matlab/kalman/likelihood/computeDLIK.m
+++ b/matlab/kalman/likelihood/computeDLIK.m
@@ -22,19 +22,19 @@ function [Da,DP,DLIK,D2a,D2P,Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,P,iF,Da,DY
persistent DK DF D2K D2F
if notsteady
-if Zflag
- [DK,DF,DP1] = computeDKalmanZ(T,DT,DOm,P,DP,DH,Z,iF,K);
- if nargout>4,
- [D2K,D2F,D2P] = computeD2KalmanZ(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
- end
-else
- [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,Z,iF,K);
- if nargout>4,
- [D2K,D2F,D2P] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
+ if Zflag
+ [DK,DF,DP1] = computeDKalmanZ(T,DT,DOm,P,DP,DH,Z,iF,K);
+ if nargout>4,
+ [D2K,D2F,D2P] = computeD2KalmanZ(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
+ end
+ else
+ [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,Z,iF,K);
+ if nargout>4,
+ [D2K,D2F,D2P] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,Z,iF,K,DK);
+ end
end
-end
-DP=DP1;
-clear DP1,
+ DP=DP1;
+ clear DP1,
else
DP=DP;
if nargout>4,
@@ -47,20 +47,20 @@ Dv=zeros(length(v),k);
for ii = 1:k
if Zflag
Dv(:,ii) = -Z*Da(:,ii) - Z*DYss(:,ii);
-% if nargout>4,
-% for jj = 1:ii
-% D2v(:,jj,ii) = -Z*D2Yss(:,jj,ii) - Z*D2a(:,jj,ii);
-% D2v(:,ii,jj) = D2v(:,jj,ii);
-% end
-% end
+ % if nargout>4,
+ % for jj = 1:ii
+ % D2v(:,jj,ii) = -Z*D2Yss(:,jj,ii) - Z*D2a(:,jj,ii);
+ % D2v(:,ii,jj) = D2v(:,jj,ii);
+ % end
+ % end
else
Dv(:,ii) = -Da(Z,ii) - DYss(Z,ii);
-% if nargout>4,
-% for jj = 1:ii
-% D2v(:,jj,ii) = -D2Yss(Z,jj,ii) - D2a(Z,jj,ii);
-% D2v(:,ii,jj) = D2v(:,jj,ii);
-% end
-% end
+ % if nargout>4,
+ % for jj = 1:ii
+ % D2v(:,jj,ii) = -D2Yss(Z,jj,ii) - D2a(Z,jj,ii);
+ % D2v(:,ii,jj) = D2v(:,jj,ii);
+ % end
+ % end
end
end
@@ -82,9 +82,9 @@ for ii = 1:k
d2Kij = D2K(:,:,jj,ii);
d2Fij = D2F(:,:,jj,ii);
d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-% dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
+ % dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
-% d2vij = D2v(:,ii,jj);
+ % d2vij = D2v(:,ii,jj);
if Zflag
d2vij = -Z*D2Yss(:,jj,ii) - Z*D2a(:,jj,ii);
else
@@ -99,7 +99,7 @@ for ii = 1:k
end
end
end
-
+
Da(:,ii) = DT(:,:,ii)*tmp + T*dtmp(:,ii);
DLIK(ii,1) = trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
end
@@ -108,16 +108,16 @@ if nargout==4,
% Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),0,iF,diFi,diFj,0,dFj,0);
vecDPmf = reshape(DF,[],k);
D2a = 2*Dv'*iF*Dv + (vecDPmf' * kron(iF,iF) * vecDPmf);
-% for ii = 1:k
-%
-% diFi = -iF*DF(:,:,ii)*iF;
-% for jj = 1:ii
-% dFj = DF(:,:,jj);
-% diFj = -iF*DF(:,:,jj)*iF;
-%
-% Hesst(ii,jj) = getHesst_ij(v*0,Dv(:,ii),Dv(:,jj),v*0,iF,diFi,diFj,0,-dFj,0);
-% end
-% end
+ % for ii = 1:k
+ %
+ % diFi = -iF*DF(:,:,ii)*iF;
+ % for jj = 1:ii
+ % dFj = DF(:,:,jj);
+ % diFj = -iF*DF(:,:,jj)*iF;
+ %
+ % Hesst(ii,jj) = getHesst_ij(v*0,Dv(:,ii),Dv(:,jj),v*0,iF,diFi,diFj,0,-dFj,0);
+ % end
+ % end
end
% end of computeDLIK
@@ -168,9 +168,9 @@ end
function [d2K,d2S,d2P1] = computeD2Kalman(A,dA,d2A,d2Om,P0,dP0,d2P1,DH,Z,iF,K0,dK0);
% computes the second derivatives of the Kalman matrices
% note: A=T in main func.
-
- k = size(dA,3);
- tmp = P0-K0*P0(Z,:);
+
+k = size(dA,3);
+tmp = P0-K0*P0(Z,:);
[ns,no] = size(K0);
% CPC = C*P0*C'; CPC = .5*(CPC+CPC');iF = inv(CPC);
@@ -186,14 +186,14 @@ jcount=0;
for ii = 1:k
dAi = dA(:,:,ii);
dFi = dP0(Z,Z,ii);
-% d2Omi = d2Om(:,:,ii);
+ % d2Omi = d2Om(:,:,ii);
diFi = -iF*dFi*iF;
dKi = dK0(:,:,ii);
for jj = 1:ii,
jcount=jcount+1;
dAj = dA(:,:,jj);
dFj = dP0(Z,Z,jj);
-% d2Omj = d2Om(:,:,jj);
+ % d2Omj = d2Om(:,:,jj);
dFj = dP0(Z,Z,jj);
diFj = -iF*dFj*iF;
dKj = dK0(:,:,jj);
@@ -201,33 +201,33 @@ for ii = 1:k
d2Aij = reshape(d2A(:,jcount),[ns ns]);
d2Pij = dyn_unvech(d2P1(:,jcount));
d2Omij = dyn_unvech(d2Om(:,jcount));
-
- % second order
-
- d2Fij = d2Pij(Z,Z) ;
-
-% d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
- d2APC = d2Pij(:,Z);
-
- d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-
- d2Kij= d2Pij(:,Z)*iF + P0(:,Z)*d2iF + dP0(:,Z,jj)*diFi + dP0(:,Z,ii)*diFj;
- d2KCP = d2Kij*P0(Z,:) + K0*d2Pij(Z,:) + dKi*dP0(Z,:,jj) + dKj*dP0(Z,:,ii) ;
-
- dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*P0(Z,:) - K0*dP0(Z,:,ii);
- dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*P0(Z,:) - K0*dP0(Z,:,jj);
- d2tmp = d2Pij - d2KCP;
-
- d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
-
- d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
- d2P1(:,jcount) = dyn_vech(d2AtmpA + d2Omij); %#ok<*NASGU>
- d2S(:,:,ii,jj) = d2Fij;
- d2K(:,:,jj,ii) = d2Kij; %#ok<NASGU>
-% d2P1(:,:,jj,ii) = d2AtmpA + d2Omij; %#ok<*NASGU>
- d2S(:,:,jj,ii) = d2Fij;
-% d2iS(:,:,ii,jj) = d2iF;
+ % second order
+
+ d2Fij = d2Pij(Z,Z) ;
+
+ % d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
+ d2APC = d2Pij(:,Z);
+
+ d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+
+ d2Kij= d2Pij(:,Z)*iF + P0(:,Z)*d2iF + dP0(:,Z,jj)*diFi + dP0(:,Z,ii)*diFj;
+
+ d2KCP = d2Kij*P0(Z,:) + K0*d2Pij(Z,:) + dKi*dP0(Z,:,jj) + dKj*dP0(Z,:,ii) ;
+
+ dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*P0(Z,:) - K0*dP0(Z,:,ii);
+ dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*P0(Z,:) - K0*dP0(Z,:,jj);
+ d2tmp = d2Pij - d2KCP;
+
+ d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
+
+ d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
+ d2P1(:,jcount) = dyn_vech(d2AtmpA + d2Omij); %#ok<*NASGU>
+ d2S(:,:,ii,jj) = d2Fij;
+ d2K(:,:,jj,ii) = d2Kij; %#ok<NASGU>
+ % d2P1(:,:,jj,ii) = d2AtmpA + d2Omij; %#ok<*NASGU>
+ d2S(:,:,jj,ii) = d2Fij;
+ % d2iS(:,:,ii,jj) = d2iF;
end
end
@@ -236,9 +236,9 @@ end
function [d2K,d2S,d2P1] = computeD2KalmanZ(A,dA,d2A,d2Om,P0,dP0,d2P1,DH,Z,iF,K0,dK0);
% computes the second derivatives of the Kalman matrices
% note: A=T in main func.
-
- k = size(dA,3);
- tmp = P0-K0*Z*P0(:,:);
+
+k = size(dA,3);
+tmp = P0-K0*Z*P0(:,:);
[ns,no] = size(K0);
% CPC = C*P0*C'; CPC = .5*(CPC+CPC');iF = inv(CPC);
@@ -254,14 +254,14 @@ jcount=0;
for ii = 1:k,
dAi = dA(:,:,ii);
dFi = Z*dP0(:,:,ii)*Z;
-% d2Omi = d2Om(:,:,ii);
+ % d2Omi = d2Om(:,:,ii);
diFi = -iF*dFi*iF;
dKi = dK0(:,:,ii);
for jj = 1:ii,
jcount=jcount+1;
dAj = dA(:,:,jj);
dFj = Z*dP0(:,:,jj)*Z;
-% d2Omj = d2Om(:,:,jj);
+ % d2Omj = d2Om(:,:,jj);
dFj = Z*dP0(:,:,jj)*Z;
diFj = -iF*dFj*iF;
dKj = dK0(:,:,jj);
@@ -269,33 +269,33 @@ for ii = 1:k,
d2Aij = reshape(d2A(:,jcount),[ns ns]);
d2Pij = dyn_unvech(d2P1(:,jcount));
d2Omij = dyn_unvech(d2Om(:,jcount));
-
- % second order
-
- d2Fij = Z*d2Pij(:,:)*Z ;
-
-% d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
- d2APC = d2Pij(:,:)*Z;
-
- d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-
- d2Kij= d2Pij(:,:)*Z*iF + P0(:,:)*Z*d2iF + dP0(:,:,jj)*Z*diFi + dP0(:,:,ii)*Z*diFj;
- d2KCP = d2Kij*Z*P0(:,:) + K0*Z*d2Pij(:,:) + dKi*Z*dP0(:,:,jj) + dKj*Z*dP0(:,:,ii) ;
-
- dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*Z*P0(:,:) - K0*Z*dP0(:,:,ii);
- dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*Z*P0(:,:) - K0*Z*dP0(:,:,jj);
- d2tmp = d2Pij - d2KCP;
-
- d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
-
- d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
- d2P1(:,jcount) = dyn_vech(d2AtmpA + d2Omij); %#ok<*NASGU>
- d2S(:,:,ii,jj) = d2Fij;
-% d2iS(:,:,ii,jj) = d2iF;
- d2K(:,:,jj,ii) = d2Kij; %#ok<NASGU>
-% d2P1(:,:,jj,ii) = d2AtmpA + d2Omij; %#ok<*NASGU>
- d2S(:,:,jj,ii) = d2Fij;
+ % second order
+
+ d2Fij = Z*d2Pij(:,:)*Z ;
+
+ % d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
+ d2APC = d2Pij(:,:)*Z;
+
+ d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
+
+ d2Kij= d2Pij(:,:)*Z*iF + P0(:,:)*Z*d2iF + dP0(:,:,jj)*Z*diFi + dP0(:,:,ii)*Z*diFj;
+
+ d2KCP = d2Kij*Z*P0(:,:) + K0*Z*d2Pij(:,:) + dKi*Z*dP0(:,:,jj) + dKj*Z*dP0(:,:,ii) ;
+
+ dtmpi = dP0(:,:,ii) - dK0(:,:,ii)*Z*P0(:,:) - K0*Z*dP0(:,:,ii);
+ dtmpj = dP0(:,:,jj) - dK0(:,:,jj)*Z*P0(:,:) - K0*Z*dP0(:,:,jj);
+ d2tmp = d2Pij - d2KCP;
+
+ d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
+
+ d2K(:,:,ii,jj) = d2Kij; %#ok<NASGU>
+ d2P1(:,jcount) = dyn_vech(d2AtmpA + d2Omij); %#ok<*NASGU>
+ d2S(:,:,ii,jj) = d2Fij;
+ % d2iS(:,:,ii,jj) = d2iF;
+ d2K(:,:,jj,ii) = d2Kij; %#ok<NASGU>
+ % d2P1(:,:,jj,ii) = d2AtmpA + d2Omij; %#ok<*NASGU>
+ d2S(:,:,jj,ii) = d2Fij;
end
end
diff --git a/matlab/kalman/likelihood/kalman_filter.m b/matlab/kalman/likelihood/kalman_filter.m
index 398ab1f4bda30f6e3f6779d879c98ae9e6b6b440..e0dcafadcb21bbdf75bbc1f3d69da0402f4f10e3 100644
--- a/matlab/kalman/likelihood/kalman_filter.m
+++ b/matlab/kalman/likelihood/kalman_filter.m
@@ -237,7 +237,7 @@ if analytic_derivation,
dlikk = dlikk/2;
if analytic_derivation==2 || asy_hess,
if asy_hess==0,
- Hess = Hess + tril(Hess,-1)';
+ Hess = Hess + tril(Hess,-1)';
end
Hess = -Hess/2;
end
diff --git a/matlab/kalman/likelihood/kalman_filter_d.m b/matlab/kalman/likelihood/kalman_filter_d.m
index a10cf6f4d2c243eac5011f56db5df5d2bb7fc399..61c4b53fea0f0a5a90da0ba101bcb135c07bc479 100644
--- a/matlab/kalman/likelihood/kalman_filter_d.m
+++ b/matlab/kalman/likelihood/kalman_filter_d.m
@@ -69,10 +69,10 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
s = t-start+1;
v = Y(:,t)-Z*a; %get prediction error v^(0) in (5.13) DK (2012)
Finf = Z*Pinf*Z'; % (5.7) in DK (2012)
- %do case distinction based on whether F_{\infty,t} has full rank or 0 rank
+ %do case distinction based on whether F_{\infty,t} has full rank or 0 rank
if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
if ~all(abs(Finf(:)) < diffuse_kalman_tol) %rank-deficient but not rank 0
- % The univariate diffuse kalman filter should be used instead.
+ % The univariate diffuse kalman filter should be used instead.
return
else %rank of F_{\infty,t} is 0
Fstar = Z*Pstar*Z' + H; % (5.7) in DK (2012)
@@ -81,8 +81,8 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
% The univariate diffuse kalman filter should be used.
return
else %rank 0
- %pathological case, discard draw
- return
+ %pathological case, discard draw
+ return
end
else
iFstar = inv(Fstar);
@@ -95,15 +95,15 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
end
end
else %F_{\infty,t} positive definite
- %To compare to DK (2012), this block makes use of the following transformation
- %Kstar=T^{-1}*K^{(1)}=M_{*}*F^{(1)}+M_{\infty}*F^{(2)}
- % =P_{*}*Z'*F^{(1)}+P_{\infty}*Z'*((-1)*(-F_{\infty}^{-1})*F_{*}*(F_{\infty}^{-1}))
- % =[P_{*}*Z'-Kinf*F_{*})]*F^{(1)}
- %Make use of L^{0}'=(T-K^{(0)}*Z)'=(T-T*M_{\infty}*F^{(1)}*Z)'
- % =(T-T*P_{\infty*Z'*F^{(1)}*Z)'=(T-T*Kinf*Z)'
- % = (T*(I-*Kinf*Z))'=(I-Z'*Kinf')*T'
- %P_{*}=T*P_{\infty}*L^{(1)}+T*P_{*}*L^{(0)}+RQR
- % =T*[(P_{\infty}*(-K^{(1)*Z}))+P_{*}*(I-Z'*Kinf')*T'+RQR]
+ %To compare to DK (2012), this block makes use of the following transformation
+ %Kstar=T^{-1}*K^{(1)}=M_{*}*F^{(1)}+M_{\infty}*F^{(2)}
+ % =P_{*}*Z'*F^{(1)}+P_{\infty}*Z'*((-1)*(-F_{\infty}^{-1})*F_{*}*(F_{\infty}^{-1}))
+ % =[P_{*}*Z'-Kinf*F_{*})]*F^{(1)}
+ %Make use of L^{0}'=(T-K^{(0)}*Z)'=(T-T*M_{\infty}*F^{(1)}*Z)'
+ % =(T-T*P_{\infty*Z'*F^{(1)}*Z)'=(T-T*Kinf*Z)'
+ % = (T*(I-*Kinf*Z))'=(I-Z'*Kinf')*T'
+ %P_{*}=T*P_{\infty}*L^{(1)}+T*P_{*}*L^{(0)}+RQR
+ % =T*[(P_{\infty}*(-K^{(1)*Z}))+P_{*}*(I-Z'*Kinf')*T'+RQR]
dlik(s)= log(det(Finf)); %set w_t to top case in bottom equation page 172, DK (2012)
iFinf = inv(Finf);
Kinf = Pinf*Z'*iFinf; %define Kinf=T^{-1}*K_0 with M_{\infty}=Pinf*Z'
diff --git a/matlab/kalman/likelihood/kalman_filter_fast.m b/matlab/kalman/likelihood/kalman_filter_fast.m
index 4319b5502f7fffa354600bef7b70d143b5a7e330..cc8c3d1f928a570c10a8b3e42f4878228bbb763f 100644
--- a/matlab/kalman/likelihood/kalman_filter_fast.m
+++ b/matlab/kalman/likelihood/kalman_filter_fast.m
@@ -199,7 +199,7 @@ if analytic_derivation,
dlikk = dlikk/2;
if analytic_derivation==2 || asy_hess,
if asy_hess==0,
- Hess = Hess + tril(Hess,-1)';
+ Hess = Hess + tril(Hess,-1)';
end
Hess = -Hess/2;
end
@@ -210,10 +210,10 @@ if t <= last
if analytic_derivation,
if analytic_derivation==2,
[tmp, tmp2] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
- analytic_derivation,Da,DT,DYss,D2a,D2T,D2Yss);
+ analytic_derivation,Da,DT,DYss,D2a,D2T,D2Yss);
else
[tmp, tmp2] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
- analytic_derivation,Da,DT,DYss,asy_hess);
+ analytic_derivation,Da,DT,DYss,asy_hess);
end
likk(s+1:end)=tmp2{1};
dlikk(s+1:end,:)=tmp2{2};
diff --git a/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m b/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
index a497d6e53f1e2af7d998af1cdf120332f9d9025a..5334c301e045789c0f6e2072eff6822b52e0e2e6 100644
--- a/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
+++ b/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
@@ -1,8 +1,8 @@
function [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(data_index,number_of_observations,no_more_missing_observations, ...
- Y, start, last, ...
- a, Pinf, Pstar, ...
- kalman_tol, diffuse_kalman_tol, riccati_tol, presample, ...
- T, R, Q, H, Z, mm, pp, rr)
+ Y, start, last, ...
+ a, Pinf, Pstar, ...
+ kalman_tol, diffuse_kalman_tol, riccati_tol, presample, ...
+ T, R, Q, H, Z, mm, pp, rr)
% Computes the diffuse likelihood of a state space model when some observations are missing.
%
% INPUTS
@@ -91,7 +91,7 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
Finf = ZZ*Pinf*ZZ'; % (5.7) in DK (2012)
if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
if ~all(abs(Finf(:)) < diffuse_kalman_tol) %rank-deficient but not rank 0
- % The univariate diffuse kalman filter should be used.
+ % The univariate diffuse kalman filter should be used.
return
else %rank of F_{\infty,t} is 0
Fstar = ZZ*Pstar*ZZ' + H(d_index,d_index); % (5.7) in DK (2012)
@@ -100,7 +100,7 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
% The univariate diffuse kalman filter should be used.
return
else %rank 0
- %pathological case, discard draw
+ %pathological case, discard draw
return
end
else
diff --git a/matlab/kalman/likelihood/univariate_computeDLIK.m b/matlab/kalman/likelihood/univariate_computeDLIK.m
index 1338a84a6284bb5a30a5b4ac958d3c7e500beb0d..3601ba754400ccc2c82a0c0c9073d178164b1eea 100644
--- a/matlab/kalman/likelihood/univariate_computeDLIK.m
+++ b/matlab/kalman/likelihood/univariate_computeDLIK.m
@@ -67,7 +67,7 @@ if notsteady,
D2K(:,i,j) = D2K(:,i,j) -PZ*D2F(j,i)/F^2 - squeeze(DP(:,Z,i))*DF(j)/F^2 - ...
squeeze(DP(:,Z,j))*DF(i)'/F^2 + 2/F^3*PZ*DF(i)'*DF(j);
D2K(:,j,i) = D2K(:,i,j);
-% D2K = squeeze(D2P(:,Z,:,:))/F;
+ % D2K = squeeze(D2P(:,Z,:,:))/F;
end
end
end
@@ -104,12 +104,12 @@ end
DLIK = DF/F + 2*Dv'/F*v - v^2/F^2*DF;
if nargout==6
Hesst = D2F/F-1/F^2*(DF*DF') + 2*D2v/F*v + 2*(Dv'*Dv)/F - 2*(DF*Dv)*v/F^2 ...
- - v^2/F^2*D2F - 2*v/F^2*(Dv'*DF') + 2*v^2/F^3*(DF*DF');
+ - v^2/F^2*D2F - 2*v/F^2*(Dv'*DF') + 2*v^2/F^3*(DF*DF');
elseif nargout==4,
D2a = 1/F^2*(DF*DF') + 2*(Dv'*Dv)/F ;
-% D2a = -1/F^2*(DF*DF') + 2*(Dv'*Dv)/F + 2*v^2/F^3*(DF*DF') ...
-% - 2*(DF*Dv)*v/F^2 - 2*v/F^2*(Dv'*DF');
-% D2a = +2*(Dv'*Dv)/F + (DF' * DF)/F^2;
+ % D2a = -1/F^2*(DF*DF') + 2*(Dv'*Dv)/F + 2*v^2/F^3*(DF*DF') ...
+ % - 2*(DF*Dv)*v/F^2 - 2*v/F^2*(Dv'*DF');
+ % D2a = +2*(Dv'*Dv)/F + (DF' * DF)/F^2;
end
Da = Da + DK*v+K*Dv;
@@ -145,9 +145,9 @@ if notsteady,
jcount = jcount+1;
tmp = dyn_unvech(D2P(:,jcount));
tmp = tmp - (tmp*Z')*K' - (DP(:,:,j)*Z')*DK(:,i)' ...
- - (DP(:,:,i)*Z')*DK(:,j)' -PZ*D2K(:,j,i)';
+ - (DP(:,:,i)*Z')*DK(:,j)' -PZ*D2K(:,j,i)';
D2P(:,jcount) = dyn_vech(tmp);
-% D2P(:,:,i,j) = D2P(:,:,j,i);
+ % D2P(:,:,i,j) = D2P(:,:,j,i);
end
end
else
@@ -160,7 +160,7 @@ if notsteady,
D2PZ = tmp(:,Z);
tmp = tmp - D2PZ*K' - DPZ(:,j)*DK(:,i)'- DPZ(:,i)*DK(:,j)' - PZ*squeeze(D2K(:,j,i))';
D2P(:,jcount) = dyn_vech(tmp);
-% D2P(:,:,i,j) = D2P(:,:,j,i);
+ % D2P(:,:,i,j) = D2P(:,:,j,i);
end
end
diff --git a/matlab/kalman/likelihood/univariate_computeDstate.m b/matlab/kalman/likelihood/univariate_computeDstate.m
index a535a0a5189919f72000f688f6d11b5e0dac4703..52e85c25533ec08f7424990355ecd0af4ed0c7af 100644
--- a/matlab/kalman/likelihood/univariate_computeDstate.m
+++ b/matlab/kalman/likelihood/univariate_computeDstate.m
@@ -43,12 +43,12 @@ if nargout>2,
if notsteady,
tmp = dyn_unvech(D2P(:,jcount));
tmp = T*tmp*T' +DT(:,:,i)*DP(:,:,j)*T'+T*DP(:,:,j)*DT(:,:,i)' + ...
- DT(:,:,j)*DP(:,:,i)*T'+T*DP(:,:,i)*DT(:,:,j)' + ...
- DT(:,:,j)*P*DT(:,:,i)'+DT(:,:,i)*P*DT(:,:,j)'+ ...
- reshape(D2T(:,jcount),size(T))*P*T'+T*P*reshape(D2T(:,jcount),size(T))' + ...
- dyn_unvech(D2Om(:,jcount));
+ DT(:,:,j)*DP(:,:,i)*T'+T*DP(:,:,i)*DT(:,:,j)' + ...
+ DT(:,:,j)*P*DT(:,:,i)'+DT(:,:,i)*P*DT(:,:,j)'+ ...
+ reshape(D2T(:,jcount),size(T))*P*T'+T*P*reshape(D2T(:,jcount),size(T))' + ...
+ dyn_unvech(D2Om(:,jcount));
D2P(:,jcount) = dyn_vech(tmp);
-% D2P(:,:,i,j) = D2P(:,:,j,i);
+ % D2P(:,:,i,j) = D2P(:,:,j,i);
end
end
end
diff --git a/matlab/kalman/likelihood/univariate_kalman_filter.m b/matlab/kalman/likelihood/univariate_kalman_filter.m
index dd4559cd5f7560b4e7a7b86194ed68a7550d68d3..9f9571a6456d9c909bd7756338e9bd58f96a7f84 100644
--- a/matlab/kalman/likelihood/univariate_kalman_filter.m
+++ b/matlab/kalman/likelihood/univariate_kalman_filter.m
@@ -229,7 +229,7 @@ if analytic_derivation,
DLIK = DLIK/2;
dlik = dlik/2;
if analytic_derivation==2 || asy_hess,
-% Hess = (Hess + Hess')/2;
+ % Hess = (Hess + Hess')/2;
Hess = -Hess/2;
end
end
@@ -239,10 +239,10 @@ if t <= last
if analytic_derivation,
if analytic_derivation==2,
[tmp, tmp2] = univariate_kalman_filter_ss(Y,t,last,a,P,kalman_tol,T,H,Z,pp,Zflag, ...
- analytic_derivation,Da,DT,DYss,DP,DH,D2a,D2T,D2Yss,D2P);
+ analytic_derivation,Da,DT,DYss,DP,DH,D2a,D2T,D2Yss,D2P);
else
[tmp, tmp2] = univariate_kalman_filter_ss(Y,t,last,a,P,kalman_tol,T,H,Z,pp,Zflag, ...
- analytic_derivation,Da,DT,DYss,DP,DH,asy_hess);
+ analytic_derivation,Da,DT,DYss,DP,DH,asy_hess);
end
lik(s+1:end,:)=tmp2{1};
dlik(s+1:end,:)=tmp2{2};
diff --git a/matlab/kalman/likelihood/univariate_kalman_filter_ss.m b/matlab/kalman/likelihood/univariate_kalman_filter_ss.m
index cbc1d5775351fc9abf61bdda94c0d17905ef1c45..dd2623b8ee59ba2ee9d8e2f5357545803d174b5e 100644
--- a/matlab/kalman/likelihood/univariate_kalman_filter_ss.m
+++ b/matlab/kalman/likelihood/univariate_kalman_filter_ss.m
@@ -171,7 +171,7 @@ if analytic_derivation,
likk = {likk, dlikk};
end
if analytic_derivation==2 || asy_hess,
-% Hess = (Hess + Hess')/2;
+ % Hess = (Hess + Hess')/2;
Hess = -Hess/2;
LIK={LIK,DLIK,Hess};
elseif analytic_derivation==1,
diff --git a/matlab/lmmcp/catstruct.m b/matlab/lmmcp/catstruct.m
index 540bc99d7aff0ad6f4581141f63fffdb872429d0..040c0b7ed9101d5e0abab9b1435bb54199ee8f00 100644
--- a/matlab/lmmcp/catstruct.m
+++ b/matlab/lmmcp/catstruct.m
@@ -1,174 +1,174 @@
-function A = catstruct(varargin)
-% CATSTRUCT Concatenate or merge structures with different fieldnames
-% X = CATSTRUCT(S1,S2,S3,...) merges the structures S1, S2, S3 ...
-% into one new structure X. X contains all fields present in the various
-% structures. An example:
-%
-% A.name = 'Me' ;
-% B.income = 99999 ;
-% X = catstruct(A,B)
-% % -> X.name = 'Me' ;
-% % X.income = 99999 ;
-%
-% If a fieldname is not unique among structures (i.e., a fieldname is
-% present in more than one structure), only the value from the last
-% structure with this field is used. In this case, the fields are
-% alphabetically sorted. A warning is issued as well. An axample:
-%
-% S1.name = 'Me' ;
-% S2.age = 20 ; S3.age = 30 ; S4.age = 40 ;
-% S5.honest = false ;
-% Y = catstruct(S1,S2,S3,S4,S5) % use value from S4
-%
-% The inputs can be array of structures. All structures should have the
-% same size. An example:
-%
-% C(1).bb = 1 ; C(2).bb = 2 ;
-% D(1).aa = 3 ; D(2).aa = 4 ;
-% CD = catstruct(C,D) % CD is a 1x2 structure array with fields bb and aa
-%
-% The last input can be the string 'sorted'. In this case,
-% CATSTRUCT(S1,S2, ..., 'sorted') will sort the fieldnames alphabetically.
-% To sort the fieldnames of a structure A, you could use
-% CATSTRUCT(A,'sorted') but I recommend ORDERFIELDS for doing that.
-%
-% When there is nothing to concatenate, the result will be an empty
-% struct (0x0 struct array with no fields).
-%
-% NOTE: To concatenate similar arrays of structs, you can use simple
-% concatenation:
-% A = dir('*.mat') ; B = dir('*.m') ; C = [A ; B] ;
-%
-% See also CAT, STRUCT, FIELDNAMES, STRUCT2CELL, ORDERFIELDS
-
-% for Matlab R13 and up
-% version 3.0 (mar 2013)
-% Originally downloaded from MATLAB central:
-% http://www.mathworks.com/matlabcentral/fileexchange/7842-catstruct
-
-% Copyright (C) 2005 Jos van der Geest <jos@jasen.nl>
-% Copyright (C) 2013 Christophe Gouel
-% Copyright (C) 2016 Dynare Team
-%
-% Redistribution and use in source and binary forms, with or without
-% modification, are permitted provided that the following conditions are
-% met:
-%
-% * Redistributions of source code must retain the above copyright
-% notice, this list of conditions and the following disclaimer.
-% * Redistributions in binary form must reproduce the above copyright
-% notice, this list of conditions and the following disclaimer in
-% the documentation and/or other materials provided with the distribution
-%
-% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-% POSSIBILITY OF SUCH DAMAGE.
-
-% History
-% Created in 2005
-% Revisions
-% 2.0 (sep 2007) removed bug when dealing with fields containing cell
-% arrays (Thanks to Rene Willemink)
-% 2.1 (sep 2008) added warning and error identifiers
-% 2.2 (oct 2008) fixed error when dealing with empty structs (Thanks to
-% Lars Barring)
-% 3.0 (mar 2013) fixed problem when the inputs were array of structures
-% (thanks to Tor Inge Birkenes for pointing this out).
-% Rephrased the help section as well.
-
-narginchk(1, Inf);
-N = nargin ;
-
-if ~isstruct(varargin{end}),
- if isequal(varargin{end},'sorted'),
- sorted = 1 ;
- N = N-1 ;
- if N<1
- error('catstruct: wrong number of input arguments') ;
- end
- else
- error('catstruct:InvalidArgument','Last argument should be a structure, or the string "sorted".') ;
- end
-else
- sorted = 0 ;
-end
-
-sz0 = [] ; % used to check that all inputs have the same size
-
-% used to check for a few trivial cases
-NonEmptyInputs = false(N,1) ;
-NonEmptyInputsN = 0 ;
-
-% used to collect the fieldnames and the inputs
-FN = cell(N,1) ;
-VAL = cell(N,1) ;
-
-% parse the inputs
-for ii=1:N,
- X = varargin{ii} ;
- if ~isstruct(X),
- error('catstruct:InvalidArgument',['Argument #' num2str(ii) ' is not a structure.']) ;
- end
-
- if ~isempty(X),
- % empty structs are ignored
- if ii > 1 && ~isempty(sz0)
- if ~isequal(size(X), sz0)
- error('catstruct:UnequalSizes','All structures should have the same size.') ;
- end
- else
- sz0 = size(X) ;
- end
- NonEmptyInputsN = NonEmptyInputsN + 1 ;
- NonEmptyInputs(ii) = true ;
- FN{ii} = fieldnames(X) ;
- VAL{ii} = struct2cell(X) ;
- end
-end
-
-if NonEmptyInputsN == 0
- % all structures were empty
- A = struct([]) ;
-elseif NonEmptyInputsN == 1,
- % there was only one non-empty structure
- A = varargin{NonEmptyInputs} ;
- if sorted,
- A = orderfields(A) ;
- end
-else
- % there is actually something to concatenate
- FN = cat(1,FN{:}) ;
- VAL = cat(1,VAL{:}) ;
- FN = squeeze(FN) ;
- VAL = squeeze(VAL) ;
- MatlabVersion = version;
- if isoctave || str2double(MatlabVersion(end-5:end-2))<2013 % Equivalent to, but faster than if verLessThan('matlab','8.1')
- [UFN,ind] = unique(FN) ;
- else
- [UFN,ind] = unique(FN,'legacy') ;
- end
-
- if numel(UFN) ~= numel(FN),
- warning('catstruct:DuplicatesFound','Fieldnames are not unique between structures.') ;
- sorted = 1 ;
- end
-
- if sorted,
- VAL = VAL(ind,:) ;
- FN = FN(ind,:) ;
- end
-
- A = cell2struct(VAL, FN);
- A = reshape(A, sz0) ; % reshape into original format
-end
-
-
-
+function A = catstruct(varargin)
+% CATSTRUCT Concatenate or merge structures with different fieldnames
+% X = CATSTRUCT(S1,S2,S3,...) merges the structures S1, S2, S3 ...
+% into one new structure X. X contains all fields present in the various
+% structures. An example:
+%
+% A.name = 'Me' ;
+% B.income = 99999 ;
+% X = catstruct(A,B)
+% % -> X.name = 'Me' ;
+% % X.income = 99999 ;
+%
+% If a fieldname is not unique among structures (i.e., a fieldname is
+% present in more than one structure), only the value from the last
+% structure with this field is used. In this case, the fields are
+% alphabetically sorted. A warning is issued as well. An axample:
+%
+% S1.name = 'Me' ;
+% S2.age = 20 ; S3.age = 30 ; S4.age = 40 ;
+% S5.honest = false ;
+% Y = catstruct(S1,S2,S3,S4,S5) % use value from S4
+%
+% The inputs can be array of structures. All structures should have the
+% same size. An example:
+%
+% C(1).bb = 1 ; C(2).bb = 2 ;
+% D(1).aa = 3 ; D(2).aa = 4 ;
+% CD = catstruct(C,D) % CD is a 1x2 structure array with fields bb and aa
+%
+% The last input can be the string 'sorted'. In this case,
+% CATSTRUCT(S1,S2, ..., 'sorted') will sort the fieldnames alphabetically.
+% To sort the fieldnames of a structure A, you could use
+% CATSTRUCT(A,'sorted') but I recommend ORDERFIELDS for doing that.
+%
+% When there is nothing to concatenate, the result will be an empty
+% struct (0x0 struct array with no fields).
+%
+% NOTE: To concatenate similar arrays of structs, you can use simple
+% concatenation:
+% A = dir('*.mat') ; B = dir('*.m') ; C = [A ; B] ;
+%
+% See also CAT, STRUCT, FIELDNAMES, STRUCT2CELL, ORDERFIELDS
+
+% for Matlab R13 and up
+% version 3.0 (mar 2013)
+% Originally downloaded from MATLAB central:
+% http://www.mathworks.com/matlabcentral/fileexchange/7842-catstruct
+
+% Copyright (C) 2005 Jos van der Geest <jos@jasen.nl>
+% Copyright (C) 2013 Christophe Gouel
+% Copyright (C) 2016 Dynare Team
+%
+% Redistribution and use in source and binary forms, with or without
+% modification, are permitted provided that the following conditions are
+% met:
+%
+% * Redistributions of source code must retain the above copyright
+% notice, this list of conditions and the following disclaimer.
+% * Redistributions in binary form must reproduce the above copyright
+% notice, this list of conditions and the following disclaimer in
+% the documentation and/or other materials provided with the distribution
+%
+% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+% POSSIBILITY OF SUCH DAMAGE.
+
+% History
+% Created in 2005
+% Revisions
+% 2.0 (sep 2007) removed bug when dealing with fields containing cell
+% arrays (Thanks to Rene Willemink)
+% 2.1 (sep 2008) added warning and error identifiers
+% 2.2 (oct 2008) fixed error when dealing with empty structs (Thanks to
+% Lars Barring)
+% 3.0 (mar 2013) fixed problem when the inputs were array of structures
+% (thanks to Tor Inge Birkenes for pointing this out).
+% Rephrased the help section as well.
+
+narginchk(1, Inf);
+N = nargin ;
+
+if ~isstruct(varargin{end}),
+ if isequal(varargin{end},'sorted'),
+ sorted = 1 ;
+ N = N-1 ;
+ if N<1
+ error('catstruct: wrong number of input arguments') ;
+ end
+ else
+ error('catstruct:InvalidArgument','Last argument should be a structure, or the string "sorted".') ;
+ end
+else
+ sorted = 0 ;
+end
+
+sz0 = [] ; % used to check that all inputs have the same size
+
+% used to check for a few trivial cases
+NonEmptyInputs = false(N,1) ;
+NonEmptyInputsN = 0 ;
+
+% used to collect the fieldnames and the inputs
+FN = cell(N,1) ;
+VAL = cell(N,1) ;
+
+% parse the inputs
+for ii=1:N,
+ X = varargin{ii} ;
+ if ~isstruct(X),
+ error('catstruct:InvalidArgument',['Argument #' num2str(ii) ' is not a structure.']) ;
+ end
+
+ if ~isempty(X),
+ % empty structs are ignored
+ if ii > 1 && ~isempty(sz0)
+ if ~isequal(size(X), sz0)
+ error('catstruct:UnequalSizes','All structures should have the same size.') ;
+ end
+ else
+ sz0 = size(X) ;
+ end
+ NonEmptyInputsN = NonEmptyInputsN + 1 ;
+ NonEmptyInputs(ii) = true ;
+ FN{ii} = fieldnames(X) ;
+ VAL{ii} = struct2cell(X) ;
+ end
+end
+
+if NonEmptyInputsN == 0
+ % all structures were empty
+ A = struct([]) ;
+elseif NonEmptyInputsN == 1,
+ % there was only one non-empty structure
+ A = varargin{NonEmptyInputs} ;
+ if sorted,
+ A = orderfields(A) ;
+ end
+else
+ % there is actually something to concatenate
+ FN = cat(1,FN{:}) ;
+ VAL = cat(1,VAL{:}) ;
+ FN = squeeze(FN) ;
+ VAL = squeeze(VAL) ;
+ MatlabVersion = version;
+ if isoctave || str2double(MatlabVersion(end-5:end-2))<2013 % Equivalent to, but faster than if verLessThan('matlab','8.1')
+ [UFN,ind] = unique(FN) ;
+ else
+ [UFN,ind] = unique(FN,'legacy') ;
+ end
+
+ if numel(UFN) ~= numel(FN),
+ warning('catstruct:DuplicatesFound','Fieldnames are not unique between structures.') ;
+ sorted = 1 ;
+ end
+
+ if sorted,
+ VAL = VAL(ind,:) ;
+ FN = FN(ind,:) ;
+ end
+
+ A = cell2struct(VAL, FN);
+ A = reshape(A, sz0) ; % reshape into original format
+end
+
+
+
diff --git a/matlab/lmmcp/dyn_lmmcp_func.m b/matlab/lmmcp/dyn_lmmcp_func.m
index 936f98306dae861e8c01a7d30f90620bdeffb308..f08da101ea1c0b3a821525c7d36a96461f6b2978 100644
--- a/matlab/lmmcp/dyn_lmmcp_func.m
+++ b/matlab/lmmcp/dyn_lmmcp_func.m
@@ -20,37 +20,37 @@ function [F,A] = dyn_lmmcp_func(x, model_dynamic, Y0, YT, exo_simul, params, ...
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- Y = [Y0; x; YT];
-
- F = zeros(periods*ny,1);
- if nargout == 2
- A = sparse([],[],[],periods*ny,periods*ny,periods*nnzA);
- end
+Y = [Y0; x; YT];
- i_rows = 1:ny;
- i_cols = find(lead_lag_incidence');
- i_cols_A = i_cols;
+F = zeros(periods*ny,1);
+if nargout == 2
+ A = sparse([],[],[],periods*ny,periods*ny,periods*nnzA);
+end
- for it = 2:(periods+1)
-
- [res,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
- steady_state,it);
- F(i_rows) = res(eq_index);
-
- if nargout == 2
- if it == 2
- A(i_rows,i_cols_A1) = jacobian(eq_index,i_cols_1);
- elseif it == periods+1
- A(i_rows,i_cols_A(i_cols_T)) = jacobian(eq_index,i_cols_T);
- else
- A(i_rows,i_cols_A) = jacobian(eq_index,i_cols_j);
- end
- end
-
- i_rows = i_rows + ny;
- i_cols = i_cols + ny;
- if nargout == 2 && it > 2
- i_cols_A = i_cols_A + ny;
+i_rows = 1:ny;
+i_cols = find(lead_lag_incidence');
+i_cols_A = i_cols;
+
+for it = 2:(periods+1)
+
+ [res,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
+ steady_state,it);
+ F(i_rows) = res(eq_index);
+
+ if nargout == 2
+ if it == 2
+ A(i_rows,i_cols_A1) = jacobian(eq_index,i_cols_1);
+ elseif it == periods+1
+ A(i_rows,i_cols_A(i_cols_T)) = jacobian(eq_index,i_cols_T);
+ else
+ A(i_rows,i_cols_A) = jacobian(eq_index,i_cols_j);
end
end
+
+ i_rows = i_rows + ny;
+ i_cols = i_cols + ny;
+ if nargout == 2 && it > 2
+ i_cols_A = i_cols_A + ny;
+ end
+end
diff --git a/matlab/lmmcp/get_complementarity_conditions.m b/matlab/lmmcp/get_complementarity_conditions.m
index d1ea1771fdd2031835f1a0130b8fc4c22b058bc2..291627f592b68a8727c46770a77bf3abc6c98772 100644
--- a/matlab/lmmcp/get_complementarity_conditions.m
+++ b/matlab/lmmcp/get_complementarity_conditions.m
@@ -55,14 +55,14 @@ for i=1:size(etags,1)
str = etags{i,3};
kop = strfind(etags{i,3},'<');
if ~isempty(kop)
- k = find(strcmp(strtrim(str(1:kop-1)),cellstr(M.endo_names))); %get variable index with restriction
- if isempty(k)
- error(sprintf(['Complementarity condition %s: variable %s is ' ...
- 'not recognized'],etags{i,3},strtrim(str(1:kop-1))))
- end
- ub(k) = str2num(str(kop+1:end));
- eq_index(etags{i,1}) = k;
- eq_index(k) = etags{i,1};
+ k = find(strcmp(strtrim(str(1:kop-1)),cellstr(M.endo_names))); %get variable index with restriction
+ if isempty(k)
+ error(sprintf(['Complementarity condition %s: variable %s is ' ...
+ 'not recognized'],etags{i,3},strtrim(str(1:kop-1))))
+ end
+ ub(k) = str2num(str(kop+1:end));
+ eq_index(etags{i,1}) = k;
+ eq_index(k) = etags{i,1};
else
kop = strfind(etags{i,3},'>');
if ~isempty(kop)
diff --git a/matlab/lmmcp/lmmcp.m b/matlab/lmmcp/lmmcp.m
index 1ac1c5e0a444a916ba17bde44212f58d6bc54923..76cb49a9872e9a5819024fa8e5157e19e98698d3 100644
--- a/matlab/lmmcp/lmmcp.m
+++ b/matlab/lmmcp/lmmcp.m
@@ -1,625 +1,625 @@
-function [x,FVAL,EXITFLAG,OUTPUT,JACOB] = lmmcp(FUN,x,lb,ub,options,varargin)
-% LMMCP solves a mixed complementarity problem.
-%
-% LMMCP uses a semismooth least squares formulation. The method applies a
-% Levenberg-Marquardt/Gauss-Newton algorithm to a least-squares formulation.
-%
-% X = LMMCP(FUN,X0) tries to solve the system of nonlinear equations F(X)=0 and
-% starts at the vector X0. FUN accepts a vector X and return a vector F of equation
-% values F evaluated at X and, as second output if required, a matrix J, the
-% Jacobian evaluated at X.
-%
-% X = LMMCP(FUN,X0,LB,UB) solves the mixed complementarity problem of the form:
-% LB =X => F(X)>0,
-% LB<=X<=UB => F(X)=0,
-% X =UB => F(X)<0.
-%
-% X = LMMCP(FUN,X0,LB,UB,OPTIONS) solves the MCP problem using the options
-% defined in the structure OPTIONS. Main fields are
-% Display : control the display of iterations, 'none' (default),
-% 'iter-detailed' or 'final-detailed'
-% Switch from phase I to phase II
-% preprocess : activate preprocessor for phase I (default = 1)
-% presteps : number of iterations in phase I (default = 20)
-% Termination parameters
-% MaxIter : Maximum number of iterations (default = 500)
-% tmin : safeguard stepsize (default = 1E-12)
-% TolFun : Termination tolerance on the function value, a positive
-% scalar (default = sqrt(eps))
-% Stepsize parameters
-% m : number of previous function values to use in the nonmonotone
-% line search rule (default = 10)
-% kwatch : maximum number of steps (default = 20 and should not be
-% smaller than m)
-% watchdog : activate the watchdog strategy (default = 1)
-% Ther are other minor parameters. Please see the code for their default values
-% and interpretation.
-%
-% [X,FVAL] = LMMCP(FUN,X0,...) returns the value of the equations FUN at X.
-%
-% [X,FVAL,EXITFLAG] = LMMCP(FUN,X0,...) returns EXITFLAG that describes the exit
-% conditions. Possible values are
-% 1 : LMMCP converged to a root
-% 0 : Too many iterations
-% -1 :
-%
-% [X,FVAL,EXITFLAG,OUTPUT] = LMMCP(FUN,X0,...) returns the structure OUTPUT that
-% contains the number of iterations (OUTPUT.iterations), the value of the merit
-% function (OUTPUT.Psix), and the norm of the derivative of the merit function
-% (OUTPUT.normDPsix).
-%
-% [X,FVAL,EXITFLAG,OUTPUT,JACOB] = LMMCP(FUN,X0,...) returns JACOB the Jacobian
-% of FUN evaluated at X.
-%
-% More details of the main program may be found in the following paper:
-%
-% Christian Kanzow and Stefania Petra: On a semismooth least squares formulation of
-% complementarity problems with gap reduction. Optimization Methods and Software
-% 19, 2004, pp. 507-525.
-%
-% In addition, the current implementation uses a preprocessor which is the
-% projected Levenberg-Marquardt step from the following preprint:
-%
-% Christian Kanzow and Stefania Petra: Projected filter trust region methods for a
-% semismooth least squares formulation of mixed complementarity
-% problems. Optimization Methods and Software
-% 22, 2007, pp. 713-735.
-%
-% A user's guide is also available:
-%
-% Christian Kanzow and Stefania Petra (2005).
-% LMMCP --- A Levenberg-Marquardt-type MATLAB Solver for Mixed Complementarity Problems.
-% University of Wuerzburg.
-% http://www.mathematik.uni-wuerzburg.de/~kanzow/software/UserGuide.pdf
-%
-% This is a modification by Christophe Gouel of the original files, which can be
-% downloaded from:
-% http://www.mathematik.uni-wuerzburg.de/~kanzow/software/LMMCP.zip
-%
-% Written by Christian Kanzow and Stefania Petra
-% Institute of Applied Mathematics and Statistics
-% University of Wuerzburg
-% Am Hubland
-% 97074 Wuerzburg
-% GERMANY
-%
-% e-mail: kanzow@mathematik.uni-wuerzburg.de
-% petra@mathematik.uni-wuerzburg.de
-%
-% Christian Kanzow sent a private message to Dynare Team on July 8, 2014,
-% confirming the free software status of lmmcp and granting unlimited
-% permission to use, copy, modifiy or redistribute the file.
-
-% Copyright (C) 2005 Christian Kanzow and Stefania Petra
-% Copyright (C) 2013 Christophe Gouel
-% Copyright (C) 2014 Dynare Team
-%
-% Unlimited permission is granted to everyone to use, copy, modify or
-% distribute this software.
-
-%% Initialization
-defaultopt = struct(...
- 'beta', 0.55,...
- 'Big', 1e10,...
- 'delta', 5,...
- 'deltamin', 1,...
- 'Display', 'none',...
- 'epsilon1', 1e-6,...
- 'eta', 0.95,...
- 'kwatch', 20,...
- 'lambda1', 0.1,...
- 'm', 10,...
- 'MaxIter', 500,...
- 'null', 1e-10,...
- 'preprocess', 1,...
- 'presteps', 20,...
- 'sigma', 1e-4,...
- 'sigma1', 0.5,...
- 'sigma2', 2,...
- 'tmin', 1e-12,...
- 'TolFun', sqrt(eps),...
- 'watchdog', 1);
-
-if nargin < 4
- ub = inf(size(x));
- if nargin < 3
- lb = -inf(size(x));
- end
-end
-
-if nargin < 5 || isempty(options) || ~isstruct(options)
- options = defaultopt;
-else
- warning('off','catstruct:DuplicatesFound')
- options = catstruct(defaultopt,options);
-end
-
-warning('off','MATLAB:rankDeficientMatrix')
-
-switch options.Display
- case {'off','none'}
- verbosity = 0;
- case {'iter','iter-detailed'}
- verbosity = 2;
- case {'final','final-detailed'}
- verbosity = 1;
- otherwise
- verbosity = 0;
-end
-
-% parameter settings
-eps1 = options.epsilon1;
-eps2 = 0.5*options.TolFun^2;
-null = options.null;
-Big = options.Big;
-
-% maximal number of iterations
-kmax = options.MaxIter;
-
-% choice of lambda
-lambda1 = options.lambda1;
-lambda2 = 1-lambda1;
-
-% steplength parameters
-beta = options.beta;
-sigma = options.sigma;
-tmin = options.tmin;
-
-% parameters watchdog and nonmonotone line search; redefined later
-m = options.m;
-kwatch = options.kwatch;
-watchdog = options.watchdog; % 1=watchdog strategy active, otherwise not
-
-% parameters for preprocessor
-preprocess = options.preprocess; % 1=preprocessor used, otherwise not
-presteps = options.presteps; % maximum number of preprocessing steps
-
-% trust-region parameters for preprocessor
-delta = options.delta;
-deltamin = options.deltamin;
-sigma1 = options.sigma1;
-sigma2 = options.sigma2;
-eta = options.eta;
-
-% initializations
-k = 0;
-k_main = 0;
-
-% compute a feasible starting point by projection
-x = max(lb,min(x,ub));
-
-n = length(x);
-OUTPUT.Dim = n;
-
-% definition of index sets I_l, I_u and I_lu
-Indexset = zeros(n,1);
-I_l = lb>-Big & ub>Big;
-I_u = lb<-Big & ub<Big;
-I_lu = lb>-Big & ub<Big;
-Indexset(I_l) = 1;
-Indexset(I_u) = 2;
-Indexset(I_lu) = 3;
-
-% function evaluations
-[Fx,DFx] = feval(FUN,x,varargin{:});
-
-% choice of NCP-function and corresponding evaluations
-Phix = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset);
-normPhix = norm(Phix);
-Psix = 0.5*(Phix'*Phix);
-DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
-DPsix = DPhix'*Phix;
-normDPsix = norm(DPsix);
-
-% save initial values
-x0 = x;
-Phix0 = Phix;
-Psix0 = Psix;
-DPhix0 = DPhix;
-DPsix0 = DPsix;
-normDPsix0 = normDPsix;
-
-% watchdog strategy
-aux = zeros(m,1);
-aux(1) = Psix;
-MaxPsi = Psix;
-
-if watchdog==1
- kbest = k;
- xbest = x;
- Phibest = Phix;
- Psibest = Psix;
- DPhibest = DPhix;
- DPsibest = DPsix;
- normDPsibest = normDPsix;
-end
-
-% initial output
-if verbosity > 1
- fprintf(' k Psi(x) || DPsi(x) || stepsize\n');
- disp('====================================================================')
- disp('********************* Output at starting point *********************')
- fprintf('%4.0f %24.5e %24.5e\n',k,Psix,normDPsix);
-end
-
-%% Preprocessor using local method
-
-if preprocess==1
-
- if verbosity > 1
- disp('************************** Preprocessor ****************************')
- end
-
- normpLM=1;
- while (k < presteps) && (Psix > eps2) && (normpLM>null)
- k = k+1;
-
- % choice of Levenberg-Marquardt parameter, note that we do not use
- % the condition estimator for large-scale problems, although this
- % may cause numerical problems in some examples
-
- i = false;
- mu = 0;
- if n<100
- i = true;
- mu = 1e-16;
- if condest(DPhix'*DPhix)>1e25
- mu = 1e-6/(k+1);
- end
- end
- if i
- pLM = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
- else
- pLM = -DPhix\Phix;
- end
- normpLM = norm(pLM);
-
- % compute the projected Levenberg-Marquard step onto box Xk
- lbnew = max(min(lb-x,0),-delta);
- ubnew = min(max(ub-x,0),delta);
- d = max(lbnew,min(pLM,ubnew));
- xnew = x+d;
-
- % function evaluations etc.
- [Fxnew,DFxnew] = feval(FUN,xnew,varargin{:});
- Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
- Psixnew = 0.5*(Phixnew'*Phixnew);
- normPhixnew = norm(Phixnew);
-
- % update of delta
- if normPhixnew<=eta*normPhix
- delta = max(deltamin,sigma2*delta);
- elseif normPhixnew>5*eta*normPhix
- delta = max(deltamin,sigma1*delta);
- end
-
- % update
- x = xnew;
- Fx = Fxnew;
- DFx = DFxnew;
- Phix = Phixnew;
- Psix = Psixnew;
- normPhix = normPhixnew;
- DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
- DPsix = DPhix'*Phix;
- normDPsix = norm(DPsix,inf);
-
- % output at each iteration
- t=1;
- if verbosity > 1
- fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
- end
- end
-end
-
-% terminate program or redefine current iterate as original initial point
-if preprocess==1 && Psix<eps2
- if verbosity > 0
- fprintf('Psix = %1.4e\nnormDPsix = %1.4e\n',Psix,normDPsix);
- disp('Approximate solution found.')
- end
- EXITFLAG = 1;
- FVAL = Fx;
- OUTPUT.iterations = k;
- OUTPUT.Psix = Psix;
- OUTPUT.normDPsix = normDPsix;
- JACOB = DFx;
- return
-elseif preprocess==1 && Psix>=eps2
- x=x0;
- Phix=Phix0;
- Psix=Psix0;
- DPhix=DPhix0;
- DPsix=DPsix0;
- if verbosity > 1
- disp('******************** Restart with initial point ********************')
- fprintf('%4.0f %24.5e %24.5e\n',k_main,Psix0,normDPsix0);
- end
-end
-
-%% Main algorithm
-
-if verbosity > 1
- disp('************************** Main program ****************************')
-end
-
-while (k < kmax) && (Psix > eps2)
-
- % choice of Levenberg-Marquardt parameter, note that we do not use
- % the condition estimator for large-scale problems, although this
- % may cause numerical problems in some examples
-
- i = false;
- if n<100
- i = true;
- mu = 1e-16;
- if condest(DPhix'*DPhix)>1e25
- mu = 1e-1/(k+1);
- end
- end
-
- % compute a Levenberg-Marquard direction
-
- if i
- d = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
- else
- d = -DPhix\Phix;
- end
-
- % computation of steplength t using the nonmonotone Armijo-rule
- % starting with the 6-th iteration
-
- % computation of steplength t using the monotone Armijo-rule if
- % d is a 'good' descent direction or k<=5
-
- t = 1;
- xnew = x+d;
- Fxnew = feval(FUN,xnew,varargin{:});
- Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
- Psixnew = 0.5*(Phixnew'*Phixnew);
- const = sigma*DPsix'*d;
-
- while (Psixnew > MaxPsi + const*t) && (t > tmin)
- t = t*beta;
- xnew = x+t*d;
- Fxnew = feval(FUN,xnew,varargin{:});
- Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
- Psixnew = 0.5*(Phixnew'*Phixnew);
- end
-
- % updatings
- x = xnew;
- Fx = Fxnew;
- Phix = Phixnew;
- Psix = Psixnew;
- [junk,DFx] = feval(FUN,x,varargin{:});
- DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
- DPsix = DPhix'*Phix;
- normDPsix = norm(DPsix);
- k = k+1;
- k_main = k_main+1;
-
- if k_main<=5
- aux(mod(k_main,m)+1) = Psix;
- MaxPsi = Psix;
- else
- aux(mod(k_main,m)+1) = Psix;
- MaxPsi = max(aux);
- end
-
- % updatings for the watchdog strategy
- if watchdog ==1
- if Psix<Psibest
- kbest = k;
- xbest = x;
- Phibest = Phix;
- Psibest = Psix;
- DPhibest = DPhix;
- DPsibest = DPsix;
- normDPsibest = normDPsix;
- elseif k-kbest>kwatch
- x=xbest;
- Phix=Phibest;
- Psix=Psibest;
- DPhix=DPhibest;
- DPsix=DPsibest;
- normDPsix=normDPsibest;
- MaxPsi=Psix;
- end
- end
-
- if verbosity > 1
- % output at each iteration
- fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
- end
-end
-
-%% Final output
-if Psix<=eps2
- EXITFLAG = 1;
- if verbosity > 0, disp('Approximate solution found.'); end
-elseif k>=kmax
- EXITFLAG = 0;
- if verbosity > 0, disp('Maximum iteration number reached.'); end
-elseif normDPsix<=eps1
- EXITFLAG = -1; % Provisoire
- if verbosity > 0, disp('Approximate stationary point found.'); end
-else
- EXITFLAG = -1; % Provisoire
- if verbosity > 0, disp('No solution found.'); end
-end
-
-FVAL = Fx;
-OUTPUT.iterations = k;
-OUTPUT.Psix = Psix;
-OUTPUT.normDPsix = normDPsix;
-JACOB = DFx;
-
-%% Subfunctions
-
-function y = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset)
-%% PHI
-
-y = zeros(2*n,1);
-phi_u = sqrt((ub-x).^2+Fx.^2)-ub+x+Fx;
-LZ = false(n,1); % logical zero
-
-I0 = Indexset==0;
-y(I0) = -lambda1*Fx(I0);
-y([LZ; I0]) = -lambda2*Fx(I0);
-
-I1 = Indexset==1;
-y(I1) = lambda1*(-x(I1)+lb(I1)-Fx(I1)+sqrt((x(I1)-lb(I1)).^2+Fx(I1).^2));
-y([LZ; I1]) = lambda2*max(0,x(I1)-lb(I1)).*max(0,Fx(I1));
-
-I2 = Indexset==2;
-y(I2) = -lambda1*phi_u(I2);
-y([LZ; I2]) = lambda2*max(0,ub(I2)-x(I2)).*max(0,-Fx(I2));
-
-I3 = Indexset==3;
-y(I3) = lambda1*(sqrt((x(I3)-lb(I3)).^2+phi_u(I3).^2)-x(I3)+lb(I3)-phi_u(I3));
-y([LZ; I3]) = lambda2*(max(0,x(I3)-lb(I3)).*max(0,Fx(I3))+max(0,ub(I3)-x(I3)).*max(0,-Fx(I3)));
-
-
-function H = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset)
-%% DPHI evaluates an element of the C-subdifferential of operator Phi
-
-null = 1e-8;
-beta_l = zeros(n,1);
-beta_u = zeros(n,1);
-alpha_l = zeros(n,1);
-alpha_u = zeros(n,1);
-
-
-z = zeros(n,1);
-H2 = sparse(n,n);
-
-I = abs(x-lb)<=null & abs(Fx)<=null;
-beta_l(I) = 1;
-z(I) = 1;
-
-I = abs(ub-x)<=null & abs(Fx)<=null;
-beta_u(I) = 1;
-z(I) = 1;
-
-I = x-lb>=-null & Fx>=-null;
-alpha_l(I) = 1;
-
-I = ub-x>=-null & Fx<=null;
-alpha_u(I) = 1;
-
-Da = zeros(n,1);
-Db = zeros(n,1);
-
-I = 1:n;
-
-I0 = Indexset==0;
-Da(I0) = 0;
-Db(I0) = -1;
-H2(I0,:) = -DFx(I0,:);
-
-I1 = Indexset==1;
-denom1 = zeros(n,1);
-denom2 = zeros(n,1);
-if any(I1)
- denom1(I1) = max(null,sqrt((x(I1)-lb(I1)).^2+Fx(I1).^2));
- denom2(I1) = max(null,sqrt(z(I1).^2+(DFx(I1,:)*z).^2));
-end
-
-I1b = Indexset==1 & beta_l==0;
-Da(I1b) = (x(I1b)-lb(I1b))./denom1(I1b)-1;
-Db(I1b) = Fx(I1b)./denom1(I1b)-1;
-I1b = Indexset==1 & beta_l~=0;
-if any(I1b)
- Da(I1b) = z(I1b)./denom2(I1b)-1;
- Db(I1b) = (DFx(I1b,:)*z)./denom2(I1b)-1;
-end
-
-I1a = I(Indexset==1 & alpha_l==1);
-if any(I1a)
- H2(I1a,:) = spdiags(x(I1a)-lb(I1a), 0, length(I1a), length(I1a))*DFx(I1a,:) +...
- sparse(1:length(I1a),I1a,Fx(I1a),length(I1a),n,length(I1a));
-end
-
-I2 = Indexset==2;
-denom1 = zeros(n,1);
-denom2 = zeros(n,1);
-if any(I2)
- denom1(I2) = max(null,sqrt((ub(I2)-x(I2)).^2+Fx(I2).^2));
- denom2(I2) = max(null,sqrt(z(I2).^2+(DFx(I2,:)*z).^2));
-end
-
-I2b = Indexset==2 & beta_u==0;
-Da(I2b) = (ub(I2b)-x(I2b))./denom1(I2b)-1;
-Db(I2b) = -Fx(I2b)./denom1(I2b)-1;
-I2b = Indexset==2 & beta_u~=0;
-if any(I2b)
- Da(I2b) = -z(I2b)./denom2(I2b)-1;
- Db(I2b) = -(DFx(I2b,:)*z)./denom2(I2b)-1;
-end
-
-I2a = I(Indexset==2 & alpha_u==1);
-if any(I2a)
- H2(I2a,:) = bsxfun(@times,x(I2a)-ub(I2a),DFx(I2a,:))+...
- sparse(1:length(I2a),I2a,Fx(I2a),length(I2a),n,length(I2a));
-end
-
-I3 = Indexset==3;
-phi = zeros(n,1);
-ai = zeros(n,1);
-bi = zeros(n,1);
-ci = zeros(n,1);
-di = zeros(n,1);
-denom1 = zeros(n,1);
-denom2 = zeros(n,1);
-denom3 = zeros(n,1);
-denom4 = zeros(n,1);
-if any(I3)
- phi(I3) = -ub(I3)+x(I3)+Fx(I3)+sqrt((ub(I3)-x(I3)).^2+Fx(I3).^2);
- denom1(I3) = max(null,sqrt((x(I3)-lb(I3)).^2+phi(I3).^2));
- denom2(I3) = max(null,sqrt(z(I3).^2+(DFx(I3,:)*z).^2));
- denom3(I3) = max(null,sqrt((ub(I3)-x(I3)).^2+Fx(I3).^2));
- denom4(I3) = max(null,sqrt(z(I3).^2));
-end
-
-I3bu = Indexset==3 & beta_u==0;
-ci(I3bu) = (x(I3bu)-ub(I3bu))./denom3(I3bu)+1;
-di(I3bu) = Fx(I3bu)./denom3(I3bu)+1;
-I3bu = Indexset==3 & beta_u~=0;
-if any(I3bu)
- ci(I3bu) = 1+z(I3bu)./denom2(I3bu);
- di(I3bu) = 1+(DFx(I3bu,:)*z)./denom2(I3bu);
-end
-
-I3bl = Indexset==3 & beta_l==0;
-ai(I3bl) = (x(I3bl)-lb(I3bl))./denom1(I3bl)-1;
-bi(I3bl) = phi(I3bl)./denom1(I3bl)-1;
-I3bl = Indexset==3 & beta_l~=0;
-if any(I3bl)
- ai(I3bl) = z(I3bl)./denom4(I3bl)-1;
- bi(I3bl) = (ci(I3bl).*z(I3bl)+(di(I3bl,ones(1,n)).*DFx(I3bl,:))*z)./denom4(I3bl)-1;
-end
-
-Da(I3) = ai(I3)+bi(I3).*ci(I3);
-Db(I3) = bi(I3).*di(I3);
-
-I3a = I(Indexset==3 & alpha_l==1 & alpha_u==1);
-if any(I3a)
- H2(I3a,:) = bsxfun(@times,-lb(I3a)-ub(I3a)+2*x(I3a),DFx(I3a,:))+...
- 2*sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
-end
-I3a = I(Indexset==3 & alpha_l==1 & alpha_u~=1);
-if any(I3a)
- H2(I3a,:) = bsxfun(@times,x(I3a)-lb(I3a),DFx(I3a,:))+...
- sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
-end
-I3a = I(Indexset==3 & alpha_l~=1 & alpha_u==1);
-if any(I3a)
- H2(I3a,:) = bsxfun(@times,x(I3a)-ub(I3a),DFx(I3a,:))+...
- sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
-end
-
-H1 = spdiags(Db,0,length(Db),length(Db))*DFx;
-H1 = H1 + spdiags(Da, 0, length(Da), length(Da));
-
-H = [lambda1*H1; lambda2*H2];
+function [x,FVAL,EXITFLAG,OUTPUT,JACOB] = lmmcp(FUN,x,lb,ub,options,varargin)
+% LMMCP solves a mixed complementarity problem.
+%
+% LMMCP uses a semismooth least squares formulation. The method applies a
+% Levenberg-Marquardt/Gauss-Newton algorithm to a least-squares formulation.
+%
+% X = LMMCP(FUN,X0) tries to solve the system of nonlinear equations F(X)=0 and
+% starts at the vector X0. FUN accepts a vector X and return a vector F of equation
+% values F evaluated at X and, as second output if required, a matrix J, the
+% Jacobian evaluated at X.
+%
+% X = LMMCP(FUN,X0,LB,UB) solves the mixed complementarity problem of the form:
+% LB =X => F(X)>0,
+% LB<=X<=UB => F(X)=0,
+% X =UB => F(X)<0.
+%
+% X = LMMCP(FUN,X0,LB,UB,OPTIONS) solves the MCP problem using the options
+% defined in the structure OPTIONS. Main fields are
+% Display : control the display of iterations, 'none' (default),
+% 'iter-detailed' or 'final-detailed'
+% Switch from phase I to phase II
+% preprocess : activate preprocessor for phase I (default = 1)
+% presteps : number of iterations in phase I (default = 20)
+% Termination parameters
+% MaxIter : Maximum number of iterations (default = 500)
+% tmin : safeguard stepsize (default = 1E-12)
+% TolFun : Termination tolerance on the function value, a positive
+% scalar (default = sqrt(eps))
+% Stepsize parameters
+% m : number of previous function values to use in the nonmonotone
+% line search rule (default = 10)
+% kwatch : maximum number of steps (default = 20 and should not be
+% smaller than m)
+% watchdog : activate the watchdog strategy (default = 1)
+% Ther are other minor parameters. Please see the code for their default values
+% and interpretation.
+%
+% [X,FVAL] = LMMCP(FUN,X0,...) returns the value of the equations FUN at X.
+%
+% [X,FVAL,EXITFLAG] = LMMCP(FUN,X0,...) returns EXITFLAG that describes the exit
+% conditions. Possible values are
+% 1 : LMMCP converged to a root
+% 0 : Too many iterations
+% -1 :
+%
+% [X,FVAL,EXITFLAG,OUTPUT] = LMMCP(FUN,X0,...) returns the structure OUTPUT that
+% contains the number of iterations (OUTPUT.iterations), the value of the merit
+% function (OUTPUT.Psix), and the norm of the derivative of the merit function
+% (OUTPUT.normDPsix).
+%
+% [X,FVAL,EXITFLAG,OUTPUT,JACOB] = LMMCP(FUN,X0,...) returns JACOB the Jacobian
+% of FUN evaluated at X.
+%
+% More details of the main program may be found in the following paper:
+%
+% Christian Kanzow and Stefania Petra: On a semismooth least squares formulation of
+% complementarity problems with gap reduction. Optimization Methods and Software
+% 19, 2004, pp. 507-525.
+%
+% In addition, the current implementation uses a preprocessor which is the
+% projected Levenberg-Marquardt step from the following preprint:
+%
+% Christian Kanzow and Stefania Petra: Projected filter trust region methods for a
+% semismooth least squares formulation of mixed complementarity
+% problems. Optimization Methods and Software
+% 22, 2007, pp. 713-735.
+%
+% A user's guide is also available:
+%
+% Christian Kanzow and Stefania Petra (2005).
+% LMMCP --- A Levenberg-Marquardt-type MATLAB Solver for Mixed Complementarity Problems.
+% University of Wuerzburg.
+% http://www.mathematik.uni-wuerzburg.de/~kanzow/software/UserGuide.pdf
+%
+% This is a modification by Christophe Gouel of the original files, which can be
+% downloaded from:
+% http://www.mathematik.uni-wuerzburg.de/~kanzow/software/LMMCP.zip
+%
+% Written by Christian Kanzow and Stefania Petra
+% Institute of Applied Mathematics and Statistics
+% University of Wuerzburg
+% Am Hubland
+% 97074 Wuerzburg
+% GERMANY
+%
+% e-mail: kanzow@mathematik.uni-wuerzburg.de
+% petra@mathematik.uni-wuerzburg.de
+%
+% Christian Kanzow sent a private message to Dynare Team on July 8, 2014,
+% confirming the free software status of lmmcp and granting unlimited
+% permission to use, copy, modifiy or redistribute the file.
+
+% Copyright (C) 2005 Christian Kanzow and Stefania Petra
+% Copyright (C) 2013 Christophe Gouel
+% Copyright (C) 2014 Dynare Team
+%
+% Unlimited permission is granted to everyone to use, copy, modify or
+% distribute this software.
+
+%% Initialization
+defaultopt = struct(...
+ 'beta', 0.55,...
+ 'Big', 1e10,...
+ 'delta', 5,...
+ 'deltamin', 1,...
+ 'Display', 'none',...
+ 'epsilon1', 1e-6,...
+ 'eta', 0.95,...
+ 'kwatch', 20,...
+ 'lambda1', 0.1,...
+ 'm', 10,...
+ 'MaxIter', 500,...
+ 'null', 1e-10,...
+ 'preprocess', 1,...
+ 'presteps', 20,...
+ 'sigma', 1e-4,...
+ 'sigma1', 0.5,...
+ 'sigma2', 2,...
+ 'tmin', 1e-12,...
+ 'TolFun', sqrt(eps),...
+ 'watchdog', 1);
+
+if nargin < 4
+ ub = inf(size(x));
+ if nargin < 3
+ lb = -inf(size(x));
+ end
+end
+
+if nargin < 5 || isempty(options) || ~isstruct(options)
+ options = defaultopt;
+else
+ warning('off','catstruct:DuplicatesFound')
+ options = catstruct(defaultopt,options);
+end
+
+warning('off','MATLAB:rankDeficientMatrix')
+
+switch options.Display
+ case {'off','none'}
+ verbosity = 0;
+ case {'iter','iter-detailed'}
+ verbosity = 2;
+ case {'final','final-detailed'}
+ verbosity = 1;
+ otherwise
+ verbosity = 0;
+end
+
+% parameter settings
+eps1 = options.epsilon1;
+eps2 = 0.5*options.TolFun^2;
+null = options.null;
+Big = options.Big;
+
+% maximal number of iterations
+kmax = options.MaxIter;
+
+% choice of lambda
+lambda1 = options.lambda1;
+lambda2 = 1-lambda1;
+
+% steplength parameters
+beta = options.beta;
+sigma = options.sigma;
+tmin = options.tmin;
+
+% parameters watchdog and nonmonotone line search; redefined later
+m = options.m;
+kwatch = options.kwatch;
+watchdog = options.watchdog; % 1=watchdog strategy active, otherwise not
+
+% parameters for preprocessor
+preprocess = options.preprocess; % 1=preprocessor used, otherwise not
+presteps = options.presteps; % maximum number of preprocessing steps
+
+% trust-region parameters for preprocessor
+delta = options.delta;
+deltamin = options.deltamin;
+sigma1 = options.sigma1;
+sigma2 = options.sigma2;
+eta = options.eta;
+
+% initializations
+k = 0;
+k_main = 0;
+
+% compute a feasible starting point by projection
+x = max(lb,min(x,ub));
+
+n = length(x);
+OUTPUT.Dim = n;
+
+% definition of index sets I_l, I_u and I_lu
+Indexset = zeros(n,1);
+I_l = lb>-Big & ub>Big;
+I_u = lb<-Big & ub<Big;
+I_lu = lb>-Big & ub<Big;
+Indexset(I_l) = 1;
+Indexset(I_u) = 2;
+Indexset(I_lu) = 3;
+
+% function evaluations
+[Fx,DFx] = feval(FUN,x,varargin{:});
+
+% choice of NCP-function and corresponding evaluations
+Phix = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset);
+normPhix = norm(Phix);
+Psix = 0.5*(Phix'*Phix);
+DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
+DPsix = DPhix'*Phix;
+normDPsix = norm(DPsix);
+
+% save initial values
+x0 = x;
+Phix0 = Phix;
+Psix0 = Psix;
+DPhix0 = DPhix;
+DPsix0 = DPsix;
+normDPsix0 = normDPsix;
+
+% watchdog strategy
+aux = zeros(m,1);
+aux(1) = Psix;
+MaxPsi = Psix;
+
+if watchdog==1
+ kbest = k;
+ xbest = x;
+ Phibest = Phix;
+ Psibest = Psix;
+ DPhibest = DPhix;
+ DPsibest = DPsix;
+ normDPsibest = normDPsix;
+end
+
+% initial output
+if verbosity > 1
+ fprintf(' k Psi(x) || DPsi(x) || stepsize\n');
+ disp('====================================================================')
+ disp('********************* Output at starting point *********************')
+ fprintf('%4.0f %24.5e %24.5e\n',k,Psix,normDPsix);
+end
+
+%% Preprocessor using local method
+
+if preprocess==1
+
+ if verbosity > 1
+ disp('************************** Preprocessor ****************************')
+ end
+
+ normpLM=1;
+ while (k < presteps) && (Psix > eps2) && (normpLM>null)
+ k = k+1;
+
+ % choice of Levenberg-Marquardt parameter, note that we do not use
+ % the condition estimator for large-scale problems, although this
+ % may cause numerical problems in some examples
+
+ i = false;
+ mu = 0;
+ if n<100
+ i = true;
+ mu = 1e-16;
+ if condest(DPhix'*DPhix)>1e25
+ mu = 1e-6/(k+1);
+ end
+ end
+ if i
+ pLM = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
+ else
+ pLM = -DPhix\Phix;
+ end
+ normpLM = norm(pLM);
+
+ % compute the projected Levenberg-Marquard step onto box Xk
+ lbnew = max(min(lb-x,0),-delta);
+ ubnew = min(max(ub-x,0),delta);
+ d = max(lbnew,min(pLM,ubnew));
+ xnew = x+d;
+
+ % function evaluations etc.
+ [Fxnew,DFxnew] = feval(FUN,xnew,varargin{:});
+ Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
+ Psixnew = 0.5*(Phixnew'*Phixnew);
+ normPhixnew = norm(Phixnew);
+
+ % update of delta
+ if normPhixnew<=eta*normPhix
+ delta = max(deltamin,sigma2*delta);
+ elseif normPhixnew>5*eta*normPhix
+ delta = max(deltamin,sigma1*delta);
+ end
+
+ % update
+ x = xnew;
+ Fx = Fxnew;
+ DFx = DFxnew;
+ Phix = Phixnew;
+ Psix = Psixnew;
+ normPhix = normPhixnew;
+ DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
+ DPsix = DPhix'*Phix;
+ normDPsix = norm(DPsix,inf);
+
+ % output at each iteration
+ t=1;
+ if verbosity > 1
+ fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
+ end
+ end
+end
+
+% terminate program or redefine current iterate as original initial point
+if preprocess==1 && Psix<eps2
+ if verbosity > 0
+ fprintf('Psix = %1.4e\nnormDPsix = %1.4e\n',Psix,normDPsix);
+ disp('Approximate solution found.')
+ end
+ EXITFLAG = 1;
+ FVAL = Fx;
+ OUTPUT.iterations = k;
+ OUTPUT.Psix = Psix;
+ OUTPUT.normDPsix = normDPsix;
+ JACOB = DFx;
+ return
+elseif preprocess==1 && Psix>=eps2
+ x=x0;
+ Phix=Phix0;
+ Psix=Psix0;
+ DPhix=DPhix0;
+ DPsix=DPsix0;
+ if verbosity > 1
+ disp('******************** Restart with initial point ********************')
+ fprintf('%4.0f %24.5e %24.5e\n',k_main,Psix0,normDPsix0);
+ end
+end
+
+%% Main algorithm
+
+if verbosity > 1
+ disp('************************** Main program ****************************')
+end
+
+while (k < kmax) && (Psix > eps2)
+
+ % choice of Levenberg-Marquardt parameter, note that we do not use
+ % the condition estimator for large-scale problems, although this
+ % may cause numerical problems in some examples
+
+ i = false;
+ if n<100
+ i = true;
+ mu = 1e-16;
+ if condest(DPhix'*DPhix)>1e25
+ mu = 1e-1/(k+1);
+ end
+ end
+
+ % compute a Levenberg-Marquard direction
+
+ if i
+ d = [DPhix; sqrt(mu)*speye(n)]\[-Phix; zeros(n,1)];
+ else
+ d = -DPhix\Phix;
+ end
+
+ % computation of steplength t using the nonmonotone Armijo-rule
+ % starting with the 6-th iteration
+
+ % computation of steplength t using the monotone Armijo-rule if
+ % d is a 'good' descent direction or k<=5
+
+ t = 1;
+ xnew = x+d;
+ Fxnew = feval(FUN,xnew,varargin{:});
+ Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
+ Psixnew = 0.5*(Phixnew'*Phixnew);
+ const = sigma*DPsix'*d;
+
+ while (Psixnew > MaxPsi + const*t) && (t > tmin)
+ t = t*beta;
+ xnew = x+t*d;
+ Fxnew = feval(FUN,xnew,varargin{:});
+ Phixnew = Phi(xnew,Fxnew,lb,ub,lambda1,lambda2,n,Indexset);
+ Psixnew = 0.5*(Phixnew'*Phixnew);
+ end
+
+ % updatings
+ x = xnew;
+ Fx = Fxnew;
+ Phix = Phixnew;
+ Psix = Psixnew;
+ [junk,DFx] = feval(FUN,x,varargin{:});
+ DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
+ DPsix = DPhix'*Phix;
+ normDPsix = norm(DPsix);
+ k = k+1;
+ k_main = k_main+1;
+
+ if k_main<=5
+ aux(mod(k_main,m)+1) = Psix;
+ MaxPsi = Psix;
+ else
+ aux(mod(k_main,m)+1) = Psix;
+ MaxPsi = max(aux);
+ end
+
+ % updatings for the watchdog strategy
+ if watchdog ==1
+ if Psix<Psibest
+ kbest = k;
+ xbest = x;
+ Phibest = Phix;
+ Psibest = Psix;
+ DPhibest = DPhix;
+ DPsibest = DPsix;
+ normDPsibest = normDPsix;
+ elseif k-kbest>kwatch
+ x=xbest;
+ Phix=Phibest;
+ Psix=Psibest;
+ DPhix=DPhibest;
+ DPsix=DPsibest;
+ normDPsix=normDPsibest;
+ MaxPsi=Psix;
+ end
+ end
+
+ if verbosity > 1
+ % output at each iteration
+ fprintf('%4.0f %24.5e %24.5e %11.7g\n',k,Psix,normDPsix,t);
+ end
+end
+
+%% Final output
+if Psix<=eps2
+ EXITFLAG = 1;
+ if verbosity > 0, disp('Approximate solution found.'); end
+elseif k>=kmax
+ EXITFLAG = 0;
+ if verbosity > 0, disp('Maximum iteration number reached.'); end
+elseif normDPsix<=eps1
+ EXITFLAG = -1; % Provisoire
+ if verbosity > 0, disp('Approximate stationary point found.'); end
+else
+ EXITFLAG = -1; % Provisoire
+ if verbosity > 0, disp('No solution found.'); end
+end
+
+FVAL = Fx;
+OUTPUT.iterations = k;
+OUTPUT.Psix = Psix;
+OUTPUT.normDPsix = normDPsix;
+JACOB = DFx;
+
+%% Subfunctions
+
+function y = Phi(x,Fx,lb,ub,lambda1,lambda2,n,Indexset)
+%% PHI
+
+y = zeros(2*n,1);
+phi_u = sqrt((ub-x).^2+Fx.^2)-ub+x+Fx;
+LZ = false(n,1); % logical zero
+
+I0 = Indexset==0;
+y(I0) = -lambda1*Fx(I0);
+y([LZ; I0]) = -lambda2*Fx(I0);
+
+I1 = Indexset==1;
+y(I1) = lambda1*(-x(I1)+lb(I1)-Fx(I1)+sqrt((x(I1)-lb(I1)).^2+Fx(I1).^2));
+y([LZ; I1]) = lambda2*max(0,x(I1)-lb(I1)).*max(0,Fx(I1));
+
+I2 = Indexset==2;
+y(I2) = -lambda1*phi_u(I2);
+y([LZ; I2]) = lambda2*max(0,ub(I2)-x(I2)).*max(0,-Fx(I2));
+
+I3 = Indexset==3;
+y(I3) = lambda1*(sqrt((x(I3)-lb(I3)).^2+phi_u(I3).^2)-x(I3)+lb(I3)-phi_u(I3));
+y([LZ; I3]) = lambda2*(max(0,x(I3)-lb(I3)).*max(0,Fx(I3))+max(0,ub(I3)-x(I3)).*max(0,-Fx(I3)));
+
+
+function H = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset)
+%% DPHI evaluates an element of the C-subdifferential of operator Phi
+
+null = 1e-8;
+beta_l = zeros(n,1);
+beta_u = zeros(n,1);
+alpha_l = zeros(n,1);
+alpha_u = zeros(n,1);
+
+
+z = zeros(n,1);
+H2 = sparse(n,n);
+
+I = abs(x-lb)<=null & abs(Fx)<=null;
+beta_l(I) = 1;
+z(I) = 1;
+
+I = abs(ub-x)<=null & abs(Fx)<=null;
+beta_u(I) = 1;
+z(I) = 1;
+
+I = x-lb>=-null & Fx>=-null;
+alpha_l(I) = 1;
+
+I = ub-x>=-null & Fx<=null;
+alpha_u(I) = 1;
+
+Da = zeros(n,1);
+Db = zeros(n,1);
+
+I = 1:n;
+
+I0 = Indexset==0;
+Da(I0) = 0;
+Db(I0) = -1;
+H2(I0,:) = -DFx(I0,:);
+
+I1 = Indexset==1;
+denom1 = zeros(n,1);
+denom2 = zeros(n,1);
+if any(I1)
+ denom1(I1) = max(null,sqrt((x(I1)-lb(I1)).^2+Fx(I1).^2));
+ denom2(I1) = max(null,sqrt(z(I1).^2+(DFx(I1,:)*z).^2));
+end
+
+I1b = Indexset==1 & beta_l==0;
+Da(I1b) = (x(I1b)-lb(I1b))./denom1(I1b)-1;
+Db(I1b) = Fx(I1b)./denom1(I1b)-1;
+I1b = Indexset==1 & beta_l~=0;
+if any(I1b)
+ Da(I1b) = z(I1b)./denom2(I1b)-1;
+ Db(I1b) = (DFx(I1b,:)*z)./denom2(I1b)-1;
+end
+
+I1a = I(Indexset==1 & alpha_l==1);
+if any(I1a)
+ H2(I1a,:) = spdiags(x(I1a)-lb(I1a), 0, length(I1a), length(I1a))*DFx(I1a,:) +...
+ sparse(1:length(I1a),I1a,Fx(I1a),length(I1a),n,length(I1a));
+end
+
+I2 = Indexset==2;
+denom1 = zeros(n,1);
+denom2 = zeros(n,1);
+if any(I2)
+ denom1(I2) = max(null,sqrt((ub(I2)-x(I2)).^2+Fx(I2).^2));
+ denom2(I2) = max(null,sqrt(z(I2).^2+(DFx(I2,:)*z).^2));
+end
+
+I2b = Indexset==2 & beta_u==0;
+Da(I2b) = (ub(I2b)-x(I2b))./denom1(I2b)-1;
+Db(I2b) = -Fx(I2b)./denom1(I2b)-1;
+I2b = Indexset==2 & beta_u~=0;
+if any(I2b)
+ Da(I2b) = -z(I2b)./denom2(I2b)-1;
+ Db(I2b) = -(DFx(I2b,:)*z)./denom2(I2b)-1;
+end
+
+I2a = I(Indexset==2 & alpha_u==1);
+if any(I2a)
+ H2(I2a,:) = bsxfun(@times,x(I2a)-ub(I2a),DFx(I2a,:))+...
+ sparse(1:length(I2a),I2a,Fx(I2a),length(I2a),n,length(I2a));
+end
+
+I3 = Indexset==3;
+phi = zeros(n,1);
+ai = zeros(n,1);
+bi = zeros(n,1);
+ci = zeros(n,1);
+di = zeros(n,1);
+denom1 = zeros(n,1);
+denom2 = zeros(n,1);
+denom3 = zeros(n,1);
+denom4 = zeros(n,1);
+if any(I3)
+ phi(I3) = -ub(I3)+x(I3)+Fx(I3)+sqrt((ub(I3)-x(I3)).^2+Fx(I3).^2);
+ denom1(I3) = max(null,sqrt((x(I3)-lb(I3)).^2+phi(I3).^2));
+ denom2(I3) = max(null,sqrt(z(I3).^2+(DFx(I3,:)*z).^2));
+ denom3(I3) = max(null,sqrt((ub(I3)-x(I3)).^2+Fx(I3).^2));
+ denom4(I3) = max(null,sqrt(z(I3).^2));
+end
+
+I3bu = Indexset==3 & beta_u==0;
+ci(I3bu) = (x(I3bu)-ub(I3bu))./denom3(I3bu)+1;
+di(I3bu) = Fx(I3bu)./denom3(I3bu)+1;
+I3bu = Indexset==3 & beta_u~=0;
+if any(I3bu)
+ ci(I3bu) = 1+z(I3bu)./denom2(I3bu);
+ di(I3bu) = 1+(DFx(I3bu,:)*z)./denom2(I3bu);
+end
+
+I3bl = Indexset==3 & beta_l==0;
+ai(I3bl) = (x(I3bl)-lb(I3bl))./denom1(I3bl)-1;
+bi(I3bl) = phi(I3bl)./denom1(I3bl)-1;
+I3bl = Indexset==3 & beta_l~=0;
+if any(I3bl)
+ ai(I3bl) = z(I3bl)./denom4(I3bl)-1;
+ bi(I3bl) = (ci(I3bl).*z(I3bl)+(di(I3bl,ones(1,n)).*DFx(I3bl,:))*z)./denom4(I3bl)-1;
+end
+
+Da(I3) = ai(I3)+bi(I3).*ci(I3);
+Db(I3) = bi(I3).*di(I3);
+
+I3a = I(Indexset==3 & alpha_l==1 & alpha_u==1);
+if any(I3a)
+ H2(I3a,:) = bsxfun(@times,-lb(I3a)-ub(I3a)+2*x(I3a),DFx(I3a,:))+...
+ 2*sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
+end
+I3a = I(Indexset==3 & alpha_l==1 & alpha_u~=1);
+if any(I3a)
+ H2(I3a,:) = bsxfun(@times,x(I3a)-lb(I3a),DFx(I3a,:))+...
+ sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
+end
+I3a = I(Indexset==3 & alpha_l~=1 & alpha_u==1);
+if any(I3a)
+ H2(I3a,:) = bsxfun(@times,x(I3a)-ub(I3a),DFx(I3a,:))+...
+ sparse(1:length(I3a),I3a,Fx(I3a),length(I3a),n,length(I3a));
+end
+
+H1 = spdiags(Db,0,length(Db),length(Db))*DFx;
+H1 = H1 + spdiags(Da, 0, length(Da), length(Da));
+
+H = [lambda1*H1; lambda2*H2];
diff --git a/matlab/load_first_mh_history_file.m b/matlab/load_first_mh_history_file.m
index 3177fafe4590b3c74d645c749de1acd72a2de261..67bd70a1c5062e2859a2881ad57a95516c3c6423 100644
--- a/matlab/load_first_mh_history_file.m
+++ b/matlab/load_first_mh_history_file.m
@@ -1,7 +1,7 @@
function info = load_first_mh_history_file(MetropolisFolder, ModelName)
% This routine requires that the MCMC draws were obtained with a dynare version greater than 4.3.3.
-
+
% Copyright (C) 2013 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/load_m_file_data_legacy.m b/matlab/load_m_file_data_legacy.m
index 5a4cdecb904e82633aa935610a64b2278eace86e..9d4b8e34c8155bc27d52a5811e376ecf00afcdd5 100644
--- a/matlab/load_m_file_data_legacy.m
+++ b/matlab/load_m_file_data_legacy.m
@@ -81,82 +81,82 @@ o2WysrOISH = dseries(eval(sprintf(VbO4y7zOlh, U7ORsJ0vy3{:})), [], U7ORsJ0vy3);
return
%@test:1
- % Write a data file
- fid = fopen('example.m','w');
- fwriten(fid, 'a = randn(100,1);');
- fwriten(fid, 'b = randn(100,1);');
- fwriten(fid, 'c = transpose(randn(100,1));');
- fwriten(fid, 'd = randn(100,1);');
- fwriten(fid, 'e = randn(100,2);');
- fwriten(fid, ' ');
- fwriten(fid, 'f = NaN(100,1);');
- fwriten(fid, 'for i=1:100');
- fwriten(fid, ' f(i) = log(rand());')
- fwriten(fid, 'end');
- fclose(fid);
- % Define a set of variables to be loaded.
- listofvariablestobeloaded = {'b', 'a'};
- % Test if we can load the data.
- try
- data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
- delete example.m
- t(1) = 1;
- catch
- t(1) = 0;
- end
- T = all(t);
+% Write a data file
+fid = fopen('example.m','w');
+fwriten(fid, 'a = randn(100,1);');
+fwriten(fid, 'b = randn(100,1);');
+fwriten(fid, 'c = transpose(randn(100,1));');
+fwriten(fid, 'd = randn(100,1);');
+fwriten(fid, 'e = randn(100,2);');
+fwriten(fid, ' ');
+fwriten(fid, 'f = NaN(100,1);');
+fwriten(fid, 'for i=1:100');
+fwriten(fid, ' f(i) = log(rand());')
+fwriten(fid, 'end');
+fclose(fid);
+% Define a set of variables to be loaded.
+listofvariablestobeloaded = {'b', 'a'};
+% Test if we can load the data.
+try
+ data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
+ delete example.m
+ t(1) = 1;
+catch
+ t(1) = 0;
+end
+T = all(t);
%@eof:1
%@test:2
- % Write a data file
- fid = fopen('example.m','w');
- fwriten(fid, 'a = randn(100,1);');
- fwriten(fid, 'b = randn(100,1);');
- fwriten(fid, 'c = transpose(randn(100,1));');
- fwriten(fid, 'd = randn(100,1);');
- fwriten(fid, 'e = randn(100,2);');
- fwriten(fid, ' ');
- fwriten(fid, 'f = NaN(100,1);');
- fwriten(fid, 'for i=1:100');
- fwriten(fid, ' f(i) = log(rand());')
- fwriten(fid, 'end');
- fclose(fid);
- % Define a set of variables to be loaded.
- listofvariablestobeloaded = {'e', 'a'};
- % Test if we can load the data.
- try
- data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
- delete example.m
- t(1) = 0;
- catch
- t(1) = 1;
- end
- T = all(t);
+% Write a data file
+fid = fopen('example.m','w');
+fwriten(fid, 'a = randn(100,1);');
+fwriten(fid, 'b = randn(100,1);');
+fwriten(fid, 'c = transpose(randn(100,1));');
+fwriten(fid, 'd = randn(100,1);');
+fwriten(fid, 'e = randn(100,2);');
+fwriten(fid, ' ');
+fwriten(fid, 'f = NaN(100,1);');
+fwriten(fid, 'for i=1:100');
+fwriten(fid, ' f(i) = log(rand());')
+fwriten(fid, 'end');
+fclose(fid);
+% Define a set of variables to be loaded.
+listofvariablestobeloaded = {'e', 'a'};
+% Test if we can load the data.
+try
+ data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
+ delete example.m
+ t(1) = 0;
+catch
+ t(1) = 1;
+end
+T = all(t);
%@eof:2
%@test:3
- % Write a data file
- fid = fopen('example.m','w');
- fwriten(fid, 'a = randn(100,1);');
- fwriten(fid, 'b = randn(100,1);');
- fwriten(fid, 'c = transpose(randn(100,1));');
- fwriten(fid, 'd = randn(100,1);');
- fwriten(fid, 'e = randn(100,2);');
- fwriten(fid, ' ');
- fwriten(fid, 'f = NaN(100,1);');
- fwriten(fid, 'for i=1:100');
- fwriten(fid, ' f(i) = log(rand());')
- fwriten(fid, 'end');
- fclose(fid);
- % Define a set of variables to be loaded.
- listofvariablestobeloaded = {'c', 'a'};
- % Test if we can load the data.
- try
- data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
- delete example.m
- t(1) = 1;
- catch
- t(1) = 0;
- end
- T = all(t);
+% Write a data file
+fid = fopen('example.m','w');
+fwriten(fid, 'a = randn(100,1);');
+fwriten(fid, 'b = randn(100,1);');
+fwriten(fid, 'c = transpose(randn(100,1));');
+fwriten(fid, 'd = randn(100,1);');
+fwriten(fid, 'e = randn(100,2);');
+fwriten(fid, ' ');
+fwriten(fid, 'f = NaN(100,1);');
+fwriten(fid, 'for i=1:100');
+fwriten(fid, ' f(i) = log(rand());')
+fwriten(fid, 'end');
+fclose(fid);
+% Define a set of variables to be loaded.
+listofvariablestobeloaded = {'c', 'a'};
+% Test if we can load the data.
+try
+ data = load_m_file_data_legacy('example.m', listofvariablestobeloaded);
+ delete example.m
+ t(1) = 1;
+catch
+ t(1) = 0;
+end
+T = all(t);
%@eof:3
diff --git a/matlab/load_mat_file_data_legacy.m b/matlab/load_mat_file_data_legacy.m
index bf8f342b1d2cf4e8b8d07d19a114834f36b1a8b0..c771013b32742da46f6df78041331aab625020ad 100644
--- a/matlab/load_mat_file_data_legacy.m
+++ b/matlab/load_mat_file_data_legacy.m
@@ -32,7 +32,7 @@ else
data_mat=[];
for var_iter=1:length(varobs)
try
- data_mat=[data_mat vec(data_file.(varobs{1,var_iter}))];
+ data_mat=[data_mat vec(data_file.(varobs{1,var_iter}))];
catch
error('makedataset: The variable %s does not have dimensions conformable with the previous one',varobs{1,var_iter});
end
diff --git a/matlab/lyapunov_symm.m b/matlab/lyapunov_symm.m
index 694415d714c08e86e9aac90263830da40cf10dc3..a4618b17111b1e4cdb120523f18c8c5cafb7bfdc 100644
--- a/matlab/lyapunov_symm.m
+++ b/matlab/lyapunov_symm.m
@@ -74,7 +74,7 @@ if method == 3
X_old = X;
X = a * X * at + b;
evol = max(sum(abs(X - X_old))); %norm_1
- %evol = max(sum(abs(X - X_old)')); %norm_inf
+ %evol = max(sum(abs(X - X_old)')); %norm_inf
it_fp = it_fp + 1;
end;
if debug
diff --git a/matlab/marginal_density.m b/matlab/marginal_density.m
index 250bb5ddffc6d1d5946afe3dbf9eb00bd1acdbd6..58e293da87ed75726ccfd72c9eb89e6a1a78cfd3 100644
--- a/matlab/marginal_density.m
+++ b/matlab/marginal_density.m
@@ -172,8 +172,8 @@ if nvx
eval(['oo_.' field_name '_std_at_mode.shocks_std.' name ' = stdh(ip);']);
ip = ip+1;
end
- end
- if nvn
+end
+if nvn
ip = nvx+1;
for i=1:nvn
name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
@@ -181,7 +181,7 @@ if nvx
eval(['oo_.' field_name '_std_at_mode.measurement_errors_std.' name ' = stdh(ip);']);
ip = ip+1;
end
- end
+end
if ncx
ip = nvx+nvn+1;
diff --git a/matlab/metropolis_draw.m b/matlab/metropolis_draw.m
index ad396b30b75becddf9b4204cfa4d7702e8055108..a8e1e602d35afc1f818a558cf6a80f99fba58819 100644
--- a/matlab/metropolis_draw.m
+++ b/matlab/metropolis_draw.m
@@ -82,7 +82,7 @@ if init
end
return
else %not initialization, return one draw
- %get random draw from random chain
+ %get random draw from random chain
ChainNumber = ceil(rand*mh_nblck);
DrawNumber = ceil(rand*NumberOfDraws);
diff --git a/matlab/metropolis_hastings_initialization.m b/matlab/metropolis_hastings_initialization.m
index e18ea098a4f13b50d443b55437b72a3c1c672da7..97a454eeeaa697021cb1a391f0b87e2313fdecdb 100644
--- a/matlab/metropolis_hastings_initialization.m
+++ b/matlab/metropolis_hastings_initialization.m
@@ -335,9 +335,9 @@ elseif options_.mh_recover
if OldMhExists
LastLineNumberInThePreviousMh = record.MhDraws(end-1,3);% Number of lines in the last mh files of the previous session.
LastFileNumberInThePreviousMh = sum(record.MhDraws(1:end-1,2),1);% Number of mh files in the the previous sessions.
- %Test if the last mh files of the previous session were not full yet
+ %Test if the last mh files of the previous session were not full yet
if LastLineNumberInThePreviousMh < MAX_nruns%not full
- %store starting point if whole chain needs to be redone
+ %store starting point if whole chain needs to be redone
NewFile = ones(NumberOfBlocks,1)*LastFileNumberInThePreviousMh;
FirstLine = ones(NumberOfBlocks,1)*(LastLineNumberInThePreviousMh+1);
LastFileFullIndicator=0;
@@ -394,7 +394,7 @@ elseif options_.mh_recover
% How many mh-files are saved in this block?
NumberOfSavedMhFilesInTheCrashedBlck = NumberOfMhFilesPerBlock(FirstBlock);
ExistingDrawsInLastMCFile=0; %initialize: no MCMC draws of current MCMC are in file from last run
- % Check whether last present file is a file included in the last MCMC run
+ % Check whether last present file is a file included in the last MCMC run
if ~LastFileFullIndicator
if NumberOfSavedMhFilesInTheCrashedBlck==NewFile(FirstBlock) %only that last file exists, but no files from current MCMC
loaded_results=load([BaseName '_mh' int2str(NewFile(FirstBlock)) '_blck' int2str(FirstBlock) '.mat']);
@@ -413,13 +413,13 @@ elseif options_.mh_recover
NewFile(FirstBlock)=NewFile(FirstBlock)+1; %set first file to be created to next one
end
end
-% % Correct the number of saved mh files if the crashed Metropolis was not the first session (so
-% % that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
-% % of the current session).
-% if OldMhExists
-% NumberOfSavedMhFilesInTheCrashedBlck = NumberOfSavedMhFilesInTheCrashedBlck - LastFileNumberInThePreviousMh;
-% end
-% NumberOfSavedMhFiles = NumberOfSavedMhFilesInTheCrashedBlck+LastFileNumberInThePreviousMh;
+ % % Correct the number of saved mh files if the crashed Metropolis was not the first session (so
+ % % that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
+ % % of the current session).
+ % if OldMhExists
+ % NumberOfSavedMhFilesInTheCrashedBlck = NumberOfSavedMhFilesInTheCrashedBlck - LastFileNumberInThePreviousMh;
+ % end
+ % NumberOfSavedMhFiles = NumberOfSavedMhFilesInTheCrashedBlck+LastFileNumberInThePreviousMh;
% Correct initial conditions.
if NumberOfSavedMhFilesInTheCrashedBlck<ExpectedNumberOfMhFilesPerBlock
diff --git a/matlab/metropolis_run_analysis.m b/matlab/metropolis_run_analysis.m
index 99b589c820b0d2a2aeb086ea7654266650ab29a6..0433b7f6f98aa1f87bc64d76857442d24ed78f58 100644
--- a/matlab/metropolis_run_analysis.m
+++ b/matlab/metropolis_run_analysis.m
@@ -39,7 +39,7 @@ params = [];
blck = 1;
for i=1:record.LastFileNumber
fname = [M.fname '/metropolis/' M.fname '_mh' int2str(i) '_blck' ...
- int2str(blck) '.mat'];
+ int2str(blck) '.mat'];
if exist(fname,'file')
o=load(fname);
logPost = [logPost; o.logpo2];
diff --git a/matlab/minus_logged_prior_density.m b/matlab/minus_logged_prior_density.m
index 38662541e1bfb192cbc8f0456b33ca0e1086b9c3..60208647f778c335a0a3b0a85339437433665779 100644
--- a/matlab/minus_logged_prior_density.m
+++ b/matlab/minus_logged_prior_density.m
@@ -128,8 +128,8 @@ M_ = set_all_parameters(xparams,EstimatedParams,DynareModel);
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85
%meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
diff --git a/matlab/missing/corrcoef/corrcoef.m b/matlab/missing/corrcoef/corrcoef.m
index c918c61043ddf89d11f1556f2ec315b651389859..ec3b670917bdb1c0ab12859f901e4f19e43cfdc9 100644
--- a/matlab/missing/corrcoef/corrcoef.m
+++ b/matlab/missing/corrcoef/corrcoef.m
@@ -25,28 +25,28 @@ function [R,sig,ci1,ci2,nan_sig] = corrcoef(X,Y,varargin)
% This replaces RANKCORR.M
%
% [...] = CORRCOEF(..., param1, value1, param2, value2, ... );
-% param value
-% 'Mode' type of correlation
-% 'Pearson','parametric'
-% 'Spearman'
-% 'rank'
-% 'rows' how do deal with missing values encoded as NaN's.
-% 'complete': remove all rows with at least one NaN
-% 'pairwise': [default]
-% 'alpha' 0.01 : significance level to compute confidence interval
+% param value
+% 'Mode' type of correlation
+% 'Pearson','parametric'
+% 'Spearman'
+% 'rank'
+% 'rows' how do deal with missing values encoded as NaN's.
+% 'complete': remove all rows with at least one NaN
+% 'pairwise': [default]
+% 'alpha' 0.01 : significance level to compute confidence interval
%
% [R,p,ci1,ci2,nansig] = CORRCOEF(...);
% R is the correlation matrix
-% R(i,j) is the correlation coefficient r between X(:,i) and Y(:,j)
+% R(i,j) is the correlation coefficient r between X(:,i) and Y(:,j)
% p gives the significance of R
-% It tests the null hypothesis that the product moment correlation coefficient is zero
+% It tests the null hypothesis that the product moment correlation coefficient is zero
% using Student's t-test on the statistic t = r*sqrt(N-2)/sqrt(1-r^2)
% where N is the number of samples (Statistics, M. Spiegel, Schaum series).
% p > alpha: do not reject the Null hypothesis: 'R is zero'.
% p < alpha: The alternative hypothesis 'R is larger than zero' is true with probability (1-alpha).
-% ci1 lower (1-alpha) confidence interval
-% ci2 upper (1-alpha) confidence interval
-% If no alpha is provided, the default alpha is 0.01. This can be changed with function flag_implicit_significance.
+% ci1 lower (1-alpha) confidence interval
+% ci2 upper (1-alpha) confidence interval
+% If no alpha is provided, the default alpha is 0.01. This can be changed with function flag_implicit_significance.
% nan_sig p-value whether H0: 'NaN''s are not correlated' could be correct
% if nan_sig < alpha, H1 ('NaNs are correlated') is very likely.
%
@@ -65,7 +65,7 @@ function [R,sig,ci1,ci2,nan_sig] = corrcoef(X,Y,varargin)
% Further recommandation related to the correlation coefficient:
% + LOOK AT THE SCATTERPLOTS to make sure that the relationship is linear
% + Correlation is not causation because
-% it is not clear which parameter is 'cause' and which is 'effect' and
+% it is not clear which parameter is 'cause' and which is 'effect' and
% the observed correlation between two variables might be due to the action of other, unobserved variables.
%
% see also: SUMSKIPNAN, COVM, COV, COR, SPEARMAN, RANKCORR, RANKS,
@@ -124,54 +124,54 @@ function [R,sig,ci1,ci2,nan_sig] = corrcoef(X,Y,varargin)
global FLAG_NANS_OCCURED;
-NARG = nargout; % needed because nargout is not reentrant in Octave, and corrcoef is recursive
+NARG = nargout; % needed because nargout is not reentrant in Octave, and corrcoef is recursive
mode = [];
if nargin==1
- Y = [];
- Mode='Pearson';
+ Y = [];
+ Mode='Pearson';
elseif nargin==0
- fprintf(2,'Error CORRCOEF: Missing argument(s)\n');
+ fprintf(2,'Error CORRCOEF: Missing argument(s)\n');
elseif nargin>1
- if ischar(Y)
- varg = [Y,varargin];
- Y=[];
- else
- varg = varargin;
- end;
+ if ischar(Y)
+ varg = [Y,varargin];
+ Y=[];
+ else
+ varg = varargin;
+ end;
- if length(varg)<1,
- Mode = 'Pearson';
- elseif length(varg)==1,
- Mode = varg{1};
- else
- for k = 2:2:length(varg),
- mode = setfield(mode,lower(varg{k-1}),varg{k});
- end;
- if isfield(mode,'mode')
- Mode = mode.mode;
- end;
+ if length(varg)<1,
+ Mode = 'Pearson';
+ elseif length(varg)==1,
+ Mode = varg{1};
+ else
+ for k = 2:2:length(varg),
+ mode = setfield(mode,lower(varg{k-1}),varg{k});
+ end;
+ if isfield(mode,'mode')
+ Mode = mode.mode;
end;
+ end;
end;
if isempty(Mode) Mode='pearson'; end;
Mode=[Mode,' '];
-FLAG_WARNING = warning; % save warning status
+FLAG_WARNING = warning; % save warning status
warning('off');
[r1,c1]=size(X);
if ~isempty(Y)
- [r2,c2]=size(Y);
- if r1~=r2,
- fprintf(2,'Error CORRCOEF: X and Y must have the same number of observations (rows).\n');
- return;
- end;
- NN = real(~isnan(X)')*real(~isnan(Y));
+ [r2,c2]=size(Y);
+ if r1~=r2,
+ fprintf(2,'Error CORRCOEF: X and Y must have the same number of observations (rows).\n');
+ return;
+ end;
+ NN = real(~isnan(X)')*real(~isnan(Y));
else
- [r2,c2]=size(X);
- NN = real(~isnan(X)')*real(~isnan(X));
+ [r2,c2]=size(X);
+ NN = real(~isnan(X)')*real(~isnan(X));
end;
%%%%% generate combinations using indices for pairwise calculation of the correlation
@@ -196,117 +196,117 @@ if YESNAN,
end;
end;
if isempty(Y),
- IX = ones(c1)-diag(ones(c1,1));
- [jx, jy ] = find(IX);
- [jxo,jyo] = find(IX);
+ IX = ones(c1)-diag(ones(c1,1));
+ [jx, jy ] = find(IX);
+ [jxo,jyo] = find(IX);
R = eye(c1);
else
- IX = sparse([],[],[],c1+c2,c1+c2,c1*c2);
- IX(1:c1,c1+(1:c2)) = 1;
- [jx,jy] = find(IX);
+ IX = sparse([],[],[],c1+c2,c1+c2,c1*c2);
+ IX(1:c1,c1+(1:c2)) = 1;
+ [jx,jy] = find(IX);
- IX = ones(c1,c2);
- [jxo,jyo] = find(IX);
+ IX = ones(c1,c2);
+ [jxo,jyo] = find(IX);
R = zeros(c1,c2);
end;
if strcmp(lower(Mode(1:7)),'pearson');
- % see http://mathworld.wolfram.com/CorrelationCoefficient.html
+ % see http://mathworld.wolfram.com/CorrelationCoefficient.html
if ~YESNAN,
- [S,N,SSQ] = sumskipnan(X,1);
- if ~isempty(Y),
- [S2,N2,SSQ2] = sumskipnan(Y,1);
- CC = X'*Y;
- M1 = S./N;
- M2 = S2./N2;
- cc = CC./NN - M1'*M2;
- R = cc./sqrt((SSQ./N-M1.*M1)'*(SSQ2./N2-M2.*M2));
- else
- CC = X'*X;
- M = S./N;
- cc = CC./NN - M'*M;
- v = SSQ./N - M.*M; %max(N-1,0);
- R = cc./sqrt(v'*v);
- end;
+ [S,N,SSQ] = sumskipnan(X,1);
+ if ~isempty(Y),
+ [S2,N2,SSQ2] = sumskipnan(Y,1);
+ CC = X'*Y;
+ M1 = S./N;
+ M2 = S2./N2;
+ cc = CC./NN - M1'*M2;
+ R = cc./sqrt((SSQ./N-M1.*M1)'*(SSQ2./N2-M2.*M2));
else
- if ~isempty(Y),
- X = [X,Y];
- end;
- for k = 1:length(jx),
- %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
- ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
- [s,n,s2] = sumskipnan(X(ik,[jx(k),jy(k)]),1);
- v = (s2-s.*s./n)./n;
- cc = X(ik,jx(k))'*X(ik,jy(k));
- cc = cc/n(1) - prod(s./n);
- %r(k) = cc./sqrt(prod(v));
- R(jxo(k),jyo(k)) = cc./sqrt(prod(v));
- end;
+ CC = X'*X;
+ M = S./N;
+ cc = CC./NN - M'*M;
+ v = SSQ./N - M.*M; %max(N-1,0);
+ R = cc./sqrt(v'*v);
+ end;
+ else
+ if ~isempty(Y),
+ X = [X,Y];
+ end;
+ for k = 1:length(jx),
+ %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
+ ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
+ [s,n,s2] = sumskipnan(X(ik,[jx(k),jy(k)]),1);
+ v = (s2-s.*s./n)./n;
+ cc = X(ik,jx(k))'*X(ik,jy(k));
+ cc = cc/n(1) - prod(s./n);
+ %r(k) = cc./sqrt(prod(v));
+ R(jxo(k),jyo(k)) = cc./sqrt(prod(v));
+ end;
end
elseif strcmp(lower(Mode(1:4)),'rank');
- % see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
+ % see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
if ~YESNAN,
- if isempty(Y)
- R = corrcoef(ranks(X));
- else
- R = corrcoef(ranks(X),ranks(Y));
- end;
+ if isempty(Y)
+ R = corrcoef(ranks(X));
else
- if ~isempty(Y),
- X = [X,Y];
- end;
- for k = 1:length(jx),
- %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
- ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
- il = ranks(X(ik,[jx(k),jy(k)]));
- R(jxo(k),jyo(k)) = corrcoef(il(:,1),il(:,2));
- end;
+ R = corrcoef(ranks(X),ranks(Y));
+ end;
+ else
+ if ~isempty(Y),
+ X = [X,Y];
+ end;
+ for k = 1:length(jx),
+ %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
+ ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
+ il = ranks(X(ik,[jx(k),jy(k)]));
+ R(jxo(k),jyo(k)) = corrcoef(il(:,1),il(:,2));
+ end;
X = ranks(X);
end;
elseif strcmp(lower(Mode(1:8)),'spearman');
- % see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
- if ~isempty(Y),
- X = [X,Y];
- end;
+ % see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
+ if ~isempty(Y),
+ X = [X,Y];
+ end;
- n = repmat(nan,c1,c2);
+ n = repmat(nan,c1,c2);
- if ~YESNAN,
- iy = ranks(X); % calculates ranks;
+ if ~YESNAN,
+ iy = ranks(X); % calculates ranks;
- for k = 1:length(jx),
- [R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((iy(:,jx(k)) - iy(:,jy(k))).^2); % NN is the number of non-missing values
- end;
- else
- for k = 1:length(jx),
- %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
- ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
- il = ranks(X(ik,[jx(k),jy(k)]));
- % NN is the number of non-missing values
- [R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((il(:,1) - il(:,2)).^2);
- end;
- X = ranks(X);
+ for k = 1:length(jx),
+ [R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((iy(:,jx(k)) - iy(:,jy(k))).^2); % NN is the number of non-missing values
+ end;
+ else
+ for k = 1:length(jx),
+ %ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
+ ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
+ il = ranks(X(ik,[jx(k),jy(k)]));
+ % NN is the number of non-missing values
+ [R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((il(:,1) - il(:,2)).^2);
end;
- R = 1 - 6 * R ./ (n.*(n.*n-1));
+ X = ranks(X);
+ end;
+ R = 1 - 6 * R ./ (n.*(n.*n-1));
elseif strcmp(lower(Mode(1:7)),'partial');
- fprintf(2,'Error CORRCOEF: use PARTCORRCOEF \n',Mode);
+ fprintf(2,'Error CORRCOEF: use PARTCORRCOEF \n',Mode);
- return;
+ return;
elseif strcmp(lower(Mode(1:7)),'kendall');
- fprintf(2,'Error CORRCOEF: mode ''%s'' not implemented yet.\n',Mode);
+ fprintf(2,'Error CORRCOEF: mode ''%s'' not implemented yet.\n',Mode);
- return;
+ return;
else
- fprintf(2,'Error CORRCOEF: unknown mode ''%s''\n',Mode);
+ fprintf(2,'Error CORRCOEF: unknown mode ''%s''\n',Mode);
end;
if (NARG<2),
- warning(FLAG_WARNING); % restore warning status
- return;
+ warning(FLAG_WARNING); % restore warning status
+ return;
end;
@@ -314,7 +314,7 @@ end;
if isfield(mode,'alpha')
alpha = mode.alpha;
elseif exist('flag_implicit_significance','file'),
- alpha = flag_implicit_significance;
+ alpha = flag_implicit_significance;
else
alpha = 0.01;
end;
@@ -324,36 +324,36 @@ end;
% SIGNIFICANCE TEST
R(isnan(R))=0;
tmp = 1 - R.*R;
-tmp(tmp<0) = 0; % prevent tmp<0 i.e. imag(t)~=0
+tmp(tmp<0) = 0; % prevent tmp<0 i.e. imag(t)~=0
t = R.*sqrt(max(NN-2,0)./tmp);
if exist('t_cdf','file');
- sig = t_cdf(t,NN-2);
+ sig = t_cdf(t,NN-2);
elseif exist('tcdf','file')>1;
- sig = tcdf(t,NN-2);
+ sig = tcdf(t,NN-2);
else
- fprintf('CORRCOEF: significance test not completed because of missing TCDF-function\n')
- sig = repmat(nan,size(R));
+ fprintf('CORRCOEF: significance test not completed because of missing TCDF-function\n')
+ sig = repmat(nan,size(R));
end;
sig = 2 * min(sig,1 - sig);
if NARG<3,
warning(FLAG_WARNING); % restore warning status
- return;
+ return;
end;
tmp = R;
-%tmp(ix1 | ix2) = nan; % avoid division-by-zero warning
+%tmp(ix1 | ix2) = nan; % avoid division-by-zero warning
z = log((1+tmp)./(1-tmp))/2; % Fisher transformation [21]
-%sz = 1./sqrt(NN-3); % standard error of z
-sz = sqrt(2)*erfinv(1-alpha)./sqrt(NN-3); % confidence interval for alpha of z
+ %sz = 1./sqrt(NN-3); % standard error of z
+sz = sqrt(2)*erfinv(1-alpha)./sqrt(NN-3); % confidence interval for alpha of z
ci1 = tanh(z-sz);
ci2 = tanh(z+sz);
-%ci1(isnan(ci1))=R(isnan(ci1)); % in case of isnan(ci), the interval limits are exactly the R value
+%ci1(isnan(ci1))=R(isnan(ci1)); % in case of isnan(ci), the interval limits are exactly the R value
%ci2(isnan(ci2))=R(isnan(ci2));
if (NARG<5) || ~YESNAN,
@@ -371,13 +371,13 @@ nan_sig(isnan(nan_R)) = nan;
nan_R(isnan(nan_R)) = 0;
if 0, any(nan_sig(:) < alpha),
- tmp = nan_sig(:); % Hack to skip NaN's in MIN(X)
- min_sig = min(tmp(~isnan(tmp))); % Necessary, because Octave returns NaN rather than min(X) for min(NaN,X)
- fprintf(1,'CORRCOFF Warning: Missing Values (i.e. NaNs) are not independent of data (p-value=%f)\n', min_sig);
- fprintf(1,' Its recommended to remove all samples (i.e. rows) with any missing value (NaN).\n');
- fprintf(1,' The null-hypotheses (NaNs are uncorrelated) is rejected for the following parameter pair(s).\n');
- [ix,iy] = find(nan_sig < alpha);
- disp([ix,iy])
+ tmp = nan_sig(:); % Hack to skip NaN's in MIN(X)
+ min_sig = min(tmp(~isnan(tmp))); % Necessary, because Octave returns NaN rather than min(X) for min(NaN,X)
+ fprintf(1,'CORRCOFF Warning: Missing Values (i.e. NaNs) are not independent of data (p-value=%f)\n', min_sig);
+ fprintf(1,' Its recommended to remove all samples (i.e. rows) with any missing value (NaN).\n');
+ fprintf(1,' The null-hypotheses (NaNs are uncorrelated) is rejected for the following parameter pair(s).\n');
+ [ix,iy] = find(nan_sig < alpha);
+ disp([ix,iy])
end;
%%%%% ----- end of independence check ------
diff --git a/matlab/missing/corrcoef/flag_implicit_skip_nan.m b/matlab/missing/corrcoef/flag_implicit_skip_nan.m
index 409b9f69648ea224f956bdcbb1b968b8f3bcc652..bf162f07188b993b8be0b7bab0eb489fa3e401a5 100644
--- a/matlab/missing/corrcoef/flag_implicit_skip_nan.m
+++ b/matlab/missing/corrcoef/flag_implicit_skip_nan.m
@@ -1,6 +1,6 @@
function FLAG = flag_implicit_skip_nan(i)
% FLAG_IMPLICIT_SKIP_NAN sets and gets default mode for handling NaNs
-% 1 skips NaN's (the default mode if no mode is set)
+% 1 skips NaN's (the default mode if no mode is set)
% 0 NaNs are propagated; input NaN's give NaN's at the output
%
% FLAG = flag_implicit_skip_nan()
@@ -9,15 +9,15 @@ function FLAG = flag_implicit_skip_nan(i)
% flag_implicit_skip_nan(FLAG) % sets mode
%
% prevFLAG = flag_implicit_skip_nan(nextFLAG)
-% gets previous set FLAG and sets FLAG for the future
+% gets previous set FLAG and sets FLAG for the future
% flag_implicit_skip_nan(prevFLAG)
-% resets FLAG to previous mode
+% resets FLAG to previous mode
%
% It is used in:
-% SUMSKIPNAN, MEDIAN, QUANTILES, TRIMEAN
+% SUMSKIPNAN, MEDIAN, QUANTILES, TRIMEAN
% and affects many other functions like:
-% CENTER, KURTOSIS, MAD, MEAN, MOMENT, RMS, SEM, SKEWNESS,
-% STATISTIC, STD, VAR, ZSCORE etc.
+% CENTER, KURTOSIS, MAD, MEAN, MOMENT, RMS, SEM, SKEWNESS,
+% STATISTIC, STD, VAR, ZSCORE etc.
%
% The mode is stored in the global variable FLAG_implicit_skip_nan
% It is recommended to use flag_implicit_skip_nan(1) as default and
@@ -40,7 +40,7 @@ function FLAG = flag_implicit_skip_nan(i)
% along with this program; if not, write to the Free Software
% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
-% $Id: flag_implicit_skip_nan.m 8351 2011-06-24 17:35:07Z carandraug $
+% $Id: flag_implicit_skip_nan.m 8351 2011-06-24 17:35:07Z carandraug $
% Copyright (C) 2001-2003,2009 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014 Dynare Team
% This function is part of the NaN-toolbox
@@ -49,7 +49,7 @@ function FLAG = flag_implicit_skip_nan(i)
persistent FLAG_implicit_skip_nan;
-%% if strcmp(version,'3.6'), FLAG_implicit_skip_nan=(1==1); end; %% hack for the use with Freemat3.6
+%% if strcmp(version,'3.6'), FLAG_implicit_skip_nan=(1==1); end; %% hack for the use with Freemat3.6
%%% set DEFAULT value of FLAG
if isempty(FLAG_implicit_skip_nan),
diff --git a/matlab/missing/corrcoef/sumskipnan.m b/matlab/missing/corrcoef/sumskipnan.m
index 78f8ecbc1e40854d7c96083f11d7315b5947b6c1..15768dc09c939dc3657df2ab30561ca43aacf476 100644
--- a/matlab/missing/corrcoef/sumskipnan.m
+++ b/matlab/missing/corrcoef/sumskipnan.m
@@ -13,13 +13,13 @@ function [o,count,SSQ] = sumskipnan(x, DIM, W)
% [Y,N,SSQ] = sumskipnan(x [,DIM])
% [...] = sumskipnan(x, DIM, W)
%
-% x input data
-% DIM dimension (default: [])
-% empty DIM sets DIM to first non singleton dimension
-% W weight vector for weighted sum, numel(W) must fit size(x,DIM)
-% Y resulting sum
-% N number of valid (not missing) elements
-% SSQ sum of squares
+% x input data
+% DIM dimension (default: [])
+% empty DIM sets DIM to first non singleton dimension
+% W weight vector for weighted sum, numel(W) must fit size(x,DIM)
+% Y resulting sum
+% N number of valid (not missing) elements
+% SSQ sum of squares
%
% the function FLAG_NANS_OCCURED() returns whether any value in x
% is a not-a-number (NaN)
@@ -47,7 +47,7 @@ function [o,count,SSQ] = sumskipnan(x, DIM, W)
% You should have received a copy of the GNU General Public License
% along with this program; If not, see <http://www.gnu.org/licenses/>.
-% $Id: sumskipnan.m 9033 2011-11-08 20:58:07Z schloegl $
+% $Id: sumskipnan.m 9033 2011-11-08 20:58:07Z schloegl $
% Copyright (C) 2000-2005,2009,2011 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014 Dynare Team
% This function is part of the NaN-toolbox
@@ -57,10 +57,10 @@ function [o,count,SSQ] = sumskipnan(x, DIM, W)
global FLAG_NANS_OCCURED;
if nargin<2,
- DIM = [];
+ DIM = [];
end;
if nargin<3,
- W = [];
+ W = [];
end;
% an efficient implementation in C of the following lines
@@ -73,14 +73,14 @@ end;
% if ~isnan(i(k))
% { o += x(k);
% count += 1;
-% tmp = x(k)*x(k)
-% o2 += tmp;
-% o3 += tmp.*tmp;
+% tmp = x(k)*x(k)
+% o2 += tmp;
+% o3 += tmp.*tmp;
% };
if isempty(DIM),
- DIM = find(size(x)>1,1);
- if isempty(DIM), DIM = 1; end;
+ DIM = find(size(x)>1,1);
+ if isempty(DIM), DIM = 1; end;
end
if (DIM<1), DIM = 1; end; %% Hack, because min([])=0 for FreeMat v3.5
@@ -169,7 +169,7 @@ end;
% count non-NaN's
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if nargout>1,
- count = sum(x==x,DIM);
+ count = sum(x==x,DIM);
FLAG_NANS_OCCURED = any(count(:)<size(x,DIM));
end;
@@ -180,8 +180,8 @@ x(x~=x) = 0;
o = sum(x,DIM);
if nargout>2,
- x = real(x).^2 + imag(x).^2;
- SSQ = sum(x,DIM);
+ x = real(x).^2 + imag(x).^2;
+ SSQ = sum(x,DIM);
end;
%!assert(sumskipnan([1,2],1),[1,2])
diff --git a/matlab/missing/corrcoef/tcdf.m b/matlab/missing/corrcoef/tcdf.m
index c282e0a1745c80b660f4a56425d08898f55ecbfb..b3827862a9ab2e00b73ccb492ea8eb55476ef555 100644
--- a/matlab/missing/corrcoef/tcdf.m
+++ b/matlab/missing/corrcoef/tcdf.m
@@ -11,8 +11,8 @@ function p = tcdf(x,n)
% Reference(s):
-% $Id: tcdf.m 9033 2011-11-08 20:58:07Z schloegl $
-% Copyright (C) 2000-2003,2009 by Alois Schloegl <alois.schloegl@gmail.com>
+% $Id: tcdf.m 9033 2011-11-08 20:58:07Z schloegl $
+% Copyright (C) 2000-2003,2009 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014 Dynare Team
% This is part of the NaN-toolbox. For more details see
% http://pub.ist.ac.at/~schloegl/matlab/NaN/
@@ -37,7 +37,7 @@ if all(size(x)==1)
elseif all(size(n)==1)
n = repmat(n,size(x));
elseif all(size(x)==size(n))
- ; %% OK, do nothing
+ ; %% OK, do nothing
else
error('size of input arguments must be equal or scalar')
end;
diff --git a/matlab/missing/ilu/ilu.m b/matlab/missing/ilu/ilu.m
index 4b4fe233829b1ba8d1a7f82a44c670cdbd9d5078..0b8f8df737ca6ec4bea84ebb4362b557a5098134 100644
--- a/matlab/missing/ilu/ilu.m
+++ b/matlab/missing/ilu/ilu.m
@@ -18,20 +18,20 @@ function [L, U, P] = ilu(A, setup)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- if nargout ~= 2
- error('Only two output arguments supported')
- end
- if nargin ~= 2
- error('Only two input arguments supported')
- end
+if nargout ~= 2
+ error('Only two output arguments supported')
+end
+if nargin ~= 2
+ error('Only two input arguments supported')
+end
+
+if isfield(setup, 'milu')
+ if setup.milu == 'off'
+ setup.milu = 0;
+ else
+ error(['Unsupported milu: ' setup.milu ])
+ end
+end
- if isfield(setup, 'milu')
- if setup.milu == 'off'
- setup.milu = 0;
- else
- error(['Unsupported milu: ' setup.milu ])
- end
- end
-
- [L, U] = luinc(A, setup);
+[L, U] = luinc(A, setup);
end
diff --git a/matlab/missing/issquare/issquare.m b/matlab/missing/issquare/issquare.m
index 4d4eb2b572c8a771080d57d35f8ffdc0904131af..dfbfe892591ae3419fd453c89e7827cd1b98d479 100644
--- a/matlab/missing/issquare/issquare.m
+++ b/matlab/missing/issquare/issquare.m
@@ -46,7 +46,7 @@ function i = issquare(A)
d = size(A);
if (length(d)==2) && (d(1)==d(2))
- i = d(1);
+ i = d(1);
else
- i = 0;
+ i = 0;
end
diff --git a/matlab/missing/stats/corr.m b/matlab/missing/stats/corr.m
index 21cdda767e6620fb3c08f072e7d5277576ed7692..c862e1e6712346d45f507b3b4b2103672a156e57 100644
--- a/matlab/missing/stats/corr.m
+++ b/matlab/missing/stats/corr.m
@@ -63,12 +63,12 @@ end
% Input validation
if (nargin==1 && ~(isnumeric (x) || islogical (x))) || ...
- (nargin==2 && ~(isnumeric (x) || islogical (x) || isnumeric (y) || islogical (y)))
+ (nargin==2 && ~(isnumeric (x) || islogical (x) || isnumeric (y) || islogical (y)))
error ('corr: X and Y must be numeric matrices or vectors');
end
if (nargin==1 && ~ismatrix(x)) || (nargin==2 && (~ismatrix(y) || ~ismatrix(x)))
-error ('corr: X and Y must be 2-D matrices or vectors');
+ error ('corr: X and Y must be 2-D matrices or vectors');
end
if (nargin == 2)
diff --git a/matlab/missing/stats/quantile.m b/matlab/missing/stats/quantile.m
index ec7dbfa64f08b7eb188cdc7096452ab0f0cc0798..54c77c3f921657d3449c30789f076647e0ab19d5 100644
--- a/matlab/missing/stats/quantile.m
+++ b/matlab/missing/stats/quantile.m
@@ -205,34 +205,34 @@ if numel(p)==numel(q)
end
function cumweights = accumulateWeights(weights, ind, n, N)
- % ACCUMULATEWEIGHTS accumulate the weights
- wSorted = weights(ind,n); % sort weights
- wSorted = wSorted*N/sum(wSorted); % normalize weights to sum to N
- cumweights = cumsum(wSorted); % cumulative weights
+% ACCUMULATEWEIGHTS accumulate the weights
+wSorted = weights(ind,n); % sort weights
+wSorted = wSorted*N/sum(wSorted); % normalize weights to sum to N
+cumweights = cumsum(wSorted); % cumulative weights
function hw = weightedIndex(huw, cumweights)
- % WEIGHTEDINDEX calculate index from cumulative weights
- ii = find(sign(cumweights-huw)<0,1,'last');
- jj = find(sign(cumweights-huw)>0,1,'first');
- if isempty(ii) || isempty(jj)
- hw = huw;
- else
- hw = ii + (huw-cumweights(ii))/(cumweights(jj)-cumweights(ii)); % weighted index
- end
+% WEIGHTEDINDEX calculate index from cumulative weights
+ii = find(sign(cumweights-huw)<0,1,'last');
+jj = find(sign(cumweights-huw)>0,1,'first');
+if isempty(ii) || isempty(jj)
+ hw = huw;
+else
+ hw = ii + (huw-cumweights(ii))/(cumweights(jj)-cumweights(ii)); % weighted index
+end
function y = isint(x)
- % ISINT check if input is whole number
- y = x==round(x);
+% ISINT check if input is whole number
+y = x==round(x);
function y = rearrange(x,order,shape)
- %REARRANGE reshape and permute to make target dim column
- y = permute(x,order);
- y = reshape(y,shape);
+%REARRANGE reshape and permute to make target dim column
+y = permute(x,order);
+y = reshape(y,shape);
function y = irearrange(x,order,shape)
- %IREARRANGE reshape and permute to original size
- y = reshape(x,shape);
- y = ipermute(y,order);
+%IREARRANGE reshape and permute to original size
+y = reshape(x,shape);
+y = ipermute(y,order);
%@test:1
diff --git a/matlab/missing_DiffuseKalmanSmootherH1_Z.m b/matlab/missing_DiffuseKalmanSmootherH1_Z.m
index a19d3fff6392455391fc18d332add47d3d04d14e..6595daf67d34dfe749e3286d98f4d4addcde2595 100644
--- a/matlab/missing_DiffuseKalmanSmootherH1_Z.m
+++ b/matlab/missing_DiffuseKalmanSmootherH1_Z.m
@@ -129,7 +129,7 @@ while rank(Pinf(:,:,t+1),diffuse_kalman_tol) && t<smpl
Finf = ZZ*Pinf(:,:,t)*ZZ'; % (5.7) in DK (2012)
if rcond(Finf) < diffuse_kalman_tol %F_{\infty,t} = 0
if ~all(abs(Finf(:)) < diffuse_kalman_tol) %rank-deficient but not rank 0
- % The univariate diffuse kalman filter should be used.
+ % The univariate diffuse kalman filter should be used.
alphahat = Inf;
return
else %rank of F_{\infty,t} is 0
@@ -206,7 +206,7 @@ while notsteady && t<smpl
atilde(:,t) = a(:,t) + PZI*v(di,t);
K(:,di,t) = T*PZI;
L(:,:,t) = T-K(:,di,t)*ZZ;
- P(:,:,t+1) = T*P(:,:,t)*L(:,:,t)' + QQ;
+ P(:,:,t+1) = T*P(:,:,t)*L(:,:,t)' + QQ;
end
a(:,t+1) = T*atilde(:,t);
Pf = P(:,:,t);
@@ -267,7 +267,7 @@ while t>d+1
end
end
if d %diffuse periods
- % initialize r_d^(0) and r_d^(1) as below DK (2012), eq. 5.23
+ % initialize r_d^(0) and r_d^(1) as below DK (2012), eq. 5.23
r0 = zeros(mm,d+1);
r0(:,d+1) = r(:,d+1); %set r0_{d}, i.e. shifted by one period
r1 = zeros(mm,d+1); %set r1_{d}, i.e. shifted by one period
@@ -284,7 +284,7 @@ if d %diffuse periods
if ~Finf_singular(1,t)
r0(:,t) = Linf(:,:,t)'*r0(:,t+1); % DK (2012), eq. 5.21 where L^(0) is named Linf
r1(:,t) = Z(di,:)'*(iFinf(di,di,t)*v(di,t)-Kstar(:,di,t)'*T'*r0(:,t+1)) ...
- + Linf(:,:,t)'*r1(:,t+1); % DK (2012), eq. 5.21, noting that i) F^(1)=(F^Inf)^(-1)(see 5.10), ii) where L^(0) is named Linf, and iii) Kstar=T^{-1}*K^(1)
+ + Linf(:,:,t)'*r1(:,t+1); % DK (2012), eq. 5.21, noting that i) F^(1)=(F^Inf)^(-1)(see 5.10), ii) where L^(0) is named Linf, and iii) Kstar=T^{-1}*K^(1)
if state_uncertainty_flag
L_1=(-T*Kstar(:,di,t)*Z(di,:)); % noting that Kstar=T^{-1}*K^(1)
N(:,:,t)=Linf(:,:,t)'*N(:,:,t+1)*Linf(:,:,t); % DK (2012), eq. 5.19, noting that L^(0) is named Linf
@@ -301,7 +301,7 @@ if d %diffuse periods
r1(:,t) = T'*r1(:,t+1); % DK (2003), eq. (14)
if state_uncertainty_flag
N(:,:,t)=Z(di,:)'*iFstar(di,di,t)*Z(di,:)...
- +Lstar(:,:,t)'*N(:,:,t+1)*Lstar(:,:,t); % DK (2003), eq. (14)
+ +Lstar(:,:,t)'*N(:,:,t+1)*Lstar(:,:,t); % DK (2003), eq. (14)
N_1(:,:,t)=T'*N_1(:,:,t+1)*Lstar(:,:,t); % DK (2003), eq. (14)
N_2(:,:,t)=T'*N_2(:,:,t+1)*T'; % DK (2003), eq. (14)
end
@@ -311,9 +311,9 @@ if d %diffuse periods
etahat(:,t) = QRt*r0(:,t); % DK (2012), p. 135
if state_uncertainty_flag
V(:,:,t)=Pstar(:,:,t)-Pstar(:,:,t)*N(:,:,t)*Pstar(:,:,t)...
- -(Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t))'...
- - Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t)...
- - Pinf(:,:,t)*N_2(:,:,t)*Pinf(:,:,t); % DK (2012), eq. 5.30
+ -(Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t))'...
+ - Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t)...
+ - Pinf(:,:,t)*N_2(:,:,t)*Pinf(:,:,t); % DK (2012), eq. 5.30
end
end
end
diff --git a/matlab/missing_DiffuseKalmanSmootherH3_Z.m b/matlab/missing_DiffuseKalmanSmootherH3_Z.m
index b03b13ee0ef02b5fcdaac162d20aadd079c1bbe8..665f285323beee3089a0a16fe8d8cfc745c3957c 100644
--- a/matlab/missing_DiffuseKalmanSmootherH3_Z.m
+++ b/matlab/missing_DiffuseKalmanSmootherH3_Z.m
@@ -81,7 +81,7 @@ function [alphahat,epsilonhat,etahat,a,P,aK,PK,decomp,V] = missing_DiffuseKalman
if size(H,2)>1
error('missing_DiffuseKalmanSmootherH3_Z:: H is not a vector. This must not happens')
end
-
+
d = 0;
decomp = [];
spinf = size(Pinf1);
@@ -152,8 +152,8 @@ while newRank && t < smpl
elseif Fstar(i,t) > kalman_tol
a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t); % KD (2000), eq. (17)
Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*Kstar(:,i,t)'/Fstar(i,t); % KD (2000), eq. (17)
- % Pinf is passed through unaltered, see eq. (17) of
- % Koopman/Durbin (2000)
+ % Pinf is passed through unaltered, see eq. (17) of
+ % Koopman/Durbin (2000)
else
% do nothing as a_{t,i+1}=a_{t,i} and P_{t,i+1}=P_{t,i}, see
% p. 157, DK (2012)
@@ -227,7 +227,7 @@ while notsteady && t<smpl
end
end
P(:,:,t+1) = T*P(:,:,t)*T' + QQ; %transition according to (6.14) in DK (2012)
- % notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<kalman_tol);
+ % notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))<kalman_tol);
end
% $$$ P_s=tril(P(:,:,t))+tril(P(:,:,t),-1)';
% $$$ P1_s=tril(P1(:,:,t))+tril(P1(:,:,t),-1)';
@@ -335,9 +335,9 @@ if d
etahat(:,t) = QRt*r(:,t); % KD (2000), eq. (27)
if state_uncertainty_flag
V(:,:,t)=Pstar(:,:,t)-Pstar(:,:,t)*N_0(:,:,t)*Pstar(:,:,t)...
- -(Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t))'...
- - Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t)...
- - Pinf(:,:,t)*N_2(:,:,t)*Pinf(:,:,t); % DK (2012), eq. 5.30
+ -(Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t))'...
+ - Pinf(:,:,t)*N_1(:,:,t)*Pstar(:,:,t)...
+ - Pinf(:,:,t)*N_2(:,:,t)*Pinf(:,:,t); % DK (2012), eq. 5.30
end
if t > 1
r0(:,t-1) = T'*r0(:,t); % KD (2000), below eq. (25) r_{t-1,p_{t-1}}=T'*r_{t,0}
diff --git a/matlab/mode_check.m b/matlab/mode_check.m
index 3217dbd1255bf6a5e4a12dda90302a6243485c97..bd003bd8e4a3114474e149fe7719070d843d9e26 100644
--- a/matlab/mode_check.m
+++ b/matlab/mode_check.m
@@ -203,7 +203,7 @@ for plt = 1:nbplt,
end
end
if TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
- fclose(fidTeX);
+ fclose(fidTeX);
end
OutputDirectoryName = CheckPath('modecheck',Model.dname);
diff --git a/matlab/model_diagnostics.m b/matlab/model_diagnostics.m
index 32a01725d5ce875b7485a7932fa398278fdec847..5b4b4221ebb87f37b7dbd70168d9ab7de2984dd9 100644
--- a/matlab/model_diagnostics.m
+++ b/matlab/model_diagnostics.m
@@ -108,7 +108,7 @@ for b=1:nb
if options.bytecode
if nb == 1
[chk, res, jacob] = bytecode(dr.ys, exo, M.params, dr.ys, 1, exo, ...
- 'evaluate', 'static');
+ 'evaluate', 'static');
else
[chk, res, jacob] = bytecode(dr.ys, exo, M.params, dr.ys, 1, exo, ...
'evaluate', 'static',['block=' ...
@@ -173,7 +173,7 @@ for b=1:nb
end
end
end
-
+
if singularity_problem
try
options_check=options;
@@ -205,26 +205,26 @@ if ~options.block
if options.order == 1
if (options.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
- M.params, dr.ys, 1);
+ M.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
else
[junk,jacobia_] = feval([M.fname '_dynamic'],z(iyr0),exo_simul, ...
- M.params, dr.ys, it_);
+ M.params, dr.ys, it_);
end;
elseif options.order >= 2
if (options.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
- M.params, dr.ys, 1);
+ M.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x];
else
[junk,jacobia_,hessian1] = feval([M.fname '_dynamic'],z(iyr0),...
- exo_simul, ...
- M.params, dr.ys, it_);
+ exo_simul, ...
+ M.params, dr.ys, it_);
end;
if options.use_dll
% In USE_DLL mode, the hessian is in the 3-column sparse representation
hessian1 = sparse(hessian1(:,1), hessian1(:,2), hessian1(:,3), ...
- size(jacobia_, 1), size(jacobia_, 2)*size(jacobia_, 2));
+ size(jacobia_, 1), size(jacobia_, 2)*size(jacobia_, 2));
end
end
diff --git a/matlab/model_info.m b/matlab/model_info.m
index d99ca48078556c0b21d07cfa4ce9dc0ea2106159..281bfa09ac919f0a89dc96a3a01613305e1865d3 100644
--- a/matlab/model_info.m
+++ b/matlab/model_info.m
@@ -30,14 +30,14 @@ else
incidence = 0;
end;
if static_
- fprintf(' Informations about %s (static model)\n',M_.fname);
- block_structre_str = 'block_structure_stat';
- nb_leadlag = 1;
- else
- fprintf(' Informations about %s (dynamic model)\n',M_.fname);
- block_structre_str = 'block_structure';
- nb_leadlag = 3;
- end;
+ fprintf(' Informations about %s (static model)\n',M_.fname);
+ block_structre_str = 'block_structure_stat';
+ nb_leadlag = 1;
+else
+ fprintf(' Informations about %s (dynamic model)\n',M_.fname);
+ block_structre_str = 'block_structure';
+ nb_leadlag = 3;
+end;
if(isfield(M_,block_structre_str))
if static_
block_structure = M_.block_structure_stat;
@@ -136,7 +136,7 @@ if(isfield(M_,block_structre_str))
fprintf('\n Gross incidence matrix\n');
fprintf(' =======================\n');
disp([topp; bott]);
-
+
%printing the reordered incidence matrix
IM_star_reordered = char([kron(ones(M_.endo_nbr, M_.endo_nbr-1), double(blanks(3))) double(blanks(M_.endo_nbr)')]);
eq(block_structure.equation_reordered) = seq;
@@ -201,7 +201,7 @@ if(isfield(M_,block_structre_str))
end;
if past_block ~= cur_block
for j = 1:i-1
- IM_star_reordered(j, 3 * (i - 1) - 1) = '|';
+ IM_star_reordered(j, 3 * (i - 1) - 1) = '|';
end;
end;
end
@@ -211,7 +211,7 @@ if(isfield(M_,block_structre_str))
fprintf(' ==========================\n');
disp([topp; bott]);
fprintf('1: non nul element, X: non nul element related to a state variable\n');
- end;
+ end;
else
fprintf('There is no block decomposition of the model.\nUse ''block'' model''s option.\n');
end;
diff --git a/matlab/model_inversion.m b/matlab/model_inversion.m
index 6c873c655052eadb24cf29d9804b0f04c34758f4..75594ecca24f092e3543c495c67d9d883ef74b63 100644
--- a/matlab/model_inversion.m
+++ b/matlab/model_inversion.m
@@ -1,6 +1,6 @@
function [endogenousvariables, exogenousvariables] = model_inversion(constraints, ...
- exogenousvariables, ...
- initialconditions, DynareModel, DynareOptions, DynareOutput)
+ exogenousvariables, ...
+ initialconditions, DynareModel, DynareOptions, DynareOutput)
% INPUTS
% - constraints [dseries] with N constrained endogenous variables from t1 to t2.
diff --git a/matlab/ms-sbvar/clean_files_for_second_type_of_mex.m b/matlab/ms-sbvar/clean_files_for_second_type_of_mex.m
index d29b532bfaff3b021d2182938344794355cd5efa..5f6d7d43cdfd921388aa2021109f206e99ed98c7 100644
--- a/matlab/ms-sbvar/clean_files_for_second_type_of_mex.m
+++ b/matlab/ms-sbvar/clean_files_for_second_type_of_mex.m
@@ -34,12 +34,12 @@ if ~strcmp(tagtouse, options_.ms.output_file_tag)
end
switch type
- case 'irf'
- clean_ms_irf_files(tagtouse);
- case 'forecast'
- clean_ms_forecast_files(tagtouse);
- case 'variance_decomposition'
- clean_ms_variance_decomposition_files(tagtouse);
- otherwise
- error('clean_files_for_second_type_of_mex: should not arrive here');
+ case 'irf'
+ clean_ms_irf_files(tagtouse);
+ case 'forecast'
+ clean_ms_forecast_files(tagtouse);
+ case 'variance_decomposition'
+ clean_ms_variance_decomposition_files(tagtouse);
+ otherwise
+ error('clean_files_for_second_type_of_mex: should not arrive here');
end
diff --git a/matlab/ms-sbvar/dyn_save_graph.m b/matlab/ms-sbvar/dyn_save_graph.m
index e3ae96326bba91498c109d393e8e6baa0b200829..9d7cde0fa6341324c385e919ac534cd26c0fced4 100644
--- a/matlab/ms-sbvar/dyn_save_graph.m
+++ b/matlab/ms-sbvar/dyn_save_graph.m
@@ -30,40 +30,40 @@ function dyn_save_graph(dirname,graph_name,graph_formats,TeX,names,texnames,capt
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- create_dir(dirname);
- graph_name = [dirname filesep regexprep(graph_name,' ','_')];
- if nargin <= 2
- TeX = 0;
- elseif nargin <= 4
- names = {};
- texnames = {};
- elseif nargin <= 6
- caption = '';
- end
-
- if graph_formats.eps || TeX
- print([ graph_name '.eps' ],'-depsc2');
- end
- if graph_formats.pdf && ~isoctave
- print(graph_name,'-dpdf');
- end
- if graph_formats.fig && ~isoctave
- print(graph_name,'-dfig');
+create_dir(dirname);
+graph_name = [dirname filesep regexprep(graph_name,' ','_')];
+if nargin <= 2
+ TeX = 0;
+elseif nargin <= 4
+ names = {};
+ texnames = {};
+elseif nargin <= 6
+ caption = '';
+end
+
+if graph_formats.eps || TeX
+ print([ graph_name '.eps' ],'-depsc2');
+end
+if graph_formats.pdf && ~isoctave
+ print(graph_name,'-dpdf');
+end
+if graph_formats.fig && ~isoctave
+ print(graph_name,'-dfig');
+end
+
+if TeX
+ fh = fopen([graph_name '.tex'],'w');
+ for i=1:length(names)
+ fprintf(fh,'\\psfrag{%s}[1][][0.5][0]{%s}\n',names{i},texnames{i});
end
-
- if TeX
- fh = fopen([graph_name '.tex'],'w');
- for i=1:length(names)
- fprintf(fh,'\\psfrag{%s}[1][][0.5][0]{%s}\n',names{i},texnames{i});
- end
- fprintf(fh,'\\centering \n');
- fprintf(fh,'\\includegraphics[width=0.8\\textwidth]{%s}\n',graph_name);
- if caption
- fprintf(fh,'\\caption{%s}',caption);
- end
- fprintf(fh,'\\label{Fig:%s}\n',graph_name);
- fprintf(fh,'\\end{figure}\n');
- fprintf(fh,'\n');
- fprintf(fh,'%% End of TeX file.\n');
- fclose(fh);
+ fprintf(fh,'\\centering \n');
+ fprintf(fh,'\\includegraphics[width=0.8\\textwidth]{%s}\n',graph_name);
+ if caption
+ fprintf(fh,'\\caption{%s}',caption);
end
+ fprintf(fh,'\\label{Fig:%s}\n',graph_name);
+ fprintf(fh,'\\end{figure}\n');
+ fprintf(fh,'\n');
+ fprintf(fh,'%% End of TeX file.\n');
+ fclose(fh);
+end
diff --git a/matlab/ms-sbvar/identification/exclusions.m b/matlab/ms-sbvar/identification/exclusions.m
index 5e7d3ef5c040da64ece9eb203f2e32bedd2b2fb7..90df49d225e424b247ee86e8d7d79c9d6e0f698e 100644
--- a/matlab/ms-sbvar/identification/exclusions.m
+++ b/matlab/ms-sbvar/identification/exclusions.m
@@ -47,25 +47,25 @@ np = zeros(nvar,1);
k = nlags*nvar+1;
for n=1:nvar
- Qi{n} = zeros(nvar,nvar);
- sQ = size(Qi1{n});
- if all(sQ) > 0
- Qi{n}(1:sQ(1),1:sQ(2)) = Qi1{n};
- end
- Ri{n} = zeros(k,k);
- sR = size(Ri1{n});
- if all(sR) > 0
- Ri{n}(1:sR(1),1:sR(2)) = Ri1{n};
- end
+ Qi{n} = zeros(nvar,nvar);
+ sQ = size(Qi1{n});
+ if all(sQ) > 0
+ Qi{n}(1:sQ(1),1:sQ(2)) = Qi1{n};
+ end
+ Ri{n} = zeros(k,k);
+ sR = size(Ri1{n});
+ if all(sR) > 0
+ Ri{n}(1:sR(1),1:sR(2)) = Ri1{n};
+ end
- if options_ms.constants_exclusion
+ if options_ms.constants_exclusion
Ri{n}(sR(1)+1,k) = 1;
- end
+ end
- Ui{n} = null(Qi{n});
- Vi{n} = null(Ri{n});
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi{n});
+ Vi{n} = null(Ri{n});
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
ixmC0Pres = NaN;
\ No newline at end of file
diff --git a/matlab/ms-sbvar/identification/lower_cholesky.m b/matlab/ms-sbvar/identification/lower_cholesky.m
index 2af96ca9b7b732011d0f2831eb02470293262c6e..b6054acf64d227f274a4d9199c080cc75d914040 100644
--- a/matlab/ms-sbvar/identification/lower_cholesky.m
+++ b/matlab/ms-sbvar/identification/lower_cholesky.m
@@ -41,9 +41,9 @@ Vi = cell(nvar,1);
n0 = zeros(nvar,1);
np = zeros(nvar,1);
if (nargin==2)
- nexo = 1;
+ nexo = 1;
elseif (nargin==3)
- indxC0Pres = 0;
+ indxC0Pres = 0;
end
k = lags*nvar+nexo;
Qi = zeros(nvar,nvar,nvar);
@@ -62,10 +62,10 @@ if options_ms.constants_exclusion
end
for n=1:nvar
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
ixmC0Pres = NaN;
\ No newline at end of file
diff --git a/matlab/ms-sbvar/identification/upper_cholesky.m b/matlab/ms-sbvar/identification/upper_cholesky.m
index 7f4d2027c56b854b64fe1e8274eeb1170a65fa99..65c403a810b004817115d1161ab0669dd6d8ee59 100644
--- a/matlab/ms-sbvar/identification/upper_cholesky.m
+++ b/matlab/ms-sbvar/identification/upper_cholesky.m
@@ -42,9 +42,9 @@ n0 = zeros(nvar,1);
np = zeros(nvar,1);
if (nargin==2)
- nexo = 1;
+ nexo = 1;
elseif (nargin==3)
- indxC0Pres = 0;
+ indxC0Pres = 0;
end
k = lags*nvar+nexo;
@@ -52,8 +52,8 @@ Qi = zeros(nvar,nvar,nvar);
Ri = zeros(k,k,nvar);
for ii=2:nvar
- Qi(ii-1,ii-1,ii)=1;
- Qi(:,:,ii)=Qi(:,:,ii)+Qi(:,:,ii-1);
+ Qi(ii-1,ii-1,ii)=1;
+ Qi(:,:,ii)=Qi(:,:,ii)+Qi(:,:,ii-1);
end
if options_ms.constants_exclusion
@@ -63,10 +63,10 @@ if options_ms.constants_exclusion
end
for n=1:nvar
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
ixmC0Pres = NaN;
\ No newline at end of file
diff --git a/matlab/ms-sbvar/ms_estimation.m b/matlab/ms-sbvar/ms_estimation.m
index 6d5eec32d704d61ff4de834515ab2cd9701788dc..67b8c34fc5de58ef6a019723288df017351ce76c 100644
--- a/matlab/ms-sbvar/ms_estimation.m
+++ b/matlab/ms-sbvar/ms_estimation.m
@@ -45,7 +45,7 @@ else
exist(['matlab_' options_.ms.file_tag '.prn'],'file') ~= 2 || ...
exist([options_.ms.file_tag '_markov_file.dat'],'file') ~= 2
error(['You must run ms_estimation without no_create_init as '...
- 'you are missing an initialization file it needs.']);
+ 'you are missing an initialization file it needs.']);
end
if ~strcmp(options_.ms.file_tag, options_.ms.output_file_tag)
clean_ms_estimation_files(options_.ms.output_file_tag);
diff --git a/matlab/ms-sbvar/ms_forecast.m b/matlab/ms-sbvar/ms_forecast.m
index 55030c9021729dcbde0a1110f920c86cf4ea9eab..e8ed8be20cd22dad4c91d171b461f95e1f1ac41b 100644
--- a/matlab/ms-sbvar/ms_forecast.m
+++ b/matlab/ms-sbvar/ms_forecast.m
@@ -89,30 +89,30 @@ if options_.ms.regimes
for regime_i=1:n_regimes
forecast_title = ['Forecast, Regimes ' num2str(regime_i)];
forecast_data = load([forecastdir filesep 'forecasts_percentiles_regime_' ...
- num2str(regime_i-1) '_' options_.ms.output_file_tag ...
- '.out'], '-ascii');
+ num2str(regime_i-1) '_' options_.ms.output_file_tag ...
+ '.out'], '-ascii');
forecast_data = reshape_ascii_forecast_data(M_.endo_nbr, ...
- percentiles_size, options_.ms.horizon, forecast_data);
+ percentiles_size, options_.ms.horizon, forecast_data);
save([forecastdir filesep 'forecast_regime_' num2str(regime_i-1) '.mat'], ...
- 'forecast_data');
+ 'forecast_data');
plot_ms_forecast(M_, options_, forecast_data, forecast_title);
end
else
if options_.ms.regime
forecast_data = load([forecastdir filesep 'forecasts_percentiles_regime_' ...
- num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
- '.out'], '-ascii');
+ num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
+ '.out'], '-ascii');
forecast_title = ['Forecast, Regime ' num2str(options_.ms.regime)];
save_filename = ['forecast_regime_' num2str(options_.ms.regime-1) '.mat'];
else
forecast_data = load([forecastdir filesep 'forecasts_percentiles_' ...
- options_.ms.output_file_tag '.out'], '-ascii');
+ options_.ms.output_file_tag '.out'], '-ascii');
forecast_title = 'Forecast';
save_filename = 'forecast.mat';
end
forecast_data = reshape_ascii_forecast_data(M_.endo_nbr, ...
- percentiles_size, options_.ms.horizon, forecast_data);
+ percentiles_size, options_.ms.horizon, forecast_data);
save([forecastdir filesep save_filename], 'forecast_data');
plot_ms_forecast(M_, options_, forecast_data, forecast_title);
end
diff --git a/matlab/ms-sbvar/ms_irf.m b/matlab/ms-sbvar/ms_irf.m
index 32718aa29c4473515c07d477cccf5498d5ce7364..9b13aaa5e17204e75edb0baee1c2c174b3a0431d 100644
--- a/matlab/ms-sbvar/ms_irf.m
+++ b/matlab/ms-sbvar/ms_irf.m
@@ -91,34 +91,34 @@ if options_.ms.regimes
for regime_i=1:n_regimes
irf_title = ['Impulse Responses, Regime ' num2str(regime_i)];
irf_data = load([irfdir filesep 'ir_percentiles_regime_' ...
- num2str(regime_i-1) '_' options_.ms.output_file_tag ...
- '.out'], '-ascii');
+ num2str(regime_i-1) '_' options_.ms.output_file_tag ...
+ '.out'], '-ascii');
irf_data = reshape_ascii_irf_data(M_.endo_nbr, percentiles_size, ...
- options_.ms.horizon, irf_data);
+ options_.ms.horizon, irf_data);
save([irfdir filesep 'irf_regime_' num2str(regime_i-1) '.mat'], 'irf_data');
plot_ms_irf(M_, options_, irf_data, irf_title, varlist);
end
else
if options_.ms.regime
irf_data = load([irfdir filesep 'ir_percentiles_regime_' ...
- num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
- '.out'], '-ascii');
+ num2str(options_.ms.regime-1) '_' options_.ms.output_file_tag ...
+ '.out'], '-ascii');
irf_title = ['Impulse Response, Regime ' num2str(options_.ms.regime)];
save_filename = ['irf_regime_' num2str(options_.ms.regime-1) '.mat'];
elseif options_.ms.filtered_probabilities
irf_data = load([irfdir filesep 'ir_percentiles_filtered_' ...
- options_.ms.output_file_tag '.out'], '-ascii');
+ options_.ms.output_file_tag '.out'], '-ascii');
irf_title = 'Impulse Response Filtered';
save_filename = 'irf.mat';
else
irf_data = load([irfdir filesep 'ir_percentiles_ergodic_' ...
- options_.ms.output_file_tag '.out'], '-ascii');
+ options_.ms.output_file_tag '.out'], '-ascii');
irf_title = 'Impulse Response Ergodic';
save_filename = 'irf.mat';
end
irf_data = reshape_ascii_irf_data(M_.endo_nbr, percentiles_size, ...
- options_.ms.horizon, irf_data);
+ options_.ms.horizon, irf_data);
save([irfdir filesep save_filename], 'irf_data');
plot_ms_irf(M_, options_, irf_data, irf_title, varlist);
end
diff --git a/matlab/ms-sbvar/ms_mardd.m b/matlab/ms-sbvar/ms_mardd.m
index 9932f0186a38c5bd44decd28eced194f31b666cd..f13b614af58c516026cd26c71403fc28da98e4db 100644
--- a/matlab/ms-sbvar/ms_mardd.m
+++ b/matlab/ms-sbvar/ms_mardd.m
@@ -25,26 +25,26 @@ function ms_mardd(options_)
msstart2 % start the program in which everyhting is initialized through msstart2.m
if ~options_.ms.indxestima
- warning(' ')
- disp('You must set IxEstima=1 in msstart to run this program')
- disp('Press ctrl-c to abort now')
- pause
+ warning(' ')
+ disp('You must set IxEstima=1 in msstart to run this program')
+ disp('Press ctrl-c to abort now')
+ pause
end
A0xhat = zeros(size(A0hat));
Apxhat = zeros(size(Aphat));
if (0)
- %Robustness check to see if the same result is obtained with the purterbation of the parameters.
- for k=1:nvar
- bk = Uiconst{k}'*A0hat(:,k);
- gk = Viconst{k}'*Aphat(:,k);
- A0xhat(:,k) = Uiconst{k}*(bk + 5.2*randn(size(bk))); % Perturbing the posterior estimate.
- Apxhat(:,k) = Viconst{k}*(gk + 5.2*randn(size(gk))); % Perturbing the posterior estimate.
- end
+ %Robustness check to see if the same result is obtained with the purterbation of the parameters.
+ for k=1:nvar
+ bk = Uiconst{k}'*A0hat(:,k);
+ gk = Viconst{k}'*Aphat(:,k);
+ A0xhat(:,k) = Uiconst{k}*(bk + 5.2*randn(size(bk))); % Perturbing the posterior estimate.
+ Apxhat(:,k) = Viconst{k}*(gk + 5.2*randn(size(gk))); % Perturbing the posterior estimate.
+ end
else
- %At the posterior estimate.
- A0xhat = A0hat; % ML estimate of A0
- Apxhat = Aphat; % ML estimate of A+
+ %At the posterior estimate.
+ A0xhat = A0hat; % ML estimate of A0
+ Apxhat = Aphat; % ML estimate of A+
end
%--- Rename variables.
YatYa = yty;
@@ -59,40 +59,40 @@ XatXa = xtx;
vlog_a0p = 0;
Yexpt=0; % exponential term for Y in p(Y|A0,A+) at some point such as the peak
Apexpt=0.0; % 0.0 because we have chosen posterior estimate of A+ as A+*. Exponential term for A+ conditional on A0 and Y
-%======= Computing the log prior pdf of a0a+ and the exponential term for Y in p(Y|A0,A+).
+ %======= Computing the log prior pdf of a0a+ and the exponential term for Y in p(Y|A0,A+).
for k=1:nvar
- a0k = A0xhat(:,k); % meaningful parameters in the kth equation.
- apk = Apxhat(:,k); % meaningful parameters in the kth equation.
-
- %--- Prior settings.
- S0bar = H0invtld{k}; %See Claim 2 on p.69b.
- Spbar = Hpinvtld{k};
- bk = Uiconst{k}'*a0k; % free parameters in the kth equation.
- gk = Viconst{k}'*apk; % free parameters in the kth equation.
- gbark = Ptld{k}*bk; % bar: prior
-
- %--- The exponential term for Y in p(Y|A0,A+)
- Yexpt = Yexpt - 0.5*(a0k'*YatYa*a0k - 2*apk'*XatYa*a0k + apk'*XatXa*apk);
- %--- The log prior pdf.
- vlog_a0p = vlog_a0p - 0.5*(size(Uiconst{k},2)+size(Viconst{k},2))*log(2*pi) + 0.5*log(abs(det(S0bar))) + ...
- 0.5*log(abs(det(Spbar))) - 0.5*(bk'*S0bar*bk+(gk-gbark)'*Spbar*(gk-gbark));
- %--- For p(A+|Y,a0) only.
- tmpd = gk - Pmat{k}*bk;
- Apexpt = Apexpt - 0.5*tmpd'*(Hpinv{k}*tmpd);
+ a0k = A0xhat(:,k); % meaningful parameters in the kth equation.
+ apk = Apxhat(:,k); % meaningful parameters in the kth equation.
+
+ %--- Prior settings.
+ S0bar = H0invtld{k}; %See Claim 2 on p.69b.
+ Spbar = Hpinvtld{k};
+ bk = Uiconst{k}'*a0k; % free parameters in the kth equation.
+ gk = Viconst{k}'*apk; % free parameters in the kth equation.
+ gbark = Ptld{k}*bk; % bar: prior
+
+ %--- The exponential term for Y in p(Y|A0,A+)
+ Yexpt = Yexpt - 0.5*(a0k'*YatYa*a0k - 2*apk'*XatYa*a0k + apk'*XatXa*apk);
+ %--- The log prior pdf.
+ vlog_a0p = vlog_a0p - 0.5*(size(Uiconst{k},2)+size(Viconst{k},2))*log(2*pi) + 0.5*log(abs(det(S0bar))) + ...
+ 0.5*log(abs(det(Spbar))) - 0.5*(bk'*S0bar*bk+(gk-gbark)'*Spbar*(gk-gbark));
+ %--- For p(A+|Y,a0) only.
+ tmpd = gk - Pmat{k}*bk;
+ Apexpt = Apexpt - 0.5*tmpd'*(Hpinv{k}*tmpd);
end
vlog_a0p
%--------- The log value of p(Y|A0,A+) at some point such as the peak. ----------
%--------- Note that logMarLHres is the same as vlog_Y_a, just to double check. ----------
vlog_Y_a = -0.5*nvar*fss*log(2*pi) + fss*log(abs(det(A0xhat))) + Yexpt
- % a: given a0 and a+
+% a: given a0 and a+
logMarLHres = 0; % Initialize log of the marginal likelihood (restricted or constant parameters).
for ki=1:fss %ndobs+1:fss % Forward recursion to get the marginal likelihood. See F on p.19 and pp. 48-49.
- %---- Restricted log marginal likelihood function (constant parameters).
- [A0l,A0u] = lu(A0xhat);
- ada = sum(log(abs(diag(A0u)))); % log|A0|
- termexp = y(ki,:)*A0xhat - phi(ki,:)*Apxhat; % 1-by-nvar
- logMarLHres = logMarLHres - (0.5*nvar)*log(2*pi) + ada - 0.5*termexp*termexp'; % log value
+ %---- Restricted log marginal likelihood function (constant parameters).
+ [A0l,A0u] = lu(A0xhat);
+ ada = sum(log(abs(diag(A0u)))); % log|A0|
+ termexp = y(ki,:)*A0xhat - phi(ki,:)*Apxhat; % 1-by-nvar
+ logMarLHres = logMarLHres - (0.5*nvar)*log(2*pi) + ada - 0.5*termexp*termexp'; % log value
end
logMarLHres
@@ -101,8 +101,8 @@ logMarLHres
totparsp = 0.0;
tmpd = 0.0;
for k=1:nvar
- totparsp = totparsp + size(Viconst{k},2);
- tmpd = tmpd + 0.5*log(abs(det(Hpinv{k})));
+ totparsp = totparsp + size(Viconst{k},2);
+ tmpd = tmpd + 0.5*log(abs(det(Hpinv{k})));
end
vlog_ap_Ya0 = -0.5*totparsp*log(2*pi) + tmpd + Apexpt;
@@ -117,65 +117,65 @@ vlog_ap_Ya0 = -0.5*totparsp*log(2*pi) + tmpd + Apexpt;
[Tinv,UT] = fn_gibbsrvar_setup(H0inv, Uiconst, Hpinv, Pmat, Viconst, nvar, fss);
%
vlog_a0_Yao = zeros(nvar,1);
- % the log value of p(a0k|Y,ao) where ao: other a's at some point such as the peak of ONLY some a0's
+% the log value of p(a0k|Y,ao) where ao: other a's at some point such as the peak of ONLY some a0's
vlog=zeros(ndraws2,1);
for k=1:nvar
- bk = Uiconst{k}'*A0xhat(:,k);
- indx_ks=[k:nvar]; % the columns that exclude 1-(k-1)th columns
- A0gbs0 = A0hat; % starting at some point such as the peak
- nk = n0(k);
-
- if k<nvar
- %--------- The 1st set of draws to be tossed away. ------------------
- for draws = 1:ndraws1
- if ~mod(draws,nbuffer)
- skipline()
- disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws))
- end
- A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
- A0gbs0=A0gbs1; % repeat the Gibbs sampling
- end
-
-
- %--------- The 2nd set of draws to be used. ------------------
- for draws = 1:ndraws2
- if ~mod(draws,nbuffer)
- skipline()
- disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws))
- end
- [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
- %------ See p.71, Forecast (II).
- %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
- Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
- gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
- [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
- %
- vlog(draws) = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
- 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
- 0.5*fss*bk'*(Vtr\(Vtr'\bk));
-
- A0gbs0=A0gbs1; % repeat the Gibbs sampling
- end
- vlogm=max(vlog);
- qlog=vlog-vlogm;
- vlogxhat=vlogm-log(ndraws2)+log(sum(exp(qlog)));
- vlog_a0_Yao(k) = vlogxhat;
- % The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's
- else
- skipline()
- disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws'))
- [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks)
- %------ See p.71, Forecast (II).
- %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
- Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
- gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
- [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
- %
- vloglast = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
- 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
- 0.5*fss*bk'*(Vtr\(Vtr'\bk));
- vlog_a0_Yao(k) = vloglast;
- end
+ bk = Uiconst{k}'*A0xhat(:,k);
+ indx_ks=[k:nvar]; % the columns that exclude 1-(k-1)th columns
+ A0gbs0 = A0hat; % starting at some point such as the peak
+ nk = n0(k);
+
+ if k<nvar
+ %--------- The 1st set of draws to be tossed away. ------------------
+ for draws = 1:ndraws1
+ if ~mod(draws,nbuffer)
+ skipline()
+ disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws))
+ end
+ A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
+ A0gbs0=A0gbs1; % repeat the Gibbs sampling
+ end
+
+
+ %--------- The 2nd set of draws to be used. ------------------
+ for draws = 1:ndraws2
+ if ~mod(draws,nbuffer)
+ skipline()
+ disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws))
+ end
+ [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
+ %------ See p.71, Forecast (II).
+ %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
+ Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
+ gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
+ [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
+ %
+ vlog(draws) = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
+ 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
+ 0.5*fss*bk'*(Vtr\(Vtr'\bk));
+
+ A0gbs0=A0gbs1; % repeat the Gibbs sampling
+ end
+ vlogm=max(vlog);
+ qlog=vlog-vlogm;
+ vlogxhat=vlogm-log(ndraws2)+log(sum(exp(qlog)));
+ vlog_a0_Yao(k) = vlogxhat;
+ % The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's
+ else
+ skipline()
+ disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws'))
+ [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks)
+ %------ See p.71, Forecast (II).
+ %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
+ Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
+ gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
+ [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
+ %
+ vloglast = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
+ 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
+ 0.5*fss*bk'*(Vtr\(Vtr'\bk));
+ vlog_a0_Yao(k) = vloglast;
+ end
end
ndraws2
diff --git a/matlab/ms-sbvar/ms_sbvar_setup.m b/matlab/ms-sbvar/ms_sbvar_setup.m
index 181a6f7d2e54029b363ec2896756374a56403f57..40e01decb3f936d5db51d5025a783870179d4944 100644
--- a/matlab/ms-sbvar/ms_sbvar_setup.m
+++ b/matlab/ms-sbvar/ms_sbvar_setup.m
@@ -29,7 +29,7 @@ function ms_sbvar_setup(options_)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
options_.data = read_variables(options_.datafile, ...
- options_.varobs, [], options_.xls_sheet, options_.xls_range);
+ options_.varobs, [], options_.xls_sheet, options_.xls_range);
[options_.ms.final_year,options_.ms.final_subperiod] = check_datafile_years_assigned(options_);
if options_.ms.upper_cholesky
@@ -277,10 +277,10 @@ else
if indxC0Pres
Fhatur0P = Fhat; % ur: unrestriced across A0 and A+
for ki = 1:size(ixmC0Pres,1) % loop through the number of equations in which
- % cross-A0-A+ restrictions occur. See St. Louis Note p.5.
+ % cross-A0-A+ restrictions occur. See St. Louis Note p.5.
ixeq = ixmC0Pres{ki}(1,1); % index for the jth equation in consideration.
Lit = Vi{ixeq}(ixmC0Pres{ki}(:,2),:); % transposed restriction matrix Li
- % V_j(i,:) in f_j(i) = V_j(i,:)*g_j
+ % V_j(i,:) in f_j(i) = V_j(i,:)*g_j
ci = ixmC0Pres{ki}(:,4) .* A0hat(ixmC0Pres{ki}(:,3),ixeq);
% s * a_j(h) in the restriction f_j(i) = s * a_j(h).
LtH = Lit/Hpinv{ixeq};
diff --git a/matlab/ms-sbvar/ms_variance_decomposition.m b/matlab/ms-sbvar/ms_variance_decomposition.m
index 553488acd184828aabc0f1b836df2601a7b1de84..83e270171ae2cfeae473b1319e7acc87dda165f9 100644
--- a/matlab/ms-sbvar/ms_variance_decomposition.m
+++ b/matlab/ms-sbvar/ms_variance_decomposition.m
@@ -85,7 +85,7 @@ if options_.ms.regime || options_.ms.regimes
outfile = [outfile 'regime_'];
if options_.ms.regime
outfile = [outfile num2str(options_.ms.regime-1) ...
- '_' options_.ms.output_file_tag '.out'];
+ '_' options_.ms.output_file_tag '.out'];
end
elseif options_.ms.filtered_probabilities
outfile = [outfile 'filtered_' options_.ms.output_file_tag '.out'];
@@ -103,7 +103,7 @@ if options_.ms.regimes
for regime_i=1:n_regimes
vd_title = ['Variance Decomposition, Regime ' num2str(regime_i)];
vd_data = load([outfile num2str(regime_i-1) '_' ...
- options_.ms.output_file_tag '.out'], '-ascii');
+ options_.ms.output_file_tag '.out'], '-ascii');
vd_data = reshape_ascii_variance_decomposition_data( ...
M_.endo_nbr, percentiles_size, options_.ms.horizon, vd_data);
save([vddir filesep 'variance_decomposition_regime_' num2str(regime_i-1) '.mat'], 'vd_data');
diff --git a/matlab/ms-sbvar/ms_write_markov_file.m b/matlab/ms-sbvar/ms_write_markov_file.m
index 02b934a3e79ed358bff1baa051a9ec7c2c83b0f5..630cfd570ceb4dbbd68d181b2615faf5fb008fcc 100644
--- a/matlab/ms-sbvar/ms_write_markov_file.m
+++ b/matlab/ms-sbvar/ms_write_markov_file.m
@@ -27,136 +27,136 @@ function ms_write_markov_file(fname, options)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- n_chains = length(options.ms.ms_chain);
- nvars = length(options.varobs);
-
- fh = fopen(fname,'w');
- %/******************************************************************************/
- %/********************* Markov State Variable Information **********************/
- %/******************************************************************************/
-
- fprintf(fh,'//== Markov State Variables with Simple Restrictions ==//\n\n');
-
-
- %//This number is NOT used but read in.
- fprintf(fh,'//== Number Observations ==//\n');
- fprintf(fh,'0\n\n');
+n_chains = length(options.ms.ms_chain);
+nvars = length(options.varobs);
- fprintf(fh,'//== Number independent state variables ==//\n');
- fprintf(fh,'%d\n\n',n_chains);
+fh = fopen(fname,'w');
+%/******************************************************************************/
+%/********************* Markov State Variable Information **********************/
+%/******************************************************************************/
- for i_chain = 1:n_chains
+fprintf(fh,'//== Markov State Variables with Simple Restrictions ==//\n\n');
- %//=====================================================//
- %//== state_variable[i] (1 <= i <= n_state_variables) ==//
- %//=====================================================//
- fprintf(fh,'//== Number of states for state_variable[%d] ==//\n', ...
- i_chain);
- n_states = length(options.ms.ms_chain(i_chain).regime);
- fprintf(fh,'%d\n\n',n_states);
-
- %//== 03/15/06: DW TVBVAR code reads the data below and overwrite the prior data read somewhere else if any.
- %//== Each column contains the parameters for a Dirichlet prior on the corresponding
- %//== column of the transition matrix. Each element must be positive. For each column,
- %//== the relative size of the prior elements determine the relative size of the elements
- %//== of the transition matrix and overall larger sizes implies a tighter prior.
- fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ...
- i_chain);
- Alpha = ones(n_states,n_states);
- for i_state = 1:n_states
- p = 1-1/options.ms.ms_chain(i_chain).regime(i_state).duration;
- Alpha(i_state,i_state) = p*(n_states-1)/(1-p);
- fprintf(fh,'%22.16f',Alpha(i_state,:));
- fprintf(fh,'\n');
+
+%//This number is NOT used but read in.
+fprintf(fh,'//== Number Observations ==//\n');
+fprintf(fh,'0\n\n');
+
+fprintf(fh,'//== Number independent state variables ==//\n');
+fprintf(fh,'%d\n\n',n_chains);
+
+for i_chain = 1:n_chains
+
+ %//=====================================================//
+ %//== state_variable[i] (1 <= i <= n_state_variables) ==//
+ %//=====================================================//
+ fprintf(fh,'//== Number of states for state_variable[%d] ==//\n', ...
+ i_chain);
+ n_states = length(options.ms.ms_chain(i_chain).regime);
+ fprintf(fh,'%d\n\n',n_states);
+
+ %//== 03/15/06: DW TVBVAR code reads the data below and overwrite the prior data read somewhere else if any.
+ %//== Each column contains the parameters for a Dirichlet prior on the corresponding
+ %//== column of the transition matrix. Each element must be positive. For each column,
+ %//== the relative size of the prior elements determine the relative size of the elements
+ %//== of the transition matrix and overall larger sizes implies a tighter prior.
+ fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ...
+ i_chain);
+ Alpha = ones(n_states,n_states);
+ for i_state = 1:n_states
+ p = 1-1/options.ms.ms_chain(i_chain).regime(i_state).duration;
+ Alpha(i_state,i_state) = p*(n_states-1)/(1-p);
+ fprintf(fh,'%22.16f',Alpha(i_state,:));
+ fprintf(fh,'\n');
+ end
+
+ fprintf(fh,['\n//== Dirichlet dimensions for state_variable[%d] ' ...
+ '==//\n'],i_chain);
+ % fprintf(fh,'%d ',repmat(n_states,1,n_states));
+ fprintf(fh,'%d ',repmat(2,1,n_states));
+ fprintf(fh,'\n\n');
+
+ %//== The jth restriction matrix is n_states-by-free[j]. Each row of the restriction
+ %//== matrix has exactly one non-zero entry and the sum of each column must be one.
+ fprintf(fh,['//== Column restrictions for state_variable[%d] ' ...
+ '==//\n'],i_chain);
+ for i_state = 1:n_states
+ if i_state == 1
+ M = eye(n_states,2);
+ elseif i_state == n_states
+ M = [zeros(n_states-2,2); eye(2)];
+ else
+ M = zeros(n_states,2);
+ M(i_state+[-1 1],1) = ones(2,1)/2;
+ M(i_state,2) = 1;
+ disp(M)
end
-
- fprintf(fh,['\n//== Dirichlet dimensions for state_variable[%d] ' ...
- '==//\n'],i_chain);
- % fprintf(fh,'%d ',repmat(n_states,1,n_states));
- fprintf(fh,'%d ',repmat(2,1,n_states));
- fprintf(fh,'\n\n');
-
- %//== The jth restriction matrix is n_states-by-free[j]. Each row of the restriction
- %//== matrix has exactly one non-zero entry and the sum of each column must be one.
- fprintf(fh,['//== Column restrictions for state_variable[%d] ' ...
- '==//\n'],i_chain);
- for i_state = 1:n_states
- if i_state == 1
- M = eye(n_states,2);
- elseif i_state == n_states
- M = [zeros(n_states-2,2); eye(2)];
- else
- M = zeros(n_states,2);
- M(i_state+[-1 1],1) = ones(2,1)/2;
- M(i_state,2) = 1;
- disp(M)
- end
- for j_state = 1:n_states
- fprintf(fh,'%f ',M(j_state,:));
- fprintf(fh,'\n');
- end
+ for j_state = 1:n_states
+ fprintf(fh,'%f ',M(j_state,:));
fprintf(fh,'\n');
end
+ fprintf(fh,'\n');
end
+end
- %/******************************************************************************/
- %/******************************* VAR Parameters *******************************/
- %/******************************************************************************/
- %//NOT read
- fprintf(fh,'//== Number Variables ==//\n');
- fprintf(fh,'%d\n\n',nvars);
+%/******************************************************************************/
+%/******************************* VAR Parameters *******************************/
+%/******************************************************************************/
+%//NOT read
+fprintf(fh,'//== Number Variables ==//\n');
+fprintf(fh,'%d\n\n',nvars);
- %//NOT read
- fprintf(fh,'//== Number Lags ==//\n');
- fprintf(fh,'%d\n\n',options.ms.nlags);
+%//NOT read
+fprintf(fh,'//== Number Lags ==//\n');
+fprintf(fh,'%d\n\n',options.ms.nlags);
- %//NOT read
- fprintf(fh,'//== Exogenous Variables ==//\n');
- fprintf(fh,'1\n\n');
+%//NOT read
+fprintf(fh,'//== Exogenous Variables ==//\n');
+fprintf(fh,'1\n\n');
- %//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
- %this state variable controls the jth column of A0 and Aplus
- fprintf(fh,['//== Controlling states variables for coefficients ==//\' ...
- 'n']);
-
- for i_var = 1:nvars
- for i_chain = 1:n_chains
- if ~isfield(options.ms.ms_chain(i_chain),'svar_coefficients') ...
- || isempty(options.ms.ms_chain(i_chain).svar_coefficients)
- i_equations = 0;
- else
- i_equations = ...
- options.ms.ms_chain(i_chain).svar_coefficients.equations;
- end
- if strcmp(i_equations,'ALL') || any(i_equations == i_var)
- fprintf(fh,'%d ',1);
- else
- fprintf(fh,'%d ',0);
- end
+%//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
+%this state variable controls the jth column of A0 and Aplus
+fprintf(fh,['//== Controlling states variables for coefficients ==//\' ...
+ 'n']);
+
+for i_var = 1:nvars
+ for i_chain = 1:n_chains
+ if ~isfield(options.ms.ms_chain(i_chain),'svar_coefficients') ...
+ || isempty(options.ms.ms_chain(i_chain).svar_coefficients)
+ i_equations = 0;
+ else
+ i_equations = ...
+ options.ms.ms_chain(i_chain).svar_coefficients.equations;
+ end
+ if strcmp(i_equations,'ALL') || any(i_equations == i_var)
+ fprintf(fh,'%d ',1);
+ else
+ fprintf(fh,'%d ',0);
end
- fprintf(fh,'\n');
end
+ fprintf(fh,'\n');
+end
- %//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
- %this state variable controls the jth diagonal element of Xi
- fprintf(fh,'\n//== Controlling states variables for variance ==//\n');
- for i_var = 1:nvars
- for i_chain = 1:n_chains
- if ~isfield(options.ms.ms_chain(i_chain),'svar_variances') ...
- || isempty(options.ms.ms_chain(i_chain).svar_variances)
- i_equations = 0;
- else
- i_equations = ...
- options.ms.ms_chain(i_chain).svar_variances.equations;
- end
- if strcmp(i_equations,'ALL') || any(i_equations == i_var)
- fprintf(fh,'%d ',1);
- else
- fprintf(fh,'%d ',0);
- end
+%//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
+%this state variable controls the jth diagonal element of Xi
+fprintf(fh,'\n//== Controlling states variables for variance ==//\n');
+for i_var = 1:nvars
+ for i_chain = 1:n_chains
+ if ~isfield(options.ms.ms_chain(i_chain),'svar_variances') ...
+ || isempty(options.ms.ms_chain(i_chain).svar_variances)
+ i_equations = 0;
+ else
+ i_equations = ...
+ options.ms.ms_chain(i_chain).svar_variances.equations;
+ end
+ if strcmp(i_equations,'ALL') || any(i_equations == i_var)
+ fprintf(fh,'%d ',1);
+ else
+ fprintf(fh,'%d ',0);
end
- fprintf(fh,'\n');
end
+ fprintf(fh,'\n');
+end
- fclose(fh);
+fclose(fh);
diff --git a/matlab/ms-sbvar/ms_write_mhm_input.m b/matlab/ms-sbvar/ms_write_mhm_input.m
index 57854d59ae3f6958fb3ca785859467f73a2b3f51..1edd4b4a2d6ae84dcba23501a7004e5aa755bb5a 100644
--- a/matlab/ms-sbvar/ms_write_mhm_input.m
+++ b/matlab/ms-sbvar/ms_write_mhm_input.m
@@ -28,41 +28,41 @@ function ms_write_mhm_input(fname, options_ms)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- fh = fopen(fname,'w');
+fh = fopen(fname,'w');
- fprintf(fh,'/**********************************************************\n');
- fprintf(fh,' *** This input file is read by swzmsbvar_mhm_1 and swzmsbvar_mhm_1.exe only, NOT by swzmsbvar_printdraws.exe.\n');
- fprintf(fh,' ***\n');
- fprintf(fh,' **********************************************************/\n');
+fprintf(fh,'/**********************************************************\n');
+fprintf(fh,' *** This input file is read by swzmsbvar_mhm_1 and swzmsbvar_mhm_1.exe only, NOT by swzmsbvar_printdraws.exe.\n');
+fprintf(fh,' ***\n');
+fprintf(fh,' **********************************************************/\n');
- fprintf(fh,'\n\n//------------- 1st set of posterior draws to find optimal scales for Metropolis (30000). ---------------\n');
- fprintf(fh,'//== number draws for first burn-in ==// //For determining the Metropolis scales only.\n');
- fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_1);
+fprintf(fh,'\n\n//------------- 1st set of posterior draws to find optimal scales for Metropolis (30000). ---------------\n');
+fprintf(fh,'//== number draws for first burn-in ==// //For determining the Metropolis scales only.\n');
+fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_1);
- fprintf(fh,'//------------- MCMC burn-in draws once the Metropolis scales (previous stage) are fixed. --------------\n');
- fprintf(fh,'//------------- 2nd set of standard burn-in posterior draws to throw away the initial draws (10000). ---------------\n');
- fprintf(fh,'//== number draws for second burn-in ==//\n');
- fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_2);
+fprintf(fh,'//------------- MCMC burn-in draws once the Metropolis scales (previous stage) are fixed. --------------\n');
+fprintf(fh,'//------------- 2nd set of standard burn-in posterior draws to throw away the initial draws (10000). ---------------\n');
+fprintf(fh,'//== number draws for second burn-in ==//\n');
+fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_2);
- fprintf(fh,'//--------------- 1st set of posterior draws to compute the mean and variance for the weighting function in the MHM (200000) ----------------\n');
- fprintf(fh,'//== number draws to estimate mean and variance ==//\n');
- fprintf(fh,'%d\n\n',options_ms.draws_nbr_mean_var_estimate);
+fprintf(fh,'//--------------- 1st set of posterior draws to compute the mean and variance for the weighting function in the MHM (200000) ----------------\n');
+fprintf(fh,'//== number draws to estimate mean and variance ==//\n');
+fprintf(fh,'%d\n\n',options_ms.draws_nbr_mean_var_estimate);
- fprintf(fh,'//--------------- Only applied to mhm_2 process: total number of MCMC draws = thinning factor * 2nd set of saved posterior draws ----------------\n');
- fprintf(fh,'//== thinning factor for modified harmonic mean process ==//\n');
- fprintf(fh,'%d\n\n',options_ms.thinning_factor);
+fprintf(fh,'//--------------- Only applied to mhm_2 process: total number of MCMC draws = thinning factor * 2nd set of saved posterior draws ----------------\n');
+fprintf(fh,'//== thinning factor for modified harmonic mean process ==//\n');
+fprintf(fh,'%d\n\n',options_ms.thinning_factor);
- fprintf(fh,'//--------------- 2nd set of saved posterior draws from MHM_2 (second stage): saved draws AFTER thinning (1000000) ----------------\n');
- fprintf(fh,'//== number draws for modified harmonic mean process ==//\n');
- fprintf(fh,'%d\n\n',options_ms.draws_nbr_modified_harmonic_mean);
+fprintf(fh,'//--------------- 2nd set of saved posterior draws from MHM_2 (second stage): saved draws AFTER thinning (1000000) ----------------\n');
+fprintf(fh,'//== number draws for modified harmonic mean process ==//\n');
+fprintf(fh,'%d\n\n',options_ms.draws_nbr_modified_harmonic_mean);
- fprintf(fh,'//------- 1st stage: computing all three tightness factors for Dirichlet. ---------\n');
- fprintf(fh,'//------- 2nd stage: hard-code the second scale factor (in principle, we can do all three). ---------\n');
- fprintf(fh,'//------- It seems that Dan''s code only use the first element of the following scales. The scale applies to the Dirichlet''s hyperparameter alpha for the diagonal of the transition matrix in the weighting function. Note that the weighting function for the transition matrix parameters is Dirichlet. ---------\n');
-
- fprintf(fh,'//== scale values for Dirichlet distribution ==//\n');
- fprintf(fh,'3\n\n');
- fprintf(fh,'%f ',options_ms.dirichlet_scale);
- fprintf(fh,'\n');
- fclose(fh);
\ No newline at end of file
+fprintf(fh,'//------- 1st stage: computing all three tightness factors for Dirichlet. ---------\n');
+fprintf(fh,'//------- 2nd stage: hard-code the second scale factor (in principle, we can do all three). ---------\n');
+fprintf(fh,'//------- It seems that Dan''s code only use the first element of the following scales. The scale applies to the Dirichlet''s hyperparameter alpha for the diagonal of the transition matrix in the weighting function. Note that the weighting function for the transition matrix parameters is Dirichlet. ---------\n');
+
+fprintf(fh,'//== scale values for Dirichlet distribution ==//\n');
+fprintf(fh,'3\n\n');
+fprintf(fh,'%f ',options_ms.dirichlet_scale);
+fprintf(fh,'\n');
+fclose(fh);
\ No newline at end of file
diff --git a/matlab/ms-sbvar/msstart2.m b/matlab/ms-sbvar/msstart2.m
index 6fa5861cd9cb981536c1fc0b21b0ca00d0f0e21c..aee78c03dd325021dd934b64353284619bb8dfb4 100644
--- a/matlab/ms-sbvar/msstart2.m
+++ b/matlab/ms-sbvar/msstart2.m
@@ -45,17 +45,17 @@ msstart_setup
%options_.ms.Indxcol = [1:nvar]; % a vector of random columns in which MC draws are made.
%
%options_.ms.indxparr = 1; % 1: parameters random; 0: no randomness in parameters
- % Note, when 0, there is no effect from the values of options_.ms.IndxAp, options_.ms.Aband, etc.
+% Note, when 0, there is no effect from the values of options_.ms.IndxAp, options_.ms.Aband, etc.
%options_.ms.indxovr = 0; % 1: distributions for other variables of interest; 0: no distribution.
- % Example: joint distribution of a(1) and a(2). Only for specific purposes
+% Example: joint distribution of a(1) and a(2). Only for specific purposes
%options_.ms.Aband = 1; % 1: error bands with only A0 and A+ random.
%options_.ms.IndxAp = 1; % 1: generate draws of A+; 0: no such draws.
- % Note: when options_.ms.IndxAp=0, there is no effect from the values of options_.ms.options_.ms.options_.ms.options_.ms.indximf, IndxFore,
- % or options_.ms.apband.
+% Note: when options_.ms.IndxAp=0, there is no effect from the values of options_.ms.options_.ms.options_.ms.options_.ms.indximf, IndxFore,
+% or options_.ms.apband.
%options_.ms.apband = 1; % 1: error bands for A+; 0: no error bands for A+.
%*** The following (impulse responses and forecasts) is used only if options_.ms.IndxAp=1
%options_.ms.indximf = 1; % 1: generate draws of impulse responses; 0: no such draws (thus no effect
- % from options_.ms.imfband)
+% from options_.ms.imfband)
%options_.ms.imfband = 1; % 1: error bands for impulse responses; 0: no error bands
%options_.ms.indxfore = 0; % 1: generate draws of forecasts; 0: no such draws (thus no effect from options_.ms.foreband)
%options_.ms.foreband = 0; % 1: error bands for out-of-sample forecasts; 0: no error bands
@@ -64,23 +64,23 @@ msstart_setup
rnum = nvar; % number of rows in the graph
cnum = 1; % number of columns in the graph
if rnum*cnum<nvar
- warning('rnum*cnum must be at least as large as nvar')
- disp('Hit any key to continue, or ctrl-c to abort')
- pause
+ warning('rnum*cnum must be at least as large as nvar')
+ disp('Hit any key to continue, or ctrl-c to abort')
+ pause
end
%options_.ms.indxgimfhat = 1; % 1: plot ML impulse responses; 0: no plot
%options_.ms.indxestima = 1; %1: ML estimation; 0: no estimation and data only
%
IndxNmlr = [1 0 0 0 0 0]; % imported by nmlzvar.m
- % Index for which normalization rule to choose
- % Only one of the elments in IndxNmlr can be non-zero
- % IndxNmlr(1): ML A distance rule (supposed to be the best)
- % IndxNmlr(2): ML Ahat distance rule (to approximate IndxNmlr(1))
- % IndxNmlr(3): ML Euclidean distance rule (not invariant to scale)
- % IndxNmlr(4): Positive diagonal rule
- % IndxNmlr(5): Positive inv(A) diagonal rule (if ~IndxNmlr(5), no need for A0inu,
- % so let A0inu=[])
- % IndxNmlr(6): Assigned postive rule (such as off-diagonal elements). Added 1/3/00
+ % Index for which normalization rule to choose
+ % Only one of the elments in IndxNmlr can be non-zero
+ % IndxNmlr(1): ML A distance rule (supposed to be the best)
+ % IndxNmlr(2): ML Ahat distance rule (to approximate IndxNmlr(1))
+ % IndxNmlr(3): ML Euclidean distance rule (not invariant to scale)
+ % IndxNmlr(4): Positive diagonal rule
+ % IndxNmlr(5): Positive inv(A) diagonal rule (if ~IndxNmlr(5), no need for A0inu,
+ % so let A0inu=[])
+ % IndxNmlr(6): Assigned postive rule (such as off-diagonal elements). Added 1/3/00
%%%%----------------------------------------
@@ -90,7 +90,7 @@ IndxNmlr = [1 0 0 0 0 0]; % imported by nmlzvar.m
nconstr1=nfqm; % number of the 1st set of constraints
nconstr2=options_.forecast ; % number of the 2nd set of constraints
nconstr=nconstr1+nconstr2; % q: 4 years -- 4*12 months.
- % When 0, no conditions directly on variables <<>>
+ % When 0, no conditions directly on variables <<>>
nconstr=0 ; %6*nconstr1;
options_.ms.eq_ms = []; % location of MS equation; if [], all shocks
PorR = [4*ones(nconstr1,1);2*ones(nconstr1,1);3*ones(nconstr1,1)]; % the variable conditioned. 1: Pcm; 3: FFR; 4: CPI
@@ -102,32 +102,32 @@ PorR = [PorR;1*ones(nconstr1,1);5*ones(nconstr1,1);6*ones(nconstr1,1)];
% Conditions directly on future shocks
%
%options_.ms.cms = 0 % 1: condition on ms shocks; 0: disable this and "fidcnderr.m" gives
- % unconditional forecasts if nconstr = 0 as well; <<>>
+% unconditional forecasts if nconstr = 0 as well; <<>>
%options_.ms.ncms = 0; % number of the stance of policy; 0 if no tightening or loosening
%options_.ms.eq_cms = 1; % location of MS shocks
options_.ms.tlindx = 1*ones(1,options_.ms.ncms); % 1-by-options_.ms.ncms vector; 1: tightening; 0: loosen
options_.ms.tlnumber = [0.5 0.5 0 0]; %94:4 % [2 2 1.5 1.5]; %79:9 %[1.5 1.5 1 1]; 90:9
- % 1-by-options_.ms.ncms vector; cut-off point for MS shocks
+ % 1-by-options_.ms.ncms vector; cut-off point for MS shocks
TLmean = zeros(1,options_.ms.ncms);
- % unconditional, i.e., 0 mean, for the final report in the paper
+% unconditional, i.e., 0 mean, for the final report in the paper
if options_.ms.cms
- options_.ms.eq_ms = [];
- % At least at this point, it makes no sense to have DLS type of options_.ms.eq_ms; 10/12/98
- if all(isfinite(options_.ms.tlnumber))
- for k=1:options_.ms.ncms
- TLmean(k) = lcnmean(options_.ms.tlnumber(k),options_.ms.tlindx(k));
- % shock mean magnitude. 1: tight; 0: loose
- % Never used for any subsequent computation but
- % simply used for the final report in the paper.
- %options_.ms.tlnumber(k) = fzero('lcutoff',0,[],[],TLmean(k))
- % get an idea about the cutoff point given TLmean instead
-
- end
- end
+ options_.ms.eq_ms = [];
+ % At least at this point, it makes no sense to have DLS type of options_.ms.eq_ms; 10/12/98
+ if all(isfinite(options_.ms.tlnumber))
+ for k=1:options_.ms.ncms
+ TLmean(k) = lcnmean(options_.ms.tlnumber(k),options_.ms.tlindx(k));
+ % shock mean magnitude. 1: tight; 0: loose
+ % Never used for any subsequent computation but
+ % simply used for the final report in the paper.
+ %options_.ms.tlnumber(k) = fzero('lcutoff',0,[],[],TLmean(k))
+ % get an idea about the cutoff point given TLmean instead
+
+ end
+ end
else
- options_.ms.ncms = 0; % only for the use of the graph by msprobg.m
- options_.ms.tlnumber = NaN*ones(1,options_.ms.ncms);
- % -infinity, only for the use of the graph by msprobg.m
+ options_.ms.ncms = 0; % only for the use of the graph by msprobg.m
+ options_.ms.tlnumber = NaN*ones(1,options_.ms.ncms);
+ % -infinity, only for the use of the graph by msprobg.m
end
@@ -135,22 +135,22 @@ end
% Soft conditions on variables
%
%cnum = 0 % # of band condtions; when 0, disable this option
- % Note (different from "fidencon") that each condition corres. to variable
+% Note (different from "fidencon") that each condition corres. to variable
%options_.ms.banact = 1; % 1: use infor on actual; 0: preset without infor on actual
if cnum
- banindx = cell(cnum,1); % index for each variable or conditon
- banstp = cell(cnum,1); % steps: annual in general
- banvar = zeros(cnum,1); % varables: annual in general
- banval = cell(cnum,1); % band value (each variable occupy a cell)
- badval{1} = zeros(length(banstp{1}),2); % 2: lower or higher bound
-
- banstp{1} = 1:4; % 3 or 4 years
- banvar(1) = 3; % 3: FFR; 5: CPI
- if ~options_.ms.banact
- for i=1:length(banstp{1})
- banval{1}(i,:) = [5.0 10.0];
- end
- end
+ banindx = cell(cnum,1); % index for each variable or conditon
+ banstp = cell(cnum,1); % steps: annual in general
+ banvar = zeros(cnum,1); % varables: annual in general
+ banval = cell(cnum,1); % band value (each variable occupy a cell)
+ badval{1} = zeros(length(banstp{1}),2); % 2: lower or higher bound
+
+ banstp{1} = 1:4; % 3 or 4 years
+ banvar(1) = 3; % 3: FFR; 5: CPI
+ if ~options_.ms.banact
+ for i=1:length(banstp{1})
+ banval{1}(i,:) = [5.0 10.0];
+ end
+ end
end
%
pause(1)
@@ -171,60 +171,60 @@ pause(1)
%(1)--------------------------------------
%
if (options_.ms.freq==12)
- nStart=(yrStart-options_.ms.initial_year )*12+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
- nEnd=(yrEnd-options_.ms.final_year )*12+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
+ nStart=(yrStart-options_.ms.initial_year )*12+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
+ nEnd=(yrEnd-options_.ms.final_year )*12+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
elseif (options_.ms.freq==4)
- nStart=(yrStart-options_.ms.initial_year )*4+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
- nEnd=(yrEnd-options_.ms.final_year )*4+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
+ nStart=(yrStart-options_.ms.initial_year )*4+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
+ nEnd=(yrEnd-options_.ms.final_year )*4+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
elseif (options_.ms.freq==1)
- nStart=(yrStart-options_.ms.initial_year )*1+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
- nEnd=(yrEnd-options_.ms.final_year )*1+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
+ nStart=(yrStart-options_.ms.initial_year )*1+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
+ nEnd=(yrEnd-options_.ms.final_year )*1+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
else
- error('Error: this code is only good for monthly/quarterly/yearly data!!!')
- return
+ error('Error: this code is only good for monthly/quarterly/yearly data!!!')
+ return
end
%
if nEnd>0 || nStart<0
- disp('Warning: this particular sample consider is out of bounds of the data!!!')
- return
+ disp('Warning: this particular sample consider is out of bounds of the data!!!')
+ return
end
%*** Note, both xdgel and xdata have the same start with the specific sample
xdgel=options_.data(nStart+1:nData+nEnd,options_.ms.vlist);
- % gel: general options_.data within sample (nSample)
+% gel: general options_.data within sample (nSample)
if ~(nSample==size(xdgel,1))
- warning('The sample size (including options_.ms.nlags ) and data are incompatible')
- disp('Check to make sure nSample and size(xdgel,1) are the same')
- return
+ warning('The sample size (including options_.ms.nlags ) and data are incompatible')
+ disp('Check to make sure nSample and size(xdgel,1) are the same')
+ return
end
%
baddata = find(isnan(xdgel));
if ~isempty(baddata)
- warning('Some data for this selected sample are actually unavailable.')
- disp('Hit any key to continue, or ctrl-c to abort')
- pause
+ warning('Some data for this selected sample are actually unavailable.')
+ disp('Hit any key to continue, or ctrl-c to abort')
+ pause
end
%
if options_.ms.initial_subperiod ==1
- yrB = options_.ms.initial_year ; qmB = options_.ms.initial_subperiod ;
+ yrB = options_.ms.initial_year ; qmB = options_.ms.initial_subperiod ;
else
- yrB = options_.ms.initial_year +1; qmB = 1;
+ yrB = options_.ms.initial_year +1; qmB = 1;
end
yrF = options_.ms.final_year ; qmF = options_.ms.final_subperiod ;
[Mdate,tmp] = fn_calyrqm(options_.ms.freq,[options_.ms.initial_year options_.ms.initial_subperiod ],[options_.ms.final_year options_.ms.final_subperiod ]);
xdatae=[Mdate options_.data(1:nData,options_.ms.vlist)];
- % beyond sample into forecast horizon until the end of the data options_.ms.final_year :options_.ms.final_subperiod
- % Note: may contain NaN data. So must be careful about its use
+% beyond sample into forecast horizon until the end of the data options_.ms.final_year :options_.ms.final_subperiod
+% Note: may contain NaN data. So must be careful about its use
%=========== Obtain prior-period, period-to-last period, and annual growth rates
[yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = fn_datana(xdatae,options_.ms.freq,options_.ms.log_var,options_.ms.percent_var,[yrB qmB],[yrF qmF]);
qdates = zeros(size(yactqmyge,1),1);
for ki=1:length(qdates)
- qdates(ki) = yactqmyge(1,1) + (yactqmyge(1,2)+ki-2)/options_.ms.freq;
+ qdates(ki) = yactqmyge(1,1) + (yactqmyge(1,2)+ki-2)/options_.ms.freq;
end
for ki=1:nvar
- figure
- plot(qdates, yactqmyge(:,2+ki)/100)
- xlabel(options_.ms.varlist{ki})
+ figure
+ plot(qdates, yactqmyge(:,2+ki)/100)
+ xlabel(options_.ms.varlist{ki})
end
save outactqmygdata.prn yactqmyge -ascii
@@ -236,11 +236,11 @@ spstr1 = 'disp([sprintf(';
spstr2 = '%4.0f %2.0f';
yactyrget=yactyrge';
for ki=1:length(options_.ms.vlist)
- if ki==length(options_.ms.vlist)
- spstr2 = [spstr2 ' %8.3f\n'];
- else
- spstr2 = [spstr2 ' %8.3f'];
- end
+ if ki==length(options_.ms.vlist)
+ spstr2 = [spstr2 ' %8.3f\n'];
+ else
+ spstr2 = [spstr2 ' %8.3f'];
+ end
end
spstr = [spstr1 'spstr2' ', yactyrget)])'];
eval(spstr)
@@ -251,11 +251,11 @@ fid = fopen('outyrqm.prn','w');
fpstr1 = 'fprintf(fid,';
fpstr2 = '%4.0f %2.0f';
for ki=1:nvar
- if ki==nvar
- fpstr2 = [fpstr2 ' %8.3f\n'];
- else
- fpstr2 = [fpstr2 ' %8.3f'];
- end
+ if ki==nvar
+ fpstr2 = [fpstr2 ' %8.3f\n'];
+ else
+ fpstr2 = [fpstr2 ' %8.3f'];
+ end
end
fpstr = [fpstr1 'fpstr2' ', yactyrget);'];
eval(fpstr)
@@ -264,479 +264,479 @@ fclose(fid);
if options_.ms.indxestima
- %(2)----------------------------------------------------------------------------
- % Estimation
- % ML forecast and impulse responses
- % Hard or soft conditions for conditional forecasts
- %(2)----------------------------------------------------------------------------
- %
- %* Arranged data information, WITHOUT dummy obs when 0 after mu is used. See fn_rnrprior_covres_dobs.m for using the dummy
- % observations as part of an explicit prior.
- [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh] = fn_dataxy(nvar,options_.ms.nlags ,xdgel,mu,0,nexo);
- if qmStart+options_.ms.nlags -options_.ms.dummy_obs >0
- qmStartEsti = rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
- if (~qmStartEsti)
- qmStartEsti = options_.ms.freq;
- end
- yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq+0.01));
+ %(2)----------------------------------------------------------------------------
+ % Estimation
+ % ML forecast and impulse responses
+ % Hard or soft conditions for conditional forecasts
+ %(2)----------------------------------------------------------------------------
+ %
+ %* Arranged data information, WITHOUT dummy obs when 0 after mu is used. See fn_rnrprior_covres_dobs.m for using the dummy
+ % observations as part of an explicit prior.
+ [xtx,xty,yty,fss,phi,y,ncoef,xr,Bh] = fn_dataxy(nvar,options_.ms.nlags ,xdgel,mu,0,nexo);
+ if qmStart+options_.ms.nlags -options_.ms.dummy_obs >0
+ qmStartEsti = rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
+ if (~qmStartEsti)
+ qmStartEsti = options_.ms.freq;
+ end
+ yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq+0.01));
% + 0.01 (or any number < 1) is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==?*options_.ms.freq doesn't give us an extra year forward.
- else
- qmStartEsti = options_.ms.freq + rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
- if (qmStart+options_.ms.nlags -options_.ms.dummy_obs ==0)
- yrStartEsti = yrStart - 1; % one year back.
- else
- yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq-0.01));
- % - 0.01 (or any number < 1) is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==-?*options_.ms.freq give us an extra year back.
- end
- end
- dateswd = fn_dataext([yrStartEsti qmStartEsti],[yrEnd qmEnd],xdatae(:,[1:2])); % dates with dummies
- phie = [dateswd phi];
- ye = [dateswd y];
-
- %* Obtain linear restrictions
- [Uiconst,Viconst,n0,np,ixmC0Pres] = feval(options_.ms.restriction_fname,nvar,nexo,options_.ms );
- if min(n0)==0
- skipline()
- warning('A0: restrictions in dlrprior.m give no free parameter in one of equations')
- disp('Press ctrl-c to abort')
- pause
- elseif min(np)==0
- skipline()
- warning('Ap: Restrictions in dlrprior.m give no free parameter in one of equations')
- disp('Press ctrl-c to abort')
- pause
- end
-
- if options_.ms.contemp_reduced_form
- Uiconst=cell(nvar,1); Viconst=cell(ncoef,1);
- for kj=1:nvar
- Uiconst{kj} = eye(nvar); Viconst{kj} = eye(ncoef);
- end
- end
-
- if options_.ms.bayesian_prior
- %*** Obtains asymmetric prior (with no linear restrictions) with dummy observations as part of an explicit prior (i.e,
- % reflected in Hpmulti and Hpinvmulti). See Forecast II, pp.69a-69b for details.
- if 1 % Liquidity effect prior on both MS and MD equations.
- [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior_covres_dobs(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu,indxDummy,hpmsmd,indxmsmdeqn);
- else
- [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu);
- end
-
- %*** Combines asymmetric prior with linear restrictions
- [Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Uiconst,Viconst,Pi,H0multi,Hpmulti,nvar);
-
- %*** Obtains the posterior matrices for estimation and inference
- [Pmat,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Uiconst,Viconst);
-
- if options_.ms.contemp_reduced_form
- %*** Obtain the ML estimate
- A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
- A0hat=inv(A0hatinv);
- a0indx = find(A0hat);
- else
- %*** Obtain the ML estimate
- % load idenml
- x = 10*rand(sum(n0),1);
- H0 = eye(sum(n0));
- crit = 1.0e-9;
- nit = 10000;
- %
- [fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
- csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
-
- A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
- A0hatinv = inv(A0hat);
- fhat
- xhat
- grad
- itct
- fcount
- retcodehat
- save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
- end
- else
- %*** Obtain the posterior matrices for estimation and inference
- [Pmat,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Uiconst,Viconst);
-
- if options_.ms.contemp_reduced_form
- %*** Obtain the ML estimate
- A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
- A0hat=inv(A0hatinv);
- a0indx = find(A0hat);
- else
- %*** Obtain the ML estimate
- % load idenml
- x = 10*rand(sum(n0),1);
- H0 = eye(sum(n0));
- crit = 1.0e-9;
- nit = 10000;
- %
- [fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
- csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
-
- A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
- A0hatinv = inv(A0hat);
- fhat
- xhat
- grad
- itct
- fcount
- retcodehat
- save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
- end
- end
-
- %**** impulse responses
- swish = A0hatinv; % each column corresponds to an equation
- if options_.ms.contemp_reduced_form
- xhat = A0hat(a0indx);
- Bhat=Pmat{1};
- Fhat = Bhat*A0hat
- ghat = NaN;
- else
- xhat = fn_tran_a2b(A0hat,Uiconst,nvar,n0);
- [Fhat,ghat] = fn_gfmean(xhat,Pmat,Viconst,nvar,ncoef,n0,np);
- if options_.ms.cross_restrictions
- Fhatur0P = Fhat; % ur: unrestriced across A0 and A+
- for ki = 1:size(ixmC0Pres,1) % loop through the number of equations in which
- % cross-A0-A+ restrictions occur. See St. Louis Note p.5.
- ixeq = ixmC0Pres{ki}(1,1); % index for the jth equation in consideration.
- Lit = Viconst{ixeq}(ixmC0Pres{ki}(:,2),:); % transposed restriction matrix Li
- % V_j(i,:) in f_j(i) = V_j(i,:)*g_j
- ci = ixmC0Pres{ki}(:,4) .* A0hat(ixmC0Pres{ki}(:,3),ixeq);
- % s * a_j(h) in the restriction f_j(i) = s * a_j(h).
- LtH = Lit/Hpinv{ixeq};
- HLV = LtH'/(LtH*Lit');
- gihat = Viconst{ixeq}'*Fhatur0P(:,ixeq);
- Fhat(:,ixeq) = Viconst{ixeq}*(gihat + HLV*(ci-Lit*gihat));
- end
- end
- Fhat
- Bhat = Fhat/A0hat; % ncoef-by-nvar reduced form lagged parameters.
- end
- nn = [nvar options_.ms.nlags imstp];
- imfhat = fn_impulse(Bhat,swish,nn); % in the form that is congenial to RATS
- imf3hat=reshape(imfhat,size(imfhat,1),nvar,nvar);
- % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
- imf3shat=permute(imf3hat,[1 3 2]);
- % imf3s: permuted so that row--steps, column--nvar shocks,
- % 3rd dimension--nvar responses
- % Note: reshape(imf3s(1,:,:),nvar,nvar) = A0in (columns -- equations)
- if options_.ms.indxgimfhat
- figure
- end
- scaleout = fn_imcgraph(imfhat,nvar,imstp,xlab,ylab,options_.ms.indxgimfhat);
- imfstd = max(abs(scaleout)'); % row: nvar (largest number); used for standard deviations
-
- %
- % %**** save stds. of both data and impulse responses in idfile1
- % temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
- % save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
- % %
- % %**** save stds. of both data and impulse responses in idfile1
- % temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
- % save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
- % if options_.ms.indxparr
- % idfile1='idenyimstd';
- % end
-
- %=====================================
- % Now, out-of-sample forecasts. Note: Hm1t does not change with A0.
- %=====================================
- %
- % * updating the last row of X (phi) with the current (last row of) y.
- tcwx = nvar*options_.ms.nlags ; % total coefficeint without exogenous variables
- phil = phi(size(phi,1),:);
- phil(nvar+1:tcwx) = phil(1:tcwx-nvar);
- phil(1:nvar) = y(end,:);
- %*** exogenous variables excluding constant terms
- if (nexo>1)
- Xexoe = fn_dataext([yrEnd qmEnd],[yrEnd qmEnd],xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
- phil(1,tcwx+1:tcwx+nexo-1) = Xexoe(1,3:end);
- end
- %
- %*** ML unconditional point forecast
- nn = [nvar options_.ms.nlags nfqm];
- if nexo<2
- yforehat = fn_forecast(Bhat,phil,nn); % nfqm-by-nvar, in log
- else
- Xfexoe = fn_dataext(fdates(1,:),fdates(numel(fdates),:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
- %Xfexoe = fn_dataext(fdates(1,:),fdates(end,:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
- yforehat = fn_forecast(Bhat,phil,nn,nexo,Xfexoe(:,3:end)); % nfqm-by-nvar, in log
- end
- yforehate = [fdates yforehat];
- %
- yact1e = fn_dataext([yrEnd-nayr 1],[yrEnd qmEnd],xdatae(:,1:nvar+2));
- if options_.ms.real_pseudo_forecast
- %yact2e = fn_dataext([yrEnd-nayr 1],E2yrqm,xdatae);
- yact2e = fn_dataext([yrEnd-nayr 1],[fdates(end,1) options_.ms.freq],xdatae(:,1:nvar+2));
- else
- yact2e=yact1e;
- end
- yafhate = [yact1e; yforehate]; % actual and forecast
- %
- %===== Converted to mg, qg, and calendar yg
- %
- [yafyrghate,yafyrhate,yafqmyghate] = fn_datana(yafhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
- % actual and forecast growth rates
- [yact2yrge,yact2yre,yact2qmyge] = fn_datana(yact2e,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
- % only actual growth rates
- yafyrghate
- if options_.ms.indxgforhat
- keyindx = [1:nvar];
- conlab=['unconditional'];
-
- figure
- yafyrghate(:,3:end) = yafyrghate(:,3:end)/100;
- yact2yrge(:,3:end) = yact2yrge(:,3:end)/100;
- fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
- end
-
- %-------------------------------------------------
- % Setup for point conditional forecast
- % ML Conditional Forecast
- %-------------------------------------------------
- %
- %% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
- %
- %% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
- %% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
- %% response at t=1, C at t=2, etc. The row of inv(A0) or C is
- %% all responses to one shock.
- %% Let r be q-by-1 (such as r(1) = r(t+1)
- %% = y(t+1) (constrained) - y(t+1) (forecast)).
- %% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
- %% where nsteps the largest constrained step. The key of the program
- %% is to creat R using impulse responses
- %% Optimal solution for shock e where R'*e=r and e is k-by-1 is
- %% e = R*inv(R'*R)*r.
- %
-
- if (nconstr > 0)
- %*** initializing
- stepcon=cell(nconstr,1); % initializing, value y conditioned
- valuecon=zeros(nconstr,1); % initializing, value y conditioned
- varcon=zeros(nconstr,1); % initializing, endogous variables conditioned
- varcon(:)=PorR; % 1: Pcm; 3: FFR; 5: CPI
-
- %
- for i=1:nconstr
- if i<=nconstr1
- stepcon{i}=i; % FFR
- elseif i<=2*nconstr1
- stepcon{i}=i-nconstr1; % FFR
- elseif i<=3*nconstr1
- stepcon{i}=i-2*nconstr1; % FFR
- elseif i<=4*nconstr1
- stepcon{i}=i-3*nconstr1; % FFR
- elseif i<=5*nconstr1
- stepcon{i}=i-4*nconstr1; % FFR
- elseif i<=6*nconstr1
- stepcon{i}=i-5*nconstr1; % FFR
- end
- end
-
-% for i=1:nconstr
-% stepcon{i}=i; % FFR
-% end
-
-% bend=12;
-% stepcon{1}=[1:bend]'; % average over
-% stepcon{nconstr1+1}=[1:options_.ms.freq-qmSub]'; % average over the remaing months in 1st forecast year
-% stepcon{nconstr1+2}=[options_.ms.freq-qmSub+1:options_.ms.freq-qmSub+12]'; % average over 12 months next year
-% stepcon{nconstr1+3}=[options_.ms.freq-qmSub+13:options_.ms.freq-qmSub+24]'; % average over 12 months. 3rd year
-% stepcon{nconstr1+4}=[options_.ms.freq-qmSub+25:options_.ms.freq-qmSub+36]'; % average over 12 months. 4th year
-
-% %**** avearage condition over, say, options_.ms.freq periods
-% if qmEnd==options_.ms.freq
-% stepcon{1}=[1:options_.ms.freq]'; % average over the remaing periods in 1st forecast year
-% else
-% stepcon{1}=[1:options_.ms.freq-qmEnd]'; % average over the remaing periods in 1st forecast year
-% end
-% for kj=2:nconstr
-% stepcon{kj}=[length(stepcon{kj-1})+1:length(stepcon{kj-1})+options_.ms.freq]'; % average over 12 months next year
-% end
-
- if options_.ms.real_pseudo_forecast
-% %*** conditions in every period
-% for i=1:nconstr
-% valuecon(i) = yact(actup+i,varcon(i));
-% %valuecon(i) = mean( yact(actup+1:actup+bend,varcon(i)) );
-% %valuecon(i) = 0.060; % 95:01
-% %valuecon(i) = (0.0475+0.055)/2; % 94:10
-% end
-
-% %*** average condtions over,say, options_.ms.freq periods.
-% for i=nconstr1+1:nconstr1+nconstr2
-% i=1;
-% valuecon(nconstr1+i) = ( ( mean(ylast12Cal(:,varcon(nconstr1+i)),1) + ...
-% log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100) )*options_.ms.freq - ...
-% yCal_1(:,varcon(nconstr1+i)) ) ./ length(stepcon{nconstr1+i});
-% % the same as unconditional "yactCalyg" 1st calendar year
-% i=2;
-% valuecon(nconstr1+i) = mean(ylast12Cal(:,varcon(nconstr1+i))) + ...
-% log(1+yactCalyg(yAg-yFg+1,varcon(nconstr1+i))/100) ...
-% + log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
-% % the same as actual "yactCalgy" 2nd calendar year
-% i=3;
-% valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
-% log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
-% % the same as actual "yactCalgy" 3rd calendar year
-% %i=4;
-% %valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
-% % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
-% % the same as actual "yactCalgy" 4th calendar year
-% end
-
- %*** conditions in every period
- vpntM = fn_dataext(E1yrqm, E2yrqm,xdatae); % point value matrix with dates
- % vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
- for i=1:nconstr
+ else
+ qmStartEsti = options_.ms.freq + rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
+ if (qmStart+options_.ms.nlags -options_.ms.dummy_obs ==0)
+ yrStartEsti = yrStart - 1; % one year back.
+ else
+ yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq-0.01));
+ % - 0.01 (or any number < 1) is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==-?*options_.ms.freq give us an extra year back.
+ end
+ end
+ dateswd = fn_dataext([yrStartEsti qmStartEsti],[yrEnd qmEnd],xdatae(:,[1:2])); % dates with dummies
+ phie = [dateswd phi];
+ ye = [dateswd y];
+
+ %* Obtain linear restrictions
+ [Uiconst,Viconst,n0,np,ixmC0Pres] = feval(options_.ms.restriction_fname,nvar,nexo,options_.ms );
+ if min(n0)==0
+ skipline()
+ warning('A0: restrictions in dlrprior.m give no free parameter in one of equations')
+ disp('Press ctrl-c to abort')
+ pause
+ elseif min(np)==0
+ skipline()
+ warning('Ap: Restrictions in dlrprior.m give no free parameter in one of equations')
+ disp('Press ctrl-c to abort')
+ pause
+ end
+
+ if options_.ms.contemp_reduced_form
+ Uiconst=cell(nvar,1); Viconst=cell(ncoef,1);
+ for kj=1:nvar
+ Uiconst{kj} = eye(nvar); Viconst{kj} = eye(ncoef);
+ end
+ end
+
+ if options_.ms.bayesian_prior
+ %*** Obtains asymmetric prior (with no linear restrictions) with dummy observations as part of an explicit prior (i.e,
+ % reflected in Hpmulti and Hpinvmulti). See Forecast II, pp.69a-69b for details.
+ if 1 % Liquidity effect prior on both MS and MD equations.
+ [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior_covres_dobs(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu,indxDummy,hpmsmd,indxmsmdeqn);
+ else
+ [Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu);
+ end
+
+ %*** Combines asymmetric prior with linear restrictions
+ [Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Uiconst,Viconst,Pi,H0multi,Hpmulti,nvar);
+
+ %*** Obtains the posterior matrices for estimation and inference
+ [Pmat,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Uiconst,Viconst);
+
+ if options_.ms.contemp_reduced_form
+ %*** Obtain the ML estimate
+ A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
+ A0hat=inv(A0hatinv);
+ a0indx = find(A0hat);
+ else
+ %*** Obtain the ML estimate
+ % load idenml
+ x = 10*rand(sum(n0),1);
+ H0 = eye(sum(n0));
+ crit = 1.0e-9;
+ nit = 10000;
+ %
+ [fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
+ csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
+
+ A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
+ A0hatinv = inv(A0hat);
+ fhat
+ xhat
+ grad
+ itct
+ fcount
+ retcodehat
+ save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
+ end
+ else
+ %*** Obtain the posterior matrices for estimation and inference
+ [Pmat,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Uiconst,Viconst);
+
+ if options_.ms.contemp_reduced_form
+ %*** Obtain the ML estimate
+ A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
+ A0hat=inv(A0hatinv);
+ a0indx = find(A0hat);
+ else
+ %*** Obtain the ML estimate
+ % load idenml
+ x = 10*rand(sum(n0),1);
+ H0 = eye(sum(n0));
+ crit = 1.0e-9;
+ nit = 10000;
+ %
+ [fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
+ csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
+
+ A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
+ A0hatinv = inv(A0hat);
+ fhat
+ xhat
+ grad
+ itct
+ fcount
+ retcodehat
+ save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
+ end
+ end
+
+ %**** impulse responses
+ swish = A0hatinv; % each column corresponds to an equation
+ if options_.ms.contemp_reduced_form
+ xhat = A0hat(a0indx);
+ Bhat=Pmat{1};
+ Fhat = Bhat*A0hat
+ ghat = NaN;
+ else
+ xhat = fn_tran_a2b(A0hat,Uiconst,nvar,n0);
+ [Fhat,ghat] = fn_gfmean(xhat,Pmat,Viconst,nvar,ncoef,n0,np);
+ if options_.ms.cross_restrictions
+ Fhatur0P = Fhat; % ur: unrestriced across A0 and A+
+ for ki = 1:size(ixmC0Pres,1) % loop through the number of equations in which
+ % cross-A0-A+ restrictions occur. See St. Louis Note p.5.
+ ixeq = ixmC0Pres{ki}(1,1); % index for the jth equation in consideration.
+ Lit = Viconst{ixeq}(ixmC0Pres{ki}(:,2),:); % transposed restriction matrix Li
+ % V_j(i,:) in f_j(i) = V_j(i,:)*g_j
+ ci = ixmC0Pres{ki}(:,4) .* A0hat(ixmC0Pres{ki}(:,3),ixeq);
+ % s * a_j(h) in the restriction f_j(i) = s * a_j(h).
+ LtH = Lit/Hpinv{ixeq};
+ HLV = LtH'/(LtH*Lit');
+ gihat = Viconst{ixeq}'*Fhatur0P(:,ixeq);
+ Fhat(:,ixeq) = Viconst{ixeq}*(gihat + HLV*(ci-Lit*gihat));
+ end
+ end
+ Fhat
+ Bhat = Fhat/A0hat; % ncoef-by-nvar reduced form lagged parameters.
+ end
+ nn = [nvar options_.ms.nlags imstp];
+ imfhat = fn_impulse(Bhat,swish,nn); % in the form that is congenial to RATS
+ imf3hat=reshape(imfhat,size(imfhat,1),nvar,nvar);
+ % imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
+ imf3shat=permute(imf3hat,[1 3 2]);
+ % imf3s: permuted so that row--steps, column--nvar shocks,
+ % 3rd dimension--nvar responses
+ % Note: reshape(imf3s(1,:,:),nvar,nvar) = A0in (columns -- equations)
+ if options_.ms.indxgimfhat
+ figure
+ end
+ scaleout = fn_imcgraph(imfhat,nvar,imstp,xlab,ylab,options_.ms.indxgimfhat);
+ imfstd = max(abs(scaleout)'); % row: nvar (largest number); used for standard deviations
+
+ %
+ % %**** save stds. of both data and impulse responses in idfile1
+ % temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
+ % save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
+ % %
+ % %**** save stds. of both data and impulse responses in idfile1
+ % temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
+ % save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
+ % if options_.ms.indxparr
+ % idfile1='idenyimstd';
+ % end
+
+ %=====================================
+ % Now, out-of-sample forecasts. Note: Hm1t does not change with A0.
+ %=====================================
+ %
+ % * updating the last row of X (phi) with the current (last row of) y.
+ tcwx = nvar*options_.ms.nlags ; % total coefficeint without exogenous variables
+ phil = phi(size(phi,1),:);
+ phil(nvar+1:tcwx) = phil(1:tcwx-nvar);
+ phil(1:nvar) = y(end,:);
+ %*** exogenous variables excluding constant terms
+ if (nexo>1)
+ Xexoe = fn_dataext([yrEnd qmEnd],[yrEnd qmEnd],xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
+ phil(1,tcwx+1:tcwx+nexo-1) = Xexoe(1,3:end);
+ end
+ %
+ %*** ML unconditional point forecast
+ nn = [nvar options_.ms.nlags nfqm];
+ if nexo<2
+ yforehat = fn_forecast(Bhat,phil,nn); % nfqm-by-nvar, in log
+ else
+ Xfexoe = fn_dataext(fdates(1,:),fdates(numel(fdates),:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
+ %Xfexoe = fn_dataext(fdates(1,:),fdates(end,:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
+ yforehat = fn_forecast(Bhat,phil,nn,nexo,Xfexoe(:,3:end)); % nfqm-by-nvar, in log
+ end
+ yforehate = [fdates yforehat];
+ %
+ yact1e = fn_dataext([yrEnd-nayr 1],[yrEnd qmEnd],xdatae(:,1:nvar+2));
+ if options_.ms.real_pseudo_forecast
+ %yact2e = fn_dataext([yrEnd-nayr 1],E2yrqm,xdatae);
+ yact2e = fn_dataext([yrEnd-nayr 1],[fdates(end,1) options_.ms.freq],xdatae(:,1:nvar+2));
+ else
+ yact2e=yact1e;
+ end
+ yafhate = [yact1e; yforehate]; % actual and forecast
+ %
+ %===== Converted to mg, qg, and calendar yg
+ %
+ [yafyrghate,yafyrhate,yafqmyghate] = fn_datana(yafhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
+ % actual and forecast growth rates
+ [yact2yrge,yact2yre,yact2qmyge] = fn_datana(yact2e,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
+ % only actual growth rates
+ yafyrghate
+ if options_.ms.indxgforhat
+ keyindx = [1:nvar];
+ conlab=['unconditional'];
+
+ figure
+ yafyrghate(:,3:end) = yafyrghate(:,3:end)/100;
+ yact2yrge(:,3:end) = yact2yrge(:,3:end)/100;
+ fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
+ end
+
+ %-------------------------------------------------
+ % Setup for point conditional forecast
+ % ML Conditional Forecast
+ %-------------------------------------------------
+ %
+ %% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
+ %
+ %% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
+ %% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
+ %% response at t=1, C at t=2, etc. The row of inv(A0) or C is
+ %% all responses to one shock.
+ %% Let r be q-by-1 (such as r(1) = r(t+1)
+ %% = y(t+1) (constrained) - y(t+1) (forecast)).
+ %% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
+ %% where nsteps the largest constrained step. The key of the program
+ %% is to creat R using impulse responses
+ %% Optimal solution for shock e where R'*e=r and e is k-by-1 is
+ %% e = R*inv(R'*R)*r.
+ %
+
+ if (nconstr > 0)
+ %*** initializing
+ stepcon=cell(nconstr,1); % initializing, value y conditioned
+ valuecon=zeros(nconstr,1); % initializing, value y conditioned
+ varcon=zeros(nconstr,1); % initializing, endogous variables conditioned
+ varcon(:)=PorR; % 1: Pcm; 3: FFR; 5: CPI
+
+ %
+ for i=1:nconstr
if i<=nconstr1
- valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates
+ stepcon{i}=i; % FFR
elseif i<=2*nconstr1
- valuecon(i) = vpntM(i-nconstr1,2+varcon(i));
+ stepcon{i}=i-nconstr1; % FFR
elseif i<=3*nconstr1
- valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i));
+ stepcon{i}=i-2*nconstr1; % FFR
elseif i<=4*nconstr1
- valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i));
+ stepcon{i}=i-3*nconstr1; % FFR
elseif i<=5*nconstr1
- valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i));
+ stepcon{i}=i-4*nconstr1; % FFR
elseif i<=6*nconstr1
- valuecon(i) = vpntM(i-5*nconstr1,2+varcon(i));
+ stepcon{i}=i-5*nconstr1; % FFR
end
- end
-
-% %*** average condtions over,say, options_.ms.freq periods.
-% if qmEnd==options_.ms.freq
-% vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
-% valuecon(1) = vaveM(1,2+varcon(1)); % 2: first 2 elements are dates
-% else
-% vaveM = fn_dataext([yrEnd 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
-% yactrem = fn_dataext([yrEnd qmEnd+1],[yrEnd options_.ms.freq],xdatae);
-% valuecon(1) = sum(yactrem(:,2+varcon(1)),1)/length(stepcon{1});
-% % 2: first 2 elements are dates
-% end
-% for kj=2:nconstr
-% valuecon(kj) = vaveM(kj,2+varcon(kj)); % 2: first 2 elements are dates
-% end
- else
- vpntM = dataext([yrEnd qmEnd+1],[yrEnd qmEnd+2],xdatae); % point value matrix with dates
- for i=1:nconstr
- if i<=nconstr1
- valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates; Poil
- elseif i<=2*nconstr1
- valuecon(i) = vpntM(i-nconstr1,2+varcon(i)); % 2: first 2 elements are dates; M2
- elseif i<=3*nconstr1
- valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; FFR
- elseif i<=4*nconstr1
- valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; CPI
- elseif i<=5*nconstr1
- valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; U
- elseif i<=5*nconstr1+nconstr2
- valuecon(i)=xdata(end,5)+(i-5*nconstr1)*log(1.001)/options_.ms.freq; %CPI
- elseif i<=5*nconstr1+2*nconstr2
- valuecon(i)=0.0725; %FFR
- else
- valuecon(i)=xdata(end,6)+(i-5*nconstr1-2*nconstr2)*0.01/nfqm; %U
+ end
+
+ % for i=1:nconstr
+ % stepcon{i}=i; % FFR
+ % end
+
+ % bend=12;
+ % stepcon{1}=[1:bend]'; % average over
+ % stepcon{nconstr1+1}=[1:options_.ms.freq-qmSub]'; % average over the remaing months in 1st forecast year
+ % stepcon{nconstr1+2}=[options_.ms.freq-qmSub+1:options_.ms.freq-qmSub+12]'; % average over 12 months next year
+ % stepcon{nconstr1+3}=[options_.ms.freq-qmSub+13:options_.ms.freq-qmSub+24]'; % average over 12 months. 3rd year
+ % stepcon{nconstr1+4}=[options_.ms.freq-qmSub+25:options_.ms.freq-qmSub+36]'; % average over 12 months. 4th year
+
+ % %**** avearage condition over, say, options_.ms.freq periods
+ % if qmEnd==options_.ms.freq
+ % stepcon{1}=[1:options_.ms.freq]'; % average over the remaing periods in 1st forecast year
+ % else
+ % stepcon{1}=[1:options_.ms.freq-qmEnd]'; % average over the remaing periods in 1st forecast year
+ % end
+ % for kj=2:nconstr
+ % stepcon{kj}=[length(stepcon{kj-1})+1:length(stepcon{kj-1})+options_.ms.freq]'; % average over 12 months next year
+ % end
+
+ if options_.ms.real_pseudo_forecast
+ % %*** conditions in every period
+ % for i=1:nconstr
+ % valuecon(i) = yact(actup+i,varcon(i));
+ % %valuecon(i) = mean( yact(actup+1:actup+bend,varcon(i)) );
+ % %valuecon(i) = 0.060; % 95:01
+ % %valuecon(i) = (0.0475+0.055)/2; % 94:10
+ % end
+
+ % %*** average condtions over,say, options_.ms.freq periods.
+ % for i=nconstr1+1:nconstr1+nconstr2
+ % i=1;
+ % valuecon(nconstr1+i) = ( ( mean(ylast12Cal(:,varcon(nconstr1+i)),1) + ...
+ % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100) )*options_.ms.freq - ...
+ % yCal_1(:,varcon(nconstr1+i)) ) ./ length(stepcon{nconstr1+i});
+ % % the same as unconditional "yactCalyg" 1st calendar year
+ % i=2;
+ % valuecon(nconstr1+i) = mean(ylast12Cal(:,varcon(nconstr1+i))) + ...
+ % log(1+yactCalyg(yAg-yFg+1,varcon(nconstr1+i))/100) ...
+ % + log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
+ % % the same as actual "yactCalgy" 2nd calendar year
+ % i=3;
+ % valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
+ % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
+ % % the same as actual "yactCalgy" 3rd calendar year
+ % %i=4;
+ % %valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
+ % % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
+ % % the same as actual "yactCalgy" 4th calendar year
+ % end
+
+ %*** conditions in every period
+ vpntM = fn_dataext(E1yrqm, E2yrqm,xdatae); % point value matrix with dates
+ % vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
+ for i=1:nconstr
+ if i<=nconstr1
+ valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates
+ elseif i<=2*nconstr1
+ valuecon(i) = vpntM(i-nconstr1,2+varcon(i));
+ elseif i<=3*nconstr1
+ valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i));
+ elseif i<=4*nconstr1
+ valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i));
+ elseif i<=5*nconstr1
+ valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i));
+ elseif i<=6*nconstr1
+ valuecon(i) = vpntM(i-5*nconstr1,2+varcon(i));
+ end
+ end
+
+ % %*** average condtions over,say, options_.ms.freq periods.
+ % if qmEnd==options_.ms.freq
+ % vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
+ % valuecon(1) = vaveM(1,2+varcon(1)); % 2: first 2 elements are dates
+ % else
+ % vaveM = fn_dataext([yrEnd 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
+ % yactrem = fn_dataext([yrEnd qmEnd+1],[yrEnd options_.ms.freq],xdatae);
+ % valuecon(1) = sum(yactrem(:,2+varcon(1)),1)/length(stepcon{1});
+ % % 2: first 2 elements are dates
+ % end
+ % for kj=2:nconstr
+ % valuecon(kj) = vaveM(kj,2+varcon(kj)); % 2: first 2 elements are dates
+ % end
+ else
+ vpntM = dataext([yrEnd qmEnd+1],[yrEnd qmEnd+2],xdatae); % point value matrix with dates
+ for i=1:nconstr
+ if i<=nconstr1
+ valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates; Poil
+ elseif i<=2*nconstr1
+ valuecon(i) = vpntM(i-nconstr1,2+varcon(i)); % 2: first 2 elements are dates; M2
+ elseif i<=3*nconstr1
+ valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; FFR
+ elseif i<=4*nconstr1
+ valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; CPI
+ elseif i<=5*nconstr1
+ valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; U
+ elseif i<=5*nconstr1+nconstr2
+ valuecon(i)=xdata(end,5)+(i-5*nconstr1)*log(1.001)/options_.ms.freq; %CPI
+ elseif i<=5*nconstr1+2*nconstr2
+ valuecon(i)=0.0725; %FFR
+ else
+ valuecon(i)=xdata(end,6)+(i-5*nconstr1-2*nconstr2)*0.01/nfqm; %U
+ end
+ end
+ %valuecon(i) = 0.060; % 95:01
+ end
+ else
+ valuecon = [];
+ stepcon = [];
+ varcon = [];
+ end
+
+ nstepsm = 0; % initializing, the maximum step in all constraints
+ for i=1:nconstr
+ nstepsm = max([nstepsm max(stepcon{i})]);
+ end
+
+ if cnum
+ if options_.ms.real_pseudo_forecast && options_.ms.banact
+ for i=1:length(banstp{1})
+ banval{1}(1:length(banstp{1}),1) = ...
+ yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) - 2;
+ banval{1}(1:length(banstp{1}),2) = ...
+ yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) + 2;
end
- end
- %valuecon(i) = 0.060; % 95:01
- end
- else
- valuecon = [];
- stepcon = [];
- varcon = [];
- end
-
- nstepsm = 0; % initializing, the maximum step in all constraints
- for i=1:nconstr
- nstepsm = max([nstepsm max(stepcon{i})]);
- end
-
- if cnum
- if options_.ms.real_pseudo_forecast && options_.ms.banact
- for i=1:length(banstp{1})
- banval{1}(1:length(banstp{1}),1) = ...
- yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) - 2;
- banval{1}(1:length(banstp{1}),2) = ...
- yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) + 2;
- end
- end
- end
-
-
- %===================================================
- % ML conditional forecast
- %===================================================
- %/*
- [ychat,Estr,rcon] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
- nconstr,options_.ms.eq_ms,nvar,options_.ms.nlags ,phil,0,0,yforehat,imf3shat,A0hat,Bhat,...
- nfqm,options_.ms.tlindx,options_.ms.tlnumber,options_.ms.ncms,options_.ms.eq_cms);
- ychate = [fdates ychat];
- yachate = [yact1e; ychate]; % actual and condtional forecast
- %===== Converted to mg, qg, and calendar yg
- [yacyrghate,yacyrhate,yacqmyghate] = fn_datana(yachate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
- % actual and conditional forecast growth rates
- if options_.ms.indxgdls && nconstr
- keyindx = [1:nvar];
- % conlab=['conditional on' ylab{PorR(1)}];
- conlab=['v-conditions'];
-
- figure
- fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
- end
-
- if options_.ms.ncsk
- Estr = zeros(nfqm,nvar);
- Estr(1:2,:) = [
- -2.1838 -1.5779 0.53064 -0.099425 -0.69269 -1.0391
- 1.9407 3.3138 -0.10563 -0.55457 -0.68772 1.3534
- ];
- Estr(3:6,3) = [0.5*ones(1,4)]'; % MD shocks
-
- Estr(3:10,2) = [1.5 1.5 1.5*ones(1,6)]'; % MS shocks
-
- %Estr(3:6,6) = 1*ones(4,1); % U shocks
- %Estr(8:11,4) = 1*ones(4,1); % y shocks
-
- %Estr(3:10,2) = [2.5 2.5 1.5*ones(1,6)]'; % MS shocks alone
-
- nn = [nvar options_.ms.noptions_.ms.nlags nfqm];
- ycEhat = forefixe(A0hat,Bhat,phil,nn,Estr);
- ycEhate = [fdates ycEhat];
- yacEhate = [yact1e; ycEhate]; % actual and condtional forecast
- %===== Converted to mg, qg, and calendar yg
- [yacEyrghate,yacEyrhate,yacEqmyghate] = datana(yacEhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
- % actual and conditional forecast growth rates
- disp([sprintf('%4.0f %2.0f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n',yacEyrghate')])
-
- if 1
- keyindx = [1:nvar];
- % conlab=['conditional on' ylab{PorR(1)}];
- conlab=['shock-conditions'];
-
- figure
- gyrfore(yacEyrghate,yact2yrge,keyindx,rnum,cnum,ylab,forelabel,conlab)
- end
- end
-
- %-----------------------------------------------------------
- % Compute structural shocks for the whole sample period excluding dummy observations.
- %-----------------------------------------------------------
- ywod = y(options_.ms.dummy_obs +1:end,:); % without dummy observations
- phiwod=phi(options_.ms.dummy_obs +1:end,:); % without dummy observations
- eplhat=ywod*A0hat-phiwod*Fhat;
- qmStartWod = mod(qmStart+options_.ms.nlags ,options_.ms.freq);
- if (~qmStartWod)
- qmStartWod = options_.ms.freq;
- end
- yrStartWod = yrStart + floor((qmStart+options_.ms.nlags -1)/options_.ms.freq);
- dateswod = fn_dataext([yrStartWod qmStartWod],[yrEnd qmEnd],xdatae(:,[1:2]));
- eplhate = [dateswod eplhat];
-
- Aphat = Fhat;
+ end
+ end
+
+
+ %===================================================
+ % ML conditional forecast
+ %===================================================
+ %/*
+ [ychat,Estr,rcon] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
+ nconstr,options_.ms.eq_ms,nvar,options_.ms.nlags ,phil,0,0,yforehat,imf3shat,A0hat,Bhat,...
+ nfqm,options_.ms.tlindx,options_.ms.tlnumber,options_.ms.ncms,options_.ms.eq_cms);
+ ychate = [fdates ychat];
+ yachate = [yact1e; ychate]; % actual and condtional forecast
+ %===== Converted to mg, qg, and calendar yg
+ [yacyrghate,yacyrhate,yacqmyghate] = fn_datana(yachate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
+ % actual and conditional forecast growth rates
+ if options_.ms.indxgdls && nconstr
+ keyindx = [1:nvar];
+ % conlab=['conditional on' ylab{PorR(1)}];
+ conlab=['v-conditions'];
+
+ figure
+ fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
+ end
+
+ if options_.ms.ncsk
+ Estr = zeros(nfqm,nvar);
+ Estr(1:2,:) = [
+ -2.1838 -1.5779 0.53064 -0.099425 -0.69269 -1.0391
+ 1.9407 3.3138 -0.10563 -0.55457 -0.68772 1.3534
+ ];
+ Estr(3:6,3) = [0.5*ones(1,4)]'; % MD shocks
+
+ Estr(3:10,2) = [1.5 1.5 1.5*ones(1,6)]'; % MS shocks
+
+ %Estr(3:6,6) = 1*ones(4,1); % U shocks
+ %Estr(8:11,4) = 1*ones(4,1); % y shocks
+
+ %Estr(3:10,2) = [2.5 2.5 1.5*ones(1,6)]'; % MS shocks alone
+
+ nn = [nvar options_.ms.noptions_.ms.nlags nfqm];
+ ycEhat = forefixe(A0hat,Bhat,phil,nn,Estr);
+ ycEhate = [fdates ycEhat];
+ yacEhate = [yact1e; ycEhate]; % actual and condtional forecast
+ %===== Converted to mg, qg, and calendar yg
+ [yacEyrghate,yacEyrhate,yacEqmyghate] = datana(yacEhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
+ % actual and conditional forecast growth rates
+ disp([sprintf('%4.0f %2.0f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n',yacEyrghate')])
+
+ if 1
+ keyindx = [1:nvar];
+ % conlab=['conditional on' ylab{PorR(1)}];
+ conlab=['shock-conditions'];
+
+ figure
+ gyrfore(yacEyrghate,yact2yrge,keyindx,rnum,cnum,ylab,forelabel,conlab)
+ end
+ end
+
+ %-----------------------------------------------------------
+ % Compute structural shocks for the whole sample period excluding dummy observations.
+ %-----------------------------------------------------------
+ ywod = y(options_.ms.dummy_obs +1:end,:); % without dummy observations
+ phiwod=phi(options_.ms.dummy_obs +1:end,:); % without dummy observations
+ eplhat=ywod*A0hat-phiwod*Fhat;
+ qmStartWod = mod(qmStart+options_.ms.nlags ,options_.ms.freq);
+ if (~qmStartWod)
+ qmStartWod = options_.ms.freq;
+ end
+ yrStartWod = yrStart + floor((qmStart+options_.ms.nlags -1)/options_.ms.freq);
+ dateswod = fn_dataext([yrStartWod qmStartWod],[yrEnd qmEnd],xdatae(:,[1:2]));
+ eplhate = [dateswod eplhat];
+
+ Aphat = Fhat;
end
%----------------------------------------
diff --git a/matlab/ms-sbvar/msstart_setup.m b/matlab/ms-sbvar/msstart_setup.m
index 37b6e27e9e05e5825264b3f40741adc40ffb73db..c951ad4eda54c486f48374627f0ac8e85cb14262 100644
--- a/matlab/ms-sbvar/msstart_setup.m
+++ b/matlab/ms-sbvar/msstart_setup.m
@@ -26,14 +26,14 @@
% Exordium I
%===========================================
format short g % format
-%
-%options_.ms.freq = 4; % quarters or months
-%options_.ms.initial_year=1959; % beginning of the year
-%options_.ms.initial_subperiod=1; % begining of the quarter or month
-%options_.ms.final_year=2005; % final year
-%options_.ms.final_subperiod=4; % final month or quarter
+ %
+ %options_.ms.freq = 4; % quarters or months
+ %options_.ms.initial_year=1959; % beginning of the year
+ %options_.ms.initial_subperiod=1; % begining of the quarter or month
+ %options_.ms.final_year=2005; % final year
+ %options_.ms.final_subperiod=4; % final month or quarter
nData=(options_.ms.final_year-options_.ms.initial_year)*options_.ms.freq + (options_.ms.final_subperiod-options_.ms.initial_subperiod+1);
- % total number of the available data -- this is all you have
+% total number of the available data -- this is all you have
%*** Load data and series
%load datainf_argen.prn % the default name for the variable is "options_.ms.data".
@@ -61,19 +61,19 @@ nvar = length(options_.varobs); % number of endogenous variables
nlogeno = length(options_.ms.log_var); % number of endogenous variables in options_.ms.log_var
npereno = length(options_.ms.percent_var); % number of endogenous variables in options_.ms.percent_var
if (nvar~=(nlogeno+npereno))
- skipline()
- warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist')
- disp('Press ctrl-c to abort')
- return
+ skipline()
+ warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist')
+ disp('Press ctrl-c to abort')
+ return
elseif (nvar==length(options_.ms.vlist))
- nexo=1; % only constants as an exogenous variable. The default setting.
+ nexo=1; % only constants as an exogenous variable. The default setting.
elseif (nvar<length(options_.ms.vlist))
- nexo=length(options_.ms.vlist)-nvar+1;
+ nexo=length(options_.ms.vlist)-nvar+1;
else
- skipline()
- warning('Make sure there are only nvar endogenous variables in options_.ms.vlist')
- disp('Press ctrl-c to abort')
- return
+ skipline()
+ warning('Make sure there are only nvar endogenous variables in options_.ms.vlist')
+ disp('Press ctrl-c to abort')
+ return
end
@@ -84,24 +84,24 @@ yrEnd=options_.ms.final_year;
qmEnd=options_.ms.final_subperiod;
%options_.forecast = 4; % number of years for forecasting
if options_.forecast<1
- error('To be safe, the number of forecast years should be at least 1')
+ error('To be safe, the number of forecast years should be at least 1')
end
forelabel = [num2str(yrEnd) ':' num2str(qmEnd) ' Forecast'];
nSample=(yrEnd-yrStart)*options_.ms.freq + (qmEnd-qmStart+1);
if qmEnd==options_.ms.freq
- E1yrqm = [yrEnd+1 1]; % first year and quarter (month) after the sample
+ E1yrqm = [yrEnd+1 1]; % first year and quarter (month) after the sample
else
- E1yrqm = [yrEnd qmEnd+1]; % first year and quarter (month) after the sample
+ E1yrqm = [yrEnd qmEnd+1]; % first year and quarter (month) after the sample
end
E2yrqm = [yrEnd+options_.forecast qmEnd]; % end at the last month (quarter) of a calendar year after the sample
[fdates,nfqm]=fn_calyrqm(options_.ms.freq,E1yrqm,E2yrqm); % forecast dates and number of forecast dates
[sdates,nsqm] = fn_calyrqm(options_.ms.freq,[yrStart qmStart],[yrEnd qmEnd]);
- % sdates: dates for the whole sample (including options_.ms.nlags)
+% sdates: dates for the whole sample (including options_.ms.nlags)
if nSample~=nsqm
- warning('Make sure that nSample is consistent with the size of sdates')
- disp('Hit any key to continue, or ctrl-c to abort')
- pause
+ warning('Make sure that nSample is consistent with the size of sdates')
+ disp('Hit any key to continue, or ctrl-c to abort')
+ pause
end
imstp = 4*options_.ms.freq; % <<>> impulse responses (4 years)
nayr = 4; %options_.forecast; % number of years before forecasting for plotting.
@@ -110,18 +110,18 @@ nayr = 4; %options_.forecast; % number of years before forecasting for plotting
%------- Prior, etc. -------
%options_.ms.nlags = 4; % number of options_.ms.nlags
%options_.ms.cross_restrictions = 0; % 1: cross-A0-and-A+ restrictions; 0: options_.ms.restriction_fname is all we have
- % Example for indxOres==1: restrictions of the form P(t) = P(t-1).
+% Example for indxOres==1: restrictions of the form P(t) = P(t-1).
%options_.ms.contemp_reduced_form = 0; % 1: contemporaneous recursive reduced form; 0: restricted (non-recursive) form
%options_.ms.real_pseudo_forecast = 0; % 1: options_.ms.real_pseudo_forecast forecasts; 0: real time forecasts
%options_.ms.bayesian_prior = 1; % 1: Bayesian prior; 0: no prior
indxDummy = options_.ms.bayesian_prior; % 1: add dummy observations to the data; 0: no dummy added.
-%options_.ms.dummy_obs = 0; % No dummy observations for xtx, phi, fss, xdatae, etc. Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
-%if indxDummy
-% options_.ms.dummy_obs=nvar+1; % number of dummy observations
-%else
-% options_.ms.dummy_obs=0; % no dummy observations
-%end
-%=== The following mu is effective only if options_.ms.bayesian_prior==1.
+ %options_.ms.dummy_obs = 0; % No dummy observations for xtx, phi, fss, xdatae, etc. Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
+ %if indxDummy
+ % options_.ms.dummy_obs=nvar+1; % number of dummy observations
+ %else
+ % options_.ms.dummy_obs=0; % no dummy observations
+ %end
+ %=== The following mu is effective only if options_.ms.bayesian_prior==1.
mu = options_.ms.coefficients_prior_hyperparameters;
@@ -142,18 +142,18 @@ tdf = 3; % degrees of freedom for t-dist for initial draw of the MC loo
nbuffer = 1000; % a block or buffer of draws (buffer) that is saved to the disk (not memory)
ndraws1=1*nbuffer; % 1st part of Monte Carlo draws
ndraws2=10*ndraws1; % 2nd part of Monte Carlo draws
-% seednumber = options_.DynareRandomStreams.seed; %7910; %472534; % if 0, random state at each clock time
-% % good one 420 for [29 45], [29 54]
-% if seednumber
-% randn('state',seednumber);
-% rand('state',seednumber);
-% else
-% randn('state',fix(100*sum(clock)));
-% rand('state',fix(100*sum(clock)));
-% end
-% nstarts=1 % number of starting points
-% imndraws = nstarts*ndraws2; % total draws for impulse responses or forecasts
-%<<<<<<<<<<<<<<<<<<<
+ % seednumber = options_.DynareRandomStreams.seed; %7910; %472534; % if 0, random state at each clock time
+ % % good one 420 for [29 45], [29 54]
+ % if seednumber
+ % randn('state',seednumber);
+ % rand('state',seednumber);
+ % else
+ % randn('state',fix(100*sum(clock)));
+ % rand('state',fix(100*sum(clock)));
+ % end
+ % nstarts=1 % number of starting points
+ % imndraws = nstarts*ndraws2; % total draws for impulse responses or forecasts
+ %<<<<<<<<<<<<<<<<<<<
diff --git a/matlab/ms-sbvar/plot_ms_forecast.m b/matlab/ms-sbvar/plot_ms_forecast.m
index c6f0661f3ca43062a02725298b4baf910cd14713..c76f8af89599892185dfca6469fb41bfa8795765 100644
--- a/matlab/ms-sbvar/plot_ms_forecast.m
+++ b/matlab/ms-sbvar/plot_ms_forecast.m
@@ -32,122 +32,122 @@ function plot_ms_forecast(M_, options_, forecast, figure_name)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- nc = 2;
- nr = 2;
- nvars = M_.endo_nbr;
- endo_names = M_.endo_names;
- var_list = endo_names(1:M_.orig_endo_nbr,:);
- names = {};
- tex_names = {};
- m = 1;
- for i = 1:size(var_list)
- tmp = strmatch(var_list(i,:),endo_names,'exact');
- if isempty(tmp)
- error([var_list(i,:) ' isn''t and endogenous variable'])
- end
- tex_name = deblank(M_.endo_names_tex(i,:));
- if ~isempty(tex_name)
- names{m} = deblank(var_list(i,:));
- tex_names{m} = tex_name;
- m = m + 1;
- end
+nc = 2;
+nr = 2;
+nvars = M_.endo_nbr;
+endo_names = M_.endo_names;
+var_list = endo_names(1:M_.orig_endo_nbr,:);
+names = {};
+tex_names = {};
+m = 1;
+for i = 1:size(var_list)
+ tmp = strmatch(var_list(i,:),endo_names,'exact');
+ if isempty(tmp)
+ error([var_list(i,:) ' isn''t and endogenous variable'])
end
-
- dims = size(forecast);
- if (length(dims) == 2)
- % Point Forecast (horizon x nvars )
- horizon = dims(1);
- num_percentiles = 1;
- elseif (length(dims) == 3)
- % Banded Forecast
- horizon = dims(2);
- num_percentiles = dims(1);
- else
- error('The impulse response matrix passed to be plotted does not appear to be the correct size');
+ tex_name = deblank(M_.endo_names_tex(i,:));
+ if ~isempty(tex_name)
+ names{m} = deblank(var_list(i,:));
+ tex_names{m} = tex_name;
+ m = m + 1;
end
+end
- if num_percentiles == 1
- plot_point_forecast(forecast, nvars, nr, nc, var_list, figure_name, ...
- options_.graph_save_formats, options_.TeX, names, tex_names, ...
- [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
- else
- plot_banded_forecast(forecast, nvars, nr, nc, var_list, num_percentiles, ...
- figure_name, options_.graph_save_formats, options_.TeX, names, tex_names, ...
- [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
- end
+dims = size(forecast);
+if (length(dims) == 2)
+ % Point Forecast (horizon x nvars )
+ horizon = dims(1);
+ num_percentiles = 1;
+elseif (length(dims) == 3)
+ % Banded Forecast
+ horizon = dims(2);
+ num_percentiles = dims(1);
+else
+ error('The impulse response matrix passed to be plotted does not appear to be the correct size');
+end
+
+if num_percentiles == 1
+ plot_point_forecast(forecast, nvars, nr, nc, var_list, figure_name, ...
+ options_.graph_save_formats, options_.TeX, names, tex_names, ...
+ [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
+else
+ plot_banded_forecast(forecast, nvars, nr, nc, var_list, num_percentiles, ...
+ figure_name, options_.graph_save_formats, options_.TeX, names, tex_names, ...
+ [options_.ms.output_file_tag filesep 'Output' filesep 'Forecast']);
+end
end
function plot_point_forecast(forecast,nvars,nr,nc,endo_names,figure_name,save_graph_formats,TeX,names,tex_names,dirname)
- if nvars > nr*nc
- graph_name = ['MS (1) ' figure_name];
- figure('Name', graph_name);
- else
- graph_name = figure_name;
- figure('Name', graph_name);
- end
- m = 1;
- n_fig = 1;
- for j=1:nvars
- if m > nr*nc
- graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
- dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
- save_graph_formats,TeX,names,tex_names,graph_name);
- n_fig =n_fig+1;
- figure('Name', graph_name);
- m = 1;
- end
- subplot(nr,nc,m);
- vn = deblank(endo_names(j,:));
- plot(forecast(:,j))
- title(vn,'Interpreter','none');
- grid on;
- m = m+1;
- end
- if m > 1
+if nvars > nr*nc
+ graph_name = ['MS (1) ' figure_name];
+ figure('Name', graph_name);
+else
+ graph_name = figure_name;
+ figure('Name', graph_name);
+end
+m = 1;
+n_fig = 1;
+for j=1:nvars
+ if m > nr*nc
+ graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
save_graph_formats,TeX,names,tex_names,graph_name);
+ n_fig =n_fig+1;
+ figure('Name', graph_name);
+ m = 1;
end
+ subplot(nr,nc,m);
+ vn = deblank(endo_names(j,:));
+ plot(forecast(:,j))
+ title(vn,'Interpreter','none');
+ grid on;
+ m = m+1;
+end
+if m > 1
+ dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
+ save_graph_formats,TeX,names,tex_names,graph_name);
+end
end
function plot_banded_forecast(forecast,nvars,nr,nc,endo_names,num_percentiles,figure_name,save_graph_formats,TeX,names,tex_names,dirname)
- if nvars > nr*nc
- graph_name = ['MS (1) ' figure_name];
- figure('Name', graph_name);
- else
- graph_name = figure_name;
- figure('Name', graph_name);
+if nvars > nr*nc
+ graph_name = ['MS (1) ' figure_name];
+ figure('Name', graph_name);
+else
+ graph_name = figure_name;
+ figure('Name', graph_name);
+end
+m = 1;
+n_fig = 1;
+for j=1:nvars
+ if m > nr*nc
+ graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
+ dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
+ save_graph_formats,TeX,names,tex_names,graph_name);
+ n_fig =n_fig+1;
+ figure('Name',graph_name);
+ m = 1;
end
- m = 1;
- n_fig = 1;
- for j=1:nvars
- if m > nr*nc
- graph_name = ['MS (' int2str(n_fig) ') ' figure_name];
- dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
- save_graph_formats,TeX,names,tex_names,graph_name);
- n_fig =n_fig+1;
- figure('Name',graph_name);
- m = 1;
+ subplot(nr,nc,m);
+ vn = deblank(endo_names(j,:));
+ for k=1:num_percentiles
+ if ceil(num_percentiles/2) == k
+ plot(forecast(k,:,j),'LineWidth',1.5)
+ else
+ plot(forecast(k,:,j),'LineWidth',1.1)
end
- subplot(nr,nc,m);
- vn = deblank(endo_names(j,:));
- for k=1:num_percentiles
- if ceil(num_percentiles/2) == k
- plot(forecast(k,:,j),'LineWidth',1.5)
- else
- plot(forecast(k,:,j),'LineWidth',1.1)
- end
- if k==1
- hold on;
- end
+ if k==1
+ hold on;
end
- title(vn,'Interpreter','none');
- hold off
- grid on;
- m = m+1;
- end
- if m > 1
- dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
- save_graph_formats,TeX,names,tex_names,graph_name);
end
+ title(vn,'Interpreter','none');
+ hold off
+ grid on;
+ m = m+1;
+end
+if m > 1
+ dyn_save_graph(dirname,['MS-forecast-' int2str(n_fig)],...
+ save_graph_formats,TeX,names,tex_names,graph_name);
+end
end
diff --git a/matlab/ms-sbvar/plot_ms_irf.m b/matlab/ms-sbvar/plot_ms_irf.m
index 82c97274791d8a2a434806e5652c4d96a73a8de6..39ca642cff802a043a6d9a98a9102581f97fd085 100644
--- a/matlab/ms-sbvar/plot_ms_irf.m
+++ b/matlab/ms-sbvar/plot_ms_irf.m
@@ -32,108 +32,108 @@ function plot_ms_irf(M_, options_, irf, figure_name, varlist)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- if nargin < 4
- figure_name = '';
- end
+if nargin < 4
+ figure_name = '';
+end
- nvars = M_.endo_nbr;
- endo_names = M_.endo_names;
+nvars = M_.endo_nbr;
+endo_names = M_.endo_names;
- if isempty(varlist)
- var_list = endo_names(1:M_.orig_endo_nbr,:);
- end
+if isempty(varlist)
+ var_list = endo_names(1:M_.orig_endo_nbr,:);
+end
- names = {};
- tex_names = {};
- m = 1;
- for i = 1:size(var_list)
- tmp = strmatch(var_list(i,:),endo_names,'exact');
- if isempty(tmp)
- error([var_list(i,:) ' isn''t and endogenous variable'])
- end
- tex_name = deblank(M_.endo_names_tex(tmp,:));
- if ~isempty(tex_name)
- names{m} = deblank(var_list(i,:));
- tex_names{m} = tex_name;
- m = m + 1;
- end
+names = {};
+tex_names = {};
+m = 1;
+for i = 1:size(var_list)
+ tmp = strmatch(var_list(i,:),endo_names,'exact');
+ if isempty(tmp)
+ error([var_list(i,:) ' isn''t and endogenous variable'])
end
-
- for i=1:M_.exo_nbr
- tex_name = deblank(M_.exo_names_tex(i,:));
- if ~isempty(tex_name)
- names{m} = deblank(M_.exo_names(i,:));
- tex_names{m} = tex_name;
- m = m + 1;
- end
+ tex_name = deblank(M_.endo_names_tex(tmp,:));
+ if ~isempty(tex_name)
+ names{m} = deblank(var_list(i,:));
+ tex_names{m} = tex_name;
+ m = m + 1;
end
+end
- dims = size(irf);
- if (length(dims) == 2)
- % Point IRF (horizon x (nvarsxnvars) )
- horizon = dims(1);
- num_percentiles = 1;
- elseif (length(dims) == 3)
- % Banded IRF
- horizon = dims(2);
- num_percentiles = dims(1);
- else
- error('The impulse response matrix passed to be plotted does not appear to be the correct size');
+for i=1:M_.exo_nbr
+ tex_name = deblank(M_.exo_names_tex(i,:));
+ if ~isempty(tex_name)
+ names{m} = deblank(M_.exo_names(i,:));
+ tex_names{m} = tex_name;
+ m = m + 1;
end
+end
- if size(endo_names,1) ~= nvars
- error('The names passed are not the same length as the number of variables');
- end
+dims = size(irf);
+if (length(dims) == 2)
+ % Point IRF (horizon x (nvarsxnvars) )
+ horizon = dims(1);
+ num_percentiles = 1;
+elseif (length(dims) == 3)
+ % Banded IRF
+ horizon = dims(2);
+ num_percentiles = dims(1);
+else
+ error('The impulse response matrix passed to be plotted does not appear to be the correct size');
+end
- if num_percentiles == 1
- % loop through the shocks
- for s=1:nvars
- shock = zeros(horizon,nvars);
- for i=1:nvars
- shock(:,i) = irf(:,((i-1) + ((s-1)*nvars)+1));
- end
- plot_point_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
- figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
+if size(endo_names,1) ~= nvars
+ error('The names passed are not the same length as the number of variables');
+end
+
+if num_percentiles == 1
+ % loop through the shocks
+ for s=1:nvars
+ shock = zeros(horizon,nvars);
+ for i=1:nvars
+ shock(:,i) = irf(:,((i-1) + ((s-1)*nvars)+1));
end
- else
- for s=1:nvars
- shock = zeros(horizon,nvars,num_percentiles);
- for n=1:num_percentiles
- for i=1:nvars
- shock(:,i,n) = irf(n,:,((i-1) + ((s-1)*nvars)+1));
- end
+ plot_point_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
+ figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
+ end
+else
+ for s=1:nvars
+ shock = zeros(horizon,nvars,num_percentiles);
+ for n=1:num_percentiles
+ for i=1:nvars
+ shock(:,i,n) = irf(n,:,((i-1) + ((s-1)*nvars)+1));
end
- plot_banded_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
- figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
end
+ plot_banded_irf_for_shock(shock, nvars,endo_names, deblank(endo_names(s,:)), ...
+ figure_name, [options_.ms.output_file_tag filesep 'Output' filesep 'IRF'], options_, names, tex_names);
end
end
+end
function [fig] = plot_point_irf_for_shock(irf,nvars,endo_names,shock_name,figure_name,dirname,options_,names,tex_names)
- fig = figure('Name',figure_name);
- for k=1:nvars
- subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
- plot(irf(:,k))
- disp([endo_names(k,:) ' shock from ' shock_name]);
- title([endo_names(k,:) ' shock from ' shock_name]);
- end
- dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
- options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
+fig = figure('Name',figure_name);
+for k=1:nvars
+ subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
+ plot(irf(:,k))
+ disp([endo_names(k,:) ' shock from ' shock_name]);
+ title([endo_names(k,:) ' shock from ' shock_name]);
+end
+dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
+ options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
end
function [fig] = plot_banded_irf_for_shock(irf,nvars, endo_names, shock_name,figure_name,dirname,options_,names,tex_names)
- fig = figure('Name',figure_name);
- npercentiles = size(irf,3);
- for k=1:nvars
- subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
- for nn=1:npercentiles
- plot(irf(:,k,nn))
- hold on
- end
- hold off
- disp([endo_names(k,:) ' shock from ' shock_name]);
- title([endo_names(k,:) ' shock from ' shock_name]);
+fig = figure('Name',figure_name);
+npercentiles = size(irf,3);
+for k=1:nvars
+ subplot(ceil(sqrt(nvars)), ceil(sqrt(nvars)),k);
+ for nn=1:npercentiles
+ plot(irf(:,k,nn))
+ hold on
end
- dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
- options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
+ hold off
+ disp([endo_names(k,:) ' shock from ' shock_name]);
+ title([endo_names(k,:) ' shock from ' shock_name]);
+end
+dyn_save_graph(dirname,[figure_name ' ' shock_name],options_.graph_save_formats, ...
+ options_.TeX,names,tex_names,[figure_name ' ' shock_name]);
end
diff --git a/matlab/ms-sbvar/plot_ms_probabilities.m b/matlab/ms-sbvar/plot_ms_probabilities.m
index 023ecfd4499ee346954a3ce63d42a739354d8aaa..3fd26eab1257a32c2c52425df32e143a1972c0b8 100644
--- a/matlab/ms-sbvar/plot_ms_probabilities.m
+++ b/matlab/ms-sbvar/plot_ms_probabilities.m
@@ -27,41 +27,41 @@ function plot_ms_probabilities(computed_probabilities, options_)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
- [T,num_grand_regimes] = size(computed_probabilities);
- num_chains = length(options_.ms.ms_chain);
- for i=1:num_chains
+
+[T,num_grand_regimes] = size(computed_probabilities);
+num_chains = length(options_.ms.ms_chain);
+for i=1:num_chains
chains(i).num_regimes = length(options_.ms.ms_chain(i).regime);
chains(i).probabilities = zeros([T,chains(i).num_regimes]);
- end
-
- for t=1:T
+end
+
+for t=1:T
chains = iterate_chain(computed_probabilities(t,:), t, chains, 1, num_chains);
- end
-
- for i=1:num_chains
+end
+
+for i=1:num_chains
graph_name = ['MS-Probabilities, Chain ' int2str(i)];
figure('Name',graph_name)
plot(chains(i).probabilities,'LineWidth', 1.2);
ltxt = {};
for j=1:chains(i).num_regimes
- ltxt{j} = ['Regime ' int2str(j)];
+ ltxt{j} = ['Regime ' int2str(j)];
end
legend(ltxt{:});
title(['Chain ' int2str(i)]);
ylim([0 1.0]);
dyn_save_graph([options_.ms.output_file_tag filesep 'Output' filesep 'Probabilities'], ['MS-Probabilities-Chain-' int2str(i)], ...
- options_.graph_save_formats,options_.TeX,[],[],graph_name);
- end
+ options_.graph_save_formats,options_.TeX,[],[],graph_name);
+end
end
function [chains] = iterate_chain(probs, t, chains, chain, num_chains)
- offset_length = length(probs)/chains(chain).num_regimes;
- for i=1:chains(chain).num_regimes
+offset_length = length(probs)/chains(chain).num_regimes;
+for i=1:chains(chain).num_regimes
p = probs( (i-1)*offset_length+1 : i*offset_length );
chains(chain).probabilities(t, i) = chains(chain).probabilities(t, i) + sum( p );
if chain < num_chains
- chains = iterate_chain(p, t, chains, chain+1, num_chains);
+ chains = iterate_chain(p, t, chains, chain+1, num_chains);
end
- end
+end
end
diff --git a/matlab/ms-sbvar/plot_ms_variance_decomposition.m b/matlab/ms-sbvar/plot_ms_variance_decomposition.m
index 7cd30dca84cd97a42f0057343ff9bd7e1b42b955..9a9e85fe7e0b4a7cea166eaffde6b026a96ba497 100644
--- a/matlab/ms-sbvar/plot_ms_variance_decomposition.m
+++ b/matlab/ms-sbvar/plot_ms_variance_decomposition.m
@@ -9,12 +9,12 @@ function plot_ms_variance_decomposition(M_, options_, vd, figure_name, varargin)
% figure_name: (string) graph name
%
% OPTIONAL INPUTS
-% 'data': the actual data, TxK with K=number of data series
-% 'steady': the steady state value, TxK
-% 'shock_names': to specify the names of the shocks
-% 'series_names': to specify the names of the different series
-% 'dates': pass a date vector to use, otherwise will just index on 1:T
-% 'colors': Jx3 list of the rgb colors to use for each shock
+% 'data': the actual data, TxK with K=number of data series
+% 'steady': the steady state value, TxK
+% 'shock_names': to specify the names of the shocks
+% 'series_names': to specify the names of the different series
+% 'dates': pass a date vector to use, otherwise will just index on 1:T
+% 'colors': Jx3 list of the rgb colors to use for each shock
%
% OUTPUTS
% none
@@ -44,130 +44,130 @@ if length(size(vd)) == 3
return;
end
- nvars = M_.endo_nbr;
- endo_names = M_.endo_names;
+nvars = M_.endo_nbr;
+endo_names = M_.endo_names;
- names = {};
- tex_names = {};
- m = 1;
- for i=1:M_.orig_endo_nbr
- tex_name = deblank(M_.endo_names_tex(i,:));
- if ~isempty(tex_name)
- names{m} = deblank(endo_names(i,:));
- tex_names{m} = tex_name;
- m = m + 1;
- end
+names = {};
+tex_names = {};
+m = 1;
+for i=1:M_.orig_endo_nbr
+ tex_name = deblank(M_.endo_names_tex(i,:));
+ if ~isempty(tex_name)
+ names{m} = deblank(endo_names(i,:));
+ tex_names{m} = tex_name;
+ m = m + 1;
end
+end
- dims = size(vd);
- if length(dims) == 3
- T = dims(1);
- K = dims(2);
- J = dims(3);
- shocks = vd;
- else
- T = dims(1);
- K = nvars;
- J = nvars;
- temp_vd = zeros(T,K,J);
- for i=1:nvars
- for j=1:nvars
- temp_vd(:,i,j) = vd(:,((j-1) + ((i-1)*nvars)+1));
- end
+dims = size(vd);
+if length(dims) == 3
+ T = dims(1);
+ K = dims(2);
+ J = dims(3);
+ shocks = vd;
+else
+ T = dims(1);
+ K = nvars;
+ J = nvars;
+ temp_vd = zeros(T,K,J);
+ for i=1:nvars
+ for j=1:nvars
+ temp_vd(:,i,j) = vd(:,((j-1) + ((i-1)*nvars)+1));
end
- shocks = temp_vd;
end
+ shocks = temp_vd;
+end
- for i=1:nvars
- shock_names{i} = endo_names(i,:);
- series_names{i} = endo_names(i,:);
- end
+for i=1:nvars
+ shock_names{i} = endo_names(i,:);
+ series_names{i} = endo_names(i,:);
+end
- x = [1:T];
- plot_dates = 0;
- data = 0;
- steady = 0;
- colors = [ .1 .1 .75
- .8 0 0
- 1 .7 .25
- 1 1 0
- .5 1 .5
- .7 .7 .1
- .5 .6 .2
- .1 .5 .1];
+x = [1:T];
+plot_dates = 0;
+data = 0;
+steady = 0;
+colors = [ .1 .1 .75
+ .8 0 0
+ 1 .7 .25
+ 1 1 0
+ .5 1 .5
+ .7 .7 .1
+ .5 .6 .2
+ .1 .5 .1];
- % overide the defaults with optional inputs
- for i=1:length(varargin)
- if strcmpi(varargin{i},'data')
- data = varargin{i+1};
- elseif strcmpi(varargin{i},'steady')
- steady = varargin{i+1};
- elseif strcmpi(varargin{i},'shock_names')
- shock_names = varargin{i+1};
- elseif strcmpi(varargin{i},'series_names')
- series_names = varargin{i+1};
- elseif strcmpi(varargin{i}, 'dates')
- x = varargin{i+1}; plot_dates = 1;
- elseif strcmpi(varargin{i},'colors')
- colors = varargin{i+1};
- end
+% overide the defaults with optional inputs
+for i=1:length(varargin)
+ if strcmpi(varargin{i},'data')
+ data = varargin{i+1};
+ elseif strcmpi(varargin{i},'steady')
+ steady = varargin{i+1};
+ elseif strcmpi(varargin{i},'shock_names')
+ shock_names = varargin{i+1};
+ elseif strcmpi(varargin{i},'series_names')
+ series_names = varargin{i+1};
+ elseif strcmpi(varargin{i}, 'dates')
+ x = varargin{i+1}; plot_dates = 1;
+ elseif strcmpi(varargin{i},'colors')
+ colors = varargin{i+1};
end
+end
- % add an extra period to the time series
- x(T+1) = x(T) + (x(T) - x(T-1));
+% add an extra period to the time series
+x(T+1) = x(T) + (x(T) - x(T-1));
- figure('Name',figure_name)
- for k=1:K
- % Go through each series
- subplot(K,1,k);
- sshocks = shocks(:,k,:);
- hold on
- % plot the stacked shocks
- for t=1:T
- % step through each time period
- pos_position = 0; neg_position = 0;
- xt = [x(t) x(t) x(t+1) x(t+1)];
- for j=1:J
- % stack each shock
- st = sshocks(t,1,j);
- if st < 0
- yi = st+neg_position;
- y = [neg_position yi yi neg_position];
- neg_position = yi;
- else
- yi = st+pos_position;
- y = [pos_position yi yi pos_position];
- pos_position = yi;
- end
- fill(xt,y,colors(j,:));
- XY(t,j,:) = y;
- end
- end
- if data
- plot(x(2:end)',data(:,k),'k','LineWidth',2.5);
- end
- if steady
- plot(x(2:end)',steady(:,k), '--k','LineWidth',2.25);
- end
- if k==K
- if isoctave
- legend(shock_names,'Location','SouthOutside');
+figure('Name',figure_name)
+for k=1:K
+ % Go through each series
+ subplot(K,1,k);
+ sshocks = shocks(:,k,:);
+ hold on
+ % plot the stacked shocks
+ for t=1:T
+ % step through each time period
+ pos_position = 0; neg_position = 0;
+ xt = [x(t) x(t) x(t+1) x(t+1)];
+ for j=1:J
+ % stack each shock
+ st = sshocks(t,1,j);
+ if st < 0
+ yi = st+neg_position;
+ y = [neg_position yi yi neg_position];
+ neg_position = yi;
else
- legend(shock_names,'Location','BestOutside','Orientation','horizontal');
+ yi = st+pos_position;
+ y = [pos_position yi yi pos_position];
+ pos_position = yi;
end
+ fill(xt,y,colors(j,:));
+ XY(t,j,:) = y;
end
-
- hold off
- if plot_dates
- datetick 'x';
+ end
+ if data
+ plot(x(2:end)',data(:,k),'k','LineWidth',2.5);
+ end
+ if steady
+ plot(x(2:end)',steady(:,k), '--k','LineWidth',2.25);
+ end
+ if k==K
+ if isoctave
+ legend(shock_names,'Location','SouthOutside');
+ else
+ legend(shock_names,'Location','BestOutside','Orientation','horizontal');
end
- xlim([min(x),max(x)])
- ylim([0 , 1])
- grid on
- title(series_names{k});
end
- dyn_save_graph([options_.ms.output_file_tag filesep 'Output' ...
- filesep 'Variance_Decomposition'], 'MS-Variance-Decomposition', ...
- options_.graph_save_formats, options_.TeX, names, tex_names, ...
- 'Variance decomposition');
+
+ hold off
+ if plot_dates
+ datetick 'x';
+ end
+ xlim([min(x),max(x)])
+ ylim([0 , 1])
+ grid on
+ title(series_names{k});
+end
+dyn_save_graph([options_.ms.output_file_tag filesep 'Output' ...
+ filesep 'Variance_Decomposition'], 'MS-Variance-Decomposition', ...
+ options_.graph_save_formats, options_.TeX, names, tex_names, ...
+ 'Variance decomposition');
end
diff --git a/matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m b/matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m
index cc03e87c7f31682b03734d78f4fde4803aa8265a..193c9b59ff0600a13c38712b4a1691a63da04ab5 100644
--- a/matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m
+++ b/matlab/ms-sbvar/plot_ms_variance_decomposition_error_bands.m
@@ -79,14 +79,14 @@ for s=1:nvars
end
end
plot_banded_vddata_for_shock(shock, nvars, endo_names, ...
- deblank(endo_names(s,:)), figure_name, ...
- [options_.ms.output_file_tag filesep 'Output' filesep 'Variance_Decomposition'], ...
- options_, names, tex_names);
+ deblank(endo_names(s,:)), figure_name, ...
+ [options_.ms.output_file_tag filesep 'Output' filesep 'Variance_Decomposition'], ...
+ options_, names, tex_names);
end
end
function [fig] = plot_banded_vddata_for_shock(vddata, nvars, endo_names, ...
- shock_name, figure_name, dirname, options_, names, tex_names)
+ shock_name, figure_name, dirname, options_, names, tex_names)
fig = figure('Name', figure_name);
npercentiles = size(vddata,3);
for k=1:nvars
@@ -100,6 +100,6 @@ for k=1:nvars
title([endo_names(k,:) ' contribution to ' shock_name]);
end
dyn_save_graph(dirname, [figure_name ' ' shock_name], ...
- options_.graph_save_formats, options_.TeX, names, tex_names, ...
- [figure_name ' ' shock_name]);
+ options_.graph_save_formats, options_.TeX, names, tex_names, ...
+ [figure_name ' ' shock_name]);
end
diff --git a/matlab/ms-sbvar/svar_global_identification_check.m b/matlab/ms-sbvar/svar_global_identification_check.m
index 549d5c7fd40150a46bd27d185de9662586c588cf..b0884defe57dbb39083f3d5d265b41c74d04dcc7 100644
--- a/matlab/ms-sbvar/svar_global_identification_check.m
+++ b/matlab/ms-sbvar/svar_global_identification_check.m
@@ -47,7 +47,7 @@ if isequal(options_.ms.restriction_fname, 'upper_cholesky') || ...
end
nvar = length(options_.varobs); % number of endogenous variables
nexo = 1;
-
+
[Uiconst,Viconst,n0,np,ixmC0Pres,Qi,Ri] = exclusions(nvar,nexo,options_.ms );
% order column constraints by rank
diff --git a/matlab/non_linear_dsge_likelihood.m b/matlab/non_linear_dsge_likelihood.m
index 57e02a0b7a0a6c0968ebcfbe592c95911fdfd869..43574d4e95d0819d0a415eb94cf6e4f6ec7960b4 100644
--- a/matlab/non_linear_dsge_likelihood.m
+++ b/matlab/non_linear_dsge_likelihood.m
@@ -229,8 +229,8 @@ end
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 || ...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85
%meaningful second entry of output that can be used
fval = Inf;
info(4) = info(2);
diff --git a/matlab/occbin/call_solve_one_constraint.m b/matlab/occbin/call_solve_one_constraint.m
index b4ec1baed97f70f15033145e759e4d6a2c6e578f..d7a83c8361cd95585e9e177af7d6efcf6ad0711f 100755
--- a/matlab/occbin/call_solve_one_constraint.m
+++ b/matlab/occbin/call_solve_one_constraint.m
@@ -1,21 +1,21 @@
-% Solve model, generate model IRFs
-[zdatalinear zdatapiecewise zdatass oobase_ Mbase_ ] = ...
- solve_one_constraint(modnam,modnamstar,...
- constraint, constraint_relax,...
- shockssequence,irfshock,nperiods,maxiter);
-
-
-
-% unpack the IRFs
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)),'_uncdifference=zdatalinear(:,i);']);
- eval([deblank(Mbase_.endo_names(i,:)),'_difference=zdatapiecewise(:,i);']);
- eval([deblank(Mbase_.endo_names(i,:)),'_ss=zdatass(i);']);
-end
-
-
-nparams = size(Mbase_.param_names,1);
-
-for i = 1:nparams
- eval([Mbase_.param_names(i,:),'= Mbase_.params(i);']);
-end
+% Solve model, generate model IRFs
+[zdatalinear zdatapiecewise zdatass oobase_ Mbase_ ] = ...
+ solve_one_constraint(modnam,modnamstar,...
+ constraint, constraint_relax,...
+ shockssequence,irfshock,nperiods,maxiter);
+
+
+
+% unpack the IRFs
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)),'_uncdifference=zdatalinear(:,i);']);
+ eval([deblank(Mbase_.endo_names(i,:)),'_difference=zdatapiecewise(:,i);']);
+ eval([deblank(Mbase_.endo_names(i,:)),'_ss=zdatass(i);']);
+end
+
+
+nparams = size(Mbase_.param_names,1);
+
+for i = 1:nparams
+ eval([Mbase_.param_names(i,:),'= Mbase_.params(i);']);
+end
diff --git a/matlab/occbin/call_solve_two_constraints.m b/matlab/occbin/call_solve_two_constraints.m
index b782e6db76b9f6c1c1192f63cfcde9e5c0a4e766..1b54718e5be369c64a40ae79c95d6d427dd9b25e 100755
--- a/matlab/occbin/call_solve_two_constraints.m
+++ b/matlab/occbin/call_solve_two_constraints.m
@@ -1,21 +1,21 @@
-[zdatalinear zdatapiecewise zdatass oobase_ Mbase_] = solve_two_constraints(...
- modnam_00,modnam_10,modnam_01,modnam_11,...
- constraint1, constraint2,...
- constraint_relax1, constraint_relax2,...
- scalefactormod,irfshock,nperiods,curb_retrench,maxiter);
-
-
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)),'_uncdifference=zdatalinear(:,i);']);
- eval([deblank(Mbase_.endo_names(i,:)),'_difference=zdatapiecewise(:,i);']);
- eval([deblank(Mbase_.endo_names(i,:)),'_ss=zdatass(i);']);
-end
-
-constraint1_difference = process_constraint(constraint1,'_difference',Mbase_.endo_names,0);
-constraint2_difference = process_constraint(constraint2,'_difference',Mbase_.endo_names,0);
-
-nparams = size(Mbase_.param_names,1);
-
-for i = 1:nparams
- eval([Mbase_.param_names(i,:),'= Mbase_.params(i);']);
-end
+[zdatalinear zdatapiecewise zdatass oobase_ Mbase_] = solve_two_constraints(...
+ modnam_00,modnam_10,modnam_01,modnam_11,...
+ constraint1, constraint2,...
+ constraint_relax1, constraint_relax2,...
+ scalefactormod,irfshock,nperiods,curb_retrench,maxiter);
+
+
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)),'_uncdifference=zdatalinear(:,i);']);
+ eval([deblank(Mbase_.endo_names(i,:)),'_difference=zdatapiecewise(:,i);']);
+ eval([deblank(Mbase_.endo_names(i,:)),'_ss=zdatass(i);']);
+end
+
+constraint1_difference = process_constraint(constraint1,'_difference',Mbase_.endo_names,0);
+constraint2_difference = process_constraint(constraint2,'_difference',Mbase_.endo_names,0);
+
+nparams = size(Mbase_.param_names,1);
+
+for i = 1:nparams
+ eval([Mbase_.param_names(i,:),'= Mbase_.params(i);']);
+end
diff --git a/matlab/occbin/get_deriv.m b/matlab/occbin/get_deriv.m
index 24f1b42aeb8866593514fa040d0b6d56edd125d8..19fe87001affd87f008a0fe28c8df55f649ba681 100755
--- a/matlab/occbin/get_deriv.m
+++ b/matlab/occbin/get_deriv.m
@@ -1,83 +1,83 @@
-function [hm1,h,hl1,j,resid] = get_deriv(M_,ys_)
-
-iy_ = M_.lead_lag_incidence;
-it_ = 1;
-
-x = zeros(1,M_.exo_nbr);
-
-% For most models, there are leads, lags and current values of variables
-if size(iy_,1)==3
- % find non-zero columns of hm1
- lag_cols = find(iy_(1,:)~=0);
- % find non-zero columns of h
- con_cols = find(iy_(2,:));
- % find non-zero columns of hl1
- lea_cols = find(iy_(3,:));
-
-% If models either lacks leads or lags, iy_ will have two rows
-% In this case, we guess that the row with more nonzeros is the row with current variables
-elseif size(iy_,1)==2
- % if first row has more nonzero entries than the second, assume model lacks lagged variables
- if length(find(iy_(1,:)))>length(find(iy_(2,:)))
- warning('Model does not have lagged endogenous variables')
- con_cols = find(iy_(1,:));
- lea_cols = find(iy_(2,:));
- lag_cols = [];
- else
- warning('Model does not have expected future endogenous variables')
- lag_cols = find(iy_(1,:));
- con_cols = find(iy_(2,:));
- lea_cols = [];
- end
-
-end
-
-
-
-% find number of entries for y vector
-ny = length(find(iy_~=0));
-
-% build steady state y
-y = ys_(lag_cols);
-y = [y;ys_(con_cols)];
-y = [y;ys_(lea_cols)];
-
-
-if ismac
-eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, ys_, it_);']);
-% Older versions of DYNARE for Mac did not include ys_ in the call structure
-%eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, it_);']);
-else
-eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, ys_, it_);']);
-end
-
-
-hm1=zeros(M_.endo_nbr);
-h = hm1;
-hl1 = hm1;
-j = zeros(M_.endo_nbr,M_.exo_nbr);
-
-
-% build hm1
-nlag_cols = length(lag_cols);
-for i=1:nlag_cols
- hm1(:,lag_cols(i)) = g1(:,i);
-end
-
-% build h
-ncon_cols = length(con_cols);
-for i=1:ncon_cols
- h(:,con_cols(i)) = g1(:,i+nlag_cols);
-end
-
-% build hl1
-nlea_cols = length(lea_cols);
-for i=1:nlea_cols
- hl1(:,lea_cols(i)) = g1(:,i+nlag_cols+ncon_cols);
-end
-
-
-for i = 1:M_.exo_nbr;
- j(:,i) =g1(:,i+ny);
-end
-
+function [hm1,h,hl1,j,resid] = get_deriv(M_,ys_)
+
+iy_ = M_.lead_lag_incidence;
+it_ = 1;
+
+x = zeros(1,M_.exo_nbr);
+
+% For most models, there are leads, lags and current values of variables
+if size(iy_,1)==3
+ % find non-zero columns of hm1
+ lag_cols = find(iy_(1,:)~=0);
+ % find non-zero columns of h
+ con_cols = find(iy_(2,:));
+ % find non-zero columns of hl1
+ lea_cols = find(iy_(3,:));
+
+ % If models either lacks leads or lags, iy_ will have two rows
+ % In this case, we guess that the row with more nonzeros is the row with current variables
+elseif size(iy_,1)==2
+ % if first row has more nonzero entries than the second, assume model lacks lagged variables
+ if length(find(iy_(1,:)))>length(find(iy_(2,:)))
+ warning('Model does not have lagged endogenous variables')
+ con_cols = find(iy_(1,:));
+ lea_cols = find(iy_(2,:));
+ lag_cols = [];
+ else
+ warning('Model does not have expected future endogenous variables')
+ lag_cols = find(iy_(1,:));
+ con_cols = find(iy_(2,:));
+ lea_cols = [];
+ end
+
+end
+
+
+
+% find number of entries for y vector
+ny = length(find(iy_~=0));
+
+% build steady state y
+y = ys_(lag_cols);
+y = [y;ys_(con_cols)];
+y = [y;ys_(lea_cols)];
+
+
+if ismac
+ eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, ys_, it_);']);
+ % Older versions of DYNARE for Mac did not include ys_ in the call structure
+ %eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, it_);']);
+else
+ eval(['[resid,g1]=',M_.fname,'_dynamic(y,x, M_.params, ys_, it_);']);
+end
+
+
+hm1=zeros(M_.endo_nbr);
+h = hm1;
+hl1 = hm1;
+j = zeros(M_.endo_nbr,M_.exo_nbr);
+
+
+% build hm1
+nlag_cols = length(lag_cols);
+for i=1:nlag_cols
+ hm1(:,lag_cols(i)) = g1(:,i);
+end
+
+% build h
+ncon_cols = length(con_cols);
+for i=1:ncon_cols
+ h(:,con_cols(i)) = g1(:,i+nlag_cols);
+end
+
+% build hl1
+nlea_cols = length(lea_cols);
+for i=1:nlea_cols
+ hl1(:,lea_cols(i)) = g1(:,i+nlag_cols+ncon_cols);
+end
+
+
+for i = 1:M_.exo_nbr;
+ j(:,i) =g1(:,i+ny);
+end
+
diff --git a/matlab/occbin/get_pq.m b/matlab/occbin/get_pq.m
index 5caab79af26462708ce14753652f00432e913146..6ef8c8ec78f5b98bf64bffc3c2d632c2be928a36 100755
--- a/matlab/occbin/get_pq.m
+++ b/matlab/occbin/get_pq.m
@@ -1,28 +1,28 @@
-function [p,q]=get_pq(dr_,nstatic,nfwrd);
-
-nvars = size(dr_.ghx,1);
-nshocks = size(dr_.ghu,2);
-statevar_pos = (nstatic +1):(nvars-nfwrd);
-
-p = zeros(nvars);
-% interlace matrix
-nnotzero = length(statevar_pos);
-for i=1:nnotzero
- p(:,statevar_pos(i)) = dr_.ghx(:,i);
-end
-
-% reorder p matrix according to order in lgy_
-inverse_order = zeros(nvars,1);
-for i=1:nvars
- inverse_order(i) = find(i==dr_.order_var);
-end
-
-p_reordered = zeros(nvars);
-q = zeros(nvars,nshocks);
-for i=1:nvars
- for j=1:nvars
- p_reordered(i,j)=p(inverse_order(i),inverse_order(j));
- end
- q(i,:)=dr_.ghu(inverse_order(i),:);
-end
+function [p,q]=get_pq(dr_,nstatic,nfwrd);
+
+nvars = size(dr_.ghx,1);
+nshocks = size(dr_.ghu,2);
+statevar_pos = (nstatic +1):(nvars-nfwrd);
+
+p = zeros(nvars);
+% interlace matrix
+nnotzero = length(statevar_pos);
+for i=1:nnotzero
+ p(:,statevar_pos(i)) = dr_.ghx(:,i);
+end
+
+% reorder p matrix according to order in lgy_
+inverse_order = zeros(nvars,1);
+for i=1:nvars
+ inverse_order(i) = find(i==dr_.order_var);
+end
+
+p_reordered = zeros(nvars);
+q = zeros(nvars,nshocks);
+for i=1:nvars
+ for j=1:nvars
+ p_reordered(i,j)=p(inverse_order(i),inverse_order(j));
+ end
+ q(i,:)=dr_.ghu(inverse_order(i),:);
+end
p=p_reordered;
\ No newline at end of file
diff --git a/matlab/occbin/makechart.m b/matlab/occbin/makechart.m
index a3801e95667af5ae6ec03e4e66960e34b994aff5..496a136ba8881415627c28ed4057935c27a4cf36 100755
--- a/matlab/occbin/makechart.m
+++ b/matlab/occbin/makechart.m
@@ -1,81 +1,81 @@
-function makechart(titlelist,legendlist,figlabel,ylabels,zdata1,zdata2,zdata3)
-
-
-
-figure
-
-titlelist = char(strrep(cellstr(titlelist),'_','.'));
-
-ndsets=3; % default, changed below as applicable
-if nargin==5
- zdata2=nan*zdata1;
- zdata3=nan*zdata1;
- ndsets =1;
-elseif nargin == 6
- zdata3 =nan*zdata1;
- ndsets=2;
-elseif ((nargin>8) | (nargin <=4))
- error ('makechart takes 5 to 6 arguments')
-end
-
-nobs = size(zdata1,1);
-xvalues = (1:nobs)';
-
-nvars = size(titlelist,1);
-if nvars==1
- nrows=1;
- ncols = 1;
-elseif nvars==2
- nrows =2;
- ncols = 1;
-elseif (nvars == 3 | nvars ==4)
- nrows = 2;
- ncols =2;
-elseif (nvars==5 |nvars ==6)
- nrows = 3;
- ncols = 2;
-elseif (nvars==7 | nvars==8)
- nrows = 4;
- ncols = 2;
-elseif (nvars==9 | nvars==10)
- nrows = 5;
- ncols = 2;
-else
- error('too many variables (makechart)')
-end
-
-for i = 1:nvars
- subplot(nrows,ncols,i)
- h1=plot(xvalues,zdata1(:,i),'b-','linewidth',2); hold on
- h1=plot(xvalues,zdata2(:,i),'r--','linewidth',2); hold on
- h2=plot(xvalues,zdata3(:,i),'b-','LineWidth',3);
- [x0 x1 y10 y11] = pickaxes(xvalues,zdata1(:,i));
- [x0 x1 y20 y21] = pickaxes(xvalues,zdata2(:,i));
- [x0 x1 y30 y31] = pickaxes(xvalues,zdata3(:,i));
- y0 = min([y10,y20,y30]);
- y1 = max([y11,y21,y31]);
- if y0==y1
- y1=y0+1;
- end
-
- axis([x0 x1 y0 y1])
- set(h1);
-
- if i==1 && isempty(legendlist)==0
- legend(legendlist)
- text('String',figlabel,'Units','normalized','Position',[1.2 1.24],...
- 'FontSize',14,'FontWeight','bold','HorizontalAlignment','center');
- end
-
- if i==nvars | i==nvars-1
- xlabel('Time');
- end
-% set(gca,'XTick',xtick)
-% set(gca,'XTickLabel',xticklabel)
-
- title([num2str(i),'. ',titlelist(i,:)]);
- ylabel(ylabels(i,:))
-end
-
-% sets printing preferences
-%printpref
+function makechart(titlelist,legendlist,figlabel,ylabels,zdata1,zdata2,zdata3)
+
+
+
+figure
+
+titlelist = char(strrep(cellstr(titlelist),'_','.'));
+
+ndsets=3; % default, changed below as applicable
+if nargin==5
+ zdata2=nan*zdata1;
+ zdata3=nan*zdata1;
+ ndsets =1;
+elseif nargin == 6
+ zdata3 =nan*zdata1;
+ ndsets=2;
+elseif ((nargin>8) | (nargin <=4))
+ error ('makechart takes 5 to 6 arguments')
+end
+
+nobs = size(zdata1,1);
+xvalues = (1:nobs)';
+
+nvars = size(titlelist,1);
+if nvars==1
+ nrows=1;
+ ncols = 1;
+elseif nvars==2
+ nrows =2;
+ ncols = 1;
+elseif (nvars == 3 | nvars ==4)
+ nrows = 2;
+ ncols =2;
+elseif (nvars==5 |nvars ==6)
+ nrows = 3;
+ ncols = 2;
+elseif (nvars==7 | nvars==8)
+ nrows = 4;
+ ncols = 2;
+elseif (nvars==9 | nvars==10)
+ nrows = 5;
+ ncols = 2;
+else
+ error('too many variables (makechart)')
+end
+
+for i = 1:nvars
+ subplot(nrows,ncols,i)
+ h1=plot(xvalues,zdata1(:,i),'b-','linewidth',2); hold on
+ h1=plot(xvalues,zdata2(:,i),'r--','linewidth',2); hold on
+ h2=plot(xvalues,zdata3(:,i),'b-','LineWidth',3);
+ [x0 x1 y10 y11] = pickaxes(xvalues,zdata1(:,i));
+ [x0 x1 y20 y21] = pickaxes(xvalues,zdata2(:,i));
+ [x0 x1 y30 y31] = pickaxes(xvalues,zdata3(:,i));
+ y0 = min([y10,y20,y30]);
+ y1 = max([y11,y21,y31]);
+ if y0==y1
+ y1=y0+1;
+ end
+
+ axis([x0 x1 y0 y1])
+ set(h1);
+
+ if i==1 && isempty(legendlist)==0
+ legend(legendlist)
+ text('String',figlabel,'Units','normalized','Position',[1.2 1.24],...
+ 'FontSize',14,'FontWeight','bold','HorizontalAlignment','center');
+ end
+
+ if i==nvars | i==nvars-1
+ xlabel('Time');
+ end
+ % set(gca,'XTick',xtick)
+ % set(gca,'XTickLabel',xticklabel)
+
+ title([num2str(i),'. ',titlelist(i,:)]);
+ ylabel(ylabels(i,:))
+end
+
+% sets printing preferences
+%printpref
diff --git a/matlab/occbin/makechart9.m b/matlab/occbin/makechart9.m
index c6e65afcdf86785e1f4e5b106f11b20bbdc16279..e876189599100494c4ca2b1f3c4f3cc67d70f389 100755
--- a/matlab/occbin/makechart9.m
+++ b/matlab/occbin/makechart9.m
@@ -1,136 +1,136 @@
-function makechart9(titlelist,legendlist,figlabel,yearshock,ylabels,...
- zdata1,zdata2,zdata3,zdata4,zdata5,zdata6,zdata7)
-
-
-
-figure
-
-titlelist = char(strrep(cellstr(titlelist),'_','.'));
-
-ndsets=7; % default, changed below as applicable
-if nargin==6
- zdata2=nan*zdata1;
- zdata3=nan*zdata1;
- zdata4=nan*zdata1;
- zdata5=nan*zdata1;
- zdata6=nan*zdata1;
- zdata7=nan*zdata1;
- ndsets =1;
-elseif nargin==7
- zdata3=nan*zdata1;
- zdata4=nan*zdata1;
- zdata5=nan*zdata1;
- zdata6=nan*zdata1;
- zdata7=nan*zdata1;
- ndsets =2;
-elseif nargin == 8
- zdata4 =nan*zdata1;
- zdata5 =nan*zdata1;
- zdata6=nan*zdata1;
- zdata7=nan*zdata1;
- ndsets=3;
-elseif nargin == 9
- zdata5 =nan*zdata1;
- zdata6=nan*zdata1;
- zdata7=nan*zdata1;
- ndsets=4;
-elseif nargin == 10
- zdata6 =nan*zdata1;
- zdata7=nan*zdata1;
- ndsets=5;
-elseif nargin == 11
- zdata7=nan*zdata1;
- ndsets=6;
-elseif ((nargin>=13) | (nargin <=3))
- error ('makechart takes 4 to 10 arguments')
-end
-
-nobs = size(zdata1,1);
-
-if yearshock>-100
-xvalues = yearshock+(0:nobs-1)'/4; % Matteo plot year on x axis
-else
-xvalues = (1:nobs)'; % Matteo plot year on x axis
-end
-
-nvars = size(titlelist,1);
-if nvars==1
- nrows=1;
- ncols = 1;
-elseif nvars==2
- nrows =2;
- ncols = 1;
-elseif nvars == 3
- nrows = 3;
- ncols = 1;
-elseif nvars==4
- nrows = 2;
- ncols = 2;
-elseif (nvars==5 | nvars ==6)
- nrows = 3;
- ncols = 2;
-elseif (nvars==7 | nvars==8)
- nrows = 4;
- ncols = 2;
-elseif nvars>8 & nvars<=12;
- nrows = 3;
- ncols = 4;
-elseif nvars>12 & nvars<=15;
- nrows = 5;
- ncols = 3;
-else
- error('too many variables (makechart)')
-end
-
-
-for i = 1:nvars
- subplot(nrows,ncols,i)
- h1=plot(xvalues,zdata1(:,i),'k',...
- xvalues,zdata2(:,i),'r',...
- xvalues,zdata3(:,i),'b',...
- xvalues,zdata4(:,i),'g',...
- xvalues,zdata5(:,i),'g',...
- xvalues,zdata6(:,i),'c',...
- xvalues,zdata7(:,i),'y');
- [x0 x1 y10 y11] = pickaxes(xvalues,zdata1(:,i));
- [x0 x1 y20 y21] = pickaxes(xvalues,zdata2(:,i));
- [x0 x1 y30 y31] = pickaxes(xvalues,zdata3(:,i));
- [x0 x1 y40 y41] = pickaxes(xvalues,zdata4(:,i));
- [x0 x1 y50 y51] = pickaxes(xvalues,zdata5(:,i));
- [x0 x1 y60 y61] = pickaxes(xvalues,zdata6(:,i));
- [x0 x1 y70 y71] = pickaxes(xvalues,zdata7(:,i));
- grid on
- y0 = min([y10,y20,y30,y40,y50,y60,y70]);
- y1 = max([y11,y21,y31,y41,y51,y61,y71]);
- if y0==y1
- y1=y0+1;
- end
-
- axis([x0 x1 y0 y1])
- set(h1,'linewidth',2);
- if i==1
- if numel(strvcat(legendlist(1,:)))
- h=legend(legendlist,'Location','Northwest');
- set(h,'Fontsize',8)
- end
- end
- if i==1
- if nvars>3
- text('String',figlabel,'Units','normalized','Position',[1.2 1.21],...
- 'FontSize',13,'FontWeight','bold','HorizontalAlignment','center');
- else
- text('String',figlabel,'Units','normalized','Position',[0.4 1.24],...
- 'FontSize',13,'FontWeight','bold','HorizontalAlignment','center');
- end
- end
-
- %set(gca,'XTick',xtick)
- %set(gca,'XTickLabel',xticklabel)
-
- title(titlelist(i,:),'Fontsize',11);
- ylabel(ylabels(i,:))
-
-end
-
-% sets printing preferences
-%printpref
+function makechart9(titlelist,legendlist,figlabel,yearshock,ylabels,...
+ zdata1,zdata2,zdata3,zdata4,zdata5,zdata6,zdata7)
+
+
+
+figure
+
+titlelist = char(strrep(cellstr(titlelist),'_','.'));
+
+ndsets=7; % default, changed below as applicable
+if nargin==6
+ zdata2=nan*zdata1;
+ zdata3=nan*zdata1;
+ zdata4=nan*zdata1;
+ zdata5=nan*zdata1;
+ zdata6=nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets =1;
+elseif nargin==7
+ zdata3=nan*zdata1;
+ zdata4=nan*zdata1;
+ zdata5=nan*zdata1;
+ zdata6=nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets =2;
+elseif nargin == 8
+ zdata4 =nan*zdata1;
+ zdata5 =nan*zdata1;
+ zdata6=nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets=3;
+elseif nargin == 9
+ zdata5 =nan*zdata1;
+ zdata6=nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets=4;
+elseif nargin == 10
+ zdata6 =nan*zdata1;
+ zdata7=nan*zdata1;
+ ndsets=5;
+elseif nargin == 11
+ zdata7=nan*zdata1;
+ ndsets=6;
+elseif ((nargin>=13) | (nargin <=3))
+ error ('makechart takes 4 to 10 arguments')
+end
+
+nobs = size(zdata1,1);
+
+if yearshock>-100
+ xvalues = yearshock+(0:nobs-1)'/4; % Matteo plot year on x axis
+else
+ xvalues = (1:nobs)'; % Matteo plot year on x axis
+end
+
+nvars = size(titlelist,1);
+if nvars==1
+ nrows=1;
+ ncols = 1;
+elseif nvars==2
+ nrows =2;
+ ncols = 1;
+elseif nvars == 3
+ nrows = 3;
+ ncols = 1;
+elseif nvars==4
+ nrows = 2;
+ ncols = 2;
+elseif (nvars==5 | nvars ==6)
+ nrows = 3;
+ ncols = 2;
+elseif (nvars==7 | nvars==8)
+ nrows = 4;
+ ncols = 2;
+elseif nvars>8 & nvars<=12;
+ nrows = 3;
+ ncols = 4;
+elseif nvars>12 & nvars<=15;
+ nrows = 5;
+ ncols = 3;
+else
+ error('too many variables (makechart)')
+end
+
+
+for i = 1:nvars
+ subplot(nrows,ncols,i)
+ h1=plot(xvalues,zdata1(:,i),'k',...
+ xvalues,zdata2(:,i),'r',...
+ xvalues,zdata3(:,i),'b',...
+ xvalues,zdata4(:,i),'g',...
+ xvalues,zdata5(:,i),'g',...
+ xvalues,zdata6(:,i),'c',...
+ xvalues,zdata7(:,i),'y');
+ [x0 x1 y10 y11] = pickaxes(xvalues,zdata1(:,i));
+ [x0 x1 y20 y21] = pickaxes(xvalues,zdata2(:,i));
+ [x0 x1 y30 y31] = pickaxes(xvalues,zdata3(:,i));
+ [x0 x1 y40 y41] = pickaxes(xvalues,zdata4(:,i));
+ [x0 x1 y50 y51] = pickaxes(xvalues,zdata5(:,i));
+ [x0 x1 y60 y61] = pickaxes(xvalues,zdata6(:,i));
+ [x0 x1 y70 y71] = pickaxes(xvalues,zdata7(:,i));
+ grid on
+ y0 = min([y10,y20,y30,y40,y50,y60,y70]);
+ y1 = max([y11,y21,y31,y41,y51,y61,y71]);
+ if y0==y1
+ y1=y0+1;
+ end
+
+ axis([x0 x1 y0 y1])
+ set(h1,'linewidth',2);
+ if i==1
+ if numel(strvcat(legendlist(1,:)))
+ h=legend(legendlist,'Location','Northwest');
+ set(h,'Fontsize',8)
+ end
+ end
+ if i==1
+ if nvars>3
+ text('String',figlabel,'Units','normalized','Position',[1.2 1.21],...
+ 'FontSize',13,'FontWeight','bold','HorizontalAlignment','center');
+ else
+ text('String',figlabel,'Units','normalized','Position',[0.4 1.24],...
+ 'FontSize',13,'FontWeight','bold','HorizontalAlignment','center');
+ end
+ end
+
+ %set(gca,'XTick',xtick)
+ %set(gca,'XTickLabel',xticklabel)
+
+ title(titlelist(i,:),'Fontsize',11);
+ ylabel(ylabels(i,:))
+
+end
+
+% sets printing preferences
+%printpref
diff --git a/matlab/occbin/map_regime.m b/matlab/occbin/map_regime.m
index a3ed88b47870b3e0cffc33dedce292072d08306f..64a87250e94e2dfd31086d1a3e609e5d63da46d5 100755
--- a/matlab/occbin/map_regime.m
+++ b/matlab/occbin/map_regime.m
@@ -1,25 +1,25 @@
-function [regime regimestart]=map_regimes(violvecbool)
-
-nperiods = length(violvecbool)-1;
-
-% analyse violvec and isolate contiguous periods in the other regime.
- regime(1) = violvecbool(1);
- regimeindx = 1;
- regimestart(1) = 1;
- for i=2:nperiods
- if violvecbool(i)~=regime(regimeindx)
- regimeindx=regimeindx+1;
- regime(regimeindx) = violvecbool(i);
- regimestart(regimeindx)=i;
- end
- end
-
-
- if (regime(1) == 1 & length(regimestart)==1)
- warning('Increase nperiods');
- end
-
- if (regime(end)==1)
- warning('Increase nperiods');
- end
-
+function [regime regimestart]=map_regime(violvecbool)
+
+nperiods = length(violvecbool)-1;
+
+% analyse violvec and isolate contiguous periods in the other regime.
+regime(1) = violvecbool(1);
+regimeindx = 1;
+regimestart(1) = 1;
+for i=2:nperiods
+ if violvecbool(i)~=regime(regimeindx)
+ regimeindx=regimeindx+1;
+ regime(regimeindx) = violvecbool(i);
+ regimestart(regimeindx)=i;
+ end
+end
+
+
+if (regime(1) == 1 & length(regimestart)==1)
+ warning('Increase nperiods');
+end
+
+if (regime(end)==1)
+ warning('Increase nperiods');
+end
+
diff --git a/matlab/occbin/mkdata.m b/matlab/occbin/mkdata.m
index a7f855743ddb228ecf8c75e65823849f054fd8b4..02b2eb703e803f4c3fd1c72f5f1d23f3751b3124 100755
--- a/matlab/occbin/mkdata.m
+++ b/matlab/occbin/mkdata.m
@@ -1,63 +1,63 @@
-function [zdata]=mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,scalefactormod,init)
-
-%[nsim, ksim, ysim, isim, csim] = mkdata(nperiods,cofb,endog_)
-
-% given decision rule
-neqs = size(endog_,1);
-
-if nargin<9
- init = zeros(neqs,1);
-end
-
-if nargin<8
- scalefactormod=1;
-end
-
-if nargin<7
- error('Not enough inputs')
-end
-
-history = zeros(neqs,nperiods+1);
-
- nshocks = size(irfshock,1);
- for i = 1:nshocks
- shockpos = strmatch(irfshock(i,:),exog_,'exact');
- if ~isempty(shockpos)
- irfshockpos(i) = shockpos;
- else
- error(['Shock ',irfshock(i,:),' is not in the model']);
- end
- end
-
-
-% generate data
-% history will contain data, the state vector at each period in time will
-% be stored columnwise.
-history = zeros(neqs,nperiods);
-history(:,1)= init;
-
-lengthshock = size(scalefactormod,1);
-
-errvec = zeros(size(exog_,1),1);
-
-for i = 2:nperiods+1
- if i<=(lengthshock+1)
- for j = 1:nshocks
- errvec(irfshockpos(j)) = scalefactormod(i-1,j);
- end
- history(:,i) = decrulea * history(:,i-1)+decruleb*errvec;
- else
- % update endogenous variables
- history(:,i) = decrulea * history(:,i-1);
- end
-end
-
-% extract desired variables
-nwish=size(wishlist,1);
-wishpos = zeros(nwish,1);
-
-history=history';
-for i=1:nwish
- wishpos(i) = strmatch(wishlist(i,:),endog_,'exact');
-end
+function [zdata]=mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,scalefactormod,init)
+
+%[nsim, ksim, ysim, isim, csim] = mkdata(nperiods,cofb,endog_)
+
+% given decision rule
+neqs = size(endog_,1);
+
+if nargin<9
+ init = zeros(neqs,1);
+end
+
+if nargin<8
+ scalefactormod=1;
+end
+
+if nargin<7
+ error('Not enough inputs')
+end
+
+history = zeros(neqs,nperiods+1);
+
+nshocks = size(irfshock,1);
+for i = 1:nshocks
+ shockpos = strmatch(irfshock(i,:),exog_,'exact');
+ if ~isempty(shockpos)
+ irfshockpos(i) = shockpos;
+ else
+ error(['Shock ',irfshock(i,:),' is not in the model']);
+ end
+end
+
+
+% generate data
+% history will contain data, the state vector at each period in time will
+% be stored columnwise.
+history = zeros(neqs,nperiods);
+history(:,1)= init;
+
+lengthshock = size(scalefactormod,1);
+
+errvec = zeros(size(exog_,1),1);
+
+for i = 2:nperiods+1
+ if i<=(lengthshock+1)
+ for j = 1:nshocks
+ errvec(irfshockpos(j)) = scalefactormod(i-1,j);
+ end
+ history(:,i) = decrulea * history(:,i-1)+decruleb*errvec;
+ else
+ % update endogenous variables
+ history(:,i) = decrulea * history(:,i-1);
+ end
+end
+
+% extract desired variables
+nwish=size(wishlist,1);
+wishpos = zeros(nwish,1);
+
+history=history';
+for i=1:nwish
+ wishpos(i) = strmatch(wishlist(i,:),endog_,'exact');
+end
zdata = history(2:end,wishpos);
\ No newline at end of file
diff --git a/matlab/occbin/mkdatap_anticipated.m b/matlab/occbin/mkdatap_anticipated.m
index 524d3dd5d78880eb42c8121ef702fa2acd3503ad..6e745e0a454f3c208732765e709ddf10528d863e 100755
--- a/matlab/occbin/mkdatap_anticipated.m
+++ b/matlab/occbin/mkdatap_anticipated.m
@@ -1,125 +1,125 @@
-function [zdata]=mkdatap_anticipated(nperiods,decrulea,decruleb,...
- cof,Jbarmat,cofstar,Jstarbarmat,Dstarbarmat,...
- regime,regimestart,violvecbool,...
- endog_,exog_,irfshock,scalefactormod,init)
-
-
-
-nvars = size(endog_,1);
-
-
-if nargin<16
- init=zeros(nvars,1);
-end
-
-if nargin<15;
- scalefactormod=1;
-end
-
-
-nshocks = size(irfshock,1);
-for i = 1:nshocks
- shockpos = strmatch(irfshock(i,:),exog_,'exact');
- if ~isempty(shockpos)
- irfshockpos(i) = shockpos;
- else
- error(['Shock ',irfshock(i,:),' is not in the model']);
- end
-end
-
-
-nregimes = length(regime);
-
-Cbarmat = cof(:,1:nvars);
-Bbarmat = cof(:,nvars+1:2*nvars);
-Abarmat = cof(:,2*nvars+1:3*nvars);
-
-
-% cofstar contains the system for the model when the constraint binds
-Cstarbarmat = cofstar(:,1:nvars);
-Bstarbarmat = cofstar(:,nvars+1:2*nvars);
-Astarbarmat = cofstar(:,2*nvars+1:3*nvars);
-
-% get the time-dependent decision rules
-
-Tmax = regimestart(nregimes)-1; % Tmax is the position of the last period
-% when the constraint binds
-
-if Tmax > 0
- P = zeros(nvars,nvars,Tmax);
- D = zeros(nvars,Tmax);
-
-
- invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
- P(:,:,Tmax) = -invmat*Cstarbarmat;
- D(:,Tmax) = -invmat*Dstarbarmat;
-
-
- % equivalent to pre-multiplying by the inverse above if the target
- % matrix is invertible. Otherwise it yields the minimum state solution
- %P(:,:,Tmax) = -(Astarbarmat*decrulea+Bstarbarmat)\Cstarbarmat;
- %D(:,Tmax) = -(Astarbarmat*decrulea+Bstarbarmat)\Dstarbarmat;
-
-
- for i = Tmax-1:-1:1
-
- if violvecbool(i)
- invmat = inv(Bstarbarmat+Astarbarmat*P(:,:,i+1));
- P(:,:,i)=-invmat*Cstarbarmat;
- D(:,i) = -invmat*(Astarbarmat*D(:,i+1)+Dstarbarmat);
- else
- invmat = inv(Bbarmat+Abarmat*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat;
- D(:,i) = -invmat*(Abarmat*D(:,i+1));
- end
- end
-
-if Tmax > 1
-if violvecbool(1)
- E = -invmat*Jstarbarmat;
-else
- E = -invmat*Jbarmat;
-end
-else
- invmat = inv(Astarbarmat*decrulea+Bstarbarmat);
- E = -invmat*Jstarbarmat;
-
-end
-
-
-end
-
-% generate data
-% history will contain data, the state vector at each period in time will
-% be stored columnwise.
-history = zeros(nvars,nperiods+1);
-history(:,1) = init;
-errvec = zeros(size(exog_,1),1);
-
-% deal with predetermined conditions
-for i = 1:nshocks
- errvec(irfshockpos(i)) = scalefactormod(i);
-end
-
-% deal with shocks
-irfpos =1;
-if irfpos <=Tmax
- history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
- D(:,irfpos) + E*errvec;
-else
- history(:,irfpos+1) = decrulea*history(:,irfpos)+decruleb*errvec;
-end
-
-% all other periods
-for irfpos=2:nperiods+1
- if irfpos <=Tmax
- history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
- D(:,irfpos);
- else
- history(:,irfpos+1) = decrulea*history(:,irfpos);
- end
-end
-
-
-history=history';
+function [zdata]=mkdatap_anticipated(nperiods,decrulea,decruleb,...
+ cof,Jbarmat,cofstar,Jstarbarmat,Dstarbarmat,...
+ regime,regimestart,violvecbool,...
+ endog_,exog_,irfshock,scalefactormod,init)
+
+
+
+nvars = size(endog_,1);
+
+
+if nargin<16
+ init=zeros(nvars,1);
+end
+
+if nargin<15;
+ scalefactormod=1;
+end
+
+
+nshocks = size(irfshock,1);
+for i = 1:nshocks
+ shockpos = strmatch(irfshock(i,:),exog_,'exact');
+ if ~isempty(shockpos)
+ irfshockpos(i) = shockpos;
+ else
+ error(['Shock ',irfshock(i,:),' is not in the model']);
+ end
+end
+
+
+nregimes = length(regime);
+
+Cbarmat = cof(:,1:nvars);
+Bbarmat = cof(:,nvars+1:2*nvars);
+Abarmat = cof(:,2*nvars+1:3*nvars);
+
+
+% cofstar contains the system for the model when the constraint binds
+Cstarbarmat = cofstar(:,1:nvars);
+Bstarbarmat = cofstar(:,nvars+1:2*nvars);
+Astarbarmat = cofstar(:,2*nvars+1:3*nvars);
+
+% get the time-dependent decision rules
+
+Tmax = regimestart(nregimes)-1; % Tmax is the position of the last period
+ % when the constraint binds
+
+if Tmax > 0
+ P = zeros(nvars,nvars,Tmax);
+ D = zeros(nvars,Tmax);
+
+
+ invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
+ P(:,:,Tmax) = -invmat*Cstarbarmat;
+ D(:,Tmax) = -invmat*Dstarbarmat;
+
+
+ % equivalent to pre-multiplying by the inverse above if the target
+ % matrix is invertible. Otherwise it yields the minimum state solution
+ %P(:,:,Tmax) = -(Astarbarmat*decrulea+Bstarbarmat)\Cstarbarmat;
+ %D(:,Tmax) = -(Astarbarmat*decrulea+Bstarbarmat)\Dstarbarmat;
+
+
+ for i = Tmax-1:-1:1
+
+ if violvecbool(i)
+ invmat = inv(Bstarbarmat+Astarbarmat*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cstarbarmat;
+ D(:,i) = -invmat*(Astarbarmat*D(:,i+1)+Dstarbarmat);
+ else
+ invmat = inv(Bbarmat+Abarmat*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat;
+ D(:,i) = -invmat*(Abarmat*D(:,i+1));
+ end
+ end
+
+ if Tmax > 1
+ if violvecbool(1)
+ E = -invmat*Jstarbarmat;
+ else
+ E = -invmat*Jbarmat;
+ end
+ else
+ invmat = inv(Astarbarmat*decrulea+Bstarbarmat);
+ E = -invmat*Jstarbarmat;
+
+ end
+
+
+end
+
+% generate data
+% history will contain data, the state vector at each period in time will
+% be stored columnwise.
+history = zeros(nvars,nperiods+1);
+history(:,1) = init;
+errvec = zeros(size(exog_,1),1);
+
+% deal with predetermined conditions
+for i = 1:nshocks
+ errvec(irfshockpos(i)) = scalefactormod(i);
+end
+
+% deal with shocks
+irfpos =1;
+if irfpos <=Tmax
+ history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
+ D(:,irfpos) + E*errvec;
+else
+ history(:,irfpos+1) = decrulea*history(:,irfpos)+decruleb*errvec;
+end
+
+% all other periods
+for irfpos=2:nperiods+1
+ if irfpos <=Tmax
+ history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
+ D(:,irfpos);
+ else
+ history(:,irfpos+1) = decrulea*history(:,irfpos);
+ end
+end
+
+
+history=history';
zdata = history(2:end,:);
\ No newline at end of file
diff --git a/matlab/occbin/mkdatap_anticipated_2constraints.m b/matlab/occbin/mkdatap_anticipated_2constraints.m
index b05c90d6dec49cd74b024b26fe1795acf9b02bdf..c18cef2f9f18b109d891f5a2b831ca9d8bb16617 100755
--- a/matlab/occbin/mkdatap_anticipated_2constraints.m
+++ b/matlab/occbin/mkdatap_anticipated_2constraints.m
@@ -1,180 +1,180 @@
-function [zdata]=mkdatap_anticipated_2constraints_alt(nperiods,decrulea,decruleb,...
- cof,Jbarmat,...
- cof10,Jbarmat10,Dbarmat10,...
- cof01,Jbarmat01,Dbarmat01,...
- cof11,Jbarmat11,Dbarmat11,...
- regime1,regimestart1,...
- regime2,regimestart2,...
- violvecbool,endog_,exog_,...
- irfshock,scalefactormod,init)
-
-
-nvars = size(endog_,1);
-
-
-if nargin<16
- init=zeros(nvars,1);
-end
-
-if nargin<15;
- scalefactormod=1;
-end
-
-
-nshocks = size(irfshock,1);
-for i = 1:nshocks
- shockpos = strmatch(irfshock(i,:),exog_,'exact');
- if ~isempty(shockpos)
- irfshockpos(i) = shockpos;
- else
- error(['Shock ',irfshock(i,:),' is not in the model']);
- end
-end
-
-
-
-Cbarmat = cof(:,1:nvars);
-Bbarmat = cof(:,nvars+1:2*nvars);
-Abarmat = cof(:,2*nvars+1:3*nvars);
-
-
-% cofstar contains the system for the model when the constraint binds
-
-
-Cbarmat10 = cof10(:,1:nvars);
-Bbarmat10 = cof10(:,nvars+1:2*nvars);
-Abarmat10 = cof10(:,2*nvars+1:3*nvars);
-
-Cbarmat01 = cof01(:,1:nvars);
-Bbarmat01 = cof01(:,nvars+1:2*nvars);
-Abarmat01 = cof01(:,2*nvars+1:3*nvars);
-
-Cbarmat11 = cof11(:,1:nvars);
-Bbarmat11 = cof11(:,nvars+1:2*nvars);
-Abarmat11 = cof11(:,2*nvars+1:3*nvars);
-
-% get the time-dependent decision rules
-nregimes1 = length(regime1);
-nregimes2 = length(regime2);
-
-Tmax = max([regimestart1(nregimes1) regimestart2(nregimes2)])-1; % Tmax is the position of the last period
-% when the constraint binds
-
-if Tmax > 0
- P = zeros(nvars,nvars,Tmax);
- D = zeros(nvars,Tmax);
-
-% invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
-% P(:,:,Tmax) = -invmat*Cstarbarmat;
-% D(:,Tmax) = -invmat*Dstarbarmat;
-
-
- if (violvecbool(Tmax,1) & ~violvecbool(Tmax,2))
- %XXX fix next three lines
- invmat = inv((Abarmat10*decrulea+Bbarmat10));
- P(:,:,Tmax) = -invmat*Cbarmat10;
- D(:,Tmax) = -invmat*Dbarmat10;
- elseif (violvecbool(Tmax,1) & violvecbool(Tmax,2))
- invmat = inv((Abarmat11*decrulea+Bbarmat11));
- P(:,:,Tmax) = -invmat*Cbarmat11;
- D(:,Tmax) = -invmat*Dbarmat11;
- else
- invmat = inv((Abarmat01*decrulea+Bbarmat01));
- P(:,:,Tmax) = -invmat*Cbarmat01;
- D(:,Tmax) = -invmat*Dbarmat01;
- end
-
-
-
-
- for i = Tmax-1:-1:1
-
- if (violvecbool(i,1) & ~violvecbool(i,2))
- invmat = inv(Bbarmat10+Abarmat10*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat10;
- D(:,i) = -invmat*(Abarmat10*D(:,i+1)+Dbarmat10);
- elseif (~violvecbool(i,1) & violvecbool(i,2))
- invmat = inv(Bbarmat01+Abarmat01*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat01;
- D(:,i) = -invmat*(Abarmat01*D(:,i+1)+Dbarmat01);
- elseif (violvecbool(i,1) & violvecbool(i,2))
- invmat = inv(Bbarmat11+Abarmat11*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat11;
- D(:,i) = -invmat*(Abarmat11*D(:,i+1)+Dbarmat11);
- else
- invmat = inv(Bbarmat+Abarmat*P(:,:,i+1));
- P(:,:,i)=-invmat*Cbarmat;
- D(:,i) = -invmat*(Abarmat*D(:,i+1));
- end
-
- end
-
-
-% Double check the appropriate invmat in each case
-% right now -- inherited from previous loop
-if Tmax > 1
-
- if ( ~violvecbool(1,1) & violvecbool(1,2) )
- E = -invmat*Jbarmat01;
- elseif ( violvecbool(1,1) & ~violvecbool(1,2) )
- E = -invmat*Jbarmat10;
- elseif ( violvecbool(1,1) & violvecbool(1,2) )
- E = -invmat*Jbarmat11;
- else
- E = -invmat*Jbarmat;
- end
-
-else % Tmax is equal to 1
-% invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
-% E = -invmat*Jstarbarmat;
-
- if ( ~violvecbool(1,1) & violvecbool(1,2) )
- invmat = inv((Abarmat01*decrulea+Bbarmat01));
- E = -invmat*Jbarmat01;
- elseif ( violvecbool(1,1) & violvecbool(1,2) )
- invmat = inv((Abarmat11*decrulea+Bbarmat11));
- E = -invmat*Jbarmat11;
- else
- invmat = inv((Abarmat10*decrulea+Bbarmat10));
- E = -invmat*Jbarmat10;
-
- end
-
-end
-
-
-end
-
-% generate data
-% history will contain data, the state vector at each period in time will
-% be stored columnwise.
-history = zeros(nvars,nperiods+1);
-history(:,1) = init;
-errvec = zeros(size(exog_,1),1);
-
-for i = 1:nshocks
- errvec(irfshockpos(i)) = scalefactormod(i);
-end
-
-% deal with shocks
-irfpos =1;
-if irfpos <=Tmax
- history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
- D(:,irfpos) + E*errvec;
-else
- history(:,irfpos+1) = decrulea*history(:,irfpos)+decruleb*errvec;
-end
-
-% all other periods
-for irfpos=2:nperiods+1
- if irfpos <=Tmax
- history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
- D(:,irfpos);
- else
- history(:,irfpos+1) = decrulea*history(:,irfpos);
- end
-end
-
-
-history=history';
+function [zdata]=mkdatap_anticipated_2constraints(nperiods,decrulea,decruleb,...
+ cof,Jbarmat,...
+ cof10,Jbarmat10,Dbarmat10,...
+ cof01,Jbarmat01,Dbarmat01,...
+ cof11,Jbarmat11,Dbarmat11,...
+ regime1,regimestart1,...
+ regime2,regimestart2,...
+ violvecbool,endog_,exog_,...
+ irfshock,scalefactormod,init)
+
+
+nvars = size(endog_,1);
+
+
+if nargin<16
+ init=zeros(nvars,1);
+end
+
+if nargin<15;
+ scalefactormod=1;
+end
+
+
+nshocks = size(irfshock,1);
+for i = 1:nshocks
+ shockpos = strmatch(irfshock(i,:),exog_,'exact');
+ if ~isempty(shockpos)
+ irfshockpos(i) = shockpos;
+ else
+ error(['Shock ',irfshock(i,:),' is not in the model']);
+ end
+end
+
+
+
+Cbarmat = cof(:,1:nvars);
+Bbarmat = cof(:,nvars+1:2*nvars);
+Abarmat = cof(:,2*nvars+1:3*nvars);
+
+
+% cofstar contains the system for the model when the constraint binds
+
+
+Cbarmat10 = cof10(:,1:nvars);
+Bbarmat10 = cof10(:,nvars+1:2*nvars);
+Abarmat10 = cof10(:,2*nvars+1:3*nvars);
+
+Cbarmat01 = cof01(:,1:nvars);
+Bbarmat01 = cof01(:,nvars+1:2*nvars);
+Abarmat01 = cof01(:,2*nvars+1:3*nvars);
+
+Cbarmat11 = cof11(:,1:nvars);
+Bbarmat11 = cof11(:,nvars+1:2*nvars);
+Abarmat11 = cof11(:,2*nvars+1:3*nvars);
+
+% get the time-dependent decision rules
+nregimes1 = length(regime1);
+nregimes2 = length(regime2);
+
+Tmax = max([regimestart1(nregimes1) regimestart2(nregimes2)])-1; % Tmax is the position of the last period
+ % when the constraint binds
+
+if Tmax > 0
+ P = zeros(nvars,nvars,Tmax);
+ D = zeros(nvars,Tmax);
+
+ % invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
+ % P(:,:,Tmax) = -invmat*Cstarbarmat;
+ % D(:,Tmax) = -invmat*Dstarbarmat;
+
+
+ if (violvecbool(Tmax,1) & ~violvecbool(Tmax,2))
+ %XXX fix next three lines
+ invmat = inv((Abarmat10*decrulea+Bbarmat10));
+ P(:,:,Tmax) = -invmat*Cbarmat10;
+ D(:,Tmax) = -invmat*Dbarmat10;
+ elseif (violvecbool(Tmax,1) & violvecbool(Tmax,2))
+ invmat = inv((Abarmat11*decrulea+Bbarmat11));
+ P(:,:,Tmax) = -invmat*Cbarmat11;
+ D(:,Tmax) = -invmat*Dbarmat11;
+ else
+ invmat = inv((Abarmat01*decrulea+Bbarmat01));
+ P(:,:,Tmax) = -invmat*Cbarmat01;
+ D(:,Tmax) = -invmat*Dbarmat01;
+ end
+
+
+
+
+ for i = Tmax-1:-1:1
+
+ if (violvecbool(i,1) & ~violvecbool(i,2))
+ invmat = inv(Bbarmat10+Abarmat10*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat10;
+ D(:,i) = -invmat*(Abarmat10*D(:,i+1)+Dbarmat10);
+ elseif (~violvecbool(i,1) & violvecbool(i,2))
+ invmat = inv(Bbarmat01+Abarmat01*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat01;
+ D(:,i) = -invmat*(Abarmat01*D(:,i+1)+Dbarmat01);
+ elseif (violvecbool(i,1) & violvecbool(i,2))
+ invmat = inv(Bbarmat11+Abarmat11*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat11;
+ D(:,i) = -invmat*(Abarmat11*D(:,i+1)+Dbarmat11);
+ else
+ invmat = inv(Bbarmat+Abarmat*P(:,:,i+1));
+ P(:,:,i)=-invmat*Cbarmat;
+ D(:,i) = -invmat*(Abarmat*D(:,i+1));
+ end
+
+ end
+
+
+ % Double check the appropriate invmat in each case
+ % right now -- inherited from previous loop
+ if Tmax > 1
+
+ if ( ~violvecbool(1,1) & violvecbool(1,2) )
+ E = -invmat*Jbarmat01;
+ elseif ( violvecbool(1,1) & ~violvecbool(1,2) )
+ E = -invmat*Jbarmat10;
+ elseif ( violvecbool(1,1) & violvecbool(1,2) )
+ E = -invmat*Jbarmat11;
+ else
+ E = -invmat*Jbarmat;
+ end
+
+ else % Tmax is equal to 1
+ % invmat = inv((Astarbarmat*decrulea+Bstarbarmat));
+ % E = -invmat*Jstarbarmat;
+
+ if ( ~violvecbool(1,1) & violvecbool(1,2) )
+ invmat = inv((Abarmat01*decrulea+Bbarmat01));
+ E = -invmat*Jbarmat01;
+ elseif ( violvecbool(1,1) & violvecbool(1,2) )
+ invmat = inv((Abarmat11*decrulea+Bbarmat11));
+ E = -invmat*Jbarmat11;
+ else
+ invmat = inv((Abarmat10*decrulea+Bbarmat10));
+ E = -invmat*Jbarmat10;
+
+ end
+
+ end
+
+
+end
+
+% generate data
+% history will contain data, the state vector at each period in time will
+% be stored columnwise.
+history = zeros(nvars,nperiods+1);
+history(:,1) = init;
+errvec = zeros(size(exog_,1),1);
+
+for i = 1:nshocks
+ errvec(irfshockpos(i)) = scalefactormod(i);
+end
+
+% deal with shocks
+irfpos =1;
+if irfpos <=Tmax
+ history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
+ D(:,irfpos) + E*errvec;
+else
+ history(:,irfpos+1) = decrulea*history(:,irfpos)+decruleb*errvec;
+end
+
+% all other periods
+for irfpos=2:nperiods+1
+ if irfpos <=Tmax
+ history(:,irfpos+1) = P(:,:,irfpos)* history(:,irfpos)+...
+ D(:,irfpos);
+ else
+ history(:,irfpos+1) = decrulea*history(:,irfpos);
+ end
+end
+
+
+history=history';
zdata = history(2:end,:);
\ No newline at end of file
diff --git a/matlab/occbin/pickaxes.m b/matlab/occbin/pickaxes.m
index 46c4b79c6db0176bb5a076573d4a95fdc7869378..c2e2d5e0d55a79bce77faa66ecbfc3d822d86923 100755
--- a/matlab/occbin/pickaxes.m
+++ b/matlab/occbin/pickaxes.m
@@ -1,16 +1,16 @@
-function [x0,x1,y0,y1] = pickaxes(xvalues,yvalues)
-
-x0=xvalues(1);
-nobs = length(xvalues);
-x1=xvalues(nobs);
-
-maxy = max(yvalues);
-miny = min(yvalues);
-
-
-y0 = miny - .05*abs(miny);
-if (miny>0 & y0<0)
- y0 = 0;
-end
-
-y1 = maxy + .05*abs(maxy);
+function [x0,x1,y0,y1] = pickaxes(xvalues,yvalues)
+
+x0=xvalues(1);
+nobs = length(xvalues);
+x1=xvalues(nobs);
+
+maxy = max(yvalues);
+miny = min(yvalues);
+
+
+y0 = miny - .05*abs(miny);
+if (miny>0 & y0<0)
+ y0 = 0;
+end
+
+y1 = maxy + .05*abs(maxy);
diff --git a/matlab/occbin/process_constraint.m b/matlab/occbin/process_constraint.m
index d209111755fde2bd1e9e192397fae953fd092624..0573eb6419504732f7971f56e458396b9e0548fd 100755
--- a/matlab/occbin/process_constraint.m
+++ b/matlab/occbin/process_constraint.m
@@ -1,38 +1,38 @@
-% this function looks for occurrences of the endogenous variables in
-% endo_names in the input string constraint
-% all occurrences of the endogenous variables are appended a suffix
-% if the invert_switch is true, the direction of the inequality in the
-% constraint is inverted
-
-function constraint1 = process_constraint(constraint,suffix,endo_names,invert_switch)
-
-% create a list of delimiters that can separate parameters and endogenoous
-% variables in the string that expresses the constraint
-delimiters = char(',',';','(',')','+','-','^','*','/',' ','>','<','=');
-
-% split the string that holds the constraint into tokens
-tokens = tokenize(constraint,delimiters);
-
-ntokens = length(tokens);
-
-% search for tokens that match the list of endogenous variables
-for i=1:ntokens
- if ~isempty(find(strcmp(tokens(i),endo_names)))
- % when there is a match with an endogenous variable append the
- % suffix
- tokens(i) = cellstr([char(tokens(i)),suffix]);
- end
-
- % if the invert_switch is true
- % reverse the direction of the inequality
- if invert_switch
- if strcmp(tokens(i),cellstr('>'))
- tokens(i) = cellstr('<');
- elseif strcmp(tokens(i),cellstr('<'))
- tokens(i) = cellstr('>');
- end
- end
-end
-
-% reassemble the tokens to create a string that expresses the constraint
+% this function looks for occurrences of the endogenous variables in
+% endo_names in the input string constraint
+% all occurrences of the endogenous variables are appended a suffix
+% if the invert_switch is true, the direction of the inequality in the
+% constraint is inverted
+
+function constraint1 = process_constraint(constraint,suffix,endo_names,invert_switch)
+
+% create a list of delimiters that can separate parameters and endogenoous
+% variables in the string that expresses the constraint
+delimiters = char(',',';','(',')','+','-','^','*','/',' ','>','<','=');
+
+% split the string that holds the constraint into tokens
+tokens = tokenize(constraint,delimiters);
+
+ntokens = length(tokens);
+
+% search for tokens that match the list of endogenous variables
+for i=1:ntokens
+ if ~isempty(find(strcmp(tokens(i),endo_names)))
+ % when there is a match with an endogenous variable append the
+ % suffix
+ tokens(i) = cellstr([char(tokens(i)),suffix]);
+ end
+
+ % if the invert_switch is true
+ % reverse the direction of the inequality
+ if invert_switch
+ if strcmp(tokens(i),cellstr('>'))
+ tokens(i) = cellstr('<');
+ elseif strcmp(tokens(i),cellstr('<'))
+ tokens(i) = cellstr('>');
+ end
+ end
+end
+
+% reassemble the tokens to create a string that expresses the constraint
constraint1 = strmerge(tokens);
\ No newline at end of file
diff --git a/matlab/occbin/setss.m b/matlab/occbin/setss.m
index d1c0e440ac6b23f14a6726bad535274a3dfc8eaa..39e269d519d8f55dae62e2999e13f4bad5ccc71a 100755
--- a/matlab/occbin/setss.m
+++ b/matlab/occbin/setss.m
@@ -1,14 +1,14 @@
-% Script that retrieves parameter values once model is solved
-
-nendog = size(Mbase_.endo_names,1);
-
-for i=1:nendog
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
-end
-
-nparams = size(Mbase_.param_names);
-
-for i = 1:nparams
- eval([Mbase_.param_names(i,:),'= M_.params(i);']);
-end
-
+% Script that retrieves parameter values once model is solved
+
+nendog = size(Mbase_.endo_names,1);
+
+for i=1:nendog
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
+end
+
+nparams = size(Mbase_.param_names);
+
+for i = 1:nparams
+ eval([Mbase_.param_names(i,:),'= M_.params(i);']);
+end
+
diff --git a/matlab/occbin/solve_no_constraint.m b/matlab/occbin/solve_no_constraint.m
index d9671409d26bc41ef12b7fda8e6ca6f8d2ea930b..d4f61a3f44c6dc6bbf913ac8437253b47054c0ca 100755
--- a/matlab/occbin/solve_no_constraint.m
+++ b/matlab/occbin/solve_no_constraint.m
@@ -1,50 +1,50 @@
-function [zdata_, zdatass_, oobase_, Mbase_ ] = ...
- solve_no_constraint(modnam,...
- shockssequence,irfshock,nperiods)
-
-global M_ oo_
-
-errlist = [];
-
-% solve model
-eval(['dynare ',modnam,' noclearall'])
-oobase_ = oo_;
-Mbase_ = M_;
-
-ys_ = oobase_.dr.ys;
-nvars = numel(ys_);
-zdatass_ = ys_ ;
-
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
-end
-
-for i = 1:size(Mbase_.param_names)
- eval([Mbase_.param_names(i,:),'= M_.params(i);']);
-end
-
-
-
-
-
-
-
-[hm1,h,hl1,Jbarmat] = get_deriv(Mbase_,ys_);
-cof = [hm1,h,hl1];
-
-[decrulea,decruleb]=get_pq(oobase_.dr);
-endog_ = M_.endo_names;
-exog_ = M_.exo_names;
-
-nvars = numel(Mbase_.endo_nbr);
-
-
-nshocks = size(shockssequence,1);
-init = zeros(nvars,1);
-
-wishlist = endog_;
-nwishes = size(wishlist,1);
-
-
-zdata_ = mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,shockssequence);
-
+function [zdata_, zdatass_, oobase_, Mbase_ ] = ...
+ solve_no_constraint(modnam,...
+ shockssequence,irfshock,nperiods)
+
+global M_ oo_
+
+errlist = [];
+
+% solve model
+eval(['dynare ',modnam,' noclearall'])
+oobase_ = oo_;
+Mbase_ = M_;
+
+ys_ = oobase_.dr.ys;
+nvars = numel(ys_);
+zdatass_ = ys_ ;
+
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
+end
+
+for i = 1:size(Mbase_.param_names)
+ eval([Mbase_.param_names(i,:),'= M_.params(i);']);
+end
+
+
+
+
+
+
+
+[hm1,h,hl1,Jbarmat] = get_deriv(Mbase_,ys_);
+cof = [hm1,h,hl1];
+
+[decrulea,decruleb]=get_pq(oobase_.dr);
+endog_ = M_.endo_names;
+exog_ = M_.exo_names;
+
+nvars = numel(Mbase_.endo_nbr);
+
+
+nshocks = size(shockssequence,1);
+init = zeros(nvars,1);
+
+wishlist = endog_;
+nwishes = size(wishlist,1);
+
+
+zdata_ = mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,shockssequence);
+
diff --git a/matlab/occbin/solve_no_constraint_noclear.m b/matlab/occbin/solve_no_constraint_noclear.m
index 309c5c546e13db96b4516146302f3fc72aaded6c..b899708e29b84f9fd54aae2b651f24da984a4df3 100755
--- a/matlab/occbin/solve_no_constraint_noclear.m
+++ b/matlab/occbin/solve_no_constraint_noclear.m
@@ -1,48 +1,48 @@
-function [zdata oobase_ Mbase_ ] = ...
- solve_no_constraint_noclear(modnam,...
- shockssequence,irfshock,nperiods);
-
-global M_ oo_
-
-errlist = [];
-
-% solve model
-eval(['dynare ',modnam,' nolog']);
-oobase_ = oo_;
-Mbase_ = M_;
-
-ys_ = oobase_.dr.ys;
-
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
-end
-
-for i = 1:size(Mbase_.param_names)
- eval([Mbase_.param_names(i,:),'= M_.params(i);']);
-end
-
-setss
-
-
-
-
-
-[hm1,h,hl1,Jbarmat] = get_deriv(Mbase_,ys_);
-cof = [hm1,h,hl1];
-
-[decrulea,decruleb]=get_pq(oobase_.dr);
-endog_ = M_.endo_names;
-exog_ = M_.exo_names;
-
-
-nvars = numel(Mbase_.endo_nbr);
-
-nshocks = size(shockssequence,1);
-init = zeros(nvars,1);
-
-wishlist = endog_;
-nwishes = size(wishlist,1);
-
-
-zdata = mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,shockssequence);
-
+function [zdata oobase_ Mbase_ ] = ...
+ solve_no_constraint_noclear(modnam,...
+ shockssequence,irfshock,nperiods);
+
+global M_ oo_
+
+errlist = [];
+
+% solve model
+eval(['dynare ',modnam,' nolog']);
+oobase_ = oo_;
+Mbase_ = M_;
+
+ys_ = oobase_.dr.ys;
+
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oo_.dr.ys(i); ']);
+end
+
+for i = 1:size(Mbase_.param_names)
+ eval([Mbase_.param_names(i,:),'= M_.params(i);']);
+end
+
+setss
+
+
+
+
+
+[hm1,h,hl1,Jbarmat] = get_deriv(Mbase_,ys_);
+cof = [hm1,h,hl1];
+
+[decrulea,decruleb]=get_pq(oobase_.dr);
+endog_ = M_.endo_names;
+exog_ = M_.exo_names;
+
+
+nvars = numel(Mbase_.endo_nbr);
+
+nshocks = size(shockssequence,1);
+init = zeros(nvars,1);
+
+wishlist = endog_;
+nwishes = size(wishlist,1);
+
+
+zdata = mkdata(nperiods,decrulea,decruleb,endog_,exog_,wishlist,irfshock,shockssequence);
+
diff --git a/matlab/occbin/solve_one_constraint.1.m b/matlab/occbin/solve_one_constraint.1.m
index 34e296496b4c535a01cb2eb05b16536fded1cf42..344e85f5aee89e19aff4a78f872ab0a912b48b24 100755
--- a/matlab/occbin/solve_one_constraint.1.m
+++ b/matlab/occbin/solve_one_constraint.1.m
@@ -1,192 +1,192 @@
-% solve_one_constraint [zdatalinear zdatapiecewise zdatass oo base M base] = solve one constraint(modnam, modnamstar, constraint, constraint relax, shockssequence, irfshock, nperiods, maxiter, init);
-%
-% Inputs:
-% modnam: name of .mod file for the reference regime (excludes the .mod extension).
-% modnamstar: name of .mod file for the alternative regime (excludes the .mod exten- sion).
-% constraint: the constraint (see notes 1 and 2 below). When the condition in constraint evaluates to true, the solution switches from the reference to the alternative regime.
-% constraint relax: when the condition in constraint relax evaluates to true, the solution returns to the reference regime.
-% shockssequence: a sequence of unforeseen shocks under which one wants to solve the model (size T�nshocks).
-% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?).
-% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
-% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
-% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is steady state). The ordering follows the definition order in the .mod files.
-%
-% Outputs:
-% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatass: theinitialpositionforthevectorofstatevariables,indeviationfromsteady state (if not specified, the default is a vectors of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
-% oobase,Mbase: structures produced by Dynare for the reference model ? see Dynare User Guide.
-
-% Log of changes:
-% 6/17/2013 -- Luca added a trailing underscore to local variables in an
-% attempt to avoid conflicts with parameter names defined in the .mod files
-% to be processed.
-% 6/17/2013 -- Luca replaced external .m file setss.m
-
-
-function [zdatalinear_ zdatapiecewise_ zdatass_ oobase_ Mbase_ ] = ...
- solve_one_constraint(modnam_,modnamstar_,...
- constraint_, constraint_relax_,...
- shockssequence_,irfshock_,nperiods_,maxiter_,init_)
-
-global M_ oo_
-
-errlist_ = [];
-
-% solve the reference model linearly
-eval(['dynare ',modnam_,' noclearall nolog '])
-oobase_ = oo_;
-Mbase_ = M_;
-
-% import locally the values of parameters assigned in the reference .mod
-% file
-for i_indx_ = 1:Mbase_.param_nbr
- eval([Mbase_.param_names(i_indx_,:),'= M_.params(i_indx_);']);
-end
-
-% Create steady state values of the variables if needed for processing the constraint
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oobase_.dr.ys(i); ']);
-end
-
-
-% parse the .mod file for the alternative regime
-eval(['dynare ',modnamstar_,' noclearall nolog '])
-oostar_ = oo_;
-Mstar_ = M_;
-
-
-% check inputs
-if ~strcmp(Mbase_.endo_names,Mstar_.endo_names)
- error('The two .mod files need to have exactly the same endogenous variables declared in the same order')
-end
-
-if ~strcmp(Mbase_.exo_names,Mstar_.exo_names)
- error('The two .mod files need to have exactly the same exogenous variables declared in the same order')
-end
-
-if ~strcmp(Mbase_.param_names,Mstar_.param_names)
- warning('The parameter list does not match across .mod files')
-end
-
-% ensure that the two models have the same parameters
-% use the parameters for the base model.
-Mstar_.params = Mbase_.params;
-
-nvars_ = Mbase_.endo_nbr;
-zdatass_ = oobase_.dr.ys;
-
-
-% get the matrices holding the first derivatives for the model
-% each regime is treated separately
-[hm1_,h_,hl1_,Jbarmat_] = get_deriv(Mbase_,zdatass_);
-cof_ = [hm1_,h_,hl1_];
-
-[hm1_,h_,hl1_,Jstarbarmat_,resid_] = get_deriv(Mstar_,zdatass_);
-cofstar_ = [hm1_,h_,hl1_];
-Dstartbarmat_ = resid_;
-
-[decrulea_,decruleb_]=get_pq(oobase_.dr);
-endog_ = M_.endo_names;
-exog_ = M_.exo_names;
-
-
-% processes the constraints specified in the call to this function
-% uppend a suffix to each endogenous variable
-constraint_difference_ = process_constraint(constraint_,'_difference',Mbase_.endo_names,0);
-
-constraint_relax_difference_ = process_constraint(constraint_relax_,'_difference',Mbase_.endo_names,0);
-
-
-
-nshocks_ = size(shockssequence_,1);
-
-% if necessary, set default values for optional arguments
-if ~exist('init_')
- init_ = zeros(nvars_,1);
-end
-
-if ~exist('maxiter_')
- maxiter_ = 20;
-end
-
-if ~exist('nperiods_')
- nperiods_ = 100;
-end
-
-
-% set some initial conditions and loop through the shocks
-% period by period
-init_orig_ = init_;
-zdatapiecewise_ = zeros(nperiods_,nvars_);
-wishlist_ = endog_;
-nwishes_ = size(wishlist_,1);
-violvecbool_ = zeros(nperiods_+1,1);
-
-
-for ishock_ = 1:nshocks_
-
- changes_=1;
- iter_ = 0;
-
-
- while (changes_ & iter_<maxiter_)
- iter_ = iter_ +1;
-
- % analyze when each regime starts based on current guess
- [regime regimestart]=map_regime(violvecbool_);
-
-
- % get the hypothesized piece wise linear solution
- [zdatalinear_]=mkdatap_anticipated(nperiods_,decrulea_,decruleb_,...
- cof_,Jbarmat_,cofstar_,Jstarbarmat_,Dstartbarmat_,...
- regime,regimestart,violvecbool_,...
- endog_,exog_,irfshock_,shockssequence_(ishock_,:),init_);
-
- for i_indx_=1:nwishes_
- eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
- end
-
-
-
- newviolvecbool_ = eval(constraint_difference_);
- relaxconstraint_ = eval(constraint_relax_difference_);
-
-
-
- % check if changes to the hypothesis of the duration for each
- % regime
- if (max(newviolvecbool_-violvecbool_>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
- changes_ = 1;
- else
- changes_ = 0;
- end
-
-
- violvecbool_ = (violvecbool_|newviolvecbool_)-(relaxconstraint_ & violvecbool_);
-
-
- end
-
- init_ = zdatalinear_(1,:);
- zdatapiecewise_(ishock_,:)=init_;
- init_= init_';
-
- % reset violvecbool_ for next period's shock -- this resetting is
- % consistent with expecting no additional shocks
- violvecbool_=[violvecbool_(2:end);0];
-
-end
-
-% if necessary, fill in the rest of the path with the remainder of the
-% last IRF computed.
-zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
-
-% get the linear responses
-zdatalinear_ = mkdata(max(nperiods_,size(shockssequence_,1)),...
- decrulea_,decruleb_,endog_,exog_,...
- wishlist_,irfshock_,shockssequence_,init_orig_);
-
-if changes_ ==1
- display('Did not converge -- increase maxiter_')
-end
+% solve_one_constraint [zdatalinear zdatapiecewise zdatass oo base M base] = solve one constraint(modnam, modnamstar, constraint, constraint relax, shockssequence, irfshock, nperiods, maxiter, init);
+%
+% Inputs:
+% modnam: name of .mod file for the reference regime (excludes the .mod extension).
+% modnamstar: name of .mod file for the alternative regime (excludes the .mod exten- sion).
+% constraint: the constraint (see notes 1 and 2 below). When the condition in constraint evaluates to true, the solution switches from the reference to the alternative regime.
+% constraint relax: when the condition in constraint relax evaluates to true, the solution returns to the reference regime.
+% shockssequence: a sequence of unforeseen shocks under which one wants to solve the model (size T�nshocks).
+% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?).
+% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
+% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
+% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is steady state). The ordering follows the definition order in the .mod files.
+%
+% Outputs:
+% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatass: theinitialpositionforthevectorofstatevariables,indeviationfromsteady state (if not specified, the default is a vectors of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
+% oobase,Mbase: structures produced by Dynare for the reference model ? see Dynare User Guide.
+
+% Log of changes:
+% 6/17/2013 -- Luca added a trailing underscore to local variables in an
+% attempt to avoid conflicts with parameter names defined in the .mod files
+% to be processed.
+% 6/17/2013 -- Luca replaced external .m file setss.m
+
+
+function [zdatalinear_ zdatapiecewise_ zdatass_ oobase_ Mbase_ ] = ...
+ solve_one_constraint.1(modnam_,modnamstar_,...
+ constraint_, constraint_relax_,...
+ shockssequence_,irfshock_,nperiods_,maxiter_,init_)
+
+global M_ oo_
+
+errlist_ = [];
+
+% solve the reference model linearly
+eval(['dynare ',modnam_,' noclearall nolog '])
+oobase_ = oo_;
+Mbase_ = M_;
+
+% import locally the values of parameters assigned in the reference .mod
+% file
+for i_indx_ = 1:Mbase_.param_nbr
+ eval([Mbase_.param_names(i_indx_,:),'= M_.params(i_indx_);']);
+end
+
+% Create steady state values of the variables if needed for processing the constraint
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oobase_.dr.ys(i); ']);
+end
+
+
+% parse the .mod file for the alternative regime
+eval(['dynare ',modnamstar_,' noclearall nolog '])
+oostar_ = oo_;
+Mstar_ = M_;
+
+
+% check inputs
+if ~strcmp(Mbase_.endo_names,Mstar_.endo_names)
+ error('The two .mod files need to have exactly the same endogenous variables declared in the same order')
+end
+
+if ~strcmp(Mbase_.exo_names,Mstar_.exo_names)
+ error('The two .mod files need to have exactly the same exogenous variables declared in the same order')
+end
+
+if ~strcmp(Mbase_.param_names,Mstar_.param_names)
+ warning('The parameter list does not match across .mod files')
+end
+
+% ensure that the two models have the same parameters
+% use the parameters for the base model.
+Mstar_.params = Mbase_.params;
+
+nvars_ = Mbase_.endo_nbr;
+zdatass_ = oobase_.dr.ys;
+
+
+% get the matrices holding the first derivatives for the model
+% each regime is treated separately
+[hm1_,h_,hl1_,Jbarmat_] = get_deriv(Mbase_,zdatass_);
+cof_ = [hm1_,h_,hl1_];
+
+[hm1_,h_,hl1_,Jstarbarmat_,resid_] = get_deriv(Mstar_,zdatass_);
+cofstar_ = [hm1_,h_,hl1_];
+Dstartbarmat_ = resid_;
+
+[decrulea_,decruleb_]=get_pq(oobase_.dr);
+endog_ = M_.endo_names;
+exog_ = M_.exo_names;
+
+
+% processes the constraints specified in the call to this function
+% uppend a suffix to each endogenous variable
+constraint_difference_ = process_constraint(constraint_,'_difference',Mbase_.endo_names,0);
+
+constraint_relax_difference_ = process_constraint(constraint_relax_,'_difference',Mbase_.endo_names,0);
+
+
+
+nshocks_ = size(shockssequence_,1);
+
+% if necessary, set default values for optional arguments
+if ~exist('init_')
+ init_ = zeros(nvars_,1);
+end
+
+if ~exist('maxiter_')
+ maxiter_ = 20;
+end
+
+if ~exist('nperiods_')
+ nperiods_ = 100;
+end
+
+
+% set some initial conditions and loop through the shocks
+% period by period
+init_orig_ = init_;
+zdatapiecewise_ = zeros(nperiods_,nvars_);
+wishlist_ = endog_;
+nwishes_ = size(wishlist_,1);
+violvecbool_ = zeros(nperiods_+1,1);
+
+
+for ishock_ = 1:nshocks_
+
+ changes_=1;
+ iter_ = 0;
+
+
+ while (changes_ & iter_<maxiter_)
+ iter_ = iter_ +1;
+
+ % analyze when each regime starts based on current guess
+ [regime regimestart]=map_regime(violvecbool_);
+
+
+ % get the hypothesized piece wise linear solution
+ [zdatalinear_]=mkdatap_anticipated(nperiods_,decrulea_,decruleb_,...
+ cof_,Jbarmat_,cofstar_,Jstarbarmat_,Dstartbarmat_,...
+ regime,regimestart,violvecbool_,...
+ endog_,exog_,irfshock_,shockssequence_(ishock_,:),init_);
+
+ for i_indx_=1:nwishes_
+ eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
+ end
+
+
+
+ newviolvecbool_ = eval(constraint_difference_);
+ relaxconstraint_ = eval(constraint_relax_difference_);
+
+
+
+ % check if changes to the hypothesis of the duration for each
+ % regime
+ if (max(newviolvecbool_-violvecbool_>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
+ changes_ = 1;
+ else
+ changes_ = 0;
+ end
+
+
+ violvecbool_ = (violvecbool_|newviolvecbool_)-(relaxconstraint_ & violvecbool_);
+
+
+ end
+
+ init_ = zdatalinear_(1,:);
+ zdatapiecewise_(ishock_,:)=init_;
+ init_= init_';
+
+ % reset violvecbool_ for next period's shock -- this resetting is
+ % consistent with expecting no additional shocks
+ violvecbool_=[violvecbool_(2:end);0];
+
+end
+
+% if necessary, fill in the rest of the path with the remainder of the
+% last IRF computed.
+zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
+
+% get the linear responses
+zdatalinear_ = mkdata(max(nperiods_,size(shockssequence_,1)),...
+ decrulea_,decruleb_,endog_,exog_,...
+ wishlist_,irfshock_,shockssequence_,init_orig_);
+
+if changes_ ==1
+ display('Did not converge -- increase maxiter_')
+end
diff --git a/matlab/occbin/solve_one_constraint.m b/matlab/occbin/solve_one_constraint.m
index c446f4c40dc164fadbf362d70cc79ceed4260a04..ee59e9a3d7f1b6fdd5c363a4d495f137c63a13d7 100755
--- a/matlab/occbin/solve_one_constraint.m
+++ b/matlab/occbin/solve_one_constraint.m
@@ -1,200 +1,200 @@
-% solve_one_constraint [zdatalinear zdatapiecewise zdatass oo base M base] = solve one constraint(modnam, modnamstar, constraint, constraint relax, shockssequence, irfshock, nperiods, maxiter, init);
-%
-% Inputs:
-% modnam: name of .mod file for the reference regime (excludes the .mod extension).
-% modnamstar: name of .mod file for the alternative regime (excludes the .mod exten- sion).
-% constraint: the constraint (see notes 1 and 2 below). When the condition in constraint evaluates to true, the solution switches from the reference to the alternative regime.
-% constraint relax: when the condition in constraint relax evaluates to true, the solution returns to the reference regime.
-% shockssequence: a sequence of unforeseen shocks under which one wants to solve the model (size T�nshocks).
-% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?).
-% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
-% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
-% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is steady state). The ordering follows the definition order in the .mod files.
-%
-% Outputs:
-% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatass: theinitialpositionforthevectorofstatevariables,indeviationfromsteady state (if not specified, the default is a vectors of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
-% oobase,Mbase: structures produced by Dynare for the reference model ? see Dynare User Guide.
-
-% Log of changes:
-% 6/17/2013 -- Luca added a trailing underscore to local variables in an
-% attempt to avoid conflicts with parameter names defined in the .mod files
-% to be processed.
-% 6/17/2013 -- Luca replaced external .m file setss.m
-
-
-function [zdatalinear_ zdatapiecewise_ zdatass_ oobase_ Mbase_ ] = ...
- solve_one_constraint(modnam_,modnamstar_,...
- constraint_, constraint_relax_,...
- shockssequence_,irfshock_,nperiods_,maxiter_,init_)
-
-global M_ oo_
-
-errlist_ = [];
-
-% solve the reference model linearly
-eval(['dynare ',modnam_,' noclearall nolog '])
-oobase_ = oo_;
-Mbase_ = M_;
-
-% import locally the values of parameters assigned in the reference .mod
-% file
-for i_indx_ = 1:Mbase_.param_nbr
- eval([Mbase_.param_names(i_indx_,:),'= M_.params(i_indx_);']);
-end
-
-% Create steady state values of the variables if needed for processing the constraint
-for i=1:Mbase_.endo_nbr
- eval([deblank(Mbase_.endo_names(i,:)) '_ss = oobase_.dr.ys(i); ']);
-end
-
-
-% parse the .mod file for the alternative regime
-eval(['dynare ',modnamstar_,' noclearall nolog '])
-oostar_ = oo_;
-Mstar_ = M_;
-
-
-% check inputs
-if ~strcmp(Mbase_.endo_names,Mstar_.endo_names)
- error('The two .mod files need to have exactly the same endogenous variables declared in the same order')
-end
-
-if ~strcmp(Mbase_.exo_names,Mstar_.exo_names)
- error('The two .mod files need to have exactly the same exogenous variables declared in the same order')
-end
-
-if ~strcmp(Mbase_.param_names,Mstar_.param_names)
- warning('The parameter list does not match across .mod files')
-end
-
-% ensure that the two models have the same parameters
-% use the parameters for the base model.
-Mstar_.params = Mbase_.params;
-
-nvars_ = Mbase_.endo_nbr;
-zdatass_ = oobase_.dr.ys;
-
-
-% get the matrices holding the first derivatives for the model
-% each regime is treated separately
-[hm1_,h_,hl1_,Jbarmat_] = get_deriv(Mbase_,zdatass_);
-cof_ = [hm1_,h_,hl1_];
-
-[hm1_,h_,hl1_,Jstarbarmat_,resid_] = get_deriv(Mstar_,zdatass_);
-cofstar_ = [hm1_,h_,hl1_];
-Dstartbarmat_ = resid_;
-
-if isfield(Mbase_,'nfwrd')
- % the latest Dynare distributions have moved nstatic and nfwrd
- [decrulea_,decruleb_]=get_pq(oobase_.dr,Mbase_.nstatic,Mbase_.nfwrd);
-else
- [decrulea_,decruleb_]=get_pq(oobase_.dr,oobase_.dr.nstatic,oobase_.dr.nfwrd);
-end
-
-endog_ = M_.endo_names;
-exog_ = M_.exo_names;
-
-
-% processes the constraints specified in the call to this function
-% uppend a suffix to each endogenous variable
-constraint_difference_ = process_constraint(constraint_,'_difference',Mbase_.endo_names,0);
-
-constraint_relax_difference_ = process_constraint(constraint_relax_,'_difference',Mbase_.endo_names,0);
-
-
-
-nshocks_ = size(shockssequence_,1);
-
-% if necessary, set default values for optional arguments
-if ~exist('init_')
- init_ = zeros(nvars_,1);
-end
-
-if ~exist('maxiter_')
- maxiter_ = 20;
-end
-
-if ~exist('nperiods_')
- nperiods_ = 100;
-end
-
-
-% set some initial conditions and loop through the shocks
-% period by period
-init_orig_ = init_;
-zdatapiecewise_ = zeros(nperiods_,nvars_);
-wishlist_ = endog_;
-nwishes_ = size(wishlist_,1);
-violvecbool_ = zeros(nperiods_+1,1);
-
-
-for ishock_ = 1:nshocks_
-
- changes_=1;
- iter_ = 0;
-
-
- while (changes_ & iter_<maxiter_)
- iter_ = iter_ +1;
-
- % analyze when each regime starts based on current guess
- [regime regimestart]=map_regime(violvecbool_);
-
-
-
- % get the hypothesized piece wise linear solution
- [zdatalinear_]=mkdatap_anticipated(nperiods_,decrulea_,decruleb_,...
- cof_,Jbarmat_,cofstar_,Jstarbarmat_,Dstartbarmat_,...
- regime,regimestart,violvecbool_,...
- endog_,exog_,irfshock_,shockssequence_(ishock_,:),init_);
-
- for i_indx_=1:nwishes_
- eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
- end
-
-
-
- newviolvecbool_ = eval(constraint_difference_);
- relaxconstraint_ = eval(constraint_relax_difference_);
-
-
-
- % check if changes to the hypothesis of the duration for each
- % regime
- if (max(newviolvecbool_-violvecbool_>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
- changes_ = 1;
- else
- changes_ = 0;
- end
-
-
-
- violvecbool_ = (violvecbool_|newviolvecbool_)-(relaxconstraint_ & violvecbool_);
-
-
- end
-
- init_ = zdatalinear_(1,:);
- zdatapiecewise_(ishock_,:)=init_;
- init_= init_';
-
- % reset violvecbool_ for next period's shock -- this resetting is
- % consistent with expecting no additional shocks
- violvecbool_=[violvecbool_(2:end);0];
-
-end
-
-% if necessary, fill in the rest of the path with the remainder of the
-% last IRF computed.
-zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
-
-% get the linear responses
-zdatalinear_ = mkdata(max(nperiods_,size(shockssequence_,1)),...
- decrulea_,decruleb_,endog_,exog_,...
- wishlist_,irfshock_,shockssequence_,init_orig_);
-
-if changes_ ==1
- display('Did not converge -- increase maxiter_')
-end
+% solve_one_constraint [zdatalinear zdatapiecewise zdatass oo base M base] = solve one constraint(modnam, modnamstar, constraint, constraint relax, shockssequence, irfshock, nperiods, maxiter, init);
+%
+% Inputs:
+% modnam: name of .mod file for the reference regime (excludes the .mod extension).
+% modnamstar: name of .mod file for the alternative regime (excludes the .mod exten- sion).
+% constraint: the constraint (see notes 1 and 2 below). When the condition in constraint evaluates to true, the solution switches from the reference to the alternative regime.
+% constraint relax: when the condition in constraint relax evaluates to true, the solution returns to the reference regime.
+% shockssequence: a sequence of unforeseen shocks under which one wants to solve the model (size T�nshocks).
+% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?).
+% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
+% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
+% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is steady state). The ordering follows the definition order in the .mod files.
+%
+% Outputs:
+% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatass: theinitialpositionforthevectorofstatevariables,indeviationfromsteady state (if not specified, the default is a vectors of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
+% oobase,Mbase: structures produced by Dynare for the reference model ? see Dynare User Guide.
+
+% Log of changes:
+% 6/17/2013 -- Luca added a trailing underscore to local variables in an
+% attempt to avoid conflicts with parameter names defined in the .mod files
+% to be processed.
+% 6/17/2013 -- Luca replaced external .m file setss.m
+
+
+function [zdatalinear_ zdatapiecewise_ zdatass_ oobase_ Mbase_ ] = ...
+ solve_one_constraint(modnam_,modnamstar_,...
+ constraint_, constraint_relax_,...
+ shockssequence_,irfshock_,nperiods_,maxiter_,init_)
+
+global M_ oo_
+
+errlist_ = [];
+
+% solve the reference model linearly
+eval(['dynare ',modnam_,' noclearall nolog '])
+oobase_ = oo_;
+Mbase_ = M_;
+
+% import locally the values of parameters assigned in the reference .mod
+% file
+for i_indx_ = 1:Mbase_.param_nbr
+ eval([Mbase_.param_names(i_indx_,:),'= M_.params(i_indx_);']);
+end
+
+% Create steady state values of the variables if needed for processing the constraint
+for i=1:Mbase_.endo_nbr
+ eval([deblank(Mbase_.endo_names(i,:)) '_ss = oobase_.dr.ys(i); ']);
+end
+
+
+% parse the .mod file for the alternative regime
+eval(['dynare ',modnamstar_,' noclearall nolog '])
+oostar_ = oo_;
+Mstar_ = M_;
+
+
+% check inputs
+if ~strcmp(Mbase_.endo_names,Mstar_.endo_names)
+ error('The two .mod files need to have exactly the same endogenous variables declared in the same order')
+end
+
+if ~strcmp(Mbase_.exo_names,Mstar_.exo_names)
+ error('The two .mod files need to have exactly the same exogenous variables declared in the same order')
+end
+
+if ~strcmp(Mbase_.param_names,Mstar_.param_names)
+ warning('The parameter list does not match across .mod files')
+end
+
+% ensure that the two models have the same parameters
+% use the parameters for the base model.
+Mstar_.params = Mbase_.params;
+
+nvars_ = Mbase_.endo_nbr;
+zdatass_ = oobase_.dr.ys;
+
+
+% get the matrices holding the first derivatives for the model
+% each regime is treated separately
+[hm1_,h_,hl1_,Jbarmat_] = get_deriv(Mbase_,zdatass_);
+cof_ = [hm1_,h_,hl1_];
+
+[hm1_,h_,hl1_,Jstarbarmat_,resid_] = get_deriv(Mstar_,zdatass_);
+cofstar_ = [hm1_,h_,hl1_];
+Dstartbarmat_ = resid_;
+
+if isfield(Mbase_,'nfwrd')
+ % the latest Dynare distributions have moved nstatic and nfwrd
+ [decrulea_,decruleb_]=get_pq(oobase_.dr,Mbase_.nstatic,Mbase_.nfwrd);
+else
+ [decrulea_,decruleb_]=get_pq(oobase_.dr,oobase_.dr.nstatic,oobase_.dr.nfwrd);
+end
+
+endog_ = M_.endo_names;
+exog_ = M_.exo_names;
+
+
+% processes the constraints specified in the call to this function
+% uppend a suffix to each endogenous variable
+constraint_difference_ = process_constraint(constraint_,'_difference',Mbase_.endo_names,0);
+
+constraint_relax_difference_ = process_constraint(constraint_relax_,'_difference',Mbase_.endo_names,0);
+
+
+
+nshocks_ = size(shockssequence_,1);
+
+% if necessary, set default values for optional arguments
+if ~exist('init_')
+ init_ = zeros(nvars_,1);
+end
+
+if ~exist('maxiter_')
+ maxiter_ = 20;
+end
+
+if ~exist('nperiods_')
+ nperiods_ = 100;
+end
+
+
+% set some initial conditions and loop through the shocks
+% period by period
+init_orig_ = init_;
+zdatapiecewise_ = zeros(nperiods_,nvars_);
+wishlist_ = endog_;
+nwishes_ = size(wishlist_,1);
+violvecbool_ = zeros(nperiods_+1,1);
+
+
+for ishock_ = 1:nshocks_
+
+ changes_=1;
+ iter_ = 0;
+
+
+ while (changes_ & iter_<maxiter_)
+ iter_ = iter_ +1;
+
+ % analyze when each regime starts based on current guess
+ [regime regimestart]=map_regime(violvecbool_);
+
+
+
+ % get the hypothesized piece wise linear solution
+ [zdatalinear_]=mkdatap_anticipated(nperiods_,decrulea_,decruleb_,...
+ cof_,Jbarmat_,cofstar_,Jstarbarmat_,Dstartbarmat_,...
+ regime,regimestart,violvecbool_,...
+ endog_,exog_,irfshock_,shockssequence_(ishock_,:),init_);
+
+ for i_indx_=1:nwishes_
+ eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
+ end
+
+
+
+ newviolvecbool_ = eval(constraint_difference_);
+ relaxconstraint_ = eval(constraint_relax_difference_);
+
+
+
+ % check if changes to the hypothesis of the duration for each
+ % regime
+ if (max(newviolvecbool_-violvecbool_>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
+ changes_ = 1;
+ else
+ changes_ = 0;
+ end
+
+
+
+ violvecbool_ = (violvecbool_|newviolvecbool_)-(relaxconstraint_ & violvecbool_);
+
+
+ end
+
+ init_ = zdatalinear_(1,:);
+ zdatapiecewise_(ishock_,:)=init_;
+ init_= init_';
+
+ % reset violvecbool_ for next period's shock -- this resetting is
+ % consistent with expecting no additional shocks
+ violvecbool_=[violvecbool_(2:end);0];
+
+end
+
+% if necessary, fill in the rest of the path with the remainder of the
+% last IRF computed.
+zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
+
+% get the linear responses
+zdatalinear_ = mkdata(max(nperiods_,size(shockssequence_,1)),...
+ decrulea_,decruleb_,endog_,exog_,...
+ wishlist_,irfshock_,shockssequence_,init_orig_);
+
+if changes_ ==1
+ display('Did not converge -- increase maxiter_')
+end
diff --git a/matlab/occbin/solve_two_constraints.m b/matlab/occbin/solve_two_constraints.m
index dfca403a2233aade5bb8a9ecf44f093f9d65c3e0..0ca5d559a0aa4763e7531a7ca9f3acbc1c4d9b4d 100755
--- a/matlab/occbin/solve_two_constraints.m
+++ b/matlab/occbin/solve_two_constraints.m
@@ -1,305 +1,305 @@
-% [zdatalinear zdatapiecewise zdatass oo 00 M 00] = solve two constraints(modnam 00,modnam 10,modnam 01,modnam 11,... constraint1, constraint2,... constraint relax1, constraint relax2,... shockssequence,irfshock,nperiods,curb retrench,maxiter,init);
-%
-% Inputs:
-% modnam 00: name of the .mod file for reference regime (excludes the .mod extension). modnam10: name of the .mod file for the alternative regime governed by the first
-% constraint.
-% modnam01: name of the .mod file for the alternative regime governed by the second constraint.
-% modnam 11: name of the .mod file for the case in which both constraints force a switch to their alternative regimes.
-% constraint1: the first constraint (see notes 1 and 2 below). If constraint1 evaluates to true, then the solution switches to the alternative regime for condition 1. In thatcase, if constraint2 (described below) evaluates to false, then the model solution switches to enforcing the conditions for an equilibrium in modnam 10. Otherwise, if constraint2 also evaluates to true, then the model solution switches to enforcing the conditions for an equilibrium in modnam 11.
-% constraint relax1: when the condition in constraint relax1 evaluates to true, the solution returns to the reference regime for constraint1.
-% constraint2: the second constraint (see notes 1 and 2 below). constraint relax2: when the condition in constraint relax2 evaluates to true, the
-% solution returns to the reference regime for constraint2. shockssequence: a sequence of unforeseen shocks under which one wants to solve the
-% model
-% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?)
-% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
-% curb retrench: a scalar equal to 0 or 1. Default is 0. When set to 0, it updates the guess based of regimes based on the previous iteration. When set to 1, it updates in a manner similar to a Gauss-Jacobi scheme, slowing the iterations down by updating the guess of regimes only one period at a time.
-% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
-% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is a vector of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
-%
-% Outputs:
-% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
-% zdatass: a vector that holds the steady state values of the endogenous variables ( following the definition order in the .mod file).
-% oo00 , M00 : structures produced by Dynare for the reference model ? see Dynare User Guide.
-
-
-% Log of changes
-% 6/17/2013 -- Luca added a trailing underscore to local variables in an
-% attempt to avoid conflicts with parameter names defined in the .mod files
-% to be processed.
-% 6/17/2013 -- Luca replaced external .m file setss.m
-
-function [ zdatalinear_ zdatapiecewise_ zdatass_ oo00_ M00_ ] = ...
- solve_two_constraints(modnam_00_,modnam_10_,modnam_01_,modnam_11_,...
- constrain1_, constrain2_,...
- constraint_relax1_, constraint_relax2_,...
- shockssequence_,irfshock_,nperiods_,curb_retrench_,maxiter_,init_)
-
-global M_ oo_
-
-
-
-% solve model
-eval(['dynare ',modnam_00_,' noclearall nolog'])
-oo00_ = oo_;
-M00_ = M_;
-
-
-for i=1:M00_.endo_nbr
- eval([deblank(M00_.endo_names(i,:)) '_ss = oo00_.dr.ys(i); ']);
-end
-
-for i_indx_ = 1:M00_.param_nbr
- eval([M00_.param_names(i_indx_,:),'= M00_.params(i_indx_);']);
-end
-
-
-
-eval(['dynare ',modnam_10_,' noclearall'])
-oo10_ = oo_;
-M10_ = M_;
-
-eval(['dynare ',modnam_01_,' noclearall'])
-oo01_ = oo_;
-M01_ = M_;
-
-eval(['dynare ',modnam_11_,' noclearall'])
-oo11_ = oo_;
-M11_ = M_;
-
-
-% do some error checking
-
-% check inputs
-if ~strcmp(M00_.endo_names,M10_.endo_names)
- error([modnam_00_,' and ',modnam_10_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.exo_names,M10_.exo_names)
- error([modnam_00_,' and ',modnam_10_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.param_names,M10_.param_names)
- warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_10_])
-end
-
-
-if ~strcmp(M00_.endo_names,M01_.endo_names)
- error([modnam_00,' and ',modnam_01_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.exo_names,M01_.exo_names)
- error([modnam_00_,' and ',modnam_01_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.param_names,M01_.param_names)
- warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_01_])
-end
-
-
-if ~strcmp(M00_.endo_names,M11_.endo_names)
- error([modnam_00_,' and ',modnam_11_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.exo_names,M11_.exo_names)
- error([modnam_00_,' and ',modnam_11_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
-end
-
-if ~strcmp(M00_.param_names,M11_.param_names)
- warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_11_])
-end
-
-
-
-
-
-nvars_ = M00_.endo_nbr;
-zdatass_ = oo00_.dr.ys;
-
-
-[hm1_,h_,hl1_,Jbarmat_] = get_deriv(M00_,zdatass_);
-cof_ = [hm1_,h_,hl1_];
-
-
-M10_.params = M00_.params;
-[hm1_,h_,hl1_,Jbarmat10_,resid_] = get_deriv(M10_,zdatass_);
-cof10_ = [hm1_,h_,hl1_];
-Dbarmat10_ = resid_;
-
-M01_.params = M00_.params;
-[hm1_,h_,hl1_,Jbarmat01_,resid_] = get_deriv(M01_,zdatass_);
-cof01_ = [hm1_,h_,hl1_];
-Dbarmat01_ = resid_;
-
-M11_.params = M00_.params;
-[hm1_,h_,hl1_,Jbarmat11_,resid_] = get_deriv(M11_,zdatass_);
-cof11_ = [hm1_,h_,hl1_];
-Dbarmat11_ = resid_;
-
-
-if isfield(M00_,'nfwrd') % needed for bakward compatibility with older Dynare releases
-[decrulea,decruleb]=get_pq(oo00_.dr,M00_.nstatic,M00_.nfwrd);
-else
-[decrulea,decruleb]=get_pq(oo00_.dr,oo00_.dr.nstatic,oo00_.dr.nfwrd);
-end
-endog_ = M00_.endo_names;
-exog_ = M00_.exo_names;
-
-
-% processes the constrain so as to uppend a suffix to each
-% endogenous variables
-constraint1_difference_ = process_constraint(constrain1_,'_difference',M00_.endo_names,0);
-
-% when the last argument in process_constraint is set to 1, the
-% direction of the inequality in the constraint is inverted
-constraint_relax1_difference_ = process_constraint(constraint_relax1_,'_difference',M00_.endo_names,0);
-
-
-% processes the constrain so as to uppend a suffix to each
-% endogenous variables
-constraint2_difference_ = process_constraint(constrain2_,'_difference',M00_.endo_names,0);
-
-% when the last argument in process_constraint is set to 1, the
-% direction of the inequality in the constraint is inverted
-constraint_relax2_difference_ = process_constraint(constraint_relax2_,'_difference',M00_.endo_names,0);
-
-
-
-nshocks = size(shockssequence_,1);
-
-
-
-
-if ~exist('init_')
- init_ = zeros(nvars_,1);
-end
-
-if ~exist('maxiter_')
- maxiter_ = 20;
-end
-
-if ~exist('curb_retrench_')
- curb_retrench_ = 0;
-end
-
-init_orig_ = init_;
-
-
-
-
-
-
-zdatapiecewise_ = zeros(nperiods_,nvars_);
-
-
-violvecbool_ = zeros(nperiods_+1,2); % This sets the first guess for when
-% the constraints are going to hold.
-% The variable is a boolean with two
-% columns. The first column refers to
-% constrain1_; the second to
-% constrain2_.
-% Each row is a period in time.
-% If the boolean is true it indicates
-% the relevant constraint is expected
-% to evaluate to true.
-% The default initial guess is
-% consistent with the base model always
-% holding -- equivalent to the linear
-% solution.
-
-wishlist_ = endog_;
-nwishes_ = size(wishlist_,1);
-for ishock_ = 1:nshocks
-
-
- changes_=1;
- iter_ = 0;
-
- while (changes_ & iter_<maxiter_)
- iter_ = iter_ +1;
-
- % analyse violvec and isolate contiguous periods in the other
- % regime.
- [regime1 regimestart1]=map_regime(violvecbool_(:,1));
- [regime2 regimestart2]=map_regime(violvecbool_(:,2));
-
-
- [zdatalinear_]=mkdatap_anticipated_2constraints(nperiods_,decrulea,decruleb,...
- cof_,Jbarmat_,...
- cof10_,Jbarmat10_,Dbarmat10_,...
- cof01_,Jbarmat01_,Dbarmat01_,...
- cof11_,Jbarmat11_,Dbarmat11_,...
- regime1,regimestart1,...
- regime2,regimestart2,...
- violvecbool_,endog_,exog_,...
- irfshock_,shockssequence_(ishock_,:),init_);
-
- for i_indx_=1:nwishes_
- eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
- end
-
-
-
-
- newviolvecbool1_ = eval(constraint1_difference_);
- relaxconstraint1_ = eval(constraint_relax1_difference_);
-
- newviolvecbool2_ = eval(constraint2_difference_);
- relaxconstraint2_ = eval(constraint_relax2_difference_);
-
-
-
- newviolvecbool_ = [newviolvecbool1_;newviolvecbool2_];
- relaxconstraint_ = [relaxconstraint1_;relaxconstraint2_];
-
-
-
- % check if changes_
- if (max(newviolvecbool_(:)-violvecbool_(:)>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
- changes_ = 1;
- else
- changes_ = 0;
- end
-
- if curb_retrench_ % apply Gauss-Sidel idea of slowing down the change in the guess
- % for the constraint -- only relax one
- % period at a time starting from the last
- % one when each of the constraints is true.
- retrench = 0*violvecbool_(:);
- if ~isempty(find(relaxconstraint1_ & violvecbool_(:,1)))
- retrenchpos = max(find(relaxconstraint1_ & violvecbool_(:,1)));
- retrench(retrenchpos) = 1;
- end
- if ~isempty(find(relaxconstraint2_ & violvecbool_(:,2)))
- retrenchpos = max(find(relaxconstraint2_ & violvecbool_(:,2)));
- retrench(retrenchpos+nperiods_+1) = 1;
- end
- violvecbool_ = (violvecbool_(:) | newviolvecbool_(:))-retrench(:);
- else
- violvecbool_ = (violvecbool_(:) | newviolvecbool_(:))-(relaxconstraint_(:) & violvecbool_(:));
- end
-
- violvecbool_ = reshape(violvecbool_,nperiods_+1,2);
-
-
-
- end
- if changes_ ==1
- display('Did not converge -- increase maxiter')
- end
-
- init_ = zdatalinear_(1,:);
- zdatapiecewise_(ishock_,:)=init_;
- init_= init_';
-
- % update the guess for constraint violations for next period
- % update is consistent with expecting no additional shocks next period
- violvecbool_=[violvecbool_(2:end,:);zeros(1,2)];
-
-end
-
-
-zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
-
-zdatalinear_ = mkdata(nperiods_,decrulea,decruleb,endog_,exog_,wishlist_,irfshock_,shockssequence_,init_orig_);
-
+% [zdatalinear zdatapiecewise zdatass oo 00 M 00] = solve two constraints(modnam 00,modnam 10,modnam 01,modnam 11,... constraint1, constraint2,... constraint relax1, constraint relax2,... shockssequence,irfshock,nperiods,curb retrench,maxiter,init);
+%
+% Inputs:
+% modnam 00: name of the .mod file for reference regime (excludes the .mod extension). modnam10: name of the .mod file for the alternative regime governed by the first
+% constraint.
+% modnam01: name of the .mod file for the alternative regime governed by the second constraint.
+% modnam 11: name of the .mod file for the case in which both constraints force a switch to their alternative regimes.
+% constraint1: the first constraint (see notes 1 and 2 below). If constraint1 evaluates to true, then the solution switches to the alternative regime for condition 1. In thatcase, if constraint2 (described below) evaluates to false, then the model solution switches to enforcing the conditions for an equilibrium in modnam 10. Otherwise, if constraint2 also evaluates to true, then the model solution switches to enforcing the conditions for an equilibrium in modnam 11.
+% constraint relax1: when the condition in constraint relax1 evaluates to true, the solution returns to the reference regime for constraint1.
+% constraint2: the second constraint (see notes 1 and 2 below). constraint relax2: when the condition in constraint relax2 evaluates to true, the
+% solution returns to the reference regime for constraint2. shockssequence: a sequence of unforeseen shocks under which one wants to solve the
+% model
+% irfshock: label for innovation for IRFs, from Dynare .mod file (one or more of the ?varexo?)
+% nperiods: simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
+% curb retrench: a scalar equal to 0 or 1. Default is 0. When set to 0, it updates the guess based of regimes based on the previous iteration. When set to 1, it updates in a manner similar to a Gauss-Jacobi scheme, slowing the iterations down by updating the guess of regimes only one period at a time.
+% maxiter: maximum number of iterations allowed for the solution algorithm (20 if not specified).
+% init: the initial position for the vector of state variables, in deviation from steady state (if not specified, the default is a vector of zero implying that the initial conditions coincide with the steady state). The ordering follows the definition order in the .mod files.
+%
+% Outputs:
+% zdatalinear: an array containing paths for all endogenous variables ignoring the occasionally binding constraint (the linear solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatapiecewise: an array containing paths for all endogenous variables satisfying the occasionally binding constraint (the occbin/piecewise solution), in deviation from steady state. Each column is a variable, the order is the definition order in the .mod files.
+% zdatass: a vector that holds the steady state values of the endogenous variables ( following the definition order in the .mod file).
+% oo00 , M00 : structures produced by Dynare for the reference model ? see Dynare User Guide.
+
+
+% Log of changes
+% 6/17/2013 -- Luca added a trailing underscore to local variables in an
+% attempt to avoid conflicts with parameter names defined in the .mod files
+% to be processed.
+% 6/17/2013 -- Luca replaced external .m file setss.m
+
+function [ zdatalinear_ zdatapiecewise_ zdatass_ oo00_ M00_ ] = ...
+ solve_two_constraints(modnam_00_,modnam_10_,modnam_01_,modnam_11_,...
+ constrain1_, constrain2_,...
+ constraint_relax1_, constraint_relax2_,...
+ shockssequence_,irfshock_,nperiods_,curb_retrench_,maxiter_,init_)
+
+global M_ oo_
+
+
+
+% solve model
+eval(['dynare ',modnam_00_,' noclearall nolog'])
+oo00_ = oo_;
+M00_ = M_;
+
+
+for i=1:M00_.endo_nbr
+ eval([deblank(M00_.endo_names(i,:)) '_ss = oo00_.dr.ys(i); ']);
+end
+
+for i_indx_ = 1:M00_.param_nbr
+ eval([M00_.param_names(i_indx_,:),'= M00_.params(i_indx_);']);
+end
+
+
+
+eval(['dynare ',modnam_10_,' noclearall'])
+oo10_ = oo_;
+M10_ = M_;
+
+eval(['dynare ',modnam_01_,' noclearall'])
+oo01_ = oo_;
+M01_ = M_;
+
+eval(['dynare ',modnam_11_,' noclearall'])
+oo11_ = oo_;
+M11_ = M_;
+
+
+% do some error checking
+
+% check inputs
+if ~strcmp(M00_.endo_names,M10_.endo_names)
+ error([modnam_00_,' and ',modnam_10_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.exo_names,M10_.exo_names)
+ error([modnam_00_,' and ',modnam_10_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.param_names,M10_.param_names)
+ warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_10_])
+end
+
+
+if ~strcmp(M00_.endo_names,M01_.endo_names)
+ error([modnam_00,' and ',modnam_01_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.exo_names,M01_.exo_names)
+ error([modnam_00_,' and ',modnam_01_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.param_names,M01_.param_names)
+ warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_01_])
+end
+
+
+if ~strcmp(M00_.endo_names,M11_.endo_names)
+ error([modnam_00_,' and ',modnam_11_,' need to have exactly the same endogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.exo_names,M11_.exo_names)
+ error([modnam_00_,' and ',modnam_11_,' need to have exactly the same exogenous variables and they need to be declared in the same order'])
+end
+
+if ~strcmp(M00_.param_names,M11_.param_names)
+ warning(['The parameter list does not match across the files ',modnam_00_,' and ',modnam_11_])
+end
+
+
+
+
+
+nvars_ = M00_.endo_nbr;
+zdatass_ = oo00_.dr.ys;
+
+
+[hm1_,h_,hl1_,Jbarmat_] = get_deriv(M00_,zdatass_);
+cof_ = [hm1_,h_,hl1_];
+
+
+M10_.params = M00_.params;
+[hm1_,h_,hl1_,Jbarmat10_,resid_] = get_deriv(M10_,zdatass_);
+cof10_ = [hm1_,h_,hl1_];
+Dbarmat10_ = resid_;
+
+M01_.params = M00_.params;
+[hm1_,h_,hl1_,Jbarmat01_,resid_] = get_deriv(M01_,zdatass_);
+cof01_ = [hm1_,h_,hl1_];
+Dbarmat01_ = resid_;
+
+M11_.params = M00_.params;
+[hm1_,h_,hl1_,Jbarmat11_,resid_] = get_deriv(M11_,zdatass_);
+cof11_ = [hm1_,h_,hl1_];
+Dbarmat11_ = resid_;
+
+
+if isfield(M00_,'nfwrd') % needed for bakward compatibility with older Dynare releases
+ [decrulea,decruleb]=get_pq(oo00_.dr,M00_.nstatic,M00_.nfwrd);
+else
+ [decrulea,decruleb]=get_pq(oo00_.dr,oo00_.dr.nstatic,oo00_.dr.nfwrd);
+end
+endog_ = M00_.endo_names;
+exog_ = M00_.exo_names;
+
+
+% processes the constrain so as to uppend a suffix to each
+% endogenous variables
+constraint1_difference_ = process_constraint(constrain1_,'_difference',M00_.endo_names,0);
+
+% when the last argument in process_constraint is set to 1, the
+% direction of the inequality in the constraint is inverted
+constraint_relax1_difference_ = process_constraint(constraint_relax1_,'_difference',M00_.endo_names,0);
+
+
+% processes the constrain so as to uppend a suffix to each
+% endogenous variables
+constraint2_difference_ = process_constraint(constrain2_,'_difference',M00_.endo_names,0);
+
+% when the last argument in process_constraint is set to 1, the
+% direction of the inequality in the constraint is inverted
+constraint_relax2_difference_ = process_constraint(constraint_relax2_,'_difference',M00_.endo_names,0);
+
+
+
+nshocks = size(shockssequence_,1);
+
+
+
+
+if ~exist('init_')
+ init_ = zeros(nvars_,1);
+end
+
+if ~exist('maxiter_')
+ maxiter_ = 20;
+end
+
+if ~exist('curb_retrench_')
+ curb_retrench_ = 0;
+end
+
+init_orig_ = init_;
+
+
+
+
+
+
+zdatapiecewise_ = zeros(nperiods_,nvars_);
+
+
+violvecbool_ = zeros(nperiods_+1,2); % This sets the first guess for when
+ % the constraints are going to hold.
+ % The variable is a boolean with two
+ % columns. The first column refers to
+ % constrain1_; the second to
+ % constrain2_.
+ % Each row is a period in time.
+ % If the boolean is true it indicates
+ % the relevant constraint is expected
+ % to evaluate to true.
+ % The default initial guess is
+ % consistent with the base model always
+ % holding -- equivalent to the linear
+ % solution.
+
+wishlist_ = endog_;
+nwishes_ = size(wishlist_,1);
+for ishock_ = 1:nshocks
+
+
+ changes_=1;
+ iter_ = 0;
+
+ while (changes_ & iter_<maxiter_)
+ iter_ = iter_ +1;
+
+ % analyse violvec and isolate contiguous periods in the other
+ % regime.
+ [regime1 regimestart1]=map_regime(violvecbool_(:,1));
+ [regime2 regimestart2]=map_regime(violvecbool_(:,2));
+
+
+ [zdatalinear_]=mkdatap_anticipated_2constraints(nperiods_,decrulea,decruleb,...
+ cof_,Jbarmat_,...
+ cof10_,Jbarmat10_,Dbarmat10_,...
+ cof01_,Jbarmat01_,Dbarmat01_,...
+ cof11_,Jbarmat11_,Dbarmat11_,...
+ regime1,regimestart1,...
+ regime2,regimestart2,...
+ violvecbool_,endog_,exog_,...
+ irfshock_,shockssequence_(ishock_,:),init_);
+
+ for i_indx_=1:nwishes_
+ eval([deblank(wishlist_(i_indx_,:)),'_difference=zdatalinear_(:,i_indx_);']);
+ end
+
+
+
+
+ newviolvecbool1_ = eval(constraint1_difference_);
+ relaxconstraint1_ = eval(constraint_relax1_difference_);
+
+ newviolvecbool2_ = eval(constraint2_difference_);
+ relaxconstraint2_ = eval(constraint_relax2_difference_);
+
+
+
+ newviolvecbool_ = [newviolvecbool1_;newviolvecbool2_];
+ relaxconstraint_ = [relaxconstraint1_;relaxconstraint2_];
+
+
+
+ % check if changes_
+ if (max(newviolvecbool_(:)-violvecbool_(:)>0)) | sum(relaxconstraint_(find(violvecbool_==1))>0)
+ changes_ = 1;
+ else
+ changes_ = 0;
+ end
+
+ if curb_retrench_ % apply Gauss-Sidel idea of slowing down the change in the guess
+ % for the constraint -- only relax one
+ % period at a time starting from the last
+ % one when each of the constraints is true.
+ retrench = 0*violvecbool_(:);
+ if ~isempty(find(relaxconstraint1_ & violvecbool_(:,1)))
+ retrenchpos = max(find(relaxconstraint1_ & violvecbool_(:,1)));
+ retrench(retrenchpos) = 1;
+ end
+ if ~isempty(find(relaxconstraint2_ & violvecbool_(:,2)))
+ retrenchpos = max(find(relaxconstraint2_ & violvecbool_(:,2)));
+ retrench(retrenchpos+nperiods_+1) = 1;
+ end
+ violvecbool_ = (violvecbool_(:) | newviolvecbool_(:))-retrench(:);
+ else
+ violvecbool_ = (violvecbool_(:) | newviolvecbool_(:))-(relaxconstraint_(:) & violvecbool_(:));
+ end
+
+ violvecbool_ = reshape(violvecbool_,nperiods_+1,2);
+
+
+
+ end
+ if changes_ ==1
+ display('Did not converge -- increase maxiter')
+ end
+
+ init_ = zdatalinear_(1,:);
+ zdatapiecewise_(ishock_,:)=init_;
+ init_= init_';
+
+ % update the guess for constraint violations for next period
+ % update is consistent with expecting no additional shocks next period
+ violvecbool_=[violvecbool_(2:end,:);zeros(1,2)];
+
+end
+
+
+zdatapiecewise_(ishock_+1:end,:)=zdatalinear_(2:nperiods_-ishock_+1,:);
+
+zdatalinear_ = mkdata(nperiods_,decrulea,decruleb,endog_,exog_,wishlist_,irfshock_,shockssequence_,init_orig_);
+
diff --git a/matlab/occbin/strmerge.m b/matlab/occbin/strmerge.m
index b8585df04d40537b79f5762d5a6f7c7a346ad104..8aebb009956fb505bf4aedf678b07f1e0cf08945 100755
--- a/matlab/occbin/strmerge.m
+++ b/matlab/occbin/strmerge.m
@@ -1,9 +1,9 @@
-function string = strmerge(tokens)
-
-ntokens = length(tokens);
-
-string = char(tokens(1));
-
-for i=2:ntokens
- string = [string,char(tokens(i))];
+function string = strmerge(tokens)
+
+ntokens = length(tokens);
+
+string = char(tokens(1));
+
+for i=2:ntokens
+ string = [string,char(tokens(i))];
end
\ No newline at end of file
diff --git a/matlab/occbin/tokenize.m b/matlab/occbin/tokenize.m
index f6e9f330c90bd4bd371532fe4fb5dbd79229b4db..257be9131357fc8ef096684bdf2f4f80187d4834 100755
--- a/matlab/occbin/tokenize.m
+++ b/matlab/occbin/tokenize.m
@@ -1,55 +1,55 @@
-function tokens = tokenize(source,delimiter)
-% syntax
-% tokens = tokenize(source,delimiters)
-%
-% source is a string to be broken into tokens
-% delimiters is a character array of single character delimiters
-% tokens is a cell string array containing the tokens
-
-
-posdelims = [];
-
-% assumes that delimiter cannot be in the first position or the last
-% position
-ndelimiters = size(delimiter,1);
-for i=1:ndelimiters
- newpositions = strfind(source,delimiter(i,:));
- if ~isempty(newpositions)
- posdelims =[posdelims, newpositions];
- end
-end
-
-% reorder posdelims in ascending order
-posdelims = sort(posdelims);
-
-if isempty(posdelims)
- tokens = cellstr(source);
-else
- ndelims = length(posdelims);
- % build positions for substrings
- delims = zeros(ndelims+1,2);
- for i=1:ndelims+1;
- if i==1
- if posdelims(1) == 1
- tokens = cellstr(source(1));
- else
- delims(i,:) = [1,posdelims(i)-1];
- tokens = cellstr(source([delims(i,1):delims(i,2)]));
- tokens = [tokens, source(posdelims(i))];
- end
- elseif i==ndelims+1
- if (posdelims(i-1) < length(source))
- delims(i,:) = [posdelims(i-1)+1,length(source)];
- tokens = [tokens, cellstr(source([delims(i,1):delims(i,2)]))];
- end
- else
- if posdelims(i)>posdelims(i-1)+1
- delims(i,:) = [posdelims(i-1)+1,posdelims(i)-1];
- tokens = [tokens, cellstr(source([delims(i,1):delims(i,2)]))];
- end
- tokens = [tokens, source(posdelims(i))];
- end
- end
-
-end
-
+function tokens = tokenize(source,delimiter)
+% syntax
+% tokens = tokenize(source,delimiters)
+%
+% source is a string to be broken into tokens
+% delimiters is a character array of single character delimiters
+% tokens is a cell string array containing the tokens
+
+
+posdelims = [];
+
+% assumes that delimiter cannot be in the first position or the last
+% position
+ndelimiters = size(delimiter,1);
+for i=1:ndelimiters
+ newpositions = strfind(source,delimiter(i,:));
+ if ~isempty(newpositions)
+ posdelims =[posdelims, newpositions];
+ end
+end
+
+% reorder posdelims in ascending order
+posdelims = sort(posdelims);
+
+if isempty(posdelims)
+ tokens = cellstr(source);
+else
+ ndelims = length(posdelims);
+ % build positions for substrings
+ delims = zeros(ndelims+1,2);
+ for i=1:ndelims+1;
+ if i==1
+ if posdelims(1) == 1
+ tokens = cellstr(source(1));
+ else
+ delims(i,:) = [1,posdelims(i)-1];
+ tokens = cellstr(source([delims(i,1):delims(i,2)]));
+ tokens = [tokens, source(posdelims(i))];
+ end
+ elseif i==ndelims+1
+ if (posdelims(i-1) < length(source))
+ delims(i,:) = [posdelims(i-1)+1,length(source)];
+ tokens = [tokens, cellstr(source([delims(i,1):delims(i,2)]))];
+ end
+ else
+ if posdelims(i)>posdelims(i-1)+1
+ delims(i,:) = [posdelims(i-1)+1,posdelims(i)-1];
+ tokens = [tokens, cellstr(source([delims(i,1):delims(i,2)]))];
+ end
+ tokens = [tokens, source(posdelims(i))];
+ end
+ end
+
+end
+
diff --git a/matlab/one_sided_hp_filter.m b/matlab/one_sided_hp_filter.m
index 38af32ea28cb8265c9003836ce8851bb48853738..46fed234375884b634078c00d36eb3ddf833df47 100644
--- a/matlab/one_sided_hp_filter.m
+++ b/matlab/one_sided_hp_filter.m
@@ -63,7 +63,7 @@ end
%Set up state space
q=1/lambda; % the signal-to-noise ration: i.e. var eta_t / var epsilon_t
F=[2,-1;
- 1,0]; % state transition matrix
+ 1,0]; % state transition matrix
H=[1,0]; % observation matrix
Q=[q,0;
0,0]; % covariance matrix state equation errors
diff --git a/matlab/optimization/cmaes.m b/matlab/optimization/cmaes.m
index 833dbe9b2f9ce82dbf8a4cfdcb7650e7e25db3a9..b4790786afdddc09e1a66f423865191e7bbd0932 100644
--- a/matlab/optimization/cmaes.m
+++ b/matlab/optimization/cmaes.m
@@ -1,161 +1,161 @@
function [xmin, ... % minimum search point of last iteration
- fmin, ... % function value of xmin
- counteval, ... % number of function evaluations done
- stopflag, ... % stop criterion reached
- out, ... % struct with various histories and solutions
- bestever ... % struct containing overall best solution (for convenience)
- ] = cmaes( ...
- fitfun, ... % name of objective/fitness function
- xstart, ... % objective variables initial point, determines N
- insigma, ... % initial coordinate wise standard deviation(s)
- inopts, ... % options struct, see defopts below
- varargin ) % arguments passed to objective function
-% cmaes.m, Version 3.56.beta, last change: February, 2012
-% CMAES implements an Evolution Strategy with Covariance Matrix
-% Adaptation (CMA-ES) for nonlinear function minimization. For
-% introductory comments and copyright (GPL) see end of file (type
-% 'type cmaes'). cmaes.m runs with MATLAB (Windows, Linux) and,
-% without data logging and plotting, it should run under Octave
-% (Linux, package octave-forge is needed).
-%
-% OPTS = CMAES returns default options.
-% OPTS = CMAES('defaults') returns default options quietly.
-% OPTS = CMAES('displayoptions') displays options.
-% OPTS = CMAES('defaults', OPTS) supplements options OPTS with default
-% options.
-%
-% XMIN = CMAES(FUN, X0, SIGMA[, OPTS]) locates the minimum XMIN of
-% function FUN starting from column vector X0 with the initial
-% coordinate wise search standard deviation SIGMA.
-%
-% Input arguments:
-%
-% FUN is a string function name like 'myfun'. FUN takes as argument a
-% column vector of size of X0 and returns a scalar. An easy way to
-% implement a hard non-linear constraint is to return NaN. Then,
-% this function evaluation is not counted and a newly sampled
-% point is tried immediately.
-%
-% X0 is a column vector, or a matrix, or a string. If X0 is a matrix,
-% mean(X0, 2) is taken as initial point. If X0 is a string like
-% '2*rand(10,1)-1', the string is evaluated first.
-%
-% SIGMA is a scalar, or a column vector of size(X0,1), or a string
-% that can be evaluated into one of these. SIGMA determines the
-% initial coordinate wise standard deviations for the search.
-% Setting SIGMA one third of the initial search region is
-% appropriate, e.g., the initial point in [0, 6]^10 and SIGMA=2
-% means cmaes('myfun', 3*rand(10,1), 2). If SIGMA is missing and
-% size(X0,2) > 1, SIGMA is set to sqrt(var(X0')'). That is, X0 is
-% used as a sample for estimating initial mean and variance of the
-% search distribution.
-%
-% OPTS (an optional argument) is a struct holding additional input
-% options. Valid field names and a short documentation can be
-% discovered by looking at the default options (type 'cmaes'
-% without arguments, see above). Empty or missing fields in OPTS
-% invoke the default value, i.e. OPTS needs not to have all valid
-% field names. Capitalization does not matter and unambiguous
-% abbreviations can be used for the field names. If a string is
-% given where a numerical value is needed, the string is evaluated
-% by eval, where 'N' expands to the problem dimension
-% (==size(X0,1)) and 'popsize' to the population size.
-%
-% [XMIN, FMIN, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = ...
-% CMAES(FITFUN, X0, SIGMA)
-% returns the best (minimal) point XMIN (found in the last
-% generation); function value FMIN of XMIN; the number of needed
-% function evaluations COUNTEVAL; a STOPFLAG value as cell array,
-% where possible entries are 'fitness', 'tolx', 'tolupx', 'tolfun',
-% 'maxfunevals', 'maxiter', 'stoptoresume', 'manual',
-% 'warnconditioncov', 'warnnoeffectcoord', 'warnnoeffectaxis',
-% 'warnequalfunvals', 'warnequalfunvalhist', 'bug' (use
-% e.g. any(strcmp(STOPFLAG, 'tolx')) or findstr(strcat(STOPFLAG,
-% 'tolx')) for further processing); a record struct OUT with some
-% more output, where the struct SOLUTIONS.BESTEVER contains the overall
-% best evaluated point X with function value F evaluated at evaluation
-% count EVALS. The last output argument BESTEVER equals
-% OUT.SOLUTIONS.BESTEVER. Moreover a history of solutions and
-% parameters is written to files according to the Log-options.
-%
-% A regular manual stop can be achieved via the file signals.par. The
-% program is terminated if the first two non-white sequences in any
-% line of this file are 'stop' and the value of the LogFilenamePrefix
-% option (by default 'outcmaes'). Also a run can be skipped.
-% Given, for example, 'skip outcmaes run 2', skips the second run
-% if option Restarts is at least 2, and another run will be started.
-%
-% To run the code completely silently set Disp, Save, and Log options
-% to 0. With OPTS.LogModulo > 0 (1 by default) the most important
-% data are written to ASCII files permitting to investigate the
-% results (e.g. plot with function plotcmaesdat) even while CMAES is
-% still running (which can be quite useful on expensive objective
-% functions). When OPTS.SaveVariables==1 (default) everything is saved
-% in file OPTS.SaveFilename (default 'variablescmaes.mat') allowing to
-% resume the search afterwards by using the resume option.
-%
-% To find the best ever evaluated point load the variables typing
-% "es=load('variablescmaes')" and investigate the variable
-% es.out.solutions.bestever.
-%
-% In case of a noisy objective function (uncertainties) set
-% OPTS.Noise.on = 1. This option interferes presumably with some
-% termination criteria, because the step-size sigma will presumably
-% not converge to zero anymore. If CMAES was provided with a
-% fifth argument (P1 in the below example, which is passed to the
-% objective function FUN), this argument is multiplied with the
-% factor given in option Noise.alphaevals, each time the detected
-% noise exceeds a threshold. This argument can be used within
-% FUN, for example, as averaging number to reduce the noise level.
-%
-% OPTS.DiagonalOnly > 1 defines the number of initial iterations,
-% where the covariance matrix remains diagonal and the algorithm has
-% internally linear time complexity. OPTS.DiagonalOnly = 1 means
-% keeping the covariance matrix always diagonal and this setting
-% also exhibits linear space complexity. This can be particularly
-% useful for dimension > 100. The default is OPTS.DiagonalOnly = 0.
-%
-% OPTS.CMA.active = 1 turns on "active CMA" with a negative update
-% of the covariance matrix and checks for positive definiteness.
-% OPTS.CMA.active = 2 does not check for pos. def. and is numerically
-% faster. Active CMA usually speeds up the adaptation and might
-% become a default in near future.
-%
-% The primary strategy parameter to play with is OPTS.PopSize, which
-% can be increased from its default value. Increasing the population
-% size (by default linked to increasing parent number OPTS.ParentNumber)
-% improves global search properties in exchange to speed. Speed
-% decreases, as a rule, at most linearely with increasing population
-% size. It is advisable to begin with the default small population
-% size. The options Restarts and IncPopSize can be used for an
-% automated multistart where the population size is increased by the
-% factor IncPopSize (two by default) before each restart. X0 (given as
-% string) is reevaluated for each restart. Stopping options
-% StopFunEvals, StopIter, MaxFunEvals, and Fitness terminate the
-% program, all others including MaxIter invoke another restart, where
-% the iteration counter is reset to zero.
-%
-% Examples:
-%
-% XMIN = cmaes('myfun', 5*ones(10,1), 1.5); starts the search at
-% 10D-point 5 and initially searches mainly between 5-3 and 5+3
-% (+- two standard deviations), but this is not a strict bound.
-% 'myfun' is a name of a function that returns a scalar from a 10D
-% column vector.
-%
-% opts.LBounds = 0; opts.UBounds = 10;
-% X=cmaes('myfun', 10*rand(10,1), 5, opts);
-% search within lower bound of 0 and upper bound of 10. Bounds can
-% also be given as column vectors. If the optimum is not located
-% on the boundary, use rather a penalty approach to handle bounds.
-%
-% opts=cmaes; opts.StopFitness=1e-10;
-% X=cmaes('myfun', rand(5,1), 0.5, opts); stops the search, if
-% the function value is smaller than 1e-10.
-%
-% [X, F, E, STOP, OUT] = cmaes('myfun2', 'rand(5,1)', 1, [], P1, P2);
-% passes two additional parameters to the function MYFUN2.
-%
+ fmin, ... % function value of xmin
+ counteval, ... % number of function evaluations done
+ stopflag, ... % stop criterion reached
+ out, ... % struct with various histories and solutions
+ bestever ... % struct containing overall best solution (for convenience)
+ ] = cmaes( ...
+ fitfun, ... % name of objective/fitness function
+ xstart, ... % objective variables initial point, determines N
+ insigma, ... % initial coordinate wise standard deviation(s)
+ inopts, ... % options struct, see defopts below
+ varargin ) % arguments passed to objective function
+ % cmaes.m, Version 3.56.beta, last change: February, 2012
+ % CMAES implements an Evolution Strategy with Covariance Matrix
+ % Adaptation (CMA-ES) for nonlinear function minimization. For
+ % introductory comments and copyright (GPL) see end of file (type
+ % 'type cmaes'). cmaes.m runs with MATLAB (Windows, Linux) and,
+ % without data logging and plotting, it should run under Octave
+ % (Linux, package octave-forge is needed).
+ %
+ % OPTS = CMAES returns default options.
+ % OPTS = CMAES('defaults') returns default options quietly.
+ % OPTS = CMAES('displayoptions') displays options.
+ % OPTS = CMAES('defaults', OPTS) supplements options OPTS with default
+ % options.
+ %
+ % XMIN = CMAES(FUN, X0, SIGMA[, OPTS]) locates the minimum XMIN of
+ % function FUN starting from column vector X0 with the initial
+ % coordinate wise search standard deviation SIGMA.
+ %
+ % Input arguments:
+ %
+ % FUN is a string function name like 'myfun'. FUN takes as argument a
+ % column vector of size of X0 and returns a scalar. An easy way to
+ % implement a hard non-linear constraint is to return NaN. Then,
+ % this function evaluation is not counted and a newly sampled
+ % point is tried immediately.
+ %
+ % X0 is a column vector, or a matrix, or a string. If X0 is a matrix,
+ % mean(X0, 2) is taken as initial point. If X0 is a string like
+ % '2*rand(10,1)-1', the string is evaluated first.
+ %
+ % SIGMA is a scalar, or a column vector of size(X0,1), or a string
+ % that can be evaluated into one of these. SIGMA determines the
+ % initial coordinate wise standard deviations for the search.
+ % Setting SIGMA one third of the initial search region is
+ % appropriate, e.g., the initial point in [0, 6]^10 and SIGMA=2
+ % means cmaes('myfun', 3*rand(10,1), 2). If SIGMA is missing and
+ % size(X0,2) > 1, SIGMA is set to sqrt(var(X0')'). That is, X0 is
+ % used as a sample for estimating initial mean and variance of the
+ % search distribution.
+ %
+ % OPTS (an optional argument) is a struct holding additional input
+ % options. Valid field names and a short documentation can be
+ % discovered by looking at the default options (type 'cmaes'
+ % without arguments, see above). Empty or missing fields in OPTS
+ % invoke the default value, i.e. OPTS needs not to have all valid
+ % field names. Capitalization does not matter and unambiguous
+ % abbreviations can be used for the field names. If a string is
+ % given where a numerical value is needed, the string is evaluated
+ % by eval, where 'N' expands to the problem dimension
+ % (==size(X0,1)) and 'popsize' to the population size.
+ %
+ % [XMIN, FMIN, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = ...
+ % CMAES(FITFUN, X0, SIGMA)
+ % returns the best (minimal) point XMIN (found in the last
+ % generation); function value FMIN of XMIN; the number of needed
+ % function evaluations COUNTEVAL; a STOPFLAG value as cell array,
+ % where possible entries are 'fitness', 'tolx', 'tolupx', 'tolfun',
+ % 'maxfunevals', 'maxiter', 'stoptoresume', 'manual',
+ % 'warnconditioncov', 'warnnoeffectcoord', 'warnnoeffectaxis',
+ % 'warnequalfunvals', 'warnequalfunvalhist', 'bug' (use
+ % e.g. any(strcmp(STOPFLAG, 'tolx')) or findstr(strcat(STOPFLAG,
+ % 'tolx')) for further processing); a record struct OUT with some
+ % more output, where the struct SOLUTIONS.BESTEVER contains the overall
+ % best evaluated point X with function value F evaluated at evaluation
+ % count EVALS. The last output argument BESTEVER equals
+ % OUT.SOLUTIONS.BESTEVER. Moreover a history of solutions and
+ % parameters is written to files according to the Log-options.
+ %
+ % A regular manual stop can be achieved via the file signals.par. The
+ % program is terminated if the first two non-white sequences in any
+ % line of this file are 'stop' and the value of the LogFilenamePrefix
+ % option (by default 'outcmaes'). Also a run can be skipped.
+ % Given, for example, 'skip outcmaes run 2', skips the second run
+ % if option Restarts is at least 2, and another run will be started.
+ %
+ % To run the code completely silently set Disp, Save, and Log options
+ % to 0. With OPTS.LogModulo > 0 (1 by default) the most important
+ % data are written to ASCII files permitting to investigate the
+ % results (e.g. plot with function plotcmaesdat) even while CMAES is
+ % still running (which can be quite useful on expensive objective
+ % functions). When OPTS.SaveVariables==1 (default) everything is saved
+ % in file OPTS.SaveFilename (default 'variablescmaes.mat') allowing to
+ % resume the search afterwards by using the resume option.
+ %
+ % To find the best ever evaluated point load the variables typing
+ % "es=load('variablescmaes')" and investigate the variable
+ % es.out.solutions.bestever.
+ %
+ % In case of a noisy objective function (uncertainties) set
+ % OPTS.Noise.on = 1. This option interferes presumably with some
+ % termination criteria, because the step-size sigma will presumably
+ % not converge to zero anymore. If CMAES was provided with a
+ % fifth argument (P1 in the below example, which is passed to the
+ % objective function FUN), this argument is multiplied with the
+ % factor given in option Noise.alphaevals, each time the detected
+ % noise exceeds a threshold. This argument can be used within
+ % FUN, for example, as averaging number to reduce the noise level.
+ %
+ % OPTS.DiagonalOnly > 1 defines the number of initial iterations,
+ % where the covariance matrix remains diagonal and the algorithm has
+ % internally linear time complexity. OPTS.DiagonalOnly = 1 means
+ % keeping the covariance matrix always diagonal and this setting
+ % also exhibits linear space complexity. This can be particularly
+ % useful for dimension > 100. The default is OPTS.DiagonalOnly = 0.
+ %
+ % OPTS.CMA.active = 1 turns on "active CMA" with a negative update
+ % of the covariance matrix and checks for positive definiteness.
+ % OPTS.CMA.active = 2 does not check for pos. def. and is numerically
+ % faster. Active CMA usually speeds up the adaptation and might
+ % become a default in near future.
+ %
+ % The primary strategy parameter to play with is OPTS.PopSize, which
+ % can be increased from its default value. Increasing the population
+ % size (by default linked to increasing parent number OPTS.ParentNumber)
+ % improves global search properties in exchange to speed. Speed
+ % decreases, as a rule, at most linearely with increasing population
+ % size. It is advisable to begin with the default small population
+ % size. The options Restarts and IncPopSize can be used for an
+ % automated multistart where the population size is increased by the
+ % factor IncPopSize (two by default) before each restart. X0 (given as
+ % string) is reevaluated for each restart. Stopping options
+ % StopFunEvals, StopIter, MaxFunEvals, and Fitness terminate the
+ % program, all others including MaxIter invoke another restart, where
+ % the iteration counter is reset to zero.
+ %
+ % Examples:
+ %
+ % XMIN = cmaes('myfun', 5*ones(10,1), 1.5); starts the search at
+ % 10D-point 5 and initially searches mainly between 5-3 and 5+3
+ % (+- two standard deviations), but this is not a strict bound.
+ % 'myfun' is a name of a function that returns a scalar from a 10D
+ % column vector.
+ %
+ % opts.LBounds = 0; opts.UBounds = 10;
+ % X=cmaes('myfun', 10*rand(10,1), 5, opts);
+ % search within lower bound of 0 and upper bound of 10. Bounds can
+ % also be given as column vectors. If the optimum is not located
+ % on the boundary, use rather a penalty approach to handle bounds.
+ %
+ % opts=cmaes; opts.StopFitness=1e-10;
+ % X=cmaes('myfun', rand(5,1), 0.5, opts); stops the search, if
+ % the function value is smaller than 1e-10.
+ %
+ % [X, F, E, STOP, OUT] = cmaes('myfun2', 'rand(5,1)', 1, [], P1, P2);
+ % passes two additional parameters to the function MYFUN2.
+ %
% Copyright (C) 2001-2012 Nikolaus Hansen,
% Copyright (C) 2012 Dynare Team
@@ -227,7 +227,7 @@ defopts.Noise.alphaevals = '1+2/(N+10) % factor for increasing 2nd arg to fitf
defopts.Noise.callback = '[] % callback function when uncertainty threshold is exceeded';
% defopts.TPA = 0;
defopts.CMA.cs = '(mueff+2)/(N+mueff+3) % cumulation constant for step-size';
- %qqq cs = (mueff^0.5)/(N^0.5+mueff^0.5) % the short time horizon version
+%qqq cs = (mueff^0.5)/(N^0.5+mueff^0.5) % the short time horizon version
defopts.CMA.damps = '1 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs % damping for step-size';
% defopts.CMA.ccum = '4/(N+4) % cumulation constant for covariance matrix';
defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N) % cumulation constant for pc';
@@ -238,22 +238,22 @@ defopts.CMA.active = '0 % active CMA 1: neg. updates with pos. def. check, 2: n
flg_future_setting = 0; % testing for possible future variant(s)
if flg_future_setting
- disp('in the future')
+ disp('in the future')
- % damps setting from Brockhoff et al 2010
- % this damps diverges with popsize 400:
- % cmaeshtml('benchmarkszero', ones(20,1)*2, 5, o, 15);
- defopts.CMA.damps = '2*mueff/lambda + 0.3 + cs % damping for step-size'; % cs: for large mueff
- % how about:
- % defopts.CMA.damps = '2*mueff/lambda + 0.3 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs % damping for step-size';
+ % damps setting from Brockhoff et al 2010
+ % this damps diverges with popsize 400:
+ % cmaeshtml('benchmarkszero', ones(20,1)*2, 5, o, 15);
+ defopts.CMA.damps = '2*mueff/lambda + 0.3 + cs % damping for step-size'; % cs: for large mueff
+ % how about:
+ % defopts.CMA.damps = '2*mueff/lambda + 0.3 + 2*max(0,sqrt((mueff-1)/(N+1))-1) + cs % damping for step-size';
- % ccum adjusted for large mueff, better on schefelmult?
- % TODO: this should also depend on diagonal option!?
- defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N) % cumulation constant for pc';
+ % ccum adjusted for large mueff, better on schefelmult?
+ % TODO: this should also depend on diagonal option!?
+ defopts.CMA.ccum = '(4 + mueff/N) / (N+4 + 2*mueff/N) % cumulation constant for pc';
- defopts.CMA.active = '1 % active CMA 1: neg. updates with pos. def. check, 2: neg. updates';
+ defopts.CMA.active = '1 % active CMA 1: neg. updates with pos. def. check, 2: neg. updates';
end
-
+
defopts.Resume = 'no % resume former run from SaveFile';
defopts.Science = 'on % off==do some additional (minor) problem capturing, NOT IN USE';
defopts.ReadSignals = 'on % from file signals.par for termination, yet a stumb';
@@ -276,1481 +276,1481 @@ defopts.UserDat2 = ''; 'for saving data/comments associated with the run';
% ---------------------- Handling Input Parameters ----------------------
if nargin < 1 || isequal(fitfun, 'defaults') % pass default options
- if nargin < 1
- disp('Default options returned (type "help cmaes" for help).');
- end
- xmin = defopts;
- if nargin > 1 % supplement second argument with default options
- xmin = getoptions(xstart, defopts);
- end
- return;
+ if nargin < 1
+ disp('Default options returned (type "help cmaes" for help).');
+ end
+ xmin = defopts;
+ if nargin > 1 % supplement second argument with default options
+ xmin = getoptions(xstart, defopts);
+ end
+ return;
end
if isequal(fitfun, 'displayoptions')
- names = fieldnames(defopts);
- for name = names'
- disp([name{:} repmat(' ', 1, 20-length(name{:})) ': ''' defopts.(name{:}) '''']);
- end
- return;
+ names = fieldnames(defopts);
+ for name = names'
+ disp([name{:} repmat(' ', 1, 20-length(name{:})) ': ''' defopts.(name{:}) '''']);
+ end
+ return;
end
input.fitfun = fitfun; % record used input
if isempty(fitfun)
- % fitfun = definput.fitfun;
- % warning(['Objective function not determined, ''' fitfun ''' used']);
- error(['Objective function not determined']);
+ % fitfun = definput.fitfun;
+ % warning(['Objective function not determined, ''' fitfun ''' used']);
+ error(['Objective function not determined']);
end
if ~ischar(fitfun)
- error('first argument FUN must be a string');
+ error('first argument FUN must be a string');
end
if nargin < 2
- xstart = [];
+ xstart = [];
end
input.xstart = xstart;
if isempty(xstart)
- % xstart = definput.xstart; % objective variables initial point
- % warning('Initial search point, and problem dimension, not determined');
- error('Initial search point, and problem dimension, not determined');
+ % xstart = definput.xstart; % objective variables initial point
+ % warning('Initial search point, and problem dimension, not determined');
+ error('Initial search point, and problem dimension, not determined');
end
if nargin < 3
- insigma = [];
+ insigma = [];
end
if isa(insigma, 'struct')
- error(['Third argument SIGMA must be (or eval to) a scalar '...
- 'or a column vector of size(X0,1)']);
+ error(['Third argument SIGMA must be (or eval to) a scalar '...
+ 'or a column vector of size(X0,1)']);
end
input.sigma = insigma;
if isempty(insigma)
- if all(size(myeval(xstart)) > 1)
- insigma = std(xstart, 0, 2);
- if any(insigma == 0)
- error(['Initial search volume is zero, choose SIGMA or X0 appropriate']);
+ if all(size(myeval(xstart)) > 1)
+ insigma = std(xstart, 0, 2);
+ if any(insigma == 0)
+ error(['Initial search volume is zero, choose SIGMA or X0 appropriate']);
+ end
+ else
+ % will be captured later
+ % error(['Initial step sizes (SIGMA) not determined']);
end
- else
- % will be captured later
- % error(['Initial step sizes (SIGMA) not determined']);
- end
end
% Compose options opts
if nargin < 4 || isempty(inopts) % no input options available
- inopts = [];
- opts = defopts;
+ inopts = [];
+ opts = defopts;
else
- opts = getoptions(inopts, defopts);
+ opts = getoptions(inopts, defopts);
end
i = strfind(opts.SaveFilename, ' '); % remove everything after white space
if ~isempty(i)
- opts.SaveFilename = opts.SaveFilename(1:i(1)-1);
+ opts.SaveFilename = opts.SaveFilename(1:i(1)-1);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
counteval = 0; countevalNaN = 0;
irun = 0;
while irun <= myeval(opts.Restarts) % for-loop does not work with resume
- irun = irun + 1;
-
-% ------------------------ Initialization -------------------------------
-
-% Handle resuming of old run
-flgresume = myevalbool(opts.Resume);
-xmean = myeval(xstart);
-if all(size(xmean) > 1)
- xmean = mean(xmean, 2); % in case if xstart is a population
-elseif size(xmean, 2) > 1
- xmean = xmean';
-end
-if ~flgresume % not resuming a former run
- % Assign settings from input parameters and options for myeval...
- N = size(xmean, 1); numberofvariables = N;
- lambda0 = floor(myeval(opts.PopSize) * myeval(opts.IncPopSize)^(irun-1));
- % lambda0 = floor(myeval(opts.PopSize) * 3^floor((irun-1)/2));
- popsize = lambda0;
- lambda = lambda0;
- insigma = myeval(insigma);
- if all(size(insigma) == [N 2])
- insigma = 0.5 * (insigma(:,2) - insigma(:,1));
- end
-else % flgresume is true, do resume former run
- tmp = whos('-file', opts.SaveFilename);
- for i = 1:length(tmp)
- if strcmp(tmp(i).name, 'localopts');
- error('Saved variables include variable "localopts", please remove');
- end
- end
- local.opts = opts; % keep stopping and display options
- local.varargin = varargin;
- load(opts.SaveFilename);
- varargin = local.varargin;
- flgresume = 1;
-
- % Overwrite old stopping and display options
- opts.StopFitness = local.opts.StopFitness;
- %%opts.MaxFunEvals = local.opts.MaxFunEvals;
- %%opts.MaxIter = local.opts.MaxIter;
- opts.StopFunEvals = local.opts.StopFunEvals;
- opts.StopIter = local.opts.StopIter;
- opts.TolX = local.opts.TolX;
- opts.TolUpX = local.opts.TolUpX;
- opts.TolFun = local.opts.TolFun;
- opts.TolHistFun = local.opts.TolHistFun;
- opts.StopOnStagnation = local.opts.StopOnStagnation;
- opts.StopOnWarnings = local.opts.StopOnWarnings;
- opts.ReadSignals = local.opts.ReadSignals;
- opts.DispFinal = local.opts.DispFinal;
- opts.LogPlot = local.opts.LogPlot;
- opts.DispModulo = local.opts.DispModulo;
- opts.SaveVariables = local.opts.SaveVariables;
- opts.LogModulo = local.opts.LogModulo;
- opts.LogTime = local.opts.LogTime;
- clear local; % otherwise local would be overwritten during load
-end
-
-%--------------------------------------------------------------
-% Evaluate options
-stopFitness = myeval(opts.StopFitness);
-stopMaxFunEvals = myeval(opts.MaxFunEvals);
-stopMaxIter = myeval(opts.MaxIter);
-stopFunEvals = myeval(opts.StopFunEvals);
-stopIter = myeval(opts.StopIter);
-stopTolX = myeval(opts.TolX);
-stopTolUpX = myeval(opts.TolUpX);
-stopTolFun = myeval(opts.TolFun);
-stopTolHistFun = myeval(opts.TolHistFun);
-stopOnStagnation = myevalbool(opts.StopOnStagnation);
-stopOnWarnings = myevalbool(opts.StopOnWarnings);
-flgreadsignals = myevalbool(opts.ReadSignals);
-flgWarnOnEqualFunctionValues = myevalbool(opts.WarnOnEqualFunctionValues);
-flgEvalParallel = myevalbool(opts.EvalParallel);
-stopOnEqualFunctionValues = myeval(opts.StopOnEqualFunctionValues);
-arrEqualFunvals = zeros(1,10+N);
-flgDiagonalOnly = myeval(opts.DiagonalOnly);
-flgActiveCMA = myeval(opts.CMA.active);
-noiseHandling = myevalbool(opts.Noise.on);
-noiseMinMaxEvals = myeval(opts.Noise.minmaxevals);
-noiseAlphaEvals = myeval(opts.Noise.alphaevals);
-noiseCallback = myeval(opts.Noise.callback);
-flgdisplay = myevalbool(opts.DispFinal);
-flgplotting = myevalbool(opts.LogPlot);
-verbosemodulo = myeval(opts.DispModulo);
-flgscience = myevalbool(opts.Science);
-flgsaving = [];
-strsaving = [];
-if strfind(opts.SaveVariables, '-v6')
- i = strfind(opts.SaveVariables, '%');
- if isempty(i) || i == 0 || strfind(opts.SaveVariables, '-v6') < i
- strsaving = '-v6';
- flgsaving = 1;
- flgsavingfinal = 1;
- end
-end
-if strncmp('final', opts.SaveVariables, 5)
- flgsaving = 0;
- flgsavingfinal = 1;
-end
-if isempty(flgsaving)
- flgsaving = myevalbool(opts.SaveVariables);
- flgsavingfinal = flgsaving;
-end
-savemodulo = myeval(opts.LogModulo);
-savetime = myeval(opts.LogTime);
-
-i = strfind(opts.LogFilenamePrefix, ' '); % remove everything after white space
-if ~isempty(i)
- opts.LogFilenamePrefix = opts.LogFilenamePrefix(1:i(1)-1);
-end
-
-% TODO here silent option? set disp, save and log options to 0
-
-%--------------------------------------------------------------
-
-if (isfinite(stopFunEvals) || isfinite(stopIter)) && ~flgsaving
- warning('To resume later the saving option needs to be set');
-end
-
-
-% Do more checking and initialization
-if flgresume % resume is on
- time.t0 = clock;
- if flgdisplay
- disp([' resumed from ' opts.SaveFilename ]);
- end
- if counteval >= stopMaxFunEvals
- error(['MaxFunEvals exceeded, use StopFunEvals as stopping ' ...
- 'criterion before resume']);
- end
- if countiter >= stopMaxIter
- error(['MaxIter exceeded, use StopIter as stopping criterion ' ...
- 'before resume']);
- end
-
-else % flgresume
- % xmean = mean(myeval(xstart), 2); % evaluate xstart again, because of irun
- maxdx = myeval(opts.DiffMaxChange); % maximal sensible variable change
- mindx = myeval(opts.DiffMinChange); % minimal sensible variable change
- % can both also be defined as Nx1 vectors
- lbounds = myeval(opts.LBounds);
- ubounds = myeval(opts.UBounds);
- if length(lbounds) == 1
- lbounds = repmat(lbounds, N, 1);
- end
- if length(ubounds) == 1
- ubounds = repmat(ubounds, N, 1);
- end
- if isempty(insigma) % last chance to set insigma
- if all(lbounds > -Inf) && all(ubounds < Inf)
- if any(lbounds>=ubounds)
- error('upper bound must be greater than lower bound');
- end
- insigma = 0.3*(ubounds-lbounds);
- stopTolX = myeval(opts.TolX); % reevaluate these
- stopTolUpX = myeval(opts.TolUpX);
- else
- error(['Initial step sizes (SIGMA) not determined']);
- end
- end
-
- % Check all vector sizes
- if size(xmean, 2) > 1 || size(xmean,1) ~= N
- error(['intial search point should be a column vector of size ' ...
- num2str(N)]);
- elseif ~(all(size(insigma) == [1 1]) || all(size(insigma) == [N 1]))
- error(['input parameter SIGMA should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(stopTolX, 2) > 1 || ~ismember(size(stopTolX, 1), [1 N])
- error(['option TolX should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(stopTolUpX, 2) > 1 || ~ismember(size(stopTolUpX, 1), [1 N])
- error(['option TolUpX should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(maxdx, 2) > 1 || ~ismember(size(maxdx, 1), [1 N])
- error(['option DiffMaxChange should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(mindx, 2) > 1 || ~ismember(size(mindx, 1), [1 N])
- error(['option DiffMinChange should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(lbounds, 2) > 1 || ~ismember(size(lbounds, 1), [1 N])
- error(['option lbounds should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- elseif size(ubounds, 2) > 1 || ~ismember(size(ubounds, 1), [1 N])
- error(['option ubounds should be (or eval to) a scalar '...
- 'or a column vector of size ' num2str(N)] );
- end
-
- % Initialize dynamic internal state parameters
- if any(insigma <= 0)
- error(['Initial search volume (SIGMA) must be greater than zero']);
- end
- if max(insigma)/min(insigma) > 1e6
- error(['Initial search volume (SIGMA) badly conditioned']);
- end
- sigma = max(insigma); % overall standard deviation
- pc = zeros(N,1); ps = zeros(N,1); % evolution paths for C and sigma
-
- if length(insigma) == 1
- insigma = insigma * ones(N,1) ;
- end
- diagD = insigma/max(insigma); % diagonal matrix D defines the scaling
- diagC = diagD.^2;
- if flgDiagonalOnly ~= 1 % use at some point full covariance matrix
- B = eye(N,N); % B defines the coordinate system
- BD = B.*repmat(diagD',N,1); % B*D for speed up only
- C = diag(diagC); % covariance matrix == BD*(BD)'
- end
- if flgDiagonalOnly
- B = 1;
- end
-
- fitness.hist=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
- fitness.histsel=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
- fitness.histbest=[]; % history of fitness values
- fitness.histmedian=[]; % history of fitness values
-
- % Initialize boundary handling
- bnd.isactive = any(lbounds > -Inf) || any(ubounds < Inf);
- if bnd.isactive
- if any(lbounds>ubounds)
- error('lower bound found to be greater than upper bound');
- end
- [xmean ti] = xintobounds(xmean, lbounds, ubounds); % just in case
- if any(ti)
- warning('Initial point was out of bounds, corrected');
- end
- bnd.weights = zeros(N,1); % weights for bound penalty
- % scaling is better in axis-parallel case, worse in rotated
- bnd.flgscale = 0; % scaling will be omitted if zero
- if bnd.flgscale ~= 0
- bnd.scale = diagC/mean(diagC);
- else
- bnd.scale = ones(N,1);
+ irun = irun + 1;
+
+ % ------------------------ Initialization -------------------------------
+
+ % Handle resuming of old run
+ flgresume = myevalbool(opts.Resume);
+ xmean = myeval(xstart);
+ if all(size(xmean) > 1)
+ xmean = mean(xmean, 2); % in case if xstart is a population
+ elseif size(xmean, 2) > 1
+ xmean = xmean';
+ end
+ if ~flgresume % not resuming a former run
+ % Assign settings from input parameters and options for myeval...
+ N = size(xmean, 1); numberofvariables = N;
+ lambda0 = floor(myeval(opts.PopSize) * myeval(opts.IncPopSize)^(irun-1));
+ % lambda0 = floor(myeval(opts.PopSize) * 3^floor((irun-1)/2));
+ popsize = lambda0;
+ lambda = lambda0;
+ insigma = myeval(insigma);
+ if all(size(insigma) == [N 2])
+ insigma = 0.5 * (insigma(:,2) - insigma(:,1));
+ end
+ else % flgresume is true, do resume former run
+ tmp = whos('-file', opts.SaveFilename);
+ for i = 1:length(tmp)
+ if strcmp(tmp(i).name, 'localopts');
+ error('Saved variables include variable "localopts", please remove');
+ end
+ end
+ local.opts = opts; % keep stopping and display options
+ local.varargin = varargin;
+ load(opts.SaveFilename);
+ varargin = local.varargin;
+ flgresume = 1;
+
+ % Overwrite old stopping and display options
+ opts.StopFitness = local.opts.StopFitness;
+ %%opts.MaxFunEvals = local.opts.MaxFunEvals;
+ %%opts.MaxIter = local.opts.MaxIter;
+ opts.StopFunEvals = local.opts.StopFunEvals;
+ opts.StopIter = local.opts.StopIter;
+ opts.TolX = local.opts.TolX;
+ opts.TolUpX = local.opts.TolUpX;
+ opts.TolFun = local.opts.TolFun;
+ opts.TolHistFun = local.opts.TolHistFun;
+ opts.StopOnStagnation = local.opts.StopOnStagnation;
+ opts.StopOnWarnings = local.opts.StopOnWarnings;
+ opts.ReadSignals = local.opts.ReadSignals;
+ opts.DispFinal = local.opts.DispFinal;
+ opts.LogPlot = local.opts.LogPlot;
+ opts.DispModulo = local.opts.DispModulo;
+ opts.SaveVariables = local.opts.SaveVariables;
+ opts.LogModulo = local.opts.LogModulo;
+ opts.LogTime = local.opts.LogTime;
+ clear local; % otherwise local would be overwritten during load
end
- idx = (lbounds > -Inf) | (ubounds < Inf);
- if length(idx) == 1
- idx = idx * ones(N,1);
- end
- bnd.isbounded = zeros(N,1);
- bnd.isbounded(find(idx)) = 1;
- maxdx = min(maxdx, (ubounds - lbounds)/2);
- if any(sigma*sqrt(diagC) > maxdx)
- fac = min(maxdx ./ sqrt(diagC))/sigma;
- sigma = min(maxdx ./ sqrt(diagC));
- warning(['Initial SIGMA multiplied by the factor ' num2str(fac) ...
- ', because it was larger than half' ...
- ' of one of the boundary intervals']);
+ %--------------------------------------------------------------
+ % Evaluate options
+ stopFitness = myeval(opts.StopFitness);
+ stopMaxFunEvals = myeval(opts.MaxFunEvals);
+ stopMaxIter = myeval(opts.MaxIter);
+ stopFunEvals = myeval(opts.StopFunEvals);
+ stopIter = myeval(opts.StopIter);
+ stopTolX = myeval(opts.TolX);
+ stopTolUpX = myeval(opts.TolUpX);
+ stopTolFun = myeval(opts.TolFun);
+ stopTolHistFun = myeval(opts.TolHistFun);
+ stopOnStagnation = myevalbool(opts.StopOnStagnation);
+ stopOnWarnings = myevalbool(opts.StopOnWarnings);
+ flgreadsignals = myevalbool(opts.ReadSignals);
+ flgWarnOnEqualFunctionValues = myevalbool(opts.WarnOnEqualFunctionValues);
+ flgEvalParallel = myevalbool(opts.EvalParallel);
+ stopOnEqualFunctionValues = myeval(opts.StopOnEqualFunctionValues);
+ arrEqualFunvals = zeros(1,10+N);
+ flgDiagonalOnly = myeval(opts.DiagonalOnly);
+ flgActiveCMA = myeval(opts.CMA.active);
+ noiseHandling = myevalbool(opts.Noise.on);
+ noiseMinMaxEvals = myeval(opts.Noise.minmaxevals);
+ noiseAlphaEvals = myeval(opts.Noise.alphaevals);
+ noiseCallback = myeval(opts.Noise.callback);
+ flgdisplay = myevalbool(opts.DispFinal);
+ flgplotting = myevalbool(opts.LogPlot);
+ verbosemodulo = myeval(opts.DispModulo);
+ flgscience = myevalbool(opts.Science);
+ flgsaving = [];
+ strsaving = [];
+ if strfind(opts.SaveVariables, '-v6')
+ i = strfind(opts.SaveVariables, '%');
+ if isempty(i) || i == 0 || strfind(opts.SaveVariables, '-v6') < i
+ strsaving = '-v6';
+ flgsaving = 1;
+ flgsavingfinal = 1;
+ end
end
- idx = (lbounds > -Inf) & (ubounds < Inf);
- dd = diagC;
- if any(5*sigma*sqrt(dd(idx)) < ubounds(idx) - lbounds(idx))
- warning(['Initial SIGMA is, in at least one coordinate, ' ...
- 'much smaller than the '...
- 'given boundary intervals. For reasonable ' ...
- 'global search performance SIGMA should be ' ...
- 'between 0.2 and 0.5 of the bounded interval in ' ...
- 'each coordinate. If all coordinates have ' ...
- 'lower and upper bounds SIGMA can be empty']);
+ if strncmp('final', opts.SaveVariables, 5)
+ flgsaving = 0;
+ flgsavingfinal = 1;
end
- bnd.dfithist = 1; % delta fit for setting weights
- bnd.aridxpoints = []; % remember complete outside points
- bnd.arfitness = []; % and their fitness
- bnd.validfitval = 0;
- bnd.iniphase = 1;
- end
-
- % ooo initial feval, for output only
- if irun == 1
- out.solutions.bestever.x = xmean;
- out.solutions.bestever.f = Inf; % for simpler comparison below
- out.solutions.bestever.evals = counteval;
- bestever = out.solutions.bestever;
- end
- if myevalbool(opts.EvalInitialX)
- fitness.hist(1)=feval(fitfun, xmean, varargin{:});
- fitness.histsel(1)=fitness.hist(1);
- counteval = counteval + 1;
- if fitness.hist(1) < out.solutions.bestever.f
- out.solutions.bestever.x = xmean;
- out.solutions.bestever.f = fitness.hist(1);
- out.solutions.bestever.evals = counteval;
- bestever = out.solutions.bestever;
+ if isempty(flgsaving)
+ flgsaving = myevalbool(opts.SaveVariables);
+ flgsavingfinal = flgsaving;
end
- else
- fitness.hist(1)=NaN;
- fitness.histsel(1)=NaN;
- end
-
- % initialize random number generator
-% $$$ if ischar(opts.Seed)
-% $$$ randn('state', eval(opts.Seed)); % random number generator state
-% $$$ else
-% $$$ randn('state', opts.Seed);
-% $$$ end
- %qqq
-% load(opts.SaveFilename, 'startseed');
-% randn('state', startseed);
-% disp(['SEED RELOADED FROM ' opts.SaveFilename]);
-% startseed = randn('state'); % for retrieving in saved variables
+ savemodulo = myeval(opts.LogModulo);
+ savetime = myeval(opts.LogTime);
- % Initialize further constants
- chiN=N^0.5*(1-1/(4*N)+1/(21*N^2)); % expectation of
- % ||N(0,I)|| == norm(randn(N,1))
-
- countiter = 0;
- % Initialize records and output
- if irun == 1
- time.t0 = clock;
-
- % TODO: keep also median solution?
- out.evals = counteval; % should be first entry
- out.stopflag = {};
-
- outiter = 0;
-
- % Write headers to output data files
- filenameprefix = opts.LogFilenamePrefix;
- if savemodulo && savetime
- filenames = {};
- filenames(end+1) = {'axlen'};
- filenames(end+1) = {'fit'};
- filenames(end+1) = {'stddev'};
- filenames(end+1) = {'xmean'};
- filenames(end+1) = {'xrecentbest'};
- str = [' (startseed=' num2str(startseed(2)) ...
- ', ' num2str(clock, '%d/%02d/%d %d:%d:%2.2f') ')'];
- for namecell = filenames(:)'
- name = namecell{:};
-
- [fid, err] = fopen(['./' filenameprefix name '.dat'], 'w');
- if fid < 1 % err ~= 0
- warning(['could not open ' filenameprefix name '.dat']);
- filenames(find(strcmp(filenames,name))) = [];
- else
-% fprintf(fid, '%s\n', ...
-% ['<CMAES-OUTPUT version="' cmaVersion '">']);
-% fprintf(fid, [' <NAME>' name '</NAME>\n']);
-% fprintf(fid, [' <DATE>' date() '</DATE>\n']);
-% fprintf(fid, ' <PARAMETERS>\n');
-% fprintf(fid, [' dimension=' num2str(N) '\n']);
-% fprintf(fid, ' </PARAMETERS>\n');
- % different cases for DATA columns annotations here
-% fprintf(fid, ' <DATA');
- if strcmp(name, 'axlen')
- fprintf(fid, ['%% columns="iteration, evaluation, sigma, ' ...
- 'max axis length, min axis length, ' ...
- 'all principal axes lengths (sorted square roots ' ...
- 'of eigenvalues of C)"' str]);
- elseif strcmp(name, 'fit')
- fprintf(fid, ['%% columns="iteration, evaluation, sigma, axis ratio, bestever,' ...
- ' best, median, worst fitness function value,' ...
- ' further objective values of best"' str]);
- elseif strcmp(name, 'stddev')
- fprintf(fid, ['%% columns=["iteration, evaluation, sigma, void, void, ' ...
- 'stds==sigma*sqrt(diag(C))"' str]);
- elseif strcmp(name, 'xmean')
- fprintf(fid, ['%% columns="iteration, evaluation, void, ' ...
- 'void, void, xmean"' str]);
- elseif strcmp(name, 'xrecentbest')
- fprintf(fid, ['%% columns="iteration, evaluation, fitness, ' ...
- 'void, void, xrecentbest"' str]);
- end
- fprintf(fid, '\n'); % DATA
- if strcmp(name, 'xmean')
- fprintf(fid, '%ld %ld 0 0 0 ', 0, counteval);
- % fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean);
-%qqq fprintf(fid, msprintf('%e ', genophenotransform(out.genopheno, xmean)) + '\n');
- fprintf(fid, '%e ', xmean);
- fprintf(fid, '\n');
- end
- fclose(fid);
- clear fid; % preventing
- end
- end % for files
- end % savemodulo
- end % irun == 1
-
-end % else flgresume
-
-% -------------------- Generation Loop --------------------------------
-stopflag = {};
-while isempty(stopflag)
- % set internal parameters
- if countiter == 0 || lambda ~= lambda_last
- if countiter > 0 && floor(log10(lambda)) ~= floor(log10(lambda_last)) ...
- && flgdisplay
- disp([' lambda = ' num2str(lambda)]);
- lambda_hist(:,end+1) = [countiter+1; lambda];
- else
- lambda_hist = [countiter+1; lambda];
- end
- lambda_last = lambda;
- % Strategy internal parameter setting: Selection
- mu = myeval(opts.ParentNumber); % number of parents/points for recombination
- if strncmp(lower(opts.RecombinationWeights), 'equal', 3)
- weights = ones(mu,1); % (mu_I,lambda)-CMA-ES
- elseif strncmp(lower(opts.RecombinationWeights), 'linear', 3)
- weights = mu+0.5-(1:mu)';
- elseif strncmp(lower(opts.RecombinationWeights), 'superlinear', 3)
- weights = log(mu+0.5)-log(1:mu)'; % muXone array for weighted recombination
- % qqq mu can be non-integer and
- % should become ceil(mu-0.5) (minor correction)
- else
- error(['Recombination weights to be "' opts.RecombinationWeights ...
- '" is not implemented']);
- end
- mueff=sum(weights)^2/sum(weights.^2); % variance-effective size of mu
- weights = weights/sum(weights); % normalize recombination weights array
- if mueff == lambda
- error(['Combination of values for PopSize, ParentNumber and ' ...
- ' and RecombinationWeights is not reasonable']);
- end
-
- % Strategy internal parameter setting: Adaptation
- cc = myeval(opts.CMA.ccum); % time constant for cumulation for covariance matrix
- cs = myeval(opts.CMA.cs);
-
- % old way TODO: remove this at some point
- % mucov = mueff; % size of mu used for calculating learning rate ccov
- % ccov = (1/mucov) * 2/(N+1.41)^2 ... % learning rate for covariance matrix
- % + (1-1/mucov) * min(1,(2*mucov-1)/((N+2)^2+mucov));
-
- % new way
- if myevalbool(opts.CMA.on)
- ccov1 = myeval(opts.CMA.ccov1);
- ccovmu = min(1-ccov1, myeval(opts.CMA.ccovmu));
- else
- ccov1 = 0;
- ccovmu = 0;
- end
-
- % flgDiagonalOnly = -lambda*4*1/ccov; % for ccov==1 it is not needed
- % 0 : C will never be diagonal anymore
- % 1 : C will always be diagonal
- % >1: C is diagonal for first iterations, set to 0 afterwards
- if flgDiagonalOnly < 1
- flgDiagonalOnly = 0;
- end
- if flgDiagonalOnly
- ccov1_sep = min(1, ccov1 * (N+1.5) / 3);
- ccovmu_sep = min(1-ccov1_sep, ccovmu * (N+1.5) / 3);
- elseif N > 98 && flgdisplay && countiter == 0
- disp('consider option DiagonalOnly for high-dimensional problems');
+ i = strfind(opts.LogFilenamePrefix, ' '); % remove everything after white space
+ if ~isempty(i)
+ opts.LogFilenamePrefix = opts.LogFilenamePrefix(1:i(1)-1);
end
- % ||ps|| is close to sqrt(mueff/N) for mueff large on linear fitness
- %damps = ... % damping for step size control, usually close to one
- % (1 + 2*max(0,sqrt((mueff-1)/(N+1))-1)) ... % limit sigma increase
- % * max(0.3, ... % reduce damps, if max. iteration number is small
- % 1 - N/min(stopMaxIter,stopMaxFunEvals/lambda)) + cs;
- damps = myeval(opts.CMA.damps);
- if noiseHandling
- noiseReevals = min(myeval(opts.Noise.reevals), lambda);
- noiseAlpha = myeval(opts.Noise.alphasigma);
- noiseEpsilon = myeval(opts.Noise.epsilon);
- noiseTheta = myeval(opts.Noise.theta);
- noisecum = myeval(opts.Noise.cum);
- noiseCutOff = myeval(opts.Noise.cutoff); % arguably of minor relevance
- else
- noiseReevals = 0; % more convenient in later coding
- end
+ % TODO here silent option? set disp, save and log options to 0
- %qqq hacking of a different parameter setting, e.g. for ccov or damps,
- % can be done here, but is not necessary anymore, see opts.CMA.
- % ccov1 = 0.0*ccov1; disp(['CAVE: ccov1=' num2str(ccov1)]);
- % ccovmu = 0.0*ccovmu; disp(['CAVE: ccovmu=' num2str(ccovmu)]);
- % damps = inf*damps; disp(['CAVE: damps=' num2str(damps)]);
- % cc = 1; disp(['CAVE: cc=' num2str(cc)]);
-
- end
-
- % Display initial message
- if countiter == 0 && flgdisplay
- if mu == 1
- strw = '100';
- elseif mu < 8
- strw = [sprintf('%.0f', 100*weights(1)) ...
- sprintf(' %.0f', 100*weights(2:end)')];
- else
- strw = [sprintf('%.2g ', 100*weights(1:2)') ...
- sprintf('%.2g', 100*weights(3)') '...' ...
- sprintf(' %.2g', 100*weights(end-1:end)') ']%, '];
- end
- if irun > 1
- strrun = [', run ' num2str(irun)];
- else
- strrun = '';
- end
- disp([' n=' num2str(N) ': (' num2str(mu) ',' ...
- num2str(lambda) ')-CMA-ES(w=[' ...
- strw ']%, ' ...
- 'mu_eff=' num2str(mueff,'%.1f') ...
- ') on function ' ...
- (fitfun) strrun]);
- if flgDiagonalOnly == 1
- disp(' C is diagonal');
- elseif flgDiagonalOnly
- disp([' C is diagonal for ' num2str(floor(flgDiagonalOnly)) ' iterations']);
+ %--------------------------------------------------------------
+
+ if (isfinite(stopFunEvals) || isfinite(stopIter)) && ~flgsaving
+ warning('To resume later the saving option needs to be set');
end
- end
-
- flush;
-
- countiter = countiter + 1;
-
- % Generate and evaluate lambda offspring
-
- fitness.raw = repmat(NaN, 1, lambda + noiseReevals);
-
- % parallel evaluation
- if flgEvalParallel
- arz = randn(N,lambda);
-
- if ~flgDiagonalOnly
- arx = repmat(xmean, 1, lambda) + sigma * (BD * arz); % Eq. (1)
- else
- arx = repmat(xmean, 1, lambda) + repmat(sigma * diagD, 1, lambda) .* arz;
- end
-
- if noiseHandling
- if noiseEpsilon == 0
- arx = [arx arx(:,1:noiseReevals)];
- elseif flgDiagonalOnly
- arx = [arx arx(:,1:noiseReevals) + ...
- repmat(noiseEpsilon * sigma * diagD, 1, noiseReevals) ...
- .* randn(N,noiseReevals)];
- else
- arx = [arx arx(:,1:noiseReevals) + ...
- noiseEpsilon * sigma * ...
- (BD * randn(N,noiseReevals))];
+
+
+ % Do more checking and initialization
+ if flgresume % resume is on
+ time.t0 = clock;
+ if flgdisplay
+ disp([' resumed from ' opts.SaveFilename ]);
end
- end
-
- % You may handle constraints here. You may either resample
- % arz(:,k) and/or multiply it with a factor between -1 and 1
- % (the latter will decrease the overall step size) and
- % recalculate arx accordingly. Do not change arx or arz in any
- % other way.
-
- if ~bnd.isactive
- arxvalid = arx;
- else
- arxvalid = xintobounds(arx, lbounds, ubounds);
- end
- % You may handle constraints here. You may copy and alter
- % (columns of) arxvalid(:,k) only for the evaluation of the
- % fitness function. arx and arxvalid should not be changed.
- fitness.raw = feval(fitfun, arxvalid, varargin{:});
- countevalNaN = countevalNaN + sum(isnan(fitness.raw));
- counteval = counteval + sum(~isnan(fitness.raw));
- end
-
- % non-parallel evaluation and remaining NaN-values
- % set also the reevaluated solution to NaN
- fitness.raw(lambda + find(isnan(fitness.raw(1:noiseReevals)))) = NaN;
- for k=find(isnan(fitness.raw)),
- % fitness.raw(k) = NaN;
- tries = 0;
- % Resample, until fitness is not NaN
- while isnan(fitness.raw(k))
- if k <= lambda % regular samples (not the re-evaluation-samples)
- arz(:,k) = randn(N,1); % (re)sample
-
- if flgDiagonalOnly
- arx(:,k) = xmean + sigma * diagD .* arz(:,k); % Eq. (1)
- else
- arx(:,k) = xmean + sigma * (BD * arz(:,k)); % Eq. (1)
+ if counteval >= stopMaxFunEvals
+ error(['MaxFunEvals exceeded, use StopFunEvals as stopping ' ...
+ 'criterion before resume']);
end
- else % re-evaluation solution with index > lambda
- if flgDiagonalOnly
- arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * diagD .* randn(N,1);
- else
- arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
+ if countiter >= stopMaxIter
+ error(['MaxIter exceeded, use StopIter as stopping criterion ' ...
+ 'before resume']);
+ end
+
+ else % flgresume
+ % xmean = mean(myeval(xstart), 2); % evaluate xstart again, because of irun
+ maxdx = myeval(opts.DiffMaxChange); % maximal sensible variable change
+ mindx = myeval(opts.DiffMinChange); % minimal sensible variable change
+ % can both also be defined as Nx1 vectors
+ lbounds = myeval(opts.LBounds);
+ ubounds = myeval(opts.UBounds);
+ if length(lbounds) == 1
+ lbounds = repmat(lbounds, N, 1);
+ end
+ if length(ubounds) == 1
+ ubounds = repmat(ubounds, N, 1);
+ end
+ if isempty(insigma) % last chance to set insigma
+ if all(lbounds > -Inf) && all(ubounds < Inf)
+ if any(lbounds>=ubounds)
+ error('upper bound must be greater than lower bound');
+ end
+ insigma = 0.3*(ubounds-lbounds);
+ stopTolX = myeval(opts.TolX); % reevaluate these
+ stopTolUpX = myeval(opts.TolUpX);
+ else
+ error(['Initial step sizes (SIGMA) not determined']);
+ end
end
- end
-
- % You may handle constraints here. You may either resample
- % arz(:,k) and/or multiply it with a factor between -1 and 1
- % (the latter will decrease the overall step size) and
- % recalculate arx accordingly. Do not change arx or arz in any
- % other way.
-
- if ~bnd.isactive
- arxvalid(:,k) = arx(:,k);
- else
- arxvalid(:,k) = xintobounds(arx(:,k), lbounds, ubounds);
- end
- % You may handle constraints here. You may copy and alter
- % (columns of) arxvalid(:,k) only for the evaluation of the
- % fitness function. arx should not be changed.
- fitness.raw(k) = feval(fitfun, arxvalid(:,k), varargin{:});
- tries = tries + 1;
- if isnan(fitness.raw(k))
- countevalNaN = countevalNaN + 1;
- end
- if mod(tries, 100) == 0
- warning([num2str(tries) ...
- ' NaN objective function values at evaluation ' ...
- num2str(counteval)]);
- end
- end
- counteval = counteval + 1; % retries due to NaN are not counted
- end
-
- fitness.sel = fitness.raw;
-
- % ----- handle boundaries -----
- if 1 < 3 && bnd.isactive
- % Get delta fitness values
- val = myprctile(fitness.raw, [25 75]);
- % more precise would be exp(mean(log(diagC)))
- val = (val(2) - val(1)) / N / mean(diagC) / sigma^2;
- %val = (myprctile(fitness.raw, 75) - myprctile(fitness.raw, 25)) ...
- % / N / mean(diagC) / sigma^2;
- % Catch non-sensible values
- if ~isfinite(val)
- warning('Non-finite fitness range');
- val = max(bnd.dfithist);
- elseif val == 0 % happens if all points are out of bounds
- val = min(bnd.dfithist(bnd.dfithist>0)); % seems not to make sense, given all solutions are out of bounds
- elseif bnd.validfitval == 0 % flag that first sensible val was found
- bnd.dfithist = [];
- bnd.validfitval = 1;
- end
-
- % Store delta fitness values
- if length(bnd.dfithist) < 20+(3*N)/lambda
- bnd.dfithist = [bnd.dfithist val];
- else
- bnd.dfithist = [bnd.dfithist(2:end) val];
- end
- [tx ti] = xintobounds(xmean, lbounds, ubounds);
-
- % Set initial weights
- if bnd.iniphase
- if any(ti)
- bnd.weights(find(bnd.isbounded)) = 2.0002 * median(bnd.dfithist);
- if bnd.flgscale == 0 % scale only initial weights then
- dd = diagC;
- idx = find(bnd.isbounded);
- dd = dd(idx) / mean(dd); % remove mean scaling
- bnd.weights(idx) = bnd.weights(idx) ./ dd;
- end
- if bnd.validfitval && countiter > 2
- bnd.iniphase = 0;
- end
- end
- end
+ % Check all vector sizes
+ if size(xmean, 2) > 1 || size(xmean,1) ~= N
+ error(['intial search point should be a column vector of size ' ...
+ num2str(N)]);
+ elseif ~(all(size(insigma) == [1 1]) || all(size(insigma) == [N 1]))
+ error(['input parameter SIGMA should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(stopTolX, 2) > 1 || ~ismember(size(stopTolX, 1), [1 N])
+ error(['option TolX should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(stopTolUpX, 2) > 1 || ~ismember(size(stopTolUpX, 1), [1 N])
+ error(['option TolUpX should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(maxdx, 2) > 1 || ~ismember(size(maxdx, 1), [1 N])
+ error(['option DiffMaxChange should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(mindx, 2) > 1 || ~ismember(size(mindx, 1), [1 N])
+ error(['option DiffMinChange should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(lbounds, 2) > 1 || ~ismember(size(lbounds, 1), [1 N])
+ error(['option lbounds should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ elseif size(ubounds, 2) > 1 || ~ismember(size(ubounds, 1), [1 N])
+ error(['option ubounds should be (or eval to) a scalar '...
+ 'or a column vector of size ' num2str(N)] );
+ end
+
+ % Initialize dynamic internal state parameters
+ if any(insigma <= 0)
+ error(['Initial search volume (SIGMA) must be greater than zero']);
+ end
+ if max(insigma)/min(insigma) > 1e6
+ error(['Initial search volume (SIGMA) badly conditioned']);
+ end
+ sigma = max(insigma); % overall standard deviation
+ pc = zeros(N,1); ps = zeros(N,1); % evolution paths for C and sigma
- % Increase weights
- if 1 < 3 && any(ti) % any coordinate of xmean out of bounds
- % judge distance of xmean to boundary
- tx = xmean - tx;
- idx = (ti ~= 0 & abs(tx) > 3*max(1,sqrt(N)/mueff) ...
- * sigma*sqrt(diagC)) ;
- % only increase if xmean is moving away
- idx = idx & (sign(tx) == sign(xmean - xold));
- if ~isempty(idx) % increase
- % the factor became 1.2 instead of 1.1, because
- % changed from max to min in version 3.52
- bnd.weights(idx) = 1.2^(min(1, mueff/10/N)) * bnd.weights(idx);
- end
- end
+ if length(insigma) == 1
+ insigma = insigma * ones(N,1) ;
+ end
+ diagD = insigma/max(insigma); % diagonal matrix D defines the scaling
+ diagC = diagD.^2;
+ if flgDiagonalOnly ~= 1 % use at some point full covariance matrix
+ B = eye(N,N); % B defines the coordinate system
+ BD = B.*repmat(diagD',N,1); % B*D for speed up only
+ C = diag(diagC); % covariance matrix == BD*(BD)'
+ end
+ if flgDiagonalOnly
+ B = 1;
+ end
- % Calculate scaling biased to unity, product is one
- if bnd.flgscale ~= 0
- bnd.scale = exp(0.9*(log(diagC)-mean(log(diagC))));
- end
+ fitness.hist=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
+ fitness.histsel=NaN*ones(1,10+ceil(3*10*N/lambda)); % history of fitness values
+ fitness.histbest=[]; % history of fitness values
+ fitness.histmedian=[]; % history of fitness values
+
+ % Initialize boundary handling
+ bnd.isactive = any(lbounds > -Inf) || any(ubounds < Inf);
+ if bnd.isactive
+ if any(lbounds>ubounds)
+ error('lower bound found to be greater than upper bound');
+ end
+ [xmean ti] = xintobounds(xmean, lbounds, ubounds); % just in case
+ if any(ti)
+ warning('Initial point was out of bounds, corrected');
+ end
+ bnd.weights = zeros(N,1); % weights for bound penalty
+ % scaling is better in axis-parallel case, worse in rotated
+ bnd.flgscale = 0; % scaling will be omitted if zero
+ if bnd.flgscale ~= 0
+ bnd.scale = diagC/mean(diagC);
+ else
+ bnd.scale = ones(N,1);
+ end
+
+ idx = (lbounds > -Inf) | (ubounds < Inf);
+ if length(idx) == 1
+ idx = idx * ones(N,1);
+ end
+ bnd.isbounded = zeros(N,1);
+ bnd.isbounded(find(idx)) = 1;
+ maxdx = min(maxdx, (ubounds - lbounds)/2);
+ if any(sigma*sqrt(diagC) > maxdx)
+ fac = min(maxdx ./ sqrt(diagC))/sigma;
+ sigma = min(maxdx ./ sqrt(diagC));
+ warning(['Initial SIGMA multiplied by the factor ' num2str(fac) ...
+ ', because it was larger than half' ...
+ ' of one of the boundary intervals']);
+ end
+ idx = (lbounds > -Inf) & (ubounds < Inf);
+ dd = diagC;
+ if any(5*sigma*sqrt(dd(idx)) < ubounds(idx) - lbounds(idx))
+ warning(['Initial SIGMA is, in at least one coordinate, ' ...
+ 'much smaller than the '...
+ 'given boundary intervals. For reasonable ' ...
+ 'global search performance SIGMA should be ' ...
+ 'between 0.2 and 0.5 of the bounded interval in ' ...
+ 'each coordinate. If all coordinates have ' ...
+ 'lower and upper bounds SIGMA can be empty']);
+ end
+ bnd.dfithist = 1; % delta fit for setting weights
+ bnd.aridxpoints = []; % remember complete outside points
+ bnd.arfitness = []; % and their fitness
+ bnd.validfitval = 0;
+ bnd.iniphase = 1;
+ end
- % Assigned penalized fitness
- bnd.arpenalty = (bnd.weights ./ bnd.scale)' * (arxvalid - arx).^2;
-
- fitness.sel = fitness.raw + bnd.arpenalty;
-
- end % handle boundaries
- % ----- end handle boundaries -----
-
- % compute noise measurement and reduce fitness arrays to size lambda
- if noiseHandling
- [noiseS] = local_noisemeasurement(fitness.sel(1:lambda), ...
- fitness.sel(lambda+(1:noiseReevals)), ...
- noiseReevals, noiseTheta, noiseCutOff);
- if countiter == 1 % TODO: improve this very rude way of initialization
- noiseSS = 0;
- noiseN = 0; % counter for mean
- end
- noiseSS = noiseSS + noisecum * (noiseS - noiseSS);
-
- % noise-handling could be done here, but the original sigma is still needed
- % disp([noiseS noiseSS noisecum])
-
- fitness.rawar12 = fitness.raw; % just documentary
- fitness.selar12 = fitness.sel; % just documentary
- % qqq refine fitness based on both values
- if 11 < 3 % TODO: in case of outliers this mean is counterproductive
- % median out of three would be ok
- fitness.raw(1:noiseReevals) = ... % not so raw anymore
- (fitness.raw(1:noiseReevals) + fitness.raw(lambda+(1:noiseReevals))) / 2;
- fitness.sel(1:noiseReevals) = ...
- (fitness.sel(1:noiseReevals) + fitness.sel(lambda+(1:noiseReevals))) / 2;
- end
- fitness.raw = fitness.raw(1:lambda);
- fitness.sel = fitness.sel(1:lambda);
- end
-
- % Sort by fitness
- [fitness.raw, fitness.idx] = sort(fitness.raw);
- [fitness.sel, fitness.idxsel] = sort(fitness.sel); % minimization
- fitness.hist(2:end) = fitness.hist(1:end-1); % record short history of
- fitness.hist(1) = fitness.raw(1); % best fitness values
- if length(fitness.histbest) < 120+ceil(30*N/lambda) || ...
- (mod(countiter, 5) == 0 && length(fitness.histbest) < 2e4) % 20 percent of 1e5 gen.
- fitness.histbest = [fitness.raw(1) fitness.histbest]; % best fitness values
- fitness.histmedian = [median(fitness.raw) fitness.histmedian]; % median fitness values
- else
- fitness.histbest(2:end) = fitness.histbest(1:end-1);
- fitness.histmedian(2:end) = fitness.histmedian(1:end-1);
- fitness.histbest(1) = fitness.raw(1); % best fitness values
- fitness.histmedian(1) = median(fitness.raw); % median fitness values
- end
- fitness.histsel(2:end) = fitness.histsel(1:end-1); % record short history of
- fitness.histsel(1) = fitness.sel(1); % best sel fitness values
-
- % Calculate new xmean, this is selection and recombination
- xold = xmean; % for speed up of Eq. (2) and (3)
- xmean = arx(:,fitness.idxsel(1:mu))*weights;
- zmean = arz(:,fitness.idxsel(1:mu))*weights;%==D^-1*B'*(xmean-xold)/sigma
- if mu == 1
- fmean = fitness.sel(1);
- else
- fmean = NaN; % [] does not work in the latter assignment
- % fmean = feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
- % counteval = counteval + 1;
- end
-
- % Cumulation: update evolution paths
- ps = (1-cs)*ps + sqrt(cs*(2-cs)*mueff) * (B*zmean); % Eq. (4)
- hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
- if flg_future_setting
- hsig = sum(ps.^2) / (1-(1-cs)^(2*countiter)) / N < 2 + 4/(N+1); % just simplified
- end
-% hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
-% hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.5 + 1/(N-0.5);
-% hsig = norm(ps) < 1.5 * sqrt(N);
-% hsig = 1;
-
- pc = (1-cc)*pc ...
- + hsig*(sqrt(cc*(2-cc)*mueff)/sigma) * (xmean-xold); % Eq. (2)
- if hsig == 0
- % disp([num2str(countiter) ' ' num2str(counteval) ' pc update stalled']);
- end
-
- % Adapt covariance matrix
- neg.ccov = 0; % TODO: move parameter setting upwards at some point
- if ccov1 + ccovmu > 0 % Eq. (3)
- if flgDiagonalOnly % internal linear(?) complexity
- diagC = (1-ccov1_sep-ccovmu_sep+(1-hsig)*ccov1_sep*cc*(2-cc)) * diagC ... % regard old matrix
- + ccov1_sep * pc.^2 ... % plus rank one update
- + ccovmu_sep ... % plus rank mu update
- * (diagC .* (arz(:,fitness.idxsel(1:mu)).^2 * weights));
-% * (repmat(diagC,1,mu) .* arz(:,fitness.idxsel(1:mu)).^2 * weights);
- diagD = sqrt(diagC); % replaces eig(C)
- else
- arpos = (arx(:,fitness.idxsel(1:mu))-repmat(xold,1,mu)) / sigma;
- % "active" CMA update: negative update, in case controlling pos. definiteness
- if flgActiveCMA > 0
- % set parameters
- neg.mu = mu;
- neg.mueff = mueff;
- if flgActiveCMA > 10 % flat weights with mu=lambda/2
- neg.mu = floor(lambda/2);
- neg.mueff = neg.mu;
+ % ooo initial feval, for output only
+ if irun == 1
+ out.solutions.bestever.x = xmean;
+ out.solutions.bestever.f = Inf; % for simpler comparison below
+ out.solutions.bestever.evals = counteval;
+ bestever = out.solutions.bestever;
end
- % neg.mu = ceil(min([N, lambda/4, mueff])); neg.mueff = mu; % i.e. neg.mu <= N
- % Parameter study: in 3-D lambda=50,100, 10-D lambda=200,400, 30-D lambda=1000,2000 a
- % three times larger neg.ccov does not work.
- % increasing all ccov rates three times does work (probably because of the factor (1-ccovmu))
- % in 30-D to looks fine
-
- neg.ccov = (1 - ccovmu) * 0.25 * neg.mueff / ((N+2)^1.5 + 2*neg.mueff);
- neg.minresidualvariance = 0.66; % keep at least 0.66 in all directions, small popsize are most critical
- neg.alphaold = 0.5; % where to make up for the variance loss, 0.5 means no idea what to do
- % 1 is slightly more robust and gives a better "guaranty" for pos. def.,
- % but does it make sense from the learning perspective for large ccovmu?
-
- neg.ccovfinal = neg.ccov;
-
- % prepare vectors, compute negative updating matrix Cneg and checking matrix Ccheck
- arzneg = arz(:,fitness.idxsel(lambda:-1:lambda - neg.mu + 1));
- % i-th longest becomes i-th shortest
- % TODO: this is not in compliance with the paper Hansen&Ros2010,
- % where simply arnorms = arnorms(end:-1:1) ?
- [arnorms idxnorms] = sort(sqrt(sum(arzneg.^2, 1)));
- [ignore idxnorms] = sort(idxnorms); % inverse index
- arnormfacs = arnorms(end:-1:1) ./ arnorms;
- % arnormfacs = arnorms(randperm(neg.mu)) ./ arnorms;
- arnorms = arnorms(end:-1:1); % for the record
- if flgActiveCMA < 20
- arzneg = arzneg .* repmat(arnormfacs(idxnorms), N, 1); % E x*x' is N
- % arzneg = sqrt(N) * arzneg ./ repmat(sqrt(sum(arzneg.^2, 1)), N, 1); % E x*x' is N
+ if myevalbool(opts.EvalInitialX)
+ fitness.hist(1)=feval(fitfun, xmean, varargin{:});
+ fitness.histsel(1)=fitness.hist(1);
+ counteval = counteval + 1;
+ if fitness.hist(1) < out.solutions.bestever.f
+ out.solutions.bestever.x = xmean;
+ out.solutions.bestever.f = fitness.hist(1);
+ out.solutions.bestever.evals = counteval;
+ bestever = out.solutions.bestever;
+ end
+ else
+ fitness.hist(1)=NaN;
+ fitness.histsel(1)=NaN;
end
- if flgActiveCMA < 10 && neg.mu == mu % weighted sum
- if mod(flgActiveCMA, 10) == 1 % TODO: prevent this with a less tight but more efficient check (see below)
- Ccheck = arzneg * diag(weights) * arzneg'; % in order to check the largest EV
- end
- artmp = BD * arzneg;
- Cneg = artmp * diag(weights) * artmp';
- else % simple sum
- if mod(flgActiveCMA, 10) == 1
- Ccheck = (1/neg.mu) * arzneg*arzneg'; % in order to check largest EV
- end
- artmp = BD * arzneg;
- Cneg = (1/neg.mu) * artmp*artmp';
+
+ % initialize random number generator
+% $$$ if ischar(opts.Seed)
+% $$$ randn('state', eval(opts.Seed)); % random number generator state
+% $$$ else
+% $$$ randn('state', opts.Seed);
+% $$$ end
+%qqq
+% load(opts.SaveFilename, 'startseed');
+% randn('state', startseed);
+% disp(['SEED RELOADED FROM ' opts.SaveFilename]);
+% startseed = randn('state'); % for retrieving in saved variables
+ % Initialize further constants
+ chiN=N^0.5*(1-1/(4*N)+1/(21*N^2)); % expectation of
+ % ||N(0,I)|| == norm(randn(N,1))
+
+ countiter = 0;
+ % Initialize records and output
+ if irun == 1
+ time.t0 = clock;
+
+ % TODO: keep also median solution?
+ out.evals = counteval; % should be first entry
+ out.stopflag = {};
+
+ outiter = 0;
+
+ % Write headers to output data files
+ filenameprefix = opts.LogFilenamePrefix;
+ if savemodulo && savetime
+ filenames = {};
+ filenames(end+1) = {'axlen'};
+ filenames(end+1) = {'fit'};
+ filenames(end+1) = {'stddev'};
+ filenames(end+1) = {'xmean'};
+ filenames(end+1) = {'xrecentbest'};
+ str = [' (startseed=' num2str(startseed(2)) ...
+ ', ' num2str(clock, '%d/%02d/%d %d:%d:%2.2f') ')'];
+ for namecell = filenames(:)'
+ name = namecell{:};
+
+ [fid, err] = fopen(['./' filenameprefix name '.dat'], 'w');
+ if fid < 1 % err ~= 0
+ warning(['could not open ' filenameprefix name '.dat']);
+ filenames(find(strcmp(filenames,name))) = [];
+ else
+ % fprintf(fid, '%s\n', ...
+ % ['<CMAES-OUTPUT version="' cmaVersion '">']);
+ % fprintf(fid, [' <NAME>' name '</NAME>\n']);
+ % fprintf(fid, [' <DATE>' date() '</DATE>\n']);
+ % fprintf(fid, ' <PARAMETERS>\n');
+ % fprintf(fid, [' dimension=' num2str(N) '\n']);
+ % fprintf(fid, ' </PARAMETERS>\n');
+ % different cases for DATA columns annotations here
+ % fprintf(fid, ' <DATA');
+ if strcmp(name, 'axlen')
+ fprintf(fid, ['%% columns="iteration, evaluation, sigma, ' ...
+ 'max axis length, min axis length, ' ...
+ 'all principal axes lengths (sorted square roots ' ...
+ 'of eigenvalues of C)"' str]);
+ elseif strcmp(name, 'fit')
+ fprintf(fid, ['%% columns="iteration, evaluation, sigma, axis ratio, bestever,' ...
+ ' best, median, worst fitness function value,' ...
+ ' further objective values of best"' str]);
+ elseif strcmp(name, 'stddev')
+ fprintf(fid, ['%% columns=["iteration, evaluation, sigma, void, void, ' ...
+ 'stds==sigma*sqrt(diag(C))"' str]);
+ elseif strcmp(name, 'xmean')
+ fprintf(fid, ['%% columns="iteration, evaluation, void, ' ...
+ 'void, void, xmean"' str]);
+ elseif strcmp(name, 'xrecentbest')
+ fprintf(fid, ['%% columns="iteration, evaluation, fitness, ' ...
+ 'void, void, xrecentbest"' str]);
+ end
+ fprintf(fid, '\n'); % DATA
+ if strcmp(name, 'xmean')
+ fprintf(fid, '%ld %ld 0 0 0 ', 0, counteval);
+ % fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean);
+ %qqq fprintf(fid, msprintf('%e ', genophenotransform(out.genopheno, xmean)) + '\n');
+ fprintf(fid, '%e ', xmean);
+ fprintf(fid, '\n');
+ end
+ fclose(fid);
+ clear fid; % preventing
+ end
+ end % for files
+ end % savemodulo
+ end % irun == 1
+
+ end % else flgresume
+
+ % -------------------- Generation Loop --------------------------------
+ stopflag = {};
+ while isempty(stopflag)
+ % set internal parameters
+ if countiter == 0 || lambda ~= lambda_last
+ if countiter > 0 && floor(log10(lambda)) ~= floor(log10(lambda_last)) ...
+ && flgdisplay
+ disp([' lambda = ' num2str(lambda)]);
+ lambda_hist(:,end+1) = [countiter+1; lambda];
+ else
+ lambda_hist = [countiter+1; lambda];
+ end
+ lambda_last = lambda;
+ % Strategy internal parameter setting: Selection
+ mu = myeval(opts.ParentNumber); % number of parents/points for recombination
+ if strncmp(lower(opts.RecombinationWeights), 'equal', 3)
+ weights = ones(mu,1); % (mu_I,lambda)-CMA-ES
+ elseif strncmp(lower(opts.RecombinationWeights), 'linear', 3)
+ weights = mu+0.5-(1:mu)';
+ elseif strncmp(lower(opts.RecombinationWeights), 'superlinear', 3)
+ weights = log(mu+0.5)-log(1:mu)'; % muXone array for weighted recombination
+ % qqq mu can be non-integer and
+ % should become ceil(mu-0.5) (minor correction)
+ else
+ error(['Recombination weights to be "' opts.RecombinationWeights ...
+ '" is not implemented']);
+ end
+ mueff=sum(weights)^2/sum(weights.^2); % variance-effective size of mu
+ weights = weights/sum(weights); % normalize recombination weights array
+ if mueff == lambda
+ error(['Combination of values for PopSize, ParentNumber and ' ...
+ ' and RecombinationWeights is not reasonable']);
+ end
+
+ % Strategy internal parameter setting: Adaptation
+ cc = myeval(opts.CMA.ccum); % time constant for cumulation for covariance matrix
+ cs = myeval(opts.CMA.cs);
+
+ % old way TODO: remove this at some point
+ % mucov = mueff; % size of mu used for calculating learning rate ccov
+ % ccov = (1/mucov) * 2/(N+1.41)^2 ... % learning rate for covariance matrix
+ % + (1-1/mucov) * min(1,(2*mucov-1)/((N+2)^2+mucov));
+
+ % new way
+ if myevalbool(opts.CMA.on)
+ ccov1 = myeval(opts.CMA.ccov1);
+ ccovmu = min(1-ccov1, myeval(opts.CMA.ccovmu));
+ else
+ ccov1 = 0;
+ ccovmu = 0;
+ end
+
+ % flgDiagonalOnly = -lambda*4*1/ccov; % for ccov==1 it is not needed
+ % 0 : C will never be diagonal anymore
+ % 1 : C will always be diagonal
+ % >1: C is diagonal for first iterations, set to 0 afterwards
+ if flgDiagonalOnly < 1
+ flgDiagonalOnly = 0;
+ end
+ if flgDiagonalOnly
+ ccov1_sep = min(1, ccov1 * (N+1.5) / 3);
+ ccovmu_sep = min(1-ccov1_sep, ccovmu * (N+1.5) / 3);
+ elseif N > 98 && flgdisplay && countiter == 0
+ disp('consider option DiagonalOnly for high-dimensional problems');
+ end
+
+ % ||ps|| is close to sqrt(mueff/N) for mueff large on linear fitness
+ %damps = ... % damping for step size control, usually close to one
+ % (1 + 2*max(0,sqrt((mueff-1)/(N+1))-1)) ... % limit sigma increase
+ % * max(0.3, ... % reduce damps, if max. iteration number is small
+ % 1 - N/min(stopMaxIter,stopMaxFunEvals/lambda)) + cs;
+ damps = myeval(opts.CMA.damps);
+ if noiseHandling
+ noiseReevals = min(myeval(opts.Noise.reevals), lambda);
+ noiseAlpha = myeval(opts.Noise.alphasigma);
+ noiseEpsilon = myeval(opts.Noise.epsilon);
+ noiseTheta = myeval(opts.Noise.theta);
+ noisecum = myeval(opts.Noise.cum);
+ noiseCutOff = myeval(opts.Noise.cutoff); % arguably of minor relevance
+ else
+ noiseReevals = 0; % more convenient in later coding
+ end
+
+ %qqq hacking of a different parameter setting, e.g. for ccov or damps,
+ % can be done here, but is not necessary anymore, see opts.CMA.
+ % ccov1 = 0.0*ccov1; disp(['CAVE: ccov1=' num2str(ccov1)]);
+ % ccovmu = 0.0*ccovmu; disp(['CAVE: ccovmu=' num2str(ccovmu)]);
+ % damps = inf*damps; disp(['CAVE: damps=' num2str(damps)]);
+ % cc = 1; disp(['CAVE: cc=' num2str(cc)]);
+
+ end
+
+ % Display initial message
+ if countiter == 0 && flgdisplay
+ if mu == 1
+ strw = '100';
+ elseif mu < 8
+ strw = [sprintf('%.0f', 100*weights(1)) ...
+ sprintf(' %.0f', 100*weights(2:end)')];
+ else
+ strw = [sprintf('%.2g ', 100*weights(1:2)') ...
+ sprintf('%.2g', 100*weights(3)') '...' ...
+ sprintf(' %.2g', 100*weights(end-1:end)') ']%, '];
+ end
+ if irun > 1
+ strrun = [', run ' num2str(irun)];
+ else
+ strrun = '';
+ end
+ disp([' n=' num2str(N) ': (' num2str(mu) ',' ...
+ num2str(lambda) ')-CMA-ES(w=[' ...
+ strw ']%, ' ...
+ 'mu_eff=' num2str(mueff,'%.1f') ...
+ ') on function ' ...
+ (fitfun) strrun]);
+ if flgDiagonalOnly == 1
+ disp(' C is diagonal');
+ elseif flgDiagonalOnly
+ disp([' C is diagonal for ' num2str(floor(flgDiagonalOnly)) ' iterations']);
+ end
end
- % check pos.def. and set learning rate neg.ccov accordingly,
- % this check makes the original choice of neg.ccov extremly failsafe
- % still assuming C == BD*BD', which is only approxim. correct
- if mod(flgActiveCMA, 10) == 1 && 1 - neg.ccov * arnorms(idxnorms).^2 * weights < neg.minresidualvariance
- % TODO: the simple and cheap way would be to set
- % fac = 1 - ccovmu - ccov1 OR 1 - mueff/lambda and
- % neg.ccov = fac*(1 - neg.minresidualvariance) / (arnorms(idxnorms).^2 * weights)
- % this is the more sophisticated way:
- % maxeigenval = eigs(arzneg * arzneg', 1, 'lm', eigsopts); % not faster
- maxeigenval = max(eig(Ccheck)); % norm is much slower, because (norm()==max(svd())
- %disp([countiter log10([neg.ccov, maxeigenval, arnorms(idxnorms).^2 * weights, max(arnorms)^2]), ...
- % neg.ccov * arnorms(idxnorms).^2 * weights])
- % pause
- % remove less than ??34*(1-cmu)%?? of variance in any direction
- % 1-ccovmu is the variance left from the old C
- neg.ccovfinal = min(neg.ccov, (1-ccovmu)*(1-neg.minresidualvariance)/maxeigenval);
- % -ccov1 removed to avoid error message??
- if neg.ccovfinal < neg.ccov
- disp(['active CMA at iteration ' num2str(countiter) ...
- ': max EV ==', num2str([maxeigenval, neg.ccov, neg.ccovfinal])]);
- end
+ flush;
+
+ countiter = countiter + 1;
+
+ % Generate and evaluate lambda offspring
+
+ fitness.raw = repmat(NaN, 1, lambda + noiseReevals);
+
+ % parallel evaluation
+ if flgEvalParallel
+ arz = randn(N,lambda);
+
+ if ~flgDiagonalOnly
+ arx = repmat(xmean, 1, lambda) + sigma * (BD * arz); % Eq. (1)
+ else
+ arx = repmat(xmean, 1, lambda) + repmat(sigma * diagD, 1, lambda) .* arz;
+ end
+
+ if noiseHandling
+ if noiseEpsilon == 0
+ arx = [arx arx(:,1:noiseReevals)];
+ elseif flgDiagonalOnly
+ arx = [arx arx(:,1:noiseReevals) + ...
+ repmat(noiseEpsilon * sigma * diagD, 1, noiseReevals) ...
+ .* randn(N,noiseReevals)];
+ else
+ arx = [arx arx(:,1:noiseReevals) + ...
+ noiseEpsilon * sigma * ...
+ (BD * randn(N,noiseReevals))];
+ end
+ end
+
+ % You may handle constraints here. You may either resample
+ % arz(:,k) and/or multiply it with a factor between -1 and 1
+ % (the latter will decrease the overall step size) and
+ % recalculate arx accordingly. Do not change arx or arz in any
+ % other way.
+
+ if ~bnd.isactive
+ arxvalid = arx;
+ else
+ arxvalid = xintobounds(arx, lbounds, ubounds);
+ end
+ % You may handle constraints here. You may copy and alter
+ % (columns of) arxvalid(:,k) only for the evaluation of the
+ % fitness function. arx and arxvalid should not be changed.
+ fitness.raw = feval(fitfun, arxvalid, varargin{:});
+ countevalNaN = countevalNaN + sum(isnan(fitness.raw));
+ counteval = counteval + sum(~isnan(fitness.raw));
end
- % xmean = xold; % the distribution does not degenerate!?
- % update C
- C = (1-ccov1-ccovmu+neg.alphaold*neg.ccovfinal+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix
- + ccov1 * pc*pc' ... % plus rank one update
- + (ccovmu + (1-neg.alphaold)*neg.ccovfinal) ... % plus rank mu update
- * arpos * (repmat(weights,1,N) .* arpos') ...
- - neg.ccovfinal * Cneg; % minus rank mu update
- else % no active (negative) update
- C = (1-ccov1-ccovmu+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix
- + ccov1 * pc*pc' ... % plus rank one update
- + ccovmu ... % plus rank mu update
- * arpos * (repmat(weights,1,N) .* arpos');
- % is now O(mu*N^2 + mu*N), was O(mu*N^2 + mu^2*N) when using diag(weights)
- % for mu=30*N it is now 10 times faster, overall 3 times faster
- end
- diagC = diag(C);
- end
- end
-
- % the following is de-preciated and will be removed in future
- % better setting for cc makes this hack obsolete
- if 11 < 2 && ~flgscience
- % remove momentum in ps, if ps is large and fitness is getting worse.
- % this should rarely happen.
- % this might very well be counterproductive in dynamic environments
- if sum(ps.^2)/N > 1.5 + 10*(2/N)^.5 && ...
- fitness.histsel(1) > max(fitness.histsel(2:3))
- ps = ps * sqrt(N*(1+max(0,log(sum(ps.^2)/N))) / sum(ps.^2));
- if flgdisplay
- disp(['Momentum in ps removed at [niter neval]=' ...
- num2str([countiter counteval]) ']']);
- end
- end
- end
-
- % Adapt sigma
- if flg_future_setting % according to a suggestion from Dirk Arnold (2000)
- % exp(1) is still not reasonably small enough
- sigma = sigma * exp(min(1, (sum(ps.^2)/N - 1)/2 * cs/damps)); % Eq. (5)
- else
- % exp(1) is still not reasonably small enough
- sigma = sigma * exp(min(1, (sqrt(sum(ps.^2))/chiN - 1) * cs/damps)); % Eq. (5)
- end
- % disp([countiter norm(ps)/chiN]);
-
- if 11 < 3 % testing with optimal step-size
- if countiter == 1
- disp('*********** sigma set to const * ||x|| ******************');
- end
- sigma = 0.04 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=1000:25e3
- sigma = 0.3 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=(40,1000):17e3
- % 75e3 with def (1.5)
- % 35e3 with damps=0.25
- end
- if 11 < 3
- if countiter == 1
- disp('*********** xmean set to const ******************');
- end
- xmean = ones(N,1);
- end
-
- % Update B and D from C
-
- if ~flgDiagonalOnly && (ccov1+ccovmu+neg.ccov) > 0 && mod(countiter, 1/(ccov1+ccovmu+neg.ccov)/N/10) < 1
- C=triu(C)+triu(C,1)'; % enforce symmetry to prevent complex numbers
- [B,tmp] = eig(C); % eigen decomposition, B==normalized eigenvectors
- % effort: approx. 15*N matrix-vector multiplications
- diagD = diag(tmp);
-
- if any(~isfinite(diagD))
- clear idx; % prevents error under octave
- save(['tmp' opts.SaveFilename]);
- error(['function eig returned non-finited eigenvalues, cond(C)=' ...
- num2str(cond(C)) ]);
- end
- if any(any(~isfinite(B)))
- clear idx; % prevents error under octave
- save(['tmp' opts.SaveFilename]);
- error(['function eig returned non-finited eigenvectors, cond(C)=' ...
- num2str(cond(C)) ]);
- end
- % limit condition of C to 1e14 + 1
- if min(diagD) <= 0
- if stopOnWarnings
- stopflag(end+1) = {'warnconditioncov'};
- else
- warning(['Iteration ' num2str(countiter) ...
- ': Eigenvalue (smaller) zero']);
- diagD(diagD<0) = 0;
- tmp = max(diagD)/1e14;
- C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1);
- end
- end
- if max(diagD) > 1e14*min(diagD)
- if stopOnWarnings
- stopflag(end+1) = {'warnconditioncov'};
- else
- warning(['Iteration ' num2str(countiter) ': condition of C ' ...
- 'at upper limit' ]);
- tmp = max(diagD)/1e14 - min(diagD);
- C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1);
- end
- end
+ % non-parallel evaluation and remaining NaN-values
+ % set also the reevaluated solution to NaN
+ fitness.raw(lambda + find(isnan(fitness.raw(1:noiseReevals)))) = NaN;
+ for k=find(isnan(fitness.raw)),
+ % fitness.raw(k) = NaN;
+ tries = 0;
+ % Resample, until fitness is not NaN
+ while isnan(fitness.raw(k))
+ if k <= lambda % regular samples (not the re-evaluation-samples)
+ arz(:,k) = randn(N,1); % (re)sample
+
+ if flgDiagonalOnly
+ arx(:,k) = xmean + sigma * diagD .* arz(:,k); % Eq. (1)
+ else
+ arx(:,k) = xmean + sigma * (BD * arz(:,k)); % Eq. (1)
+ end
+ else % re-evaluation solution with index > lambda
+ if flgDiagonalOnly
+ arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * diagD .* randn(N,1);
+ else
+ arx(:,k) = arx(:,k-lambda) + (noiseEpsilon * sigma) * (BD * randn(N,1));
+ end
+ end
+
+ % You may handle constraints here. You may either resample
+ % arz(:,k) and/or multiply it with a factor between -1 and 1
+ % (the latter will decrease the overall step size) and
+ % recalculate arx accordingly. Do not change arx or arz in any
+ % other way.
+
+ if ~bnd.isactive
+ arxvalid(:,k) = arx(:,k);
+ else
+ arxvalid(:,k) = xintobounds(arx(:,k), lbounds, ubounds);
+ end
+ % You may handle constraints here. You may copy and alter
+ % (columns of) arxvalid(:,k) only for the evaluation of the
+ % fitness function. arx should not be changed.
+ fitness.raw(k) = feval(fitfun, arxvalid(:,k), varargin{:});
+ tries = tries + 1;
+ if isnan(fitness.raw(k))
+ countevalNaN = countevalNaN + 1;
+ end
+ if mod(tries, 100) == 0
+ warning([num2str(tries) ...
+ ' NaN objective function values at evaluation ' ...
+ num2str(counteval)]);
+ end
+ end
+ counteval = counteval + 1; % retries due to NaN are not counted
+ end
- diagC = diag(C);
- diagD = sqrt(diagD); % D contains standard deviations now
- % diagD = diagD / prod(diagD)^(1/N); C = C / prod(diagD)^(2/N);
- BD = B.*repmat(diagD',N,1); % O(n^2)
- end % if mod
-
- % Align/rescale order of magnitude of scales of sigma and C for nicer output
- % not a very usual case
- if 1 < 2 && sigma > 1e10*max(diagD)
- fac = sigma / max(diagD);
- sigma = sigma/fac;
- pc = fac * pc;
- diagD = fac * diagD;
- if ~flgDiagonalOnly
- C = fac^2 * C; % disp(fac);
- BD = B.*repmat(diagD',N,1); % O(n^2), but repmat might be inefficient todo?
- end
- diagC = fac^2 * diagC;
- end
-
- if flgDiagonalOnly > 1 && countiter > flgDiagonalOnly
- % full covariance matrix from now on
- flgDiagonalOnly = 0;
- B = eye(N,N);
- BD = diag(diagD);
- C = diag(diagC); % is better, because correlations are spurious anyway
- end
-
- if noiseHandling
- if countiter == 1 % assign firstvarargin for noise treatment e.g. as #reevaluations
- if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1})
- if irun == 1
- firstvarargin = varargin{1};
- else
- varargin{1} = firstvarargin; % reset varargin{1}
- end
- else
- firstvarargin = 0;
- end
- end
- if noiseSS < 0 && noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
- varargin{1} = max(noiseMinMaxEvals(1), varargin{1} / noiseAlphaEvals^(1/4)); % still experimental
- elseif noiseSS > 0
- if ~isempty(noiseCallback) % to be removed?
- res = feval(noiseCallback); % should also work without output argument!?
- if ~isempty(res) && res > 1 % TODO: decide for interface of callback
- % also a dynamic popsize could be done here
- sigma = sigma * noiseAlpha;
- end
- else
- if noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
- varargin{1} = min(noiseMinMaxEvals(2), varargin{1} * noiseAlphaEvals);
- end
-
- sigma = sigma * noiseAlpha;
- % lambda = ceil(0.1 * sqrt(lambda) + lambda);
- % TODO: find smallest increase of lambda with log-linear
- % convergence in iterations
- end
- % qqq experimental: take a mean to estimate the true optimum
- noiseN = noiseN + 1;
- if noiseN == 1
- noiseX = xmean;
- else
- noiseX = noiseX + (3/noiseN) * (xmean - noiseX);
- end
- end
- end
-
- % ----- numerical error management -----
- % Adjust maximal coordinate axis deviations
- if any(sigma*sqrt(diagC) > maxdx)
- sigma = min(maxdx ./ sqrt(diagC));
- %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
- % 'deviation at upper limit of ' num2str(maxdx)]);
- % stopflag(end+1) = {'maxcoorddev'};
- end
- % Adjust minimal coordinate axis deviations
- if any(sigma*sqrt(diagC) < mindx)
- sigma = max(mindx ./ sqrt(diagC)) * exp(0.05+cs/damps);
- %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
- % 'deviation at lower limit of ' num2str(mindx)]);
- % stopflag(end+1) = {'mincoorddev'};;
- end
- % Adjust too low coordinate axis deviations
- if any(xmean == xmean + 0.2*sigma*sqrt(diagC))
- if stopOnWarnings
- stopflag(end+1) = {'warnnoeffectcoord'};
- else
- warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
- 'deviation too low' ]);
- if flgDiagonalOnly
- diagC = diagC + (ccov1_sep+ccovmu_sep) * (diagC .* ...
- (xmean == xmean + 0.2*sigma*sqrt(diagC)));
- else
- C = C + (ccov1+ccovmu) * diag(diagC .* ...
- (xmean == xmean + 0.2*sigma*sqrt(diagC)));
- end
- sigma = sigma * exp(0.05+cs/damps);
- end
- end
- % Adjust step size in case of (numerical) precision problem
- if flgDiagonalOnly
- tmp = 0.1*sigma*diagD;
- else
- tmp = 0.1*sigma*BD(:,1+floor(mod(countiter,N)));
- end
- if all(xmean == xmean + tmp)
- i = 1+floor(mod(countiter,N));
- if stopOnWarnings
- stopflag(end+1) = {'warnnoeffectaxis'};
- else
- warning(['Iteration ' num2str(countiter) ...
- ': main axis standard deviation ' ...
- num2str(sigma*diagD(i)) ' has no effect' ]);
- sigma = sigma * exp(0.2+cs/damps);
- end
- end
- % Adjust step size in case of equal function values (flat fitness)
- % isequalfuncvalues = 0;
- if fitness.sel(1) == fitness.sel(1+ceil(0.1+lambda/4))
- % isequalfuncvalues = 1;
- if stopOnEqualFunctionValues
- arrEqualFunvals = [countiter arrEqualFunvals(1:end-1)];
- % stop if this happens in more than 33%
- if arrEqualFunvals(end) > countiter - 3 * length(arrEqualFunvals)
- stopflag(end+1) = {'equalfunvals'};
- end
- else
- if flgWarnOnEqualFunctionValues
- warning(['Iteration ' num2str(countiter) ...
- ': equal function values f=' num2str(fitness.sel(1)) ...
- ' at maximal main axis sigma ' ...
- num2str(sigma*max(diagD))]);
- end
- sigma = sigma * exp(0.2+cs/damps);
- end
- end
- % Adjust step size in case of equal function values
- if countiter > 2 && myrange([fitness.hist fitness.sel(1)]) == 0
- if stopOnWarnings
- stopflag(end+1) = {'warnequalfunvalhist'};
- else
- warning(['Iteration ' num2str(countiter) ...
- ': equal function values in history at maximal main ' ...
- 'axis sigma ' num2str(sigma*max(diagD))]);
- sigma = sigma * exp(0.2+cs/damps);
- end
- end
-
- % ----- end numerical error management -----
-
- % Keep overall best solution
- out.evals = counteval;
- out.solutions.evals = counteval;
- out.solutions.mean.x = xmean;
- out.solutions.mean.f = fmean;
- out.solutions.mean.evals = counteval;
- out.solutions.recentbest.x = arxvalid(:, fitness.idx(1));
- out.solutions.recentbest.f = fitness.raw(1);
- out.solutions.recentbest.evals = counteval + fitness.idx(1) - lambda;
- out.solutions.recentworst.x = arxvalid(:, fitness.idx(end));
- out.solutions.recentworst.f = fitness.raw(end);
- out.solutions.recentworst.evals = counteval + fitness.idx(end) - lambda;
- if fitness.hist(1) < out.solutions.bestever.f
- out.solutions.bestever.x = arxvalid(:, fitness.idx(1));
- out.solutions.bestever.f = fitness.hist(1);
- out.solutions.bestever.evals = counteval + fitness.idx(1) - lambda;
- bestever = out.solutions.bestever;
- end
-
- % Set stop flag
- if fitness.raw(1) <= stopFitness, stopflag(end+1) = {'fitness'}; end
- if counteval >= stopMaxFunEvals, stopflag(end+1) = {'maxfunevals'}; end
- if countiter >= stopMaxIter, stopflag(end+1) = {'maxiter'}; end
- if all(sigma*(max(abs(pc), sqrt(diagC))) < stopTolX)
- stopflag(end+1) = {'tolx'};
- end
- if any(sigma*sqrt(diagC) > stopTolUpX)
- stopflag(end+1) = {'tolupx'};
- end
- if sigma*max(diagD) == 0 % should never happen
- stopflag(end+1) = {'bug'};
- end
- if countiter > 2 && myrange([fitness.sel fitness.hist]) <= stopTolFun
- stopflag(end+1) = {'tolfun'};
- end
- if countiter >= length(fitness.hist) && myrange(fitness.hist) <= stopTolHistFun
- stopflag(end+1) = {'tolhistfun'};
- end
- l = floor(length(fitness.histbest)/3);
- if 1 < 2 && stopOnStagnation && ... % leads sometimes early stop on ftablet, fcigtab
- countiter > N * (5+100/lambda) && ...
- length(fitness.histbest) > 100 && ...
- median(fitness.histmedian(1:l)) >= median(fitness.histmedian(end-l:end)) && ...
- median(fitness.histbest(1:l)) >= median(fitness.histbest(end-l:end))
- stopflag(end+1) = {'stagnation'};
- end
-
- if counteval >= stopFunEvals || countiter >= stopIter
- stopflag(end+1) = {'stoptoresume'};
- if length(stopflag) == 1 && flgsaving == 0
- error('To resume later the saving option needs to be set');
- end
- end
- % read stopping message from file signals.par
- if flgreadsignals
- fid = fopen('./signals.par', 'rt'); % can be performance critical
- while fid > 0
- strline = fgetl(fid); %fgets(fid, 300);
- if strline < 0 % fgets and fgetl returns -1 at end of file
- break;
- end
- % 'stop filename' sets stopflag to manual
- str = sscanf(strline, ' %s %s', 2);
- if strcmp(str, ['stop' opts.LogFilenamePrefix])
- stopflag(end+1) = {'manual'};
- break;
- end
- % 'skip filename run 3' skips a run, but not the last
- str = sscanf(strline, ' %s %s %s', 3);
- if strcmp(str, ['skip' opts.LogFilenamePrefix 'run'])
- i = strfind(strline, 'run');
- if irun == sscanf(strline(i+3:end), ' %d ', 1) && irun <= myeval(opts.Restarts)
- stopflag(end+1) = {'skipped'};
- end
- end
- end % while, break
- if fid > 0
- fclose(fid);
- clear fid; % prevents strange error under octave
- end
- end
-
- out.stopflag = stopflag;
-
- % ----- output generation -----
- if verbosemodulo > 0 && isfinite(verbosemodulo)
- if countiter == 1 || mod(countiter, 10*verbosemodulo) < 1
- disp(['Iterat, #Fevals: Function Value (median,worst) ' ...
- '|Axis Ratio|' ...
- 'idx:Min SD idx:Max SD']);
- end
- if mod(countiter, verbosemodulo) < 1 ...
- || (verbosemodulo > 0 && isfinite(verbosemodulo) && ...
- (countiter < 3 || ~isempty(stopflag)))
- [minstd minstdidx] = min(sigma*sqrt(diagC));
- [maxstd maxstdidx] = max(sigma*sqrt(diagC));
- % format display nicely
- disp([repmat(' ',1,4-floor(log10(countiter))) ...
- num2str(countiter) ' , ' ...
- repmat(' ',1,5-floor(log10(counteval))) ...
- num2str(counteval) ' : ' ...
- num2str(fitness.hist(1), '%.13e') ...
- ' +(' num2str(median(fitness.raw)-fitness.hist(1), '%.0e ') ...
- ',' num2str(max(fitness.raw)-fitness.hist(1), '%.0e ') ...
- ') | ' ...
- num2str(max(diagD)/min(diagD), '%4.2e') ' | ' ...
- repmat(' ',1,1-floor(log10(minstdidx))) num2str(minstdidx) ':' ...
- num2str(minstd, ' %.1e') ' ' ...
- repmat(' ',1,1-floor(log10(maxstdidx))) num2str(maxstdidx) ':' ...
- num2str(maxstd, ' %.1e')]);
- end
- end
-
- % measure time for recording data
- if countiter < 3
- time.c = 0.05;
- time.nonoutput = 0;
- time.recording = 0;
- time.saving = 0.15; % first saving after 3 seconds of 100 iterations
- time.plotting = 0;
- elseif countiter > 300
- % set backward horizon, must be long enough to cover infrequent plotting etc
- % time.c = min(1, time.nonoutput/3 + 1e-9);
- time.c = max(1e-5, 0.1/sqrt(countiter)); % mean over all or 1e-5
- end
- % get average time per iteration
- time.t1 = clock;
- time.act = max(0,etime(time.t1, time.t0));
- time.nonoutput = (1-time.c) * time.nonoutput ...
- + time.c * time.act;
-
- time.recording = (1-time.c) * time.recording; % per iteration
- time.saving = (1-time.c) * time.saving;
- time.plotting = (1-time.c) * time.plotting;
-
- % record output data, concerning time issues
- if savemodulo && savetime && (countiter < 1e2 || ~isempty(stopflag) || ...
- countiter >= outiter + savemodulo)
- outiter = countiter;
- % Save output data to files
- for namecell = filenames(:)'
- name = namecell{:};
-
- [fid, err] = fopen(['./' filenameprefix name '.dat'], 'a');
- if fid < 1 % err ~= 0
- warning(['could not open ' filenameprefix name '.dat']);
- else
- if strcmp(name, 'axlen')
- fprintf(fid, '%d %d %e %e %e ', countiter, counteval, sigma, ...
- max(diagD), min(diagD));
- fprintf(fid, '%e ', sort(diagD));
- fprintf(fid, '\n');
- elseif strcmp(name, 'disp') % TODO
- elseif strcmp(name, 'fit')
- fprintf(fid, '%ld %ld %e %e %25.18e %25.18e %25.18e %25.18e', ...
- countiter, counteval, sigma, max(diagD)/min(diagD), ...
- out.solutions.bestever.f, ...
- fitness.raw(1), median(fitness.raw), fitness.raw(end));
- if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1}) && varargin{1} ~= 0
- fprintf(fid, ' %f', varargin{1});
- end
- fprintf(fid, '\n');
- elseif strcmp(name, 'stddev')
- fprintf(fid, '%ld %ld %e 0 0 ', countiter, counteval, sigma);
- fprintf(fid, '%e ', sigma*sqrt(diagC));
- fprintf(fid, '\n');
- elseif strcmp(name, 'xmean')
- if isnan(fmean)
- fprintf(fid, '%ld %ld 0 0 0 ', countiter, counteval);
- else
- fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean);
- end
- fprintf(fid, '%e ', xmean);
- fprintf(fid, '\n');
- elseif strcmp(name, 'xrecentbest')
- % TODO: fitness is inconsistent with x-value
- fprintf(fid, '%ld %ld %25.18e 0 0 ', countiter, counteval, fitness.raw(1));
- fprintf(fid, '%e ', arx(:,fitness.idx(1)));
- fprintf(fid, '\n');
- end
- fclose(fid);
- end
- end
-
- % get average time for recording data
- time.t2 = clock;
- time.recording = time.recording + time.c * max(0,etime(time.t2, time.t1));
-
- % plot
- if flgplotting && countiter > 1
- if countiter == 2
- iterplotted = 0;
- end
- if ~isempty(stopflag) || ...
- ((time.nonoutput+time.recording) * (countiter - iterplotted) > 1 && ...
- time.plotting < 0.05 * (time.nonoutput+time.recording))
- local_plotcmaesdat(324, filenameprefix);
- iterplotted = countiter;
- % outplot(out); % outplot defined below
- if time.plotting == 0 % disregard opening of the window
- time.plotting = time.nonoutput+time.recording;
+ fitness.sel = fitness.raw;
+
+ % ----- handle boundaries -----
+ if 1 < 3 && bnd.isactive
+ % Get delta fitness values
+ val = myprctile(fitness.raw, [25 75]);
+ % more precise would be exp(mean(log(diagC)))
+ val = (val(2) - val(1)) / N / mean(diagC) / sigma^2;
+ %val = (myprctile(fitness.raw, 75) - myprctile(fitness.raw, 25)) ...
+ % / N / mean(diagC) / sigma^2;
+ % Catch non-sensible values
+ if ~isfinite(val)
+ warning('Non-finite fitness range');
+ val = max(bnd.dfithist);
+ elseif val == 0 % happens if all points are out of bounds
+ val = min(bnd.dfithist(bnd.dfithist>0)); % seems not to make sense, given all solutions are out of bounds
+ elseif bnd.validfitval == 0 % flag that first sensible val was found
+ bnd.dfithist = [];
+ bnd.validfitval = 1;
+ end
+
+ % Store delta fitness values
+ if length(bnd.dfithist) < 20+(3*N)/lambda
+ bnd.dfithist = [bnd.dfithist val];
+ else
+ bnd.dfithist = [bnd.dfithist(2:end) val];
+ end
+
+ [tx ti] = xintobounds(xmean, lbounds, ubounds);
+
+ % Set initial weights
+ if bnd.iniphase
+ if any(ti)
+ bnd.weights(find(bnd.isbounded)) = 2.0002 * median(bnd.dfithist);
+ if bnd.flgscale == 0 % scale only initial weights then
+ dd = diagC;
+ idx = find(bnd.isbounded);
+ dd = dd(idx) / mean(dd); % remove mean scaling
+ bnd.weights(idx) = bnd.weights(idx) ./ dd;
+ end
+ if bnd.validfitval && countiter > 2
+ bnd.iniphase = 0;
+ end
+ end
+ end
+
+ % Increase weights
+ if 1 < 3 && any(ti) % any coordinate of xmean out of bounds
+ % judge distance of xmean to boundary
+ tx = xmean - tx;
+ idx = (ti ~= 0 & abs(tx) > 3*max(1,sqrt(N)/mueff) ...
+ * sigma*sqrt(diagC)) ;
+ % only increase if xmean is moving away
+ idx = idx & (sign(tx) == sign(xmean - xold));
+ if ~isempty(idx) % increase
+ % the factor became 1.2 instead of 1.1, because
+ % changed from max to min in version 3.52
+ bnd.weights(idx) = 1.2^(min(1, mueff/10/N)) * bnd.weights(idx);
+ end
+ end
+
+ % Calculate scaling biased to unity, product is one
+ if bnd.flgscale ~= 0
+ bnd.scale = exp(0.9*(log(diagC)-mean(log(diagC))));
+ end
+
+ % Assigned penalized fitness
+ bnd.arpenalty = (bnd.weights ./ bnd.scale)' * (arxvalid - arx).^2;
+
+ fitness.sel = fitness.raw + bnd.arpenalty;
+
+ end % handle boundaries
+ % ----- end handle boundaries -----
+
+ % compute noise measurement and reduce fitness arrays to size lambda
+ if noiseHandling
+ [noiseS] = local_noisemeasurement(fitness.sel(1:lambda), ...
+ fitness.sel(lambda+(1:noiseReevals)), ...
+ noiseReevals, noiseTheta, noiseCutOff);
+ if countiter == 1 % TODO: improve this very rude way of initialization
+ noiseSS = 0;
+ noiseN = 0; % counter for mean
+ end
+ noiseSS = noiseSS + noisecum * (noiseS - noiseSS);
+
+ % noise-handling could be done here, but the original sigma is still needed
+ % disp([noiseS noiseSS noisecum])
+
+ fitness.rawar12 = fitness.raw; % just documentary
+ fitness.selar12 = fitness.sel; % just documentary
+ % qqq refine fitness based on both values
+ if 11 < 3 % TODO: in case of outliers this mean is counterproductive
+ % median out of three would be ok
+ fitness.raw(1:noiseReevals) = ... % not so raw anymore
+ (fitness.raw(1:noiseReevals) + fitness.raw(lambda+(1:noiseReevals))) / 2;
+ fitness.sel(1:noiseReevals) = ...
+ (fitness.sel(1:noiseReevals) + fitness.sel(lambda+(1:noiseReevals))) / 2;
+ end
+ fitness.raw = fitness.raw(1:lambda);
+ fitness.sel = fitness.sel(1:lambda);
+ end
+
+ % Sort by fitness
+ [fitness.raw, fitness.idx] = sort(fitness.raw);
+ [fitness.sel, fitness.idxsel] = sort(fitness.sel); % minimization
+ fitness.hist(2:end) = fitness.hist(1:end-1); % record short history of
+ fitness.hist(1) = fitness.raw(1); % best fitness values
+ if length(fitness.histbest) < 120+ceil(30*N/lambda) || ...
+ (mod(countiter, 5) == 0 && length(fitness.histbest) < 2e4) % 20 percent of 1e5 gen.
+ fitness.histbest = [fitness.raw(1) fitness.histbest]; % best fitness values
+ fitness.histmedian = [median(fitness.raw) fitness.histmedian]; % median fitness values
+ else
+ fitness.histbest(2:end) = fitness.histbest(1:end-1);
+ fitness.histmedian(2:end) = fitness.histmedian(1:end-1);
+ fitness.histbest(1) = fitness.raw(1); % best fitness values
+ fitness.histmedian(1) = median(fitness.raw); % median fitness values
+ end
+ fitness.histsel(2:end) = fitness.histsel(1:end-1); % record short history of
+ fitness.histsel(1) = fitness.sel(1); % best sel fitness values
+
+ % Calculate new xmean, this is selection and recombination
+ xold = xmean; % for speed up of Eq. (2) and (3)
+ xmean = arx(:,fitness.idxsel(1:mu))*weights;
+ zmean = arz(:,fitness.idxsel(1:mu))*weights;%==D^-1*B'*(xmean-xold)/sigma
+ if mu == 1
+ fmean = fitness.sel(1);
+ else
+ fmean = NaN; % [] does not work in the latter assignment
+ % fmean = feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
+ % counteval = counteval + 1;
+ end
+
+ % Cumulation: update evolution paths
+ ps = (1-cs)*ps + sqrt(cs*(2-cs)*mueff) * (B*zmean); % Eq. (4)
+ hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
+ if flg_future_setting
+ hsig = sum(ps.^2) / (1-(1-cs)^(2*countiter)) / N < 2 + 4/(N+1); % just simplified
+ end
+ % hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.4 + 2/(N+1);
+ % hsig = norm(ps)/sqrt(1-(1-cs)^(2*countiter))/chiN < 1.5 + 1/(N-0.5);
+ % hsig = norm(ps) < 1.5 * sqrt(N);
+ % hsig = 1;
+
+ pc = (1-cc)*pc ...
+ + hsig*(sqrt(cc*(2-cc)*mueff)/sigma) * (xmean-xold); % Eq. (2)
+ if hsig == 0
+ % disp([num2str(countiter) ' ' num2str(counteval) ' pc update stalled']);
+ end
+
+ % Adapt covariance matrix
+ neg.ccov = 0; % TODO: move parameter setting upwards at some point
+ if ccov1 + ccovmu > 0 % Eq. (3)
+ if flgDiagonalOnly % internal linear(?) complexity
+ diagC = (1-ccov1_sep-ccovmu_sep+(1-hsig)*ccov1_sep*cc*(2-cc)) * diagC ... % regard old matrix
+ + ccov1_sep * pc.^2 ... % plus rank one update
+ + ccovmu_sep ... % plus rank mu update
+ * (diagC .* (arz(:,fitness.idxsel(1:mu)).^2 * weights));
+ % * (repmat(diagC,1,mu) .* arz(:,fitness.idxsel(1:mu)).^2 * weights);
+ diagD = sqrt(diagC); % replaces eig(C)
+ else
+ arpos = (arx(:,fitness.idxsel(1:mu))-repmat(xold,1,mu)) / sigma;
+ % "active" CMA update: negative update, in case controlling pos. definiteness
+ if flgActiveCMA > 0
+ % set parameters
+ neg.mu = mu;
+ neg.mueff = mueff;
+ if flgActiveCMA > 10 % flat weights with mu=lambda/2
+ neg.mu = floor(lambda/2);
+ neg.mueff = neg.mu;
+ end
+ % neg.mu = ceil(min([N, lambda/4, mueff])); neg.mueff = mu; % i.e. neg.mu <= N
+ % Parameter study: in 3-D lambda=50,100, 10-D lambda=200,400, 30-D lambda=1000,2000 a
+ % three times larger neg.ccov does not work.
+ % increasing all ccov rates three times does work (probably because of the factor (1-ccovmu))
+ % in 30-D to looks fine
+
+ neg.ccov = (1 - ccovmu) * 0.25 * neg.mueff / ((N+2)^1.5 + 2*neg.mueff);
+ neg.minresidualvariance = 0.66; % keep at least 0.66 in all directions, small popsize are most critical
+ neg.alphaold = 0.5; % where to make up for the variance loss, 0.5 means no idea what to do
+ % 1 is slightly more robust and gives a better "guaranty" for pos. def.,
+ % but does it make sense from the learning perspective for large ccovmu?
+
+ neg.ccovfinal = neg.ccov;
+
+ % prepare vectors, compute negative updating matrix Cneg and checking matrix Ccheck
+ arzneg = arz(:,fitness.idxsel(lambda:-1:lambda - neg.mu + 1));
+ % i-th longest becomes i-th shortest
+ % TODO: this is not in compliance with the paper Hansen&Ros2010,
+ % where simply arnorms = arnorms(end:-1:1) ?
+ [arnorms idxnorms] = sort(sqrt(sum(arzneg.^2, 1)));
+ [ignore idxnorms] = sort(idxnorms); % inverse index
+ arnormfacs = arnorms(end:-1:1) ./ arnorms;
+ % arnormfacs = arnorms(randperm(neg.mu)) ./ arnorms;
+ arnorms = arnorms(end:-1:1); % for the record
+ if flgActiveCMA < 20
+ arzneg = arzneg .* repmat(arnormfacs(idxnorms), N, 1); % E x*x' is N
+ % arzneg = sqrt(N) * arzneg ./ repmat(sqrt(sum(arzneg.^2, 1)), N, 1); % E x*x' is N
+ end
+ if flgActiveCMA < 10 && neg.mu == mu % weighted sum
+ if mod(flgActiveCMA, 10) == 1 % TODO: prevent this with a less tight but more efficient check (see below)
+ Ccheck = arzneg * diag(weights) * arzneg'; % in order to check the largest EV
+ end
+ artmp = BD * arzneg;
+ Cneg = artmp * diag(weights) * artmp';
+ else % simple sum
+ if mod(flgActiveCMA, 10) == 1
+ Ccheck = (1/neg.mu) * arzneg*arzneg'; % in order to check largest EV
+ end
+ artmp = BD * arzneg;
+ Cneg = (1/neg.mu) * artmp*artmp';
+
+ end
+
+ % check pos.def. and set learning rate neg.ccov accordingly,
+ % this check makes the original choice of neg.ccov extremly failsafe
+ % still assuming C == BD*BD', which is only approxim. correct
+ if mod(flgActiveCMA, 10) == 1 && 1 - neg.ccov * arnorms(idxnorms).^2 * weights < neg.minresidualvariance
+ % TODO: the simple and cheap way would be to set
+ % fac = 1 - ccovmu - ccov1 OR 1 - mueff/lambda and
+ % neg.ccov = fac*(1 - neg.minresidualvariance) / (arnorms(idxnorms).^2 * weights)
+ % this is the more sophisticated way:
+ % maxeigenval = eigs(arzneg * arzneg', 1, 'lm', eigsopts); % not faster
+ maxeigenval = max(eig(Ccheck)); % norm is much slower, because (norm()==max(svd())
+ %disp([countiter log10([neg.ccov, maxeigenval, arnorms(idxnorms).^2 * weights, max(arnorms)^2]), ...
+ % neg.ccov * arnorms(idxnorms).^2 * weights])
+ % pause
+ % remove less than ??34*(1-cmu)%?? of variance in any direction
+ % 1-ccovmu is the variance left from the old C
+ neg.ccovfinal = min(neg.ccov, (1-ccovmu)*(1-neg.minresidualvariance)/maxeigenval);
+ % -ccov1 removed to avoid error message??
+ if neg.ccovfinal < neg.ccov
+ disp(['active CMA at iteration ' num2str(countiter) ...
+ ': max EV ==', num2str([maxeigenval, neg.ccov, neg.ccovfinal])]);
+ end
+ end
+ % xmean = xold; % the distribution does not degenerate!?
+ % update C
+ C = (1-ccov1-ccovmu+neg.alphaold*neg.ccovfinal+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix
+ + ccov1 * pc*pc' ... % plus rank one update
+ + (ccovmu + (1-neg.alphaold)*neg.ccovfinal) ... % plus rank mu update
+ * arpos * (repmat(weights,1,N) .* arpos') ...
+ - neg.ccovfinal * Cneg; % minus rank mu update
+ else % no active (negative) update
+ C = (1-ccov1-ccovmu+(1-hsig)*ccov1*cc*(2-cc)) * C ... % regard old matrix
+ + ccov1 * pc*pc' ... % plus rank one update
+ + ccovmu ... % plus rank mu update
+ * arpos * (repmat(weights,1,N) .* arpos');
+ % is now O(mu*N^2 + mu*N), was O(mu*N^2 + mu^2*N) when using diag(weights)
+ % for mu=30*N it is now 10 times faster, overall 3 times faster
+ end
+ diagC = diag(C);
+ end
+ end
+
+ % the following is de-preciated and will be removed in future
+ % better setting for cc makes this hack obsolete
+ if 11 < 2 && ~flgscience
+ % remove momentum in ps, if ps is large and fitness is getting worse.
+ % this should rarely happen.
+ % this might very well be counterproductive in dynamic environments
+ if sum(ps.^2)/N > 1.5 + 10*(2/N)^.5 && ...
+ fitness.histsel(1) > max(fitness.histsel(2:3))
+ ps = ps * sqrt(N*(1+max(0,log(sum(ps.^2)/N))) / sum(ps.^2));
+ if flgdisplay
+ disp(['Momentum in ps removed at [niter neval]=' ...
+ num2str([countiter counteval]) ']']);
+ end
+ end
+ end
+
+ % Adapt sigma
+ if flg_future_setting % according to a suggestion from Dirk Arnold (2000)
+ % exp(1) is still not reasonably small enough
+ sigma = sigma * exp(min(1, (sum(ps.^2)/N - 1)/2 * cs/damps)); % Eq. (5)
+ else
+ % exp(1) is still not reasonably small enough
+ sigma = sigma * exp(min(1, (sqrt(sum(ps.^2))/chiN - 1) * cs/damps)); % Eq. (5)
+ end
+ % disp([countiter norm(ps)/chiN]);
+
+ if 11 < 3 % testing with optimal step-size
+ if countiter == 1
+ disp('*********** sigma set to const * ||x|| ******************');
+ end
+ sigma = 0.04 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=1000:25e3
+ sigma = 0.3 * mueff * sqrt(sum(xmean.^2)) / N; % 20D,lam=(40,1000):17e3
+ % 75e3 with def (1.5)
+ % 35e3 with damps=0.25
+ end
+ if 11 < 3
+ if countiter == 1
+ disp('*********** xmean set to const ******************');
+ end
+ xmean = ones(N,1);
+ end
+
+ % Update B and D from C
+
+ if ~flgDiagonalOnly && (ccov1+ccovmu+neg.ccov) > 0 && mod(countiter, 1/(ccov1+ccovmu+neg.ccov)/N/10) < 1
+ C=triu(C)+triu(C,1)'; % enforce symmetry to prevent complex numbers
+ [B,tmp] = eig(C); % eigen decomposition, B==normalized eigenvectors
+ % effort: approx. 15*N matrix-vector multiplications
+ diagD = diag(tmp);
+
+ if any(~isfinite(diagD))
+ clear idx; % prevents error under octave
+ save(['tmp' opts.SaveFilename]);
+ error(['function eig returned non-finited eigenvalues, cond(C)=' ...
+ num2str(cond(C)) ]);
+ end
+ if any(any(~isfinite(B)))
+ clear idx; % prevents error under octave
+ save(['tmp' opts.SaveFilename]);
+ error(['function eig returned non-finited eigenvectors, cond(C)=' ...
+ num2str(cond(C)) ]);
+ end
+
+ % limit condition of C to 1e14 + 1
+ if min(diagD) <= 0
+ if stopOnWarnings
+ stopflag(end+1) = {'warnconditioncov'};
+ else
+ warning(['Iteration ' num2str(countiter) ...
+ ': Eigenvalue (smaller) zero']);
+ diagD(diagD<0) = 0;
+ tmp = max(diagD)/1e14;
+ C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1);
+ end
+ end
+ if max(diagD) > 1e14*min(diagD)
+ if stopOnWarnings
+ stopflag(end+1) = {'warnconditioncov'};
+ else
+ warning(['Iteration ' num2str(countiter) ': condition of C ' ...
+ 'at upper limit' ]);
+ tmp = max(diagD)/1e14 - min(diagD);
+ C = C + tmp*eye(N,N); diagD = diagD + tmp*ones(N,1);
+ end
+ end
+
+ diagC = diag(C);
+ diagD = sqrt(diagD); % D contains standard deviations now
+ % diagD = diagD / prod(diagD)^(1/N); C = C / prod(diagD)^(2/N);
+ BD = B.*repmat(diagD',N,1); % O(n^2)
+ end % if mod
+
+ % Align/rescale order of magnitude of scales of sigma and C for nicer output
+ % not a very usual case
+ if 1 < 2 && sigma > 1e10*max(diagD)
+ fac = sigma / max(diagD);
+ sigma = sigma/fac;
+ pc = fac * pc;
+ diagD = fac * diagD;
+ if ~flgDiagonalOnly
+ C = fac^2 * C; % disp(fac);
+ BD = B.*repmat(diagD',N,1); % O(n^2), but repmat might be inefficient todo?
+ end
+ diagC = fac^2 * diagC;
+ end
+
+ if flgDiagonalOnly > 1 && countiter > flgDiagonalOnly
+ % full covariance matrix from now on
+ flgDiagonalOnly = 0;
+ B = eye(N,N);
+ BD = diag(diagD);
+ C = diag(diagC); % is better, because correlations are spurious anyway
+ end
+
+ if noiseHandling
+ if countiter == 1 % assign firstvarargin for noise treatment e.g. as #reevaluations
+ if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1})
+ if irun == 1
+ firstvarargin = varargin{1};
+ else
+ varargin{1} = firstvarargin; % reset varargin{1}
+ end
+ else
+ firstvarargin = 0;
+ end
+ end
+ if noiseSS < 0 && noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
+ varargin{1} = max(noiseMinMaxEvals(1), varargin{1} / noiseAlphaEvals^(1/4)); % still experimental
+ elseif noiseSS > 0
+ if ~isempty(noiseCallback) % to be removed?
+ res = feval(noiseCallback); % should also work without output argument!?
+ if ~isempty(res) && res > 1 % TODO: decide for interface of callback
+ % also a dynamic popsize could be done here
+ sigma = sigma * noiseAlpha;
+ end
+ else
+ if noiseMinMaxEvals(2) > noiseMinMaxEvals(1) && firstvarargin
+ varargin{1} = min(noiseMinMaxEvals(2), varargin{1} * noiseAlphaEvals);
+ end
+
+ sigma = sigma * noiseAlpha;
+ % lambda = ceil(0.1 * sqrt(lambda) + lambda);
+ % TODO: find smallest increase of lambda with log-linear
+ % convergence in iterations
+ end
+ % qqq experimental: take a mean to estimate the true optimum
+ noiseN = noiseN + 1;
+ if noiseN == 1
+ noiseX = xmean;
+ else
+ noiseX = noiseX + (3/noiseN) * (xmean - noiseX);
+ end
+ end
+ end
+
+ % ----- numerical error management -----
+ % Adjust maximal coordinate axis deviations
+ if any(sigma*sqrt(diagC) > maxdx)
+ sigma = min(maxdx ./ sqrt(diagC));
+ %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+ % 'deviation at upper limit of ' num2str(maxdx)]);
+ % stopflag(end+1) = {'maxcoorddev'};
+ end
+ % Adjust minimal coordinate axis deviations
+ if any(sigma*sqrt(diagC) < mindx)
+ sigma = max(mindx ./ sqrt(diagC)) * exp(0.05+cs/damps);
+ %warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+ % 'deviation at lower limit of ' num2str(mindx)]);
+ % stopflag(end+1) = {'mincoorddev'};;
+ end
+ % Adjust too low coordinate axis deviations
+ if any(xmean == xmean + 0.2*sigma*sqrt(diagC))
+ if stopOnWarnings
+ stopflag(end+1) = {'warnnoeffectcoord'};
+ else
+ warning(['Iteration ' num2str(countiter) ': coordinate axis std ' ...
+ 'deviation too low' ]);
+ if flgDiagonalOnly
+ diagC = diagC + (ccov1_sep+ccovmu_sep) * (diagC .* ...
+ (xmean == xmean + 0.2*sigma*sqrt(diagC)));
+ else
+ C = C + (ccov1+ccovmu) * diag(diagC .* ...
+ (xmean == xmean + 0.2*sigma*sqrt(diagC)));
+ end
+ sigma = sigma * exp(0.05+cs/damps);
+ end
+ end
+ % Adjust step size in case of (numerical) precision problem
+ if flgDiagonalOnly
+ tmp = 0.1*sigma*diagD;
+ else
+ tmp = 0.1*sigma*BD(:,1+floor(mod(countiter,N)));
+ end
+ if all(xmean == xmean + tmp)
+ i = 1+floor(mod(countiter,N));
+ if stopOnWarnings
+ stopflag(end+1) = {'warnnoeffectaxis'};
+ else
+ warning(['Iteration ' num2str(countiter) ...
+ ': main axis standard deviation ' ...
+ num2str(sigma*diagD(i)) ' has no effect' ]);
+ sigma = sigma * exp(0.2+cs/damps);
+ end
+ end
+ % Adjust step size in case of equal function values (flat fitness)
+ % isequalfuncvalues = 0;
+ if fitness.sel(1) == fitness.sel(1+ceil(0.1+lambda/4))
+ % isequalfuncvalues = 1;
+ if stopOnEqualFunctionValues
+ arrEqualFunvals = [countiter arrEqualFunvals(1:end-1)];
+ % stop if this happens in more than 33%
+ if arrEqualFunvals(end) > countiter - 3 * length(arrEqualFunvals)
+ stopflag(end+1) = {'equalfunvals'};
+ end
+ else
+ if flgWarnOnEqualFunctionValues
+ warning(['Iteration ' num2str(countiter) ...
+ ': equal function values f=' num2str(fitness.sel(1)) ...
+ ' at maximal main axis sigma ' ...
+ num2str(sigma*max(diagD))]);
+ end
+ sigma = sigma * exp(0.2+cs/damps);
+ end
+ end
+ % Adjust step size in case of equal function values
+ if countiter > 2 && myrange([fitness.hist fitness.sel(1)]) == 0
+ if stopOnWarnings
+ stopflag(end+1) = {'warnequalfunvalhist'};
+ else
+ warning(['Iteration ' num2str(countiter) ...
+ ': equal function values in history at maximal main ' ...
+ 'axis sigma ' num2str(sigma*max(diagD))]);
+ sigma = sigma * exp(0.2+cs/damps);
+ end
+ end
+
+ % ----- end numerical error management -----
+
+ % Keep overall best solution
+ out.evals = counteval;
+ out.solutions.evals = counteval;
+ out.solutions.mean.x = xmean;
+ out.solutions.mean.f = fmean;
+ out.solutions.mean.evals = counteval;
+ out.solutions.recentbest.x = arxvalid(:, fitness.idx(1));
+ out.solutions.recentbest.f = fitness.raw(1);
+ out.solutions.recentbest.evals = counteval + fitness.idx(1) - lambda;
+ out.solutions.recentworst.x = arxvalid(:, fitness.idx(end));
+ out.solutions.recentworst.f = fitness.raw(end);
+ out.solutions.recentworst.evals = counteval + fitness.idx(end) - lambda;
+ if fitness.hist(1) < out.solutions.bestever.f
+ out.solutions.bestever.x = arxvalid(:, fitness.idx(1));
+ out.solutions.bestever.f = fitness.hist(1);
+ out.solutions.bestever.evals = counteval + fitness.idx(1) - lambda;
+ bestever = out.solutions.bestever;
+ end
+
+ % Set stop flag
+ if fitness.raw(1) <= stopFitness, stopflag(end+1) = {'fitness'}; end
+ if counteval >= stopMaxFunEvals, stopflag(end+1) = {'maxfunevals'}; end
+ if countiter >= stopMaxIter, stopflag(end+1) = {'maxiter'}; end
+ if all(sigma*(max(abs(pc), sqrt(diagC))) < stopTolX)
+ stopflag(end+1) = {'tolx'};
+ end
+ if any(sigma*sqrt(diagC) > stopTolUpX)
+ stopflag(end+1) = {'tolupx'};
+ end
+ if sigma*max(diagD) == 0 % should never happen
+ stopflag(end+1) = {'bug'};
+ end
+ if countiter > 2 && myrange([fitness.sel fitness.hist]) <= stopTolFun
+ stopflag(end+1) = {'tolfun'};
+ end
+ if countiter >= length(fitness.hist) && myrange(fitness.hist) <= stopTolHistFun
+ stopflag(end+1) = {'tolhistfun'};
+ end
+ l = floor(length(fitness.histbest)/3);
+ if 1 < 2 && stopOnStagnation && ... % leads sometimes early stop on ftablet, fcigtab
+ countiter > N * (5+100/lambda) && ...
+ length(fitness.histbest) > 100 && ...
+ median(fitness.histmedian(1:l)) >= median(fitness.histmedian(end-l:end)) && ...
+ median(fitness.histbest(1:l)) >= median(fitness.histbest(end-l:end))
+ stopflag(end+1) = {'stagnation'};
+ end
+
+ if counteval >= stopFunEvals || countiter >= stopIter
+ stopflag(end+1) = {'stoptoresume'};
+ if length(stopflag) == 1 && flgsaving == 0
+ error('To resume later the saving option needs to be set');
+ end
+ end
+ % read stopping message from file signals.par
+ if flgreadsignals
+ fid = fopen('./signals.par', 'rt'); % can be performance critical
+ while fid > 0
+ strline = fgetl(fid); %fgets(fid, 300);
+ if strline < 0 % fgets and fgetl returns -1 at end of file
+ break;
+ end
+ % 'stop filename' sets stopflag to manual
+ str = sscanf(strline, ' %s %s', 2);
+ if strcmp(str, ['stop' opts.LogFilenamePrefix])
+ stopflag(end+1) = {'manual'};
+ break;
+ end
+ % 'skip filename run 3' skips a run, but not the last
+ str = sscanf(strline, ' %s %s %s', 3);
+ if strcmp(str, ['skip' opts.LogFilenamePrefix 'run'])
+ i = strfind(strline, 'run');
+ if irun == sscanf(strline(i+3:end), ' %d ', 1) && irun <= myeval(opts.Restarts)
+ stopflag(end+1) = {'skipped'};
+ end
+ end
+ end % while, break
+ if fid > 0
+ fclose(fid);
+ clear fid; % prevents strange error under octave
+ end
+ end
+
+ out.stopflag = stopflag;
+
+ % ----- output generation -----
+ if verbosemodulo > 0 && isfinite(verbosemodulo)
+ if countiter == 1 || mod(countiter, 10*verbosemodulo) < 1
+ disp(['Iterat, #Fevals: Function Value (median,worst) ' ...
+ '|Axis Ratio|' ...
+ 'idx:Min SD idx:Max SD']);
+ end
+ if mod(countiter, verbosemodulo) < 1 ...
+ || (verbosemodulo > 0 && isfinite(verbosemodulo) && ...
+ (countiter < 3 || ~isempty(stopflag)))
+ [minstd minstdidx] = min(sigma*sqrt(diagC));
+ [maxstd maxstdidx] = max(sigma*sqrt(diagC));
+ % format display nicely
+ disp([repmat(' ',1,4-floor(log10(countiter))) ...
+ num2str(countiter) ' , ' ...
+ repmat(' ',1,5-floor(log10(counteval))) ...
+ num2str(counteval) ' : ' ...
+ num2str(fitness.hist(1), '%.13e') ...
+ ' +(' num2str(median(fitness.raw)-fitness.hist(1), '%.0e ') ...
+ ',' num2str(max(fitness.raw)-fitness.hist(1), '%.0e ') ...
+ ') | ' ...
+ num2str(max(diagD)/min(diagD), '%4.2e') ' | ' ...
+ repmat(' ',1,1-floor(log10(minstdidx))) num2str(minstdidx) ':' ...
+ num2str(minstd, ' %.1e') ' ' ...
+ repmat(' ',1,1-floor(log10(maxstdidx))) num2str(maxstdidx) ':' ...
+ num2str(maxstd, ' %.1e')]);
+ end
+ end
+
+ % measure time for recording data
+ if countiter < 3
+ time.c = 0.05;
+ time.nonoutput = 0;
+ time.recording = 0;
+ time.saving = 0.15; % first saving after 3 seconds of 100 iterations
+ time.plotting = 0;
+ elseif countiter > 300
+ % set backward horizon, must be long enough to cover infrequent plotting etc
+ % time.c = min(1, time.nonoutput/3 + 1e-9);
+ time.c = max(1e-5, 0.1/sqrt(countiter)); % mean over all or 1e-5
+ end
+ % get average time per iteration
+ time.t1 = clock;
+ time.act = max(0,etime(time.t1, time.t0));
+ time.nonoutput = (1-time.c) * time.nonoutput ...
+ + time.c * time.act;
+
+ time.recording = (1-time.c) * time.recording; % per iteration
+ time.saving = (1-time.c) * time.saving;
+ time.plotting = (1-time.c) * time.plotting;
+
+ % record output data, concerning time issues
+ if savemodulo && savetime && (countiter < 1e2 || ~isempty(stopflag) || ...
+ countiter >= outiter + savemodulo)
+ outiter = countiter;
+ % Save output data to files
+ for namecell = filenames(:)'
+ name = namecell{:};
+
+ [fid, err] = fopen(['./' filenameprefix name '.dat'], 'a');
+ if fid < 1 % err ~= 0
+ warning(['could not open ' filenameprefix name '.dat']);
+ else
+ if strcmp(name, 'axlen')
+ fprintf(fid, '%d %d %e %e %e ', countiter, counteval, sigma, ...
+ max(diagD), min(diagD));
+ fprintf(fid, '%e ', sort(diagD));
+ fprintf(fid, '\n');
+ elseif strcmp(name, 'disp') % TODO
+ elseif strcmp(name, 'fit')
+ fprintf(fid, '%ld %ld %e %e %25.18e %25.18e %25.18e %25.18e', ...
+ countiter, counteval, sigma, max(diagD)/min(diagD), ...
+ out.solutions.bestever.f, ...
+ fitness.raw(1), median(fitness.raw), fitness.raw(end));
+ if ~isempty(varargin) && length(varargin{1}) == 1 && isnumeric(varargin{1}) && varargin{1} ~= 0
+ fprintf(fid, ' %f', varargin{1});
+ end
+ fprintf(fid, '\n');
+ elseif strcmp(name, 'stddev')
+ fprintf(fid, '%ld %ld %e 0 0 ', countiter, counteval, sigma);
+ fprintf(fid, '%e ', sigma*sqrt(diagC));
+ fprintf(fid, '\n');
+ elseif strcmp(name, 'xmean')
+ if isnan(fmean)
+ fprintf(fid, '%ld %ld 0 0 0 ', countiter, counteval);
+ else
+ fprintf(fid, '%ld %ld 0 0 %e ', countiter, counteval, fmean);
+ end
+ fprintf(fid, '%e ', xmean);
+ fprintf(fid, '\n');
+ elseif strcmp(name, 'xrecentbest')
+ % TODO: fitness is inconsistent with x-value
+ fprintf(fid, '%ld %ld %25.18e 0 0 ', countiter, counteval, fitness.raw(1));
+ fprintf(fid, '%e ', arx(:,fitness.idx(1)));
+ fprintf(fid, '\n');
+ end
+ fclose(fid);
+ end
+ end
+
+ % get average time for recording data
+ time.t2 = clock;
+ time.recording = time.recording + time.c * max(0,etime(time.t2, time.t1));
+
+ % plot
+ if flgplotting && countiter > 1
+ if countiter == 2
+ iterplotted = 0;
+ end
+ if ~isempty(stopflag) || ...
+ ((time.nonoutput+time.recording) * (countiter - iterplotted) > 1 && ...
+ time.plotting < 0.05 * (time.nonoutput+time.recording))
+ local_plotcmaesdat(324, filenameprefix);
+ iterplotted = countiter;
+ % outplot(out); % outplot defined below
+ if time.plotting == 0 % disregard opening of the window
+ time.plotting = time.nonoutput+time.recording;
+ else
+ time.plotting = time.plotting + time.c * max(0,etime(clock, time.t2));
+ end
+ end
+ end
+ if countiter > 100 + 20 && savemodulo && ...
+ time.recording * countiter > 0.1 && ... % absolute time larger 0.1 second
+ time.recording > savetime * (time.nonoutput+time.recording) / 100
+ savemodulo = floor(1.1 * savemodulo) + 1;
+ % disp('++savemodulo'); %qqq
+ end
+ end % if output
+
+ % save everything
+ time.t3 = clock;
+ if ~isempty(stopflag) || time.saving < 0.05 * time.nonoutput || countiter == 100
+ xmin = arxvalid(:, fitness.idx(1));
+ fmin = fitness.raw(1);
+ if flgsaving && countiter > 2
+ clear idx; % prevents error under octave
+ % -v6 : non-compressed non-unicode for version 6 and earlier
+ if ~isempty(strsaving) && ~isoctave
+ save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart
+ else
+ save('-mat', opts.SaveFilename); % for inspection and possible restart
+ end
+ time.saving = time.saving + time.c * max(0,etime(clock, time.t3));
+ end
+ end
+ time.t0 = clock;
+
+ % ----- end output generation -----
+
+ end % while, end generation loop
+
+ % -------------------- Final Procedures -------------------------------
+
+ % Evaluate xmean and return best recent point in xmin
+ fmin = fitness.raw(1);
+ xmin = arxvalid(:, fitness.idx(1)); % Return best point of last generation.
+ if length(stopflag) > sum(strcmp(stopflag, 'stoptoresume')) % final stopping
+ out.solutions.mean.f = ...
+ feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
+ counteval = counteval + 1;
+ out.solutions.mean.evals = counteval;
+ if out.solutions.mean.f < fitness.raw(1)
+ fmin = out.solutions.mean.f;
+ xmin = xintobounds(xmean, lbounds, ubounds); % Return xmean as best point
+ end
+ if out.solutions.mean.f < out.solutions.bestever.f
+ out.solutions.bestever = out.solutions.mean; % Return xmean as bestever point
+ out.solutions.bestever.x = xintobounds(xmean, lbounds, ubounds);
+ bestever = out.solutions.bestever;
+ end
+ end
+
+ % Save everything and display final message
+ if flgsavingfinal
+ clear idx; % prevents error under octave
+ if ~isempty(strsaving) && ~isoctave
+ save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart
+ else
+ save('-mat', opts.SaveFilename); % for inspection and possible restart
+ end
+ message = [' (saved to ' opts.SaveFilename ')'];
else
- time.plotting = time.plotting + time.c * max(0,etime(clock, time.t2));
+ message = [];
+ end
+
+ if flgdisplay
+ disp(['#Fevals: f(returned x) | bestever.f | stopflag' ...
+ message]);
+ if isoctave
+ strstop = stopflag(:);
+ else
+ strcat(stopflag(:), '.');
+ end
+ strstop = stopflag(:); %strcat(stopflag(:), '.');
+ disp([repmat(' ',1,6-floor(log10(counteval))) ...
+ num2str(counteval, '%6.0f') ': ' num2str(fmin, '%.11e') ' | ' ...
+ num2str(out.solutions.bestever.f, '%.11e') ' | ' ...
+ strstop{1:end}]);
+ if N < 102
+ disp(['mean solution:' sprintf(' %+.1e', xmean)]);
+ disp(['std deviation:' sprintf(' %.1e', sigma*sqrt(diagC))]);
+ disp(sprintf('use plotcmaesdat.m for plotting the output at any time (option LogModulo must not be zero)'));
+ end
+ if exist('sfile', 'var')
+ disp(['Results saved in ' sfile]);
+ end
+ end
+
+ out.arstopflags{irun} = stopflag;
+ if any(strcmp(stopflag, 'fitness')) ...
+ || any(strcmp(stopflag, 'maxfunevals')) ...
+ || any(strcmp(stopflag, 'stoptoresume')) ...
+ || any(strcmp(stopflag, 'manual'))
+ break;
end
- end
- end
- if countiter > 100 + 20 && savemodulo && ...
- time.recording * countiter > 0.1 && ... % absolute time larger 0.1 second
- time.recording > savetime * (time.nonoutput+time.recording) / 100
- savemodulo = floor(1.1 * savemodulo) + 1;
- % disp('++savemodulo'); %qqq
- end
- end % if output
-
- % save everything
- time.t3 = clock;
- if ~isempty(stopflag) || time.saving < 0.05 * time.nonoutput || countiter == 100
- xmin = arxvalid(:, fitness.idx(1));
- fmin = fitness.raw(1);
- if flgsaving && countiter > 2
- clear idx; % prevents error under octave
- % -v6 : non-compressed non-unicode for version 6 and earlier
- if ~isempty(strsaving) && ~isoctave
- save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart
- else
- save('-mat', opts.SaveFilename); % for inspection and possible restart
- end
- time.saving = time.saving + time.c * max(0,etime(clock, time.t3));
- end
- end
- time.t0 = clock;
-
- % ----- end output generation -----
-
-end % while, end generation loop
-
-% -------------------- Final Procedures -------------------------------
-
-% Evaluate xmean and return best recent point in xmin
-fmin = fitness.raw(1);
-xmin = arxvalid(:, fitness.idx(1)); % Return best point of last generation.
-if length(stopflag) > sum(strcmp(stopflag, 'stoptoresume')) % final stopping
- out.solutions.mean.f = ...
- feval(fitfun, xintobounds(xmean, lbounds, ubounds), varargin{:});
- counteval = counteval + 1;
- out.solutions.mean.evals = counteval;
- if out.solutions.mean.f < fitness.raw(1)
- fmin = out.solutions.mean.f;
- xmin = xintobounds(xmean, lbounds, ubounds); % Return xmean as best point
- end
- if out.solutions.mean.f < out.solutions.bestever.f
- out.solutions.bestever = out.solutions.mean; % Return xmean as bestever point
- out.solutions.bestever.x = xintobounds(xmean, lbounds, ubounds);
- bestever = out.solutions.bestever;
- end
-end
-
-% Save everything and display final message
-if flgsavingfinal
- clear idx; % prevents error under octave
- if ~isempty(strsaving) && ~isoctave
- save('-mat', strsaving, opts.SaveFilename); % for inspection and possible restart
- else
- save('-mat', opts.SaveFilename); % for inspection and possible restart
- end
- message = [' (saved to ' opts.SaveFilename ')'];
-else
- message = [];
-end
-
-if flgdisplay
- disp(['#Fevals: f(returned x) | bestever.f | stopflag' ...
- message]);
- if isoctave
- strstop = stopflag(:);
- else
- strcat(stopflag(:), '.');
- end
- strstop = stopflag(:); %strcat(stopflag(:), '.');
- disp([repmat(' ',1,6-floor(log10(counteval))) ...
- num2str(counteval, '%6.0f') ': ' num2str(fmin, '%.11e') ' | ' ...
- num2str(out.solutions.bestever.f, '%.11e') ' | ' ...
- strstop{1:end}]);
- if N < 102
- disp(['mean solution:' sprintf(' %+.1e', xmean)]);
- disp(['std deviation:' sprintf(' %.1e', sigma*sqrt(diagC))]);
- disp(sprintf('use plotcmaesdat.m for plotting the output at any time (option LogModulo must not be zero)'));
- end
- if exist('sfile', 'var')
- disp(['Results saved in ' sfile]);
- end
-end
-
- out.arstopflags{irun} = stopflag;
- if any(strcmp(stopflag, 'fitness')) ...
- || any(strcmp(stopflag, 'maxfunevals')) ...
- || any(strcmp(stopflag, 'stoptoresume')) ...
- || any(strcmp(stopflag, 'manual'))
- break;
- end
end % while irun <= Restarts
% ---------------------------------------------------------------
@@ -1759,32 +1759,32 @@ function [x, idx] = xintobounds(x, lbounds, ubounds)
%
% x can be a column vector or a matrix consisting of column vectors
%
- if ~isempty(lbounds)
+if ~isempty(lbounds)
if length(lbounds) == 1
- idx = x < lbounds;
- x(idx) = lbounds;
+ idx = x < lbounds;
+ x(idx) = lbounds;
else
- arbounds = repmat(lbounds, 1, size(x,2));
- idx = x < arbounds;
- x(idx) = arbounds(idx);
+ arbounds = repmat(lbounds, 1, size(x,2));
+ idx = x < arbounds;
+ x(idx) = arbounds(idx);
end
- else
+else
idx = 0;
- end
- if ~isempty(ubounds)
+end
+if ~isempty(ubounds)
if length(ubounds) == 1
- idx2 = x > ubounds;
- x(idx2) = ubounds;
+ idx2 = x > ubounds;
+ x(idx2) = ubounds;
else
- arbounds = repmat(ubounds, 1, size(x,2));
- idx2 = x > arbounds;
- x(idx2) = arbounds(idx2);
+ arbounds = repmat(ubounds, 1, size(x,2));
+ idx2 = x > arbounds;
+ x(idx2) = arbounds(idx2);
end
- else
+else
idx2 = 0;
- end
- idx = idx2-idx;
-
+end
+idx = idx2-idx;
+
% ---------------------------------------------------------------
% ---------------------------------------------------------------
function opts=getoptions(inopts, defopts)
@@ -1838,122 +1838,122 @@ function opts=getoptions(inopts, defopts)
%
if nargin < 2 || isempty(defopts) % no default options available
- opts=inopts;
- return;
+ opts=inopts;
+ return;
elseif isempty(inopts) % empty inopts invoke default options
- opts = defopts;
- return;
-elseif ~isstruct(defopts) % handle a single option value
- if isempty(inopts)
opts = defopts;
- elseif ~isstruct(inopts)
- opts = inopts;
- else
- error('Input options are a struct, while default options are not');
- end
- return;
+ return;
+elseif ~isstruct(defopts) % handle a single option value
+ if isempty(inopts)
+ opts = defopts;
+ elseif ~isstruct(inopts)
+ opts = inopts;
+ else
+ error('Input options are a struct, while default options are not');
+ end
+ return;
elseif ~isstruct(inopts) % no valid input options
- error('The options need to be a struct or empty');
+ error('The options need to be a struct or empty');
end
- opts = defopts; % start from defopts
- % if necessary overwrite opts fields by inopts values
- defnames = fieldnames(defopts);
- idxmatched = []; % indices of defopts that already matched
- for name = fieldnames(inopts)'
+opts = defopts; % start from defopts
+ % if necessary overwrite opts fields by inopts values
+defnames = fieldnames(defopts);
+idxmatched = []; % indices of defopts that already matched
+for name = fieldnames(inopts)'
name = name{1}; % name of i-th inopts-field
if isoctave
- for i = 1:size(defnames, 1)
- idx(i) = strncmp(lower(defnames(i)), lower(name), length(name));
- end
+ for i = 1:size(defnames, 1)
+ idx(i) = strncmp(lower(defnames(i)), lower(name), length(name));
+ end
else
- idx = strncmpi(defnames, name, length(name));
+ idx = strncmpi(defnames, name, length(name));
end
if sum(idx) > 1
- error(['option "' name '" is not an unambigous abbreviation. ' ...
- 'Use opts=RMFIELD(opts, ''' name, ...
- ''') to remove the field from the struct.']);
+ error(['option "' name '" is not an unambigous abbreviation. ' ...
+ 'Use opts=RMFIELD(opts, ''' name, ...
+ ''') to remove the field from the struct.']);
end
if sum(idx) == 1
- defname = defnames{find(idx)};
- if ismember(find(idx), idxmatched)
- error(['input options match more than ones with "' ...
- defname '". ' ...
- 'Use opts=RMFIELD(opts, ''' name, ...
- ''') to remove the field from the struct.']);
- end
- idxmatched = [idxmatched find(idx)];
- val = getfield(inopts, name);
- % next line can replace previous line from MATLAB version 6.5.0 on and in octave
- % val = inopts.(name);
- if isstruct(val) % valid syntax only from version 6.5.0
- opts = setfield(opts, defname, ...
- getoptions(val, getfield(defopts, defname)));
- elseif isstruct(getfield(defopts, defname))
- % next three lines can replace previous three lines from MATLAB
- % version 6.5.0 on
- % opts.(defname) = ...
- % getoptions(val, defopts.(defname));
- % elseif isstruct(defopts.(defname))
- warning(['option "' name '" disregarded (must be struct)']);
- elseif ~isempty(val) % empty value: do nothing, i.e. stick to default
- opts = setfield(opts, defnames{find(idx)}, val);
- % next line can replace previous line from MATLAB version 6.5.0 on
- % opts.(defname) = inopts.(name);
- end
+ defname = defnames{find(idx)};
+ if ismember(find(idx), idxmatched)
+ error(['input options match more than ones with "' ...
+ defname '". ' ...
+ 'Use opts=RMFIELD(opts, ''' name, ...
+ ''') to remove the field from the struct.']);
+ end
+ idxmatched = [idxmatched find(idx)];
+ val = getfield(inopts, name);
+ % next line can replace previous line from MATLAB version 6.5.0 on and in octave
+ % val = inopts.(name);
+ if isstruct(val) % valid syntax only from version 6.5.0
+ opts = setfield(opts, defname, ...
+ getoptions(val, getfield(defopts, defname)));
+ elseif isstruct(getfield(defopts, defname))
+ % next three lines can replace previous three lines from MATLAB
+ % version 6.5.0 on
+ % opts.(defname) = ...
+ % getoptions(val, defopts.(defname));
+ % elseif isstruct(defopts.(defname))
+ warning(['option "' name '" disregarded (must be struct)']);
+ elseif ~isempty(val) % empty value: do nothing, i.e. stick to default
+ opts = setfield(opts, defnames{find(idx)}, val);
+ % next line can replace previous line from MATLAB version 6.5.0 on
+ % opts.(defname) = inopts.(name);
+ end
else
- warning(['option "' name '" disregarded (unknown field name)']);
+ warning(['option "' name '" disregarded (unknown field name)']);
end
- end
+end
% ---------------------------------------------------------------
% ---------------------------------------------------------------
function res=myeval(s)
- if ischar(s)
+if ischar(s)
res = evalin('caller', s);
- else
+else
res = s;
- end
-
+end
+
% ---------------------------------------------------------------
% ---------------------------------------------------------------
function res=myevalbool(s)
- if ~ischar(s) % s may not and cannot be empty
+if ~ischar(s) % s may not and cannot be empty
res = s;
- else % evaluation string s
+else % evaluation string s
if strncmpi(lower(s), 'yes', 3) || strncmpi(s, 'on', 2) ...
- || strncmpi(s, 'true', 4) || strncmp(s, '1 ', 2)
- res = 1;
+ || strncmpi(s, 'true', 4) || strncmp(s, '1 ', 2)
+ res = 1;
elseif strncmpi(s, 'no', 2) || strncmpi(s, 'off', 3) ...
- || strncmpi(s, 'false', 5) || strncmp(s, '0 ', 2)
- res = 0;
+ || strncmpi(s, 'false', 5) || strncmp(s, '0 ', 2)
+ res = 0;
else
- try res = evalin('caller', s); catch
- error(['String value "' s '" cannot be evaluated']);
- end
- try res ~= 0; catch
- error(['String value "' s '" cannot be evaluated reasonably']);
- end
+ try res = evalin('caller', s); catch
+ error(['String value "' s '" cannot be evaluated']);
+ end
+ try res ~= 0; catch
+ error(['String value "' s '" cannot be evaluated reasonably']);
+ end
end
- end
-
+end
+
% ---------------------------------------------------------------
% ---------------------------------------------------------------
function res = isoctave
% any hack to find out whether we are running octave
- s = version;
- res = 0;
- if exist('fflush', 'builtin') && eval(s(1)) < 7
+s = version;
+res = 0;
+if exist('fflush', 'builtin') && eval(s(1)) < 7
res = 1;
- end
+end
% ---------------------------------------------------------------
% ---------------------------------------------------------------
function flush
- if isoctave
+if isoctave
feval('fflush', stdout);
- end
+end
% ---------------------------------------------------------------
% ---------------------------------------------------------------
@@ -1961,8 +1961,8 @@ function flush
% ---------------------------------------------------------------
% ---------------------------------------------------------------
function res=myrange(x)
- res = max(x) - min(x);
-
+res = max(x) - min(x);
+
% ---------------------------------------------------------------
% ---------------------------------------------------------------
function res = myprctile(inar, perc, idx)
@@ -1977,43 +1977,43 @@ flgtranspose = 0;
% sizes
if size(perc,1) > 1
- perc = perc';
- flgtranspose = 1;
- if size(perc,1) > 1
- error('perc must not be a matrix');
- end
+ perc = perc';
+ flgtranspose = 1;
+ if size(perc,1) > 1
+ error('perc must not be a matrix');
+ end
end
if size(inar, 1) > 1 && size(inar,2) > 1
- error('data inar must not be a matrix');
+ error('data inar must not be a matrix');
end
-
+
% sort inar
if nargin < 3 || isempty(idx)
- [sar idx] = sort(inar);
+ [sar idx] = sort(inar);
else
- sar = inar(idx);
+ sar = inar(idx);
end
res = [];
for p = perc
- if p <= 100*(0.5/N)
- res(end+1) = sar(1);
- elseif p >= 100*((N-0.5)/N)
- res(end+1) = sar(N);
- else
- % find largest index smaller than required percentile
- availablepercentiles = 100*((1:N)-0.5)/N;
- i = max(find(p > availablepercentiles));
- % interpolate linearly
- res(end+1) = sar(i) ...
- + (sar(i+1)-sar(i))*(p - availablepercentiles(i)) ...
- / (availablepercentiles(i+1) - availablepercentiles(i));
-
- end
+ if p <= 100*(0.5/N)
+ res(end+1) = sar(1);
+ elseif p >= 100*((N-0.5)/N)
+ res(end+1) = sar(N);
+ else
+ % find largest index smaller than required percentile
+ availablepercentiles = 100*((1:N)-0.5)/N;
+ i = max(find(p > availablepercentiles));
+ % interpolate linearly
+ res(end+1) = sar(i) ...
+ + (sar(i+1)-sar(i))*(p - availablepercentiles(i)) ...
+ / (availablepercentiles(i+1) - availablepercentiles(i));
+
+ end
end
if flgtranspose
- res = res';
+ res = res';
end
@@ -2055,25 +2055,25 @@ function [s ranks rankDelta] = local_noisemeasurement(arf1, arf2, lamreev, theta
%%% verify input argument sizes
if size(arf1,1) ~= 1
- error('arf1 must be an 1xlambda array');
+ error('arf1 must be an 1xlambda array');
elseif size(arf2,1) ~= 1
- error('arf2 must be an 1xsomething array');
+ error('arf2 must be an 1xsomething array');
elseif size(arf1,2) < size(arf2,2) % not really necessary, but saver
- error('arf2 must not be smaller than arf1 in length');
+ error('arf2 must not be smaller than arf1 in length');
end
lam = size(arf1, 2);
if size(arf1,2) ~= size(arf2,2)
- arf2(end+1:lam) = arf1((size(arf2,2)+1):lam);
+ arf2(end+1:lam) = arf1((size(arf2,2)+1):lam);
end
if nargin < 5
- cutlimit = inf;
+ cutlimit = inf;
end
%%% capture unusual values
if any(diff(arf1) == 0)
- % this will presumably interpreted as rank change, because
- % sort(ones(...)) returns 1,2,3,...
- warning([num2str(sum(diff(arf1)==0)) ' equal function values']);
+ % this will presumably interpreted as rank change, because
+ % sort(ones(...)) returns 1,2,3,...
+ warning([num2str(sum(diff(arf1)==0)) ' equal function values']);
end
%%% compute rank changes into rankDelta
@@ -2086,12 +2086,12 @@ rankDelta = ranks(1,:) - ranks(2,:) - sign(ranks(1,:) - ranks(2,:));
%%% compute rank change limits using both ranks(1,...) and ranks(2,...)
for i = 1:lamreev
- sumlim(i) = ...
- 0.5 * (...
- myprctile(abs((1:2*lam-1) - (ranks(1,i) - (ranks(1,i)>ranks(2,i)))), ...
- theta*50) ...
- + myprctile(abs((1:2*lam-1) - (ranks(2,i) - (ranks(2,i)>ranks(1,i)))), ...
- theta*50));
+ sumlim(i) = ...
+ 0.5 * (...
+ myprctile(abs((1:2*lam-1) - (ranks(1,i) - (ranks(1,i)>ranks(2,i)))), ...
+ theta*50) ...
+ + myprctile(abs((1:2*lam-1) - (ranks(2,i) - (ranks(2,i)>ranks(1,i)))), ...
+ theta*50));
end
%%% compute measurement
@@ -2139,193 +2139,193 @@ function local_plotcmaesdat(figNb, filenameprefix, filenameextension, objectvarn
manual_mode = 0;
- if nargin < 1 || isempty(figNb)
+if nargin < 1 || isempty(figNb)
figNb = 325;
- end
- if nargin < 2 || isempty(filenameprefix)
+end
+if nargin < 2 || isempty(filenameprefix)
filenameprefix = 'outcmaes';
- end
- if nargin < 3 || isempty(filenameextension)
+end
+if nargin < 3 || isempty(filenameextension)
filenameextension = '.dat';
- end
- if nargin < 4 || isempty(objectvarname)
+end
+if nargin < 4 || isempty(objectvarname)
objectvarname = 'xmean';
objectvarname = 'xrecentbest';
- end
- % load data
- d.x = load([filenameprefix objectvarname filenameextension]);
- % d.x = load([filenameprefix 'xmean' filenameextension]);
- % d.x = load([filenameprefix 'xrecentbest' filenameextension]);
- d.f = load([filenameprefix 'fit' filenameextension]);
- d.std = load([filenameprefix 'stddev' filenameextension]);
- d.D = load([filenameprefix 'axlen' filenameextension]);
-
- % interpret entries in figNb for cutting out some data
- if length(figNb) > 1
+end
+% load data
+d.x = load([filenameprefix objectvarname filenameextension]);
+% d.x = load([filenameprefix 'xmean' filenameextension]);
+% d.x = load([filenameprefix 'xrecentbest' filenameextension]);
+d.f = load([filenameprefix 'fit' filenameextension]);
+d.std = load([filenameprefix 'stddev' filenameextension]);
+d.D = load([filenameprefix 'axlen' filenameextension]);
+
+% interpret entries in figNb for cutting out some data
+if length(figNb) > 1
iend = inf;
istart = figNb(2);
if length(figNb) > 2
- iend = figNb(3);
+ iend = figNb(3);
end
figNb = figNb(1);
d.x = d.x(d.x(:,1) >= istart & d.x(:,1) <= iend, :);
d.f = d.f(d.f(:,1) >= istart & d.f(:,1) <= iend, :);
d.std = d.std(d.std(:,1) >= istart & d.std(:,1) <= iend, :);
d.D = d.D(d.D(:,1) >= istart & d.D(:,1) <= iend, :);
- end
+end
- % decide for x-axis
- iabscissa = 2; % 1== versus iterations, 2==versus fevals
- if mod(figNb,100) == 1
+% decide for x-axis
+iabscissa = 2; % 1== versus iterations, 2==versus fevals
+if mod(figNb,100) == 1
iabscissa = 1; % a short hack
- end
- if iabscissa == 1
+end
+if iabscissa == 1
xlab ='iterations';
- elseif iabscissa == 2
+elseif iabscissa == 2
xlab = 'function evaluations';
- end
+end
- if size(d.x, 2) < 1000
+if size(d.x, 2) < 1000
minxend = 1.03*d.x(end, iabscissa);
- else
+else
minxend = 0;
- end
+end
- % set up figure window
- if manual_mode
+% set up figure window
+if manual_mode
figure(figNb); % just create and raise the figure window
- else
+else
if 1 < 3 && evalin('caller', 'iterplotted') == 0 && evalin('caller', 'irun') == 1
- figure(figNb); % incomment this, if figure raise in the beginning is not desired
+ figure(figNb); % incomment this, if figure raise in the beginning is not desired
elseif ismember(figNb, findobj('Type', 'figure'))
- set(0, 'CurrentFigure', figNb); % prevents raise of existing figure window
+ set(0, 'CurrentFigure', figNb); % prevents raise of existing figure window
else
- figure(figNb);
+ figure(figNb);
end
- end
-
- % plot fitness etc
- foffset = 1e-99;
- dfit = d.f(:,6)-min(d.f(:,6));
- [ignore idxbest] = min(dfit);
- dfit(dfit<1e-98) = NaN;
- subplot(2,2,1); hold off;
- dd = abs(d.f(:,7:8)) + foffset;
- dd(d.f(:,7:8)==0) = NaN;
- semilogy(d.f(:,iabscissa), dd, '-k'); hold on;
- % additional fitness data, for example constraints values
- if size(d.f,2) > 8
+end
+
+% plot fitness etc
+foffset = 1e-99;
+dfit = d.f(:,6)-min(d.f(:,6));
+[ignore idxbest] = min(dfit);
+dfit(dfit<1e-98) = NaN;
+subplot(2,2,1); hold off;
+dd = abs(d.f(:,7:8)) + foffset;
+dd(d.f(:,7:8)==0) = NaN;
+semilogy(d.f(:,iabscissa), dd, '-k'); hold on;
+% additional fitness data, for example constraints values
+if size(d.f,2) > 8
dd = abs(d.f(:,9:end)) + 10*foffset; % a hack
- % dd(d.f(:,9:end)==0) = NaN;
+ % dd(d.f(:,9:end)==0) = NaN;
semilogy(d.f(:,iabscissa), dd, '-m'); hold on;
if size(d.f,2) > 12
- semilogy(d.f(:,iabscissa),abs(d.f(:,[7 8 11 13]))+foffset,'-k'); hold on;
+ semilogy(d.f(:,iabscissa),abs(d.f(:,[7 8 11 13]))+foffset,'-k'); hold on;
end
- end
+end
- idx = find(d.f(:,6)>1e-98); % positive values
- if ~isempty(idx) % otherwise non-log plot gets hold
+idx = find(d.f(:,6)>1e-98); % positive values
+if ~isempty(idx) % otherwise non-log plot gets hold
semilogy(d.f(idx,iabscissa), d.f(idx,6)+foffset, '.b'); hold on;
- end
- idx = find(d.f(:,6) < -1e-98); % negative values
- if ~isempty(idx)
+end
+idx = find(d.f(:,6) < -1e-98); % negative values
+if ~isempty(idx)
semilogy(d.f(idx, iabscissa), abs(d.f(idx,6))+foffset,'.r'); hold on;
- end
- semilogy(d.f(:,iabscissa),abs(d.f(:,6))+foffset,'-b'); hold on;
- semilogy(d.f(:,iabscissa),dfit,'-c'); hold on;
- semilogy(d.f(:,iabscissa),(d.f(:,4)),'-r'); hold on; % AR
- semilogy(d.std(:,iabscissa), [max(d.std(:,6:end)')' ...
- min(d.std(:,6:end)')'], '-m'); % max,min std
- maxval = max(d.std(end,6:end));
- minval = min(d.std(end,6:end));
- text(d.std(end,iabscissa), maxval, sprintf('%.0e', maxval));
- text(d.std(end,iabscissa), minval, sprintf('%.0e', minval));
-
- semilogy(d.std(:,iabscissa),(d.std(:,3)),'-g'); % sigma
- % plot best f
- semilogy(d.f(idxbest,iabscissa),min(dfit),'*c'); hold on;
- semilogy(d.f(idxbest,iabscissa),abs(d.f(idxbest,6))+foffset,'*r'); hold on;
-
- ax = axis;
- ax(2) = max(minxend, ax(2));
- axis(ax);
-
- yannote = 10^(log10(ax(3)) + 0.05*(log10(ax(4))-log10(ax(3))));
- text(ax(1), yannote, ...
- [ 'f=' num2str(d.f(end,6), '%.15g') ]);
-
- title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio');
- grid on;
-
- subplot(2,2,2); hold off;
- plot(d.x(:,iabscissa), d.x(:,6:end),'-'); hold on;
- ax = axis;
- ax(2) = max(minxend, ax(2));
- axis(ax);
-
- % add some annotation lines
- [ignore idx] = sort(d.x(end,6:end));
- % choose no more than 25 indices
- idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
- yy = repmat(NaN, 2, size(d.x,2)-5);
- yy(1,:) = d.x(end, 6:end);
- yy(2,idx(idxs)) = linspace(ax(3), ax(4), length(idxs));
- plot([d.x(end,iabscissa) ax(2)], yy, '-');
- plot(repmat(d.x(end,iabscissa),2), [ax(3) ax(4)], 'k-');
- for i = idx(idxs)
+end
+semilogy(d.f(:,iabscissa),abs(d.f(:,6))+foffset,'-b'); hold on;
+semilogy(d.f(:,iabscissa),dfit,'-c'); hold on;
+semilogy(d.f(:,iabscissa),(d.f(:,4)),'-r'); hold on; % AR
+semilogy(d.std(:,iabscissa), [max(d.std(:,6:end)')' ...
+ min(d.std(:,6:end)')'], '-m'); % max,min std
+maxval = max(d.std(end,6:end));
+minval = min(d.std(end,6:end));
+text(d.std(end,iabscissa), maxval, sprintf('%.0e', maxval));
+text(d.std(end,iabscissa), minval, sprintf('%.0e', minval));
+
+semilogy(d.std(:,iabscissa),(d.std(:,3)),'-g'); % sigma
+ % plot best f
+semilogy(d.f(idxbest,iabscissa),min(dfit),'*c'); hold on;
+semilogy(d.f(idxbest,iabscissa),abs(d.f(idxbest,6))+foffset,'*r'); hold on;
+
+ax = axis;
+ax(2) = max(minxend, ax(2));
+axis(ax);
+
+yannote = 10^(log10(ax(3)) + 0.05*(log10(ax(4))-log10(ax(3))));
+text(ax(1), yannote, ...
+ [ 'f=' num2str(d.f(end,6), '%.15g') ]);
+
+title('blue:abs(f), cyan:f-min(f), green:sigma, red:axis ratio');
+grid on;
+
+subplot(2,2,2); hold off;
+plot(d.x(:,iabscissa), d.x(:,6:end),'-'); hold on;
+ax = axis;
+ax(2) = max(minxend, ax(2));
+axis(ax);
+
+% add some annotation lines
+[ignore idx] = sort(d.x(end,6:end));
+% choose no more than 25 indices
+idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
+yy = repmat(NaN, 2, size(d.x,2)-5);
+yy(1,:) = d.x(end, 6:end);
+yy(2,idx(idxs)) = linspace(ax(3), ax(4), length(idxs));
+plot([d.x(end,iabscissa) ax(2)], yy, '-');
+plot(repmat(d.x(end,iabscissa),2), [ax(3) ax(4)], 'k-');
+for i = idx(idxs)
text(ax(2), yy(2,i), ...
['x(' num2str(i) ')=' num2str(yy(1,i))]);
- end
-
- lam = 'NA';
- if size(d.x, 1) > 1 && d.x(end, 1) > d.x(end-1, 1)
+end
+
+lam = 'NA';
+if size(d.x, 1) > 1 && d.x(end, 1) > d.x(end-1, 1)
lam = num2str((d.x(end, 2) - d.x(end-1, 2)) / (d.x(end, 1) - d.x(end-1, 1)));
- end
- title(['Object Variables (' num2str(size(d.x, 2)-5) ...
- '-D, popsize~' lam ')']);grid on;
-
- subplot(2,2,3); hold off; semilogy(d.D(:,iabscissa), d.D(:,6:end), '-');
- ax = axis;
- ax(2) = max(minxend, ax(2));
- axis(ax);
- title('Principal Axes Lengths');grid on;
- xlabel(xlab);
-
- subplot(2,2,4); hold off;
- % semilogy(d.std(:,iabscissa), d.std(:,6:end), 'k-'); hold on;
- % remove sigma from stds
- d.std(:,6:end) = d.std(:,6:end) ./ (d.std(:,3) * ones(1,size(d.std,2)-5));
- semilogy(d.std(:,iabscissa), d.std(:,6:end), '-'); hold on;
- if 11 < 3 % max and min std
+end
+title(['Object Variables (' num2str(size(d.x, 2)-5) ...
+ '-D, popsize~' lam ')']);grid on;
+
+subplot(2,2,3); hold off; semilogy(d.D(:,iabscissa), d.D(:,6:end), '-');
+ax = axis;
+ax(2) = max(minxend, ax(2));
+axis(ax);
+title('Principal Axes Lengths');grid on;
+xlabel(xlab);
+
+subplot(2,2,4); hold off;
+% semilogy(d.std(:,iabscissa), d.std(:,6:end), 'k-'); hold on;
+% remove sigma from stds
+d.std(:,6:end) = d.std(:,6:end) ./ (d.std(:,3) * ones(1,size(d.std,2)-5));
+semilogy(d.std(:,iabscissa), d.std(:,6:end), '-'); hold on;
+if 11 < 3 % max and min std
semilogy(d.std(:,iabscissa), [d.std(:,3).*max(d.std(:,6:end)')' ...
d.std(:,3).*min(d.std(:,6:end)')'], '-m', 'linewidth', 2);
maxval = max(d.std(end,6:end));
minval = min(d.std(end,6:end));
text(d.std(end,iabscissa), d.std(end,3)*maxval, sprintf('max=%.0e', maxval));
text(d.std(end,iabscissa), d.std(end,3)*minval, sprintf('min=%.0e', minval));
- end
- ax = axis;
- ax(2) = max(minxend, ax(2));
- axis(ax);
- % add some annotation lines
- [ignore idx] = sort(d.std(end,6:end));
- % choose no more than 25 indices
- idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
- yy = repmat(NaN, 2, size(d.std,2)-5);
- yy(1,:) = d.std(end, 6:end);
- yy(2,idx(idxs)) = logspace(log10(ax(3)), log10(ax(4)), length(idxs));
- semilogy([d.std(end,iabscissa) ax(2)], yy, '-');
- semilogy(repmat(d.std(end,iabscissa),2), [ax(3) ax(4)], 'k-');
- for i = idx(idxs)
+end
+ax = axis;
+ax(2) = max(minxend, ax(2));
+axis(ax);
+% add some annotation lines
+[ignore idx] = sort(d.std(end,6:end));
+% choose no more than 25 indices
+idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
+yy = repmat(NaN, 2, size(d.std,2)-5);
+yy(1,:) = d.std(end, 6:end);
+yy(2,idx(idxs)) = logspace(log10(ax(3)), log10(ax(4)), length(idxs));
+semilogy([d.std(end,iabscissa) ax(2)], yy, '-');
+semilogy(repmat(d.std(end,iabscissa),2), [ax(3) ax(4)], 'k-');
+for i = idx(idxs)
text(ax(2), yy(2,i), [' ' num2str(i)]);
- end
- title('Standard Deviations in Coordinates divided by sigma');grid on;
- xlabel(xlab);
+end
+title('Standard Deviations in Coordinates divided by sigma');grid on;
+xlabel(xlab);
- if figNb ~= 324
+if figNb ~= 324
% zoom on; % does not work in Octave
- end
- drawnow;
+end
+drawnow;
% ---------------------------------------------------------------
% --------------- TEST OBJECTIVE FUNCTIONS ----------------------
@@ -2337,24 +2337,24 @@ function f=fjens1(x)
%
% use population size about 2*N
%
- f = sum((x>0) .* x.^1, 1);
- if any(any(x<0))
+f = sum((x>0) .* x.^1, 1);
+if any(any(x<0))
idx = sum(x < 0, 1) > 0;
f(idx) = 1e3;
-% f = f + 1e3 * sum(x<0, 1);
-% f = f + 10 * sum((x<0) .* x.^2, 1);
+ % f = f + 1e3 * sum(x<0, 1);
+ % f = f + 10 * sum((x<0) .* x.^2, 1);
f(idx) = f(idx) + 1e-3*abs(randn(1,sum(idx)));
-% f(idx) = NaN;
- end
+ % f(idx) = NaN;
+end
function f=fsphere(x)
- f = sum(x.^2,1);
+f = sum(x.^2,1);
function f=fmax(x)
- f = max(abs(x), [], 1);
+f = max(abs(x), [], 1);
function f=fssphere(x)
- f=sqrt(sum(x.^2, 1));
+f=sqrt(sum(x.^2, 1));
% lb = -0.512; ub = 512;
% xfeas = x;
@@ -2362,99 +2362,99 @@ function f=fssphere(x)
% xfeas(x>ub) = ub;
% f=sum(xfeas.^2, 1);
% f = f + 1e-9 * sum((xfeas-x).^2);
-
+
function f=fspherenoise(x, Nevals)
- if nargin < 2 || isempty(Nevals)
+if nargin < 2 || isempty(Nevals)
Nevals = 1;
- end
- [N,popsi] = size(x);
+end
+[N,popsi] = size(x);
% x = x .* (1 + 0.3e-0 * randn(N, popsi)/(2*N)); % actuator noise
- fsum = 10.^(0*(0:N-1)/(N-1)) * x.^2;
+fsum = 10.^(0*(0:N-1)/(N-1)) * x.^2;
% f = 0*rand(1,1) ...
% + fsum ...
% + fsum .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N)) ...
% + 1*fsum.^0.9 .* 2*randn(1,popsi) / (2*N); %
% f = fsum .* exp(0.1*randn(1,popsi));
- f = fsum .* (1 + (10/(N+10)/sqrt(Nevals))*randn(1,popsi));
+f = fsum .* (1 + (10/(N+10)/sqrt(Nevals))*randn(1,popsi));
% f = fsum .* (1 + (0.1/N)*randn(1,popsi)./randn(1,popsi).^1);
- idx = rand(1,popsi) < 0.0;
- if sum(idx) > 0
+idx = rand(1,popsi) < 0.0;
+if sum(idx) > 0
f(idx) = f(idx) + 1e3*exp(randn(1,sum(idx)));
- end
-
+end
+
function f=fmixranks(x)
- N = size(x,1);
- f=(10.^(0*(0:(N-1))/(N-1))*x.^2).^0.5;
- if size(x, 2) > 1 % compute ranks, if it is a population
+N = size(x,1);
+f=(10.^(0*(0:(N-1))/(N-1))*x.^2).^0.5;
+if size(x, 2) > 1 % compute ranks, if it is a population
[ignore, idx] = sort(f);
[ignore, ranks] = sort(idx);
k = 9; % number of solutions randomly permuted, lambda/2-1
% works still quite well (two time slower)
for i = k+1:k-0:size(x,2)
- idx(i-k+(1:k)) = idx(i-k+randperm(k));
+ idx(i-k+(1:k)) = idx(i-k+randperm(k));
end
%disp([ranks' f'])
[ignore, ranks] = sort(idx);
%disp([ranks' f'])
%pause
f = ranks+1e-9*randn(1,1);
- end
-
+end
+
function f = fsphereoneax(x)
- f = x(1)^2;
- f = mean(x)^2;
-
+f = x(1)^2;
+f = mean(x)^2;
+
function f=frandsphere(x)
- N = size(x,1);
- idx = ceil(N*rand(7,1));
- f=sum(x(idx).^2);
+N = size(x,1);
+idx = ceil(N*rand(7,1));
+f=sum(x(idx).^2);
function f=fspherelb0(x, M) % lbound at zero for 1:M needed
- if nargin < 2 M = 0; end
- N = size(x,1);
- % M active bounds, f_i = 1 for x = 0
- f = -M + sum((x(1:M) + 1).^2);
- f = f + sum(x(M+1:N).^2);
-
+if nargin < 2 M = 0; end
+N = size(x,1);
+% M active bounds, f_i = 1 for x = 0
+f = -M + sum((x(1:M) + 1).^2);
+f = f + sum(x(M+1:N).^2);
+
function f=fspherehull(x)
- % Patton, Dexter, Goodman, Punch
- % in -500..500
- % spherical ridge through zeros(N,1)
- % worst case start point seems x = 2*100*sqrt(N)
- % and small step size
- N = size(x,1);
- f = norm(x) + (norm(x-100*sqrt(N)) - 100*N)^2;
-
+% Patton, Dexter, Goodman, Punch
+% in -500..500
+% spherical ridge through zeros(N,1)
+% worst case start point seems x = 2*100*sqrt(N)
+% and small step size
+N = size(x,1);
+f = norm(x) + (norm(x-100*sqrt(N)) - 100*N)^2;
+
function f=fellilb0(x, idxM, scal) % lbound at zero for 1:M needed
- N = size(x,1);
- if nargin < 3 || isempty(scal)
+N = size(x,1);
+if nargin < 3 || isempty(scal)
scal = 100;
- end
- scale=scal.^((0:N-1)/(N-1));
- if nargin < 2 || isempty(idxM)
+end
+scale=scal.^((0:N-1)/(N-1));
+if nargin < 2 || isempty(idxM)
idxM = 1:N;
- end
- %scale(N) = 1e0;
- % M active bounds
- xopt = 0.1;
- x(idxM) = x(idxM) + xopt;
- f = scale.^2*x.^2;
- f = f - sum((xopt*scale(idxM)).^2);
+end
+%scale(N) = 1e0;
+% M active bounds
+xopt = 0.1;
+x(idxM) = x(idxM) + xopt;
+f = scale.^2*x.^2;
+f = f - sum((xopt*scale(idxM)).^2);
% f = exp(f) - 1;
% f = log10(f+1e-19) + 19;
- f = f + 1e-19;
-
+f = f + 1e-19;
+
function f=fcornersphere(x)
- w = ones(size(x,1));
- w(1) = 2.5; w(2)=2.5;
- idx = x < 0;
- f = sum(x(idx).^2);
- idx = x > 0;
- f = f + 2^2*sum(w(idx).*x(idx).^2);
-
+w = ones(size(x,1));
+w(1) = 2.5; w(2)=2.5;
+idx = x < 0;
+f = sum(x(idx).^2);
+idx = x > 0;
+f = f + 2^2*sum(w(idx).*x(idx).^2);
+
function f=fsectorsphere(x, scal)
%
% This is deceptive for cumulative sigma control CSA in large dimension:
@@ -2467,407 +2467,407 @@ function f=fsectorsphere(x, scal)
% reasonable, but I do not know better alternatives. In particular:
% TPA takes longer to converge than CSA when the latter still works.
%
- if nargin < 2 || isempty (scal)
+if nargin < 2 || isempty (scal)
scal = 1e3;
- end
- f=sum(x.^2,1);
- idx = x<0;
- f = f + (scal^2 - 1) * sum((idx.*x).^2,1);
- if 11 < 3
+end
+f=sum(x.^2,1);
+idx = x<0;
+f = f + (scal^2 - 1) * sum((idx.*x).^2,1);
+if 11 < 3
idxpen = find(f>1e9);
if ~isempty(idxpen)
- f(idxpen) = f(idxpen) + 1e8*sum(x(:,idxpen).^2,1);
+ f(idxpen) = f(idxpen) + 1e8*sum(x(:,idxpen).^2,1);
end
- end
-
+end
+
function f=fstepsphere(x, scal)
- if nargin < 2 || isempty (scal)
+if nargin < 2 || isempty (scal)
scal = 1e0;
- end
- N = size(x,1);
- f=1e-11+sum(scal.^((0:N-1)/(N-1))*floor(x+0.5).^2);
- f=1e-11+sum(floor(scal.^((0:N-1)/(N-1))'.*x+0.5).^2);
+end
+N = size(x,1);
+f=1e-11+sum(scal.^((0:N-1)/(N-1))*floor(x+0.5).^2);
+f=1e-11+sum(floor(scal.^((0:N-1)/(N-1))'.*x+0.5).^2);
% f=1e-11+sum(floor(x+0.5).^2);
function f=fstep(x)
- % in -5.12..5.12 (bounded)
- N = size(x,1);
- f=1e-11+6*N+sum(floor(x));
+% in -5.12..5.12 (bounded)
+N = size(x,1);
+f=1e-11+6*N+sum(floor(x));
function f=flnorm(x, scal, e)
if nargin < 2 || isempty(scal)
- scal = 1;
+ scal = 1;
end
if nargin < 3 || isempty(e)
- e = 1;
+ e = 1;
end
if e==inf
- f = max(abs(x));
+ f = max(abs(x));
else
- N = size(x,1);
- scale = scal.^((0:N-1)/(N-1))';
- f=sum(abs(scale.*x).^e);
+ N = size(x,1);
+ scale = scal.^((0:N-1)/(N-1))';
+ f=sum(abs(scale.*x).^e);
end
function f=fneumaier3(x)
- % in -n^2..n^2
- % x^*-i = i(n+1-i)
- N = size(x,1);
+% in -n^2..n^2
+% x^*-i = i(n+1-i)
+N = size(x,1);
% f = N*(N+4)*(N-1)/6 + sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
- f = sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
+f = sum((x-1).^2) - sum(x(1:N-1).*x(2:N));
function f = fmaxmindist(y)
- % y in [-1,1], y(1:2) is first point on a plane, y(3:4) second etc
- % points best
- % 5 1.4142
- % 8 1.03527618
- % 10 0.842535997
- % 20 0.5997
- pop = size(y,2);
- N = size(y,1)/2;
- f = [];
- for ipop = 1:pop
+% y in [-1,1], y(1:2) is first point on a plane, y(3:4) second etc
+% points best
+% 5 1.4142
+% 8 1.03527618
+% 10 0.842535997
+% 20 0.5997
+pop = size(y,2);
+N = size(y,1)/2;
+f = [];
+for ipop = 1:pop
if any(abs(y(:,ipop)) > 1)
- f(ipop) = NaN;
+ f(ipop) = NaN;
else
- x = reshape(y(:,ipop), [2, N]);
- f(ipop) = inf;
- for i = 1:N
- f(ipop) = min(f(ipop), min(sqrt(sum((x(:,[1:i-1 i+1:N]) - repmat(x(:,i), 1, N-1)).^2, 1))));
- end
+ x = reshape(y(:,ipop), [2, N]);
+ f(ipop) = inf;
+ for i = 1:N
+ f(ipop) = min(f(ipop), min(sqrt(sum((x(:,[1:i-1 i+1:N]) - repmat(x(:,i), 1, N-1)).^2, 1))));
+ end
end
- end
- f = -f;
+end
+f = -f;
function f=fchangingsphere(x)
- N = size(x,1);
- global scale_G; global count_G; if isempty(count_G) count_G=-1; end
- count_G = count_G+1;
- if mod(count_G,10) == 0
+N = size(x,1);
+global scale_G; global count_G; if isempty(count_G) count_G=-1; end
+count_G = count_G+1;
+if mod(count_G,10) == 0
scale_G = 10.^(2*rand(1,N));
- end
- %disp(scale(1));
- f = scale_G*x.^2;
-
+end
+%disp(scale(1));
+f = scale_G*x.^2;
+
function f= flogsphere(x)
- f = 1-exp(-sum(x.^2));
-
+f = 1-exp(-sum(x.^2));
+
function f= fexpsphere(x)
- f = exp(sum(x.^2)) - 1;
-
+f = exp(sum(x.^2)) - 1;
+
function f=fbaluja(x)
- % in [-0.16 0.16]
- y = x(1);
- for i = 2:length(x)
+% in [-0.16 0.16]
+y = x(1);
+for i = 2:length(x)
y(i) = x(i) + y(i-1);
- end
- f = 1e5 - 1/(1e-5 + sum(abs(y)));
+end
+f = 1e5 - 1/(1e-5 + sum(abs(y)));
function f=fschwefel(x)
- f = 0;
- for i = 1:size(x,1),
+f = 0;
+for i = 1:size(x,1),
f = f+sum(x(1:i))^2;
- end
+end
function f=fcigar(x, ar)
- if nargin < 2 || isempty(ar)
+if nargin < 2 || isempty(ar)
ar = 1e3;
- end
- f = x(1,:).^2 + ar^2*sum(x(2:end,:).^2,1);
-
+end
+f = x(1,:).^2 + ar^2*sum(x(2:end,:).^2,1);
+
function f=fcigtab(x)
- f = x(1,:).^2 + 1e8*x(end,:).^2 + 1e4*sum(x(2:(end-1),:).^2, 1);
-
+f = x(1,:).^2 + 1e8*x(end,:).^2 + 1e4*sum(x(2:(end-1),:).^2, 1);
+
function f=ftablet(x)
- f = 1e6*x(1,:).^2 + sum(x(2:end,:).^2, 1);
+f = 1e6*x(1,:).^2 + sum(x(2:end,:).^2, 1);
function f=felli(x, lgscal, expon, expon2)
- % lgscal: log10(axis ratio)
- % expon: x_i^expon, sphere==2
- N = size(x,1); if N < 2 error('dimension must be greater one'); end
+% lgscal: log10(axis ratio)
+% expon: x_i^expon, sphere==2
+N = size(x,1); if N < 2 error('dimension must be greater one'); end
% x = x - repmat(-0.5+(1:N)',1,size(x,2)); % optimum in 1:N
- if nargin < 2 || isempty(lgscal), lgscal = 3; end
- if nargin < 3 || isempty(expon), expon = 2; end
- if nargin < 4 || isempty(expon2), expon2 = 1; end
+if nargin < 2 || isempty(lgscal), lgscal = 3; end
+if nargin < 3 || isempty(expon), expon = 2; end
+if nargin < 4 || isempty(expon2), expon2 = 1; end
- f=((10^(lgscal*expon)).^((0:N-1)/(N-1)) * abs(x).^expon).^(1/expon2);
+f=((10^(lgscal*expon)).^((0:N-1)/(N-1)) * abs(x).^expon).^(1/expon2);
% if rand(1,1) > 0.015
% f = NaN;
% end
% f = f + randn(size(f));
function f=fellitest(x)
- beta = 0.9;
- N = size(x,1);
- f = (1e6.^((0:(N-1))/(N-1))).^beta * (x.^2).^beta;
+beta = 0.9;
+N = size(x,1);
+f = (1e6.^((0:(N-1))/(N-1))).^beta * (x.^2).^beta;
+
-
function f=fellii(x, scal)
- N = size(x,1); if N < 2 error('dimension must be greater one'); end
- if nargin < 2
+N = size(x,1); if N < 2 error('dimension must be greater one'); end
+if nargin < 2
scal = 1;
- end
- f= (scal*(1:N)).^2 * (x).^2;
+end
+f= (scal*(1:N)).^2 * (x).^2;
function f=fellirot(x)
- N = size(x,1);
- global ORTHOGONALCOORSYSTEM_G
- if isempty(ORTHOGONALCOORSYSTEM_G) ...
- || length(ORTHOGONALCOORSYSTEM_G) < N ...
- || isempty(ORTHOGONALCOORSYSTEM_G{N})
+N = size(x,1);
+global ORTHOGONALCOORSYSTEM_G
+if isempty(ORTHOGONALCOORSYSTEM_G) ...
+ || length(ORTHOGONALCOORSYSTEM_G) < N ...
+ || isempty(ORTHOGONALCOORSYSTEM_G{N})
coordinatesystem(N);
- end
- f = felli(ORTHOGONALCOORSYSTEM_G{N}*x);
-
+end
+f = felli(ORTHOGONALCOORSYSTEM_G{N}*x);
+
function f=frot(x, fun, varargin)
- N = size(x,1);
- global ORTHOGONALCOORSYSTEM_G
- if isempty(ORTHOGONALCOORSYSTEM_G) ...
- || length(ORTHOGONALCOORSYSTEM_G) < N ...
- || isempty(ORTHOGONALCOORSYSTEM_G{N})
+N = size(x,1);
+global ORTHOGONALCOORSYSTEM_G
+if isempty(ORTHOGONALCOORSYSTEM_G) ...
+ || length(ORTHOGONALCOORSYSTEM_G) < N ...
+ || isempty(ORTHOGONALCOORSYSTEM_G{N})
coordinatesystem(N);
- end
- f = feval(fun, ORTHOGONALCOORSYSTEM_G{N}*x, varargin{:});
-
+end
+f = feval(fun, ORTHOGONALCOORSYSTEM_G{N}*x, varargin{:});
+
function coordinatesystem(N)
- if nargin < 1 || isempty(N)
+if nargin < 1 || isempty(N)
arN = 2:30;
- else
+else
arN = N;
- end
- global ORTHOGONALCOORSYSTEM_G
- ORTHOGONALCOORSYSTEM_G{1} = 1;
- for N = arN
+end
+global ORTHOGONALCOORSYSTEM_G
+ORTHOGONALCOORSYSTEM_G{1} = 1;
+for N = arN
ar = randn(N,N);
for i = 1:N
- for j = 1:i-1
- ar(:,i) = ar(:,i) - ar(:,i)'*ar(:,j) * ar(:,j);
- end
- ar(:,i) = ar(:,i) / norm(ar(:,i));
+ for j = 1:i-1
+ ar(:,i) = ar(:,i) - ar(:,i)'*ar(:,j) * ar(:,j);
+ end
+ ar(:,i) = ar(:,i) / norm(ar(:,i));
end
ORTHOGONALCOORSYSTEM_G{N} = ar;
- end
+end
function f=fplane(x)
- f=x(1);
+f=x(1);
function f=ftwoaxes(x)
- f = sum(x(1:floor(end/2),:).^2, 1) + 1e6*sum(x(floor(1+end/2):end,:).^2, 1);
+f = sum(x(1:floor(end/2),:).^2, 1) + 1e6*sum(x(floor(1+end/2):end,:).^2, 1);
function f=fparabR(x)
- f = -x(1,:) + 100*sum(x(2:end,:).^2,1);
+f = -x(1,:) + 100*sum(x(2:end,:).^2,1);
function f=fsharpR(x)
- f = abs(-x(1, :)).^2 + 100 * sqrt(sum(x(2:end,:).^2, 1));
-
+f = abs(-x(1, :)).^2 + 100 * sqrt(sum(x(2:end,:).^2, 1));
+
function f=frosen(x)
- if size(x,1) < 2 error('dimension must be greater one'); end
- N = size(x,1);
- popsi = size(x,2);
- f = 1e2*sum((x(1:end-1,:).^2 - x(2:end,:)).^2,1) + sum((x(1:end-1,:)-1).^2,1);
- % f = f + f^0.9 .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N));
+if size(x,1) < 2 error('dimension must be greater one'); end
+N = size(x,1);
+popsi = size(x,2);
+f = 1e2*sum((x(1:end-1,:).^2 - x(2:end,:)).^2,1) + sum((x(1:end-1,:)-1).^2,1);
+% f = f + f^0.9 .* (2*randn(1,popsi) ./ randn(1,popsi).^0 / (2*N));
function f=frosenlin(x)
- if size(x,1) < 2 error('dimension must be greater one'); end
+if size(x,1) < 2 error('dimension must be greater one'); end
- x_org = x;
- x(x>30) = 30;
- x(x<-30) = -30;
+x_org = x;
+x(x>30) = 30;
+x(x<-30) = -30;
- f = 1e2*sum(-(x(1:end-1,:).^2 - x(2:end,:)),1) + ...
- sum((x(1:end-1,:)-1).^2,1);
+f = 1e2*sum(-(x(1:end-1,:).^2 - x(2:end,:)),1) + ...
+ sum((x(1:end-1,:)-1).^2,1);
- f = f + sum((x-x_org).^2,1);
+f = f + sum((x-x_org).^2,1);
% f(any(abs(x)>30,1)) = NaN;
function f=frosenrot(x)
- N = size(x,1);
- global ORTHOGONALCOORSYSTEM_G
- if isempty(ORTHOGONALCOORSYSTEM_G) ...
- || length(ORTHOGONALCOORSYSTEM_G) < N ...
- || isempty(ORTHOGONALCOORSYSTEM_G{N})
+N = size(x,1);
+global ORTHOGONALCOORSYSTEM_G
+if isempty(ORTHOGONALCOORSYSTEM_G) ...
+ || length(ORTHOGONALCOORSYSTEM_G) < N ...
+ || isempty(ORTHOGONALCOORSYSTEM_G{N})
coordinatesystem(N);
- end
- f = frosen(ORTHOGONALCOORSYSTEM_G{N}*x);
+end
+f = frosen(ORTHOGONALCOORSYSTEM_G{N}*x);
function f=frosenmodif(x)
- f = 74 + 100*(x(2)-x(1)^2)^2 + (1-x(1))^2 ...
- - 400*exp(-sum((x+1).^2)/2/0.05);
-
+f = 74 + 100*(x(2)-x(1)^2)^2 + (1-x(1))^2 ...
+ - 400*exp(-sum((x+1).^2)/2/0.05);
+
function f=fschwefelrosen1(x)
- % in [-10 10]
- f=sum((x.^2-x(1)).^2 + (x-1).^2);
-
+% in [-10 10]
+f=sum((x.^2-x(1)).^2 + (x-1).^2);
+
function f=fschwefelrosen2(x)
- % in [-10 10]
- f=sum((x(2:end).^2-x(1)).^2 + (x(2:end)-1).^2);
+% in [-10 10]
+f=sum((x(2:end).^2-x(1)).^2 + (x(2:end)-1).^2);
function f=fdiffpow(x)
- [N popsi] = size(x); if N < 2 error('dimension must be greater one'); end
+[N popsi] = size(x); if N < 2 error('dimension must be greater one'); end
- f = sum(abs(x).^repmat(2+10*(0:N-1)'/(N-1), 1, popsi), 1);
- f = sqrt(f);
+f = sum(abs(x).^repmat(2+10*(0:N-1)'/(N-1), 1, popsi), 1);
+f = sqrt(f);
function f=fabsprod(x)
- f = sum(abs(x),1) + prod(abs(x),1);
+f = sum(abs(x),1) + prod(abs(x),1);
function f=ffloor(x)
- f = sum(floor(x+0.5).^2,1);
+f = sum(floor(x+0.5).^2,1);
function f=fmaxx(x)
- f = max(abs(x), [], 1);
+f = max(abs(x), [], 1);
%%% Multimodal functions
function f=fbirastrigin(x)
% todo: the volume needs to be a constant
- N = size(x,1);
- idx = (sum(x, 1) < 0.5*N); % global optimum
- f = zeros(1,size(x,2));
- f(idx) = 10*(N-sum(cos(2*pi*x(:,idx)),1)) + sum(x(:,idx).^2,1);
- idx = ~idx;
- f(idx) = 0.1 + 10*(N-sum(cos(2*pi*(x(:,idx)-2)),1)) + sum((x(:,idx)-2).^2,1);
+N = size(x,1);
+idx = (sum(x, 1) < 0.5*N); % global optimum
+f = zeros(1,size(x,2));
+f(idx) = 10*(N-sum(cos(2*pi*x(:,idx)),1)) + sum(x(:,idx).^2,1);
+idx = ~idx;
+f(idx) = 0.1 + 10*(N-sum(cos(2*pi*(x(:,idx)-2)),1)) + sum((x(:,idx)-2).^2,1);
function f=fackley(x)
- % -32.768..32.768
- % Adding a penalty outside the interval is recommended,
- % because for large step sizes, fackley imposes like frand
- %
- N = size(x,1);
- f = 20-20*exp(-0.2*sqrt(sum(x.^2)/N));
- f = f + (exp(1) - exp(sum(cos(2*pi*x))/N));
- % add penalty outside the search interval
- f = f + sum((x(x>32.768)-32.768).^2) + sum((x(x<-32.768)+32.768).^2);
-
+% -32.768..32.768
+% Adding a penalty outside the interval is recommended,
+% because for large step sizes, fackley imposes like frand
+%
+N = size(x,1);
+f = 20-20*exp(-0.2*sqrt(sum(x.^2)/N));
+f = f + (exp(1) - exp(sum(cos(2*pi*x))/N));
+% add penalty outside the search interval
+f = f + sum((x(x>32.768)-32.768).^2) + sum((x(x<-32.768)+32.768).^2);
+
function f = fbohachevsky(x)
- % -15..15
- f = sum(x(1:end-1).^2 + 2 * x(2:end).^2 - 0.3 * cos(3*pi*x(1:end-1)) ...
- - 0.4 * cos(4*pi*x(2:end)) + 0.7);
-
+% -15..15
+f = sum(x(1:end-1).^2 + 2 * x(2:end).^2 - 0.3 * cos(3*pi*x(1:end-1)) ...
+ - 0.4 * cos(4*pi*x(2:end)) + 0.7);
+
function f=fconcentric(x)
- % in +-600
- s = sum(x.^2);
- f = s^0.25 * (sin(50*s^0.1)^2 + 1);
+% in +-600
+s = sum(x.^2);
+f = s^0.25 * (sin(50*s^0.1)^2 + 1);
function f=fgriewank(x)
- % in [-600 600]
- [N, P] = size(x);
- f = 1 - prod(cos(x'./sqrt(1:N))) + sum(x.^2)/4e3;
- scale = repmat(sqrt(1:N)', 1, P);
- f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
- % f = f + 1e4*sum(x(abs(x)>5).^2);
- % if sum(x(abs(x)>5).^2) > 0
- % f = 1e4 * sum(x(abs(x)>5).^2) + 1e8 * sum(x(x>5)).^2;
- % end
+% in [-600 600]
+[N, P] = size(x);
+f = 1 - prod(cos(x'./sqrt(1:N))) + sum(x.^2)/4e3;
+scale = repmat(sqrt(1:N)', 1, P);
+f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
+% f = f + 1e4*sum(x(abs(x)>5).^2);
+% if sum(x(abs(x)>5).^2) > 0
+% f = 1e4 * sum(x(abs(x)>5).^2) + 1e8 * sum(x(x>5)).^2;
+% end
function f=fgriewrosen(x)
% F13 or F8F2
- [N, P] = size(x);
- scale = repmat(sqrt(1:N)', 1, P);
- y = [x(2:end,:); x(1,:)];
- x = 100 * (x.^2 - y) + (x - 1).^2; % Rosenbrock part
- f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
- f = sum(1 - cos(x) + x.^2/4e3, 1);
+[N, P] = size(x);
+scale = repmat(sqrt(1:N)', 1, P);
+y = [x(2:end,:); x(1,:)];
+x = 100 * (x.^2 - y) + (x - 1).^2; % Rosenbrock part
+f = 1 - prod(cos(x./scale), 1) + sum(x.^2, 1)/4e3;
+f = sum(1 - cos(x) + x.^2/4e3, 1);
function f=fspallpseudorastrigin(x, scal, skewfac, skewstart, amplitude)
% by default multi-modal about between -30 and 30
- if nargin < 5 || isempty(amplitude)
+if nargin < 5 || isempty(amplitude)
amplitude = 40;
- end
- if nargin < 4 || isempty(skewstart)
+end
+if nargin < 4 || isempty(skewstart)
skewstart = 0;
- end
- if nargin < 3 || isempty(skewfac)
+end
+if nargin < 3 || isempty(skewfac)
skewfac = 1;
- end
- if nargin < 2 || isempty(scal)
+end
+if nargin < 2 || isempty(scal)
scal = 1;
- end
- N = size(x,1);
- scale = 1;
- if N > 1
+end
+N = size(x,1);
+scale = 1;
+if N > 1
scale=scal.^((0:N-1)'/(N-1));
- end
- % simple version:
- % f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
-
- % skew version:
- y = repmat(scale, 1, size(x,2)) .* x;
- idx = find(x > skewstart);
- if ~isempty(idx)
+end
+% simple version:
+% f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
+
+% skew version:
+y = repmat(scale, 1, size(x,2)) .* x;
+idx = find(x > skewstart);
+if ~isempty(idx)
y(idx) = skewfac*y(idx);
- end
- f = amplitude * (0*N-prod(cos((2*pi)^0*y),1)) + 0.05 * sum(y.^2,1) ...
- + randn(1,1);
+end
+f = amplitude * (0*N-prod(cos((2*pi)^0*y),1)) + 0.05 * sum(y.^2,1) ...
+ + randn(1,1);
function f=frastrigin(x, scal, skewfac, skewstart, amplitude)
% by default multi-modal about between -30 and 30
- if nargin < 5 || isempty(amplitude)
+if nargin < 5 || isempty(amplitude)
amplitude = 10;
- end
- if nargin < 4 || isempty(skewstart)
+end
+if nargin < 4 || isempty(skewstart)
skewstart = 0;
- end
- if nargin < 3 || isempty(skewfac)
+end
+if nargin < 3 || isempty(skewfac)
skewfac = 1;
- end
- if nargin < 2 || isempty(scal)
+end
+if nargin < 2 || isempty(scal)
scal = 1;
- end
- N = size(x,1);
- scale = 1;
- if N > 1
+end
+N = size(x,1);
+scale = 1;
+if N > 1
scale=scal.^((0:N-1)'/(N-1));
- end
- % simple version:
- % f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
-
- % skew version:
- y = repmat(scale, 1, size(x,2)) .* x;
- idx = find(x > skewstart);
- % idx = intersect(idx, 2:2:10);
- if ~isempty(idx)
+end
+% simple version:
+% f = amplitude*(N - sum(cos(2*pi*(scale.*x)))) + sum((scale.*x).^2);
+
+% skew version:
+y = repmat(scale, 1, size(x,2)) .* x;
+idx = find(x > skewstart);
+% idx = intersect(idx, 2:2:10);
+if ~isempty(idx)
y(idx) = skewfac*y(idx);
- end
- f = amplitude * (N-sum(cos(2*pi*y),1)) + sum(y.^2,1);
-
+end
+f = amplitude * (N-sum(cos(2*pi*y),1)) + sum(y.^2,1);
+
function f=frastriginmax(x)
- N = size(x,1);
- f = (N/20)*807.06580387678 - (10 * (N-sum(cos(2*pi*x),1)) + sum(x.^2,1));
- f(any(abs(x) > 5.12)) = 1e2*N;
+N = size(x,1);
+f = (N/20)*807.06580387678 - (10 * (N-sum(cos(2*pi*x),1)) + sum(x.^2,1));
+f(any(abs(x) > 5.12)) = 1e2*N;
function f = fschaffer(x)
- % -100..100
- N = size(x,1);
- s = x(1:N-1,:).^2 + x(2:N,:).^2;
- f = sum(s.^0.25 .* (sin(50*s.^0.1).^2+1), 1);
+% -100..100
+N = size(x,1);
+s = x(1:N-1,:).^2 + x(2:N,:).^2;
+f = sum(s.^0.25 .* (sin(50*s.^0.1).^2+1), 1);
function f=fschwefelmult(x)
- % -500..500
- %
- N = size(x,1);
- f = - sum(x.*sin(sqrt(abs(x))), 1);
- f = 418.9829*N - 1.27275661e-5*N - sum(x.*sin(sqrt(abs(x))), 1);
- % penalty term
- f = f + 1e4*sum((abs(x)>500) .* (abs(x)-500).^2, 1);
-
+% -500..500
+%
+N = size(x,1);
+f = - sum(x.*sin(sqrt(abs(x))), 1);
+f = 418.9829*N - 1.27275661e-5*N - sum(x.*sin(sqrt(abs(x))), 1);
+% penalty term
+f = f + 1e4*sum((abs(x)>500) .* (abs(x)-500).^2, 1);
+
function f=ftwomax(x)
- % Boundaries at +/-5
- N = size(x,1);
- f = -abs(sum(x)) + 5*N;
+% Boundaries at +/-5
+N = size(x,1);
+f = -abs(sum(x)) + 5*N;
function f=ftwomaxtwo(x)
- % Boundaries at +/-10
- N = size(x,1);
- f = abs(sum(x));
- if f > 30
+% Boundaries at +/-10
+N = size(x,1);
+f = abs(sum(x));
+if f > 30
f = f - 30;
- end
- f = -f;
-
+end
+f = -f;
+
function f=frand(x)
- f=1./(1-rand(1, size(x,2))) - 1;
+f=1./(1-rand(1, size(x,2))) - 1;
% CHANGES
% 12/02/19: "future" setting of ccum, correcting for large mueff, is default now
diff --git a/matlab/optimization/csminit1.m b/matlab/optimization/csminit1.m
index 1a07951957269e1ec3426f0d5e1fa5468f660965..633e7dc9b94ad99c62082c3971a622042cdd598a 100644
--- a/matlab/optimization/csminit1.m
+++ b/matlab/optimization/csminit1.m
@@ -122,7 +122,7 @@ else
disp_verbose(sprintf('Correct for low angle: %g',a),Verbose)
end
end
- disp_verbose(sprintf('Predicted improvement: %18.9f',-dfhat/2),Verbose)
+ disp_verbose(sprintf('Predicted improvement: %18.9f',-dfhat/2),Verbose)
%
% Have OK dx, now adjust length of step (lambda) until min and
% max improvement rate criteria are met.
@@ -145,7 +145,7 @@ else
%ARGLIST
%f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);
% f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);
- disp_verbose(sprintf('lambda = %10.5g; f = %20.7f',lambda,f ),Verbose)
+ disp_verbose(sprintf('lambda = %10.5g; f = %20.7f',lambda,f ),Verbose)
%debug
%disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))
if f<fhat
diff --git a/matlab/optimization/csminwel1.m b/matlab/optimization/csminwel1.m
index 52a018792979401f666456ea08cf7b34fe2415aa..af341b11db9e5794c9770bc7e25806eac1b69574 100644
--- a/matlab/optimization/csminwel1.m
+++ b/matlab/optimization/csminwel1.m
@@ -302,7 +302,7 @@ while ~done
error('Unaccounted Case, please contact the developers',Verbose)
end
end
-
+
f=fh;
x=xh;
g=gh;
@@ -312,18 +312,18 @@ end
end
function [g, badg]=get_num_grad(method,fcn,penalty,f0,x0,epsilon,varargin)
- switch method
- case 2
- [g,badg] = numgrad2(fcn, f0, x0, penalty, epsilon, varargin{:});
- case 3
- [g,badg] = numgrad3(fcn, f0, x0, penalty, epsilon, varargin{:});
- case 5
- [g,badg] = numgrad5(fcn, f0, x0, penalty, epsilon, varargin{:});
- case 13
- [g,badg] = numgrad3_(fcn, f0, x0, penalty, epsilon, varargin{:});
- case 15
- [g,badg] = numgrad5_(fcn, f0, x0, penalty, epsilon, varargin{:});
- otherwise
- error('csminwel1: Unknown method for gradient evaluation!')
- end
+switch method
+ case 2
+ [g,badg] = numgrad2(fcn, f0, x0, penalty, epsilon, varargin{:});
+ case 3
+ [g,badg] = numgrad3(fcn, f0, x0, penalty, epsilon, varargin{:});
+ case 5
+ [g,badg] = numgrad5(fcn, f0, x0, penalty, epsilon, varargin{:});
+ case 13
+ [g,badg] = numgrad3_(fcn, f0, x0, penalty, epsilon, varargin{:});
+ case 15
+ [g,badg] = numgrad5_(fcn, f0, x0, penalty, epsilon, varargin{:});
+ otherwise
+ error('csminwel1: Unknown method for gradient evaluation!')
+end
end
\ No newline at end of file
diff --git a/matlab/optimization/dynare_minimize_objective.m b/matlab/optimization/dynare_minimize_objective.m
index 30705f54aae3ac85ff61517dd2bb77a59b8a674b..8aca0d2e1090bbfa7674390ff9ad75fe3b2ce1a8 100644
--- a/matlab/optimization/dynare_minimize_objective.m
+++ b/matlab/optimization/dynare_minimize_objective.m
@@ -339,14 +339,14 @@ switch minimizer_algorithm
end
case 'SaveFiles'
if options_list{i,2}==0
- cmaesOptions.SaveVariables='off';
- cmaesOptions.LogModulo = '0'; % [0:Inf] if >1 record data less frequently after gen=100';
- cmaesOptions.LogTime = '0'; % [0:100] max. percentage of time for recording data';
+ cmaesOptions.SaveVariables='off';
+ cmaesOptions.LogModulo = '0'; % [0:Inf] if >1 record data less frequently after gen=100';
+ cmaesOptions.LogTime = '0'; % [0:100] max. percentage of time for recording data';
end
case 'CMAESResume'
- if options_list{i,2}==1
+ if options_list{i,2}==1
cmaesOptions.Resume = 'yes';
- end
+ end
otherwise
warning(['cmaes: Unknown option (' options_list{i,1} ')!'])
end
@@ -386,11 +386,11 @@ switch minimizer_algorithm
case 'MaxFunEvals'
simpsaOptions.MAX_FUN_EVALS = options_list{i,2};
case 'verbosity'
- if options_list{i,2} == 0
+ if options_list{i,2} == 0
simpsaOptions.DISPLAY = 'none';
- else
+ else
simpsaOptions.DISPLAY = 'iter';
- end
+ end
otherwise
warning(['simpsa: Unknown option (' options_list{i,1} ')!'])
end
@@ -414,10 +414,10 @@ switch minimizer_algorithm
if ~isempty(options_.optim_opt)
options_list = read_key_value_string(options_.optim_opt);
SupportedListOfOptions = {'CreationFcn', 'Display', 'DisplayInterval', 'FunctionTolerance', ...
- 'FunValCheck', 'HybridFcn', 'InertiaRange', 'InitialSwarmMatrix', 'InitialSwarmSpan', ...
- 'MaxIterations', 'MaxStallIterations', 'MaxStallTime', 'MaxTime', ...
- 'MinNeighborsFraction', 'ObjectiveLimit', 'OutputFcn', 'PlotFcn', 'SelfAdjustmentWeight', ...
- 'SocialAdjustmentWeight', 'SwarmSize', 'UseParallel', 'UseVectorized'};
+ 'FunValCheck', 'HybridFcn', 'InertiaRange', 'InitialSwarmMatrix', 'InitialSwarmSpan', ...
+ 'MaxIterations', 'MaxStallIterations', 'MaxStallTime', 'MaxTime', ...
+ 'MinNeighborsFraction', 'ObjectiveLimit', 'OutputFcn', 'PlotFcn', 'SelfAdjustmentWeight', ...
+ 'SocialAdjustmentWeight', 'SwarmSize', 'UseParallel', 'UseVectorized'};
for i=1:rows(options_list)
if ismember(options_list{i,1}, SupportedListOfOptions)
particleswarmOptions = optimoptions(particleswarmOptions, options_list{i,1}, options_list{i,2});
@@ -514,8 +514,8 @@ end
end
function [LB, UB]=set_bounds_to_finite_values(bounds, huge_number)
- LB=bounds(:,1);
- LB(isinf(LB))=-huge_number;
- UB=bounds(:,2);
- UB(isinf(UB))=huge_number;
+LB=bounds(:,1);
+LB(isinf(LB))=-huge_number;
+UB=bounds(:,2);
+UB(isinf(UB))=huge_number;
end
diff --git a/matlab/optimization/gmhmaxlik.m b/matlab/optimization/gmhmaxlik.m
index 3710d8d65ef7b0fd4065170a30e0fe499ddab3c9..bc800f8f84b8f108c72085b63c2b7035fb69d1ca 100644
--- a/matlab/optimization/gmhmaxlik.m
+++ b/matlab/optimization/gmhmaxlik.m
@@ -16,7 +16,7 @@ function [PostMode, HessianMatrix, Scale, ModeValue] = gmhmaxlik(fun, xinit, Hin
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
% Set default options
if ~isempty(Hinit);
diff --git a/matlab/optimization/mr_hessian.m b/matlab/optimization/mr_hessian.m
index eb1c0752e6e594ed9314c47b83ea67a9bb2dff9f..c3ed2934578193d470b5ab2c92167fe363d4cf21 100644
--- a/matlab/optimization/mr_hessian.m
+++ b/matlab/optimization/mr_hessian.m
@@ -223,7 +223,7 @@ if outer_product_gradient,
hh_mat=gga'*gga; % rescaled outer product hessian
hh_mat0=ggh'*ggh; % outer product hessian
A=diag(2.*hess_info.h1); % rescaling matrix
- % igg=inv(hh_mat); % inverted rescaled outer product hessian
+ % igg=inv(hh_mat); % inverted rescaled outer product hessian
ihh=A'*(hh_mat\A); % inverted outer product hessian
if hflag>0 && min(eig(reshape(hessian_mat,n,n)))>0
hh0 = A*reshape(hessian_mat,n,n)*A'; %rescaled second order derivatives
@@ -244,12 +244,12 @@ if outer_product_gradient,
igg=inv(A)'*ihh*inv(A); % inverted rescaled outer product hessian with modified std's
hh_mat=inv(igg); % outer product rescaled hessian with modified std's
hh_mat0=inv(A)'*hh_mat*inv(A); % outer product hessian with modified std's
- % sd0=sqrt(1./diag(hh0)); %rescaled 'standard errors' using second order derivatives
- % sd=sqrt(diag(igg)); %rescaled 'standard errors' using outer product
- % igg=igg./(sd*sd').*(sd0*sd0'); %rescaled inverse outer product with 'true' std's
- % hh_mat=inv(igg); % rescaled outer product hessian with 'true' std's
- % ihh=A'*igg*A; % inverted outer product hessian
- % hh_mat0=inv(A)'*hh_mat*inv(A); % outer product hessian with 'true' std's
+ % sd0=sqrt(1./diag(hh0)); %rescaled 'standard errors' using second order derivatives
+ % sd=sqrt(diag(igg)); %rescaled 'standard errors' using outer product
+ % igg=igg./(sd*sd').*(sd0*sd0'); %rescaled inverse outer product with 'true' std's
+ % hh_mat=inv(igg); % rescaled outer product hessian with 'true' std's
+ % ihh=A'*igg*A; % inverted outer product hessian
+ % hh_mat0=inv(A)'*hh_mat*inv(A); % outer product hessian with 'true' std's
end
if hflag<2
hessian_mat=hh_mat0(:);
diff --git a/matlab/optimization/newrat.m b/matlab/optimization/newrat.m
index 8085316d057f8d280720a51263c1f485fdd3d680..4d1ab5293202389a07aaddb9dfefff36f022d449 100644
--- a/matlab/optimization/newrat.m
+++ b/matlab/optimization/newrat.m
@@ -200,21 +200,21 @@ while norm(gg)>gtol && check==0 && jit<nit
hhg=hh;
H = inv(hh);
else
- if flagit==2
- hh=hh0;
- elseif flagg>0
- [dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagg,ftol0,hess_info,varargin{:});
- if flagg==2
- hh = reshape(dum,nx,nx);
- ee=eig(hh);
- if min(ee)<0
+ if flagit==2
+ hh=hh0;
+ elseif flagg>0
+ [dum, gg, htol0, igg, hhg, h1, hess_info]=mr_hessian(xparam1,func0,penalty,flagg,ftol0,hess_info,varargin{:});
+ if flagg==2
+ hh = reshape(dum,nx,nx);
+ ee=eig(hh);
+ if min(ee)<0
+ hh=hhg;
+ end
+ else
hh=hhg;
end
- else
- hh=hhg;
end
end
- end
disp_verbose(['Actual dxnorm ',num2str(norm(x(:,end)-x(:,end-1)))],Verbose)
disp_verbose(['FVAL ',num2str(fval)],Verbose)
disp_verbose(['Improvement ',num2str(fval0(icount)-fval)],Verbose)
diff --git a/matlab/optimization/simplex_optimization_routine.m b/matlab/optimization/simplex_optimization_routine.m
index 58b3ff1b097a9a633d74dc8bfbc4cf03a01c6e27..aff23b2468ca04d87e408153a75e5b53c9ea8355 100644
--- a/matlab/optimization/simplex_optimization_routine.m
+++ b/matlab/optimization/simplex_optimization_routine.m
@@ -251,7 +251,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
fxr = feval(objective_function,x,varargin{:});
func_count = func_count+1;
if fxr < fv(1)% xr is better than previous best point v(:,1).
- % Calculate the expansion point
+ % Calculate the expansion point
xe = xbar + rho*chi*(xbar-v(:,end));
x = xe;
fxe = feval(objective_function,x,varargin{:});
@@ -499,7 +499,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
else
break
end
- end
+ end
end% while loop.
x(:) = v(:,1);
@@ -520,43 +520,43 @@ end
function [v,fv,delta] = simplex_initialization(objective_function,point,point_score,delta,zero_delta,check_delta,varargin)
- n = length(point);
- v = zeros(n,n+1);
- v(:,1) = point;
- fv = zeros(n+1,1);
- fv(1) = point_score;
- if length(delta)==1
- delta = repmat(delta,n,1);
+n = length(point);
+v = zeros(n,n+1);
+v(:,1) = point;
+fv = zeros(n+1,1);
+fv(1) = point_score;
+if length(delta)==1
+ delta = repmat(delta,n,1);
+end
+for j = 1:n
+ y = point;
+ if y(j) ~= 0
+ y(j) = (1 + delta(j))*y(j);
+ else
+ y(j) = zero_delta;
end
- for j = 1:n
- y = point;
- if y(j) ~= 0
- y(j) = (1 + delta(j))*y(j);
- else
- y(j) = zero_delta;
- end
- v(:,j+1) = y;
- x = y;
- [fv(j+1),junk1,junk2,nopenalty_flag] = feval(objective_function,x,varargin{:});
- if check_delta
- while ~nopenalty_flag
- if y(j)~=0
- delta(j) = delta(j)/1.1;
- else
- zero_delta = zero_delta/1.1;
- end
- y = point;
- if y(j) ~= 0
- y(j) = (1 + delta(j))*y(j);
- else
- y(j) = zero_delta;
- end
- v(:,j+1) = y;
- x = y;
- [fv(j+1),junk1,junk2,nopenalty_flag] = feval(objective_function,x,varargin{:});
+ v(:,j+1) = y;
+ x = y;
+ [fv(j+1),junk1,junk2,nopenalty_flag] = feval(objective_function,x,varargin{:});
+ if check_delta
+ while ~nopenalty_flag
+ if y(j)~=0
+ delta(j) = delta(j)/1.1;
+ else
+ zero_delta = zero_delta/1.1;
end
+ y = point;
+ if y(j) ~= 0
+ y(j) = (1 + delta(j))*y(j);
+ else
+ y(j) = zero_delta;
+ end
+ v(:,j+1) = y;
+ x = y;
+ [fv(j+1),junk1,junk2,nopenalty_flag] = feval(objective_function,x,varargin{:});
end
end
- % Sort by increasing order of the objective function values.
- [fv,sort_idx] = sort(fv);
- v = v(:,sort_idx);
\ No newline at end of file
+end
+% Sort by increasing order of the objective function values.
+[fv,sort_idx] = sort(fv);
+v = v(:,sort_idx);
\ No newline at end of file
diff --git a/matlab/optimization/simpsa.m b/matlab/optimization/simpsa.m
index 260607a24097cbf598990a1d3b8dd141a15d6f2e..228a6bad21ed8c90b5695bc6de257f5e2845f139 100644
--- a/matlab/optimization/simpsa.m
+++ b/matlab/optimization/simpsa.m
@@ -122,20 +122,20 @@ NDIM = length(X0);
% set default options
DEFAULT_OPTIONS = simpsaset('TEMP_START',[],... % starting temperature (if none provided, an optimal one will be estimated)
- 'TEMP_END',.1,... % end temperature
- 'COOL_RATE',10,... % small values (<1) means slow convergence,large values (>1) means fast convergence
- 'INITIAL_ACCEPTANCE_RATIO',0.95,... % when initial temperature is estimated, this will be the initial acceptance ratio in the first round
- 'MIN_COOLING_FACTOR',0.9,... % minimum cooling factor (<1)
- 'MAX_ITER_TEMP_FIRST',50,... % number of iterations in the preliminary temperature loop
- 'MAX_ITER_TEMP_LAST',2000,... % number of iterations in the last temperature loop (pure simplex)
- 'MAX_ITER_TEMP',10,... % number of iterations in the remaining temperature loops
- 'MAX_ITER_TOTAL',2500,... % maximum number of iterations tout court
- 'MAX_TIME',2500,... % maximum duration of optimization
- 'MAX_FUN_EVALS',20000,... % maximum number of function evaluations
- 'TOLX',1e-6,... % maximum difference between best and worst function evaluation in simplex
- 'TOLFUN',1e-6,... % maximum difference between the coordinates of the vertices
- 'DISPLAY','iter',... % 'iter' or 'none' indicating whether user wants feedback
- 'OUTPUT_FCN',[]); % string with output function name
+ 'TEMP_END',.1,... % end temperature
+ 'COOL_RATE',10,... % small values (<1) means slow convergence,large values (>1) means fast convergence
+ 'INITIAL_ACCEPTANCE_RATIO',0.95,... % when initial temperature is estimated, this will be the initial acceptance ratio in the first round
+ 'MIN_COOLING_FACTOR',0.9,... % minimum cooling factor (<1)
+ 'MAX_ITER_TEMP_FIRST',50,... % number of iterations in the preliminary temperature loop
+ 'MAX_ITER_TEMP_LAST',2000,... % number of iterations in the last temperature loop (pure simplex)
+ 'MAX_ITER_TEMP',10,... % number of iterations in the remaining temperature loops
+ 'MAX_ITER_TOTAL',2500,... % maximum number of iterations tout court
+ 'MAX_TIME',2500,... % maximum duration of optimization
+ 'MAX_FUN_EVALS',20000,... % maximum number of function evaluations
+ 'TOLX',1e-6,... % maximum difference between best and worst function evaluation in simplex
+ 'TOLFUN',1e-6,... % maximum difference between the coordinates of the vertices
+ 'DISPLAY','iter',... % 'iter' or 'none' indicating whether user wants feedback
+ 'OUTPUT_FCN',[]); % string with output function name
% update default options with supplied options
@@ -329,7 +329,7 @@ while 1,
%% 2. maximum difference between the coordinates of the vertices in simplex is less than TOLX
%% 3. no convergence,but maximum number of iterations has been reached
%% 4. no convergence,but maximum time has been reached
-
+
if (abs(max(Y)-min(Y)) < OPTIONS.TOLFUN) && (TEMP_LOOP_NUMBER ~= 1),
if strcmp(OPTIONS.DISPLAY,'iter'),
disp('Change in the objective function value less than the specified tolerance (TOLFUN).')
diff --git a/matlab/optimization/simpsaget.m b/matlab/optimization/simpsaget.m
index e482a3e0011ca3dd6d4f533c69a6e32bd696ceb2..c22c82df4c5f63ab12574a975f8f68c7aa57b9f9 100644
--- a/matlab/optimization/simpsaget.m
+++ b/matlab/optimization/simpsaget.m
@@ -38,25 +38,25 @@ function o = simpsaget(options,name,default,flag)
% undocumented usage for fast access with no error checking
if (nargin == 4) && isequal(flag,'fast')
- o = getknownfield(options,name,default);
- return
+ o = getknownfield(options,name,default);
+ return
end
if nargin < 2
- error('MATLAB:odeget:NotEnoughInputs','Not enough input arguments.');
+ error('MATLAB:odeget:NotEnoughInputs','Not enough input arguments.');
end
if nargin < 3
- default = [];
+ default = [];
end
if ~isempty(options) && ~isa(options,'struct')
- error('MATLAB:odeget:Arg1NotODESETstruct',...
- 'First argument must be an options structure created with ODESET.');
+ error('MATLAB:odeget:Arg1NotODESETstruct',...
+ 'First argument must be an options structure created with ODESET.');
end
if isempty(options)
- o = default;
- return;
+ o = default;
+ return;
end
Names = [
@@ -75,39 +75,39 @@ Names = [
'TOLFUN '
'DISPLAY '
'OUTPUT_FCN '
- ];
+ ];
names = lower(Names);
lowName = lower(name);
j = strmatch(lowName,names);
if isempty(j) % if no matches
- error('MATLAB:odeget:InvalidPropName',...
- ['Unrecognized property name ''%s''. ' ...
- 'See ODESET for possibilities.'], name);
+ error('MATLAB:odeget:InvalidPropName',...
+ ['Unrecognized property name ''%s''. ' ...
+ 'See ODESET for possibilities.'], name);
elseif length(j) > 1 % if more than one match
- % Check for any exact matches (in case any names are subsets of others)
- k = strmatch(lowName,names,'exact');
- if length(k) == 1
- j = k;
- else
- msg = sprintf('Ambiguous property name ''%s'' ', name);
- msg = [msg '(' deblank(Names(j(1),:))];
- for k = j(2:length(j))'
- msg = [msg ', ' deblank(Names(k,:))];
+ % Check for any exact matches (in case any names are subsets of others)
+ k = strmatch(lowName,names,'exact');
+ if length(k) == 1
+ j = k;
+ else
+ msg = sprintf('Ambiguous property name ''%s'' ', name);
+ msg = [msg '(' deblank(Names(j(1),:))];
+ for k = j(2:length(j))'
+ msg = [msg ', ' deblank(Names(k,:))];
+ end
+ msg = sprintf('%s).', msg);
+ error('MATLAB:odeget:AmbiguousPropName', msg);
end
- msg = sprintf('%s).', msg);
- error('MATLAB:odeget:AmbiguousPropName', msg);
- end
end
if any(strcmp(fieldnames(options),deblank(Names(j,:))))
- o = options.(deblank(Names(j,:)));
- if isempty(o)
- o = default;
- end
+ o = options.(deblank(Names(j,:)));
+ if isempty(o)
+ o = default;
+ end
else
- o = default;
+ o = default;
end
% --------------------------------------------------------------------------
@@ -115,11 +115,11 @@ function v = getknownfield(s, f, d)
%GETKNOWNFIELD Get field f from struct s, or else yield default d.
if isfield(s,f) % s could be empty.
- v = subsref(s, struct('type','.','subs',f));
- if isempty(v)
- v = d;
- end
+ v = subsref(s, struct('type','.','subs',f));
+ if isempty(v)
+ v = d;
+ end
else
- v = d;
+ v = d;
end
diff --git a/matlab/optimization/simpsaset.m b/matlab/optimization/simpsaset.m
index ca687891a8d1e3d127d208ed933c237aee4c4db8..1944e024fc72efcfbc9bc0a1b2b70dda8fd6e136 100644
--- a/matlab/optimization/simpsaset.m
+++ b/matlab/optimization/simpsaset.m
@@ -64,7 +64,7 @@ if (nargin == 0) && (nargout == 0)
fprintf(' DISPLAY: [ ''iter'' or ''none'' {''iter''} ]\n');
fprintf(' OUTPUT_FCN: [ function_handle ]\n');
fprintf('\n');
-return;
+ return;
end
Names = [
@@ -83,7 +83,7 @@ Names = [
'TOLFUN '
'DISPLAY '
'OUTPUT_FCN '
- ];
+ ];
m = size(Names,1);
names = lower(Names);
@@ -91,82 +91,82 @@ names = lower(Names);
% Combine all leading options structures o1, o2, ... in odeset(o1,o2,...).
options = [];
for j = 1:m
- options.(deblank(Names(j,:))) = [];
+ options.(deblank(Names(j,:))) = [];
end
i = 1;
while i <= nargin
- arg = varargin{i};
- if ischar(arg) % arg is an option name
- break;
- end
- if ~isempty(arg) % [] is a valid options argument
- if ~isa(arg,'struct')
- error('MATLAB:odeset:NoPropNameOrStruct',...
- ['Expected argument %d to be a string property name ' ...
- 'or an options structure\ncreated with SIMANSET.'], i);
+ arg = varargin{i};
+ if ischar(arg) % arg is an option name
+ break;
end
- for j = 1:m
- if any(strcmp(fieldnames(arg),deblank(Names(j,:))))
- val = arg.(deblank(Names(j,:)));
- else
- val = [];
- end
- if ~isempty(val)
- options.(deblank(Names(j,:))) = val;
- end
+ if ~isempty(arg) % [] is a valid options argument
+ if ~isa(arg,'struct')
+ error('MATLAB:odeset:NoPropNameOrStruct',...
+ ['Expected argument %d to be a string property name ' ...
+ 'or an options structure\ncreated with SIMANSET.'], i);
+ end
+ for j = 1:m
+ if any(strcmp(fieldnames(arg),deblank(Names(j,:))))
+ val = arg.(deblank(Names(j,:)));
+ else
+ val = [];
+ end
+ if ~isempty(val)
+ options.(deblank(Names(j,:))) = val;
+ end
+ end
end
- end
- i = i + 1;
+ i = i + 1;
end
% A finite state machine to parse name-value pairs.
if rem(nargin-i+1,2) ~= 0
- error('MATLAB:odeset:ArgNameValueMismatch',...
- 'Arguments must occur in name-value pairs.');
+ error('MATLAB:odeset:ArgNameValueMismatch',...
+ 'Arguments must occur in name-value pairs.');
end
expectval = 0; % start expecting a name, not a value
while i <= nargin
- arg = varargin{i};
-
- if ~expectval
- if ~ischar(arg)
- error('MATLAB:odeset:NoPropName',...
- 'Expected argument %d to be a string property name.', i);
- end
+ arg = varargin{i};
- lowArg = lower(arg);
- j = strmatch(lowArg,names);
- if isempty(j) % if no matches
- error('MATLAB:odeset:InvalidPropName',...
- 'Unrecognized property name ''%s''.', arg);
- elseif length(j) > 1 % if more than one match
- % Check for any exact matches (in case any names are subsets of others)
- k = strmatch(lowArg,names,'exact');
- if length(k) == 1
- j = k;
- else
- msg = sprintf('Ambiguous property name ''%s'' ', arg);
- msg = [msg '(' deblank(Names(j(1),:))];
- for k = j(2:length(j))'
- msg = [msg ', ' deblank(Names(k,:))];
+ if ~expectval
+ if ~ischar(arg)
+ error('MATLAB:odeset:NoPropName',...
+ 'Expected argument %d to be a string property name.', i);
+ end
+
+ lowArg = lower(arg);
+ j = strmatch(lowArg,names);
+ if isempty(j) % if no matches
+ error('MATLAB:odeset:InvalidPropName',...
+ 'Unrecognized property name ''%s''.', arg);
+ elseif length(j) > 1 % if more than one match
+ % Check for any exact matches (in case any names are subsets of others)
+ k = strmatch(lowArg,names,'exact');
+ if length(k) == 1
+ j = k;
+ else
+ msg = sprintf('Ambiguous property name ''%s'' ', arg);
+ msg = [msg '(' deblank(Names(j(1),:))];
+ for k = j(2:length(j))'
+ msg = [msg ', ' deblank(Names(k,:))];
+ end
+ msg = sprintf('%s).', msg);
+ error('MATLAB:odeset:AmbiguousPropName', msg);
+ end
end
- msg = sprintf('%s).', msg);
- error('MATLAB:odeset:AmbiguousPropName', msg);
- end
+ expectval = 1; % we expect a value next
+
+ else
+ options.(deblank(Names(j,:))) = arg;
+ expectval = 0;
+
end
- expectval = 1; % we expect a value next
-
- else
- options.(deblank(Names(j,:))) = arg;
- expectval = 0;
-
- end
- i = i + 1;
+ i = i + 1;
end
if expectval
- error('MATLAB:odeset:NoValueForProp',...
- 'Expected value for property ''%s''.', arg);
+ error('MATLAB:odeset:NoValueForProp',...
+ 'Expected value for property ''%s''.', arg);
end
end
diff --git a/matlab/optimization/simulated_annealing.m b/matlab/optimization/simulated_annealing.m
index 5b505b71d6246d288da9c2e72b60e2c46dafaaf1..b2cfc52eecaa10e9e75fc891501b015faf221943 100644
--- a/matlab/optimization/simulated_annealing.m
+++ b/matlab/optimization/simulated_annealing.m
@@ -314,9 +314,9 @@ while (1>0);
if(pp<p);
if(optim.verbosity >=3);
if(optim.maximizer_indicator);
- fprintf('though lower, point accepted\n');
+ fprintf('though lower, point accepted\n');
else
- fprintf('though higher, point accepted\n');
+ fprintf('though higher, point accepted\n');
end;
end;
x=xp;
@@ -401,7 +401,7 @@ end
function print_current_invalid_try(max,xp,x,fp,f)
fprintf('\n');
- disp(['Current x ' num2str(x(:)')]);
+disp(['Current x ' num2str(x(:)')]);
if(max);
disp(['Current f ' num2str(f)]);
else
diff --git a/matlab/options2cell.m b/matlab/options2cell.m
index 3ff8ef22741e7e39030dd162f9afde04acd8c8b5..1296df22d5cee8c698531decdd7924288aef65ad 100644
--- a/matlab/options2cell.m
+++ b/matlab/options2cell.m
@@ -28,7 +28,7 @@ function c = options2cell(o)
s = fieldnames(o);
c = {};
j = 1;
-
+
for i=1:length(s)
c(j) = {s{i}};
c(j+1) = {o.(s{i})};
diff --git a/matlab/osr1.m b/matlab/osr1.m
index cf13da166d40e449c149e8df76ef72d90323086b..a92dfedd52a23e38a498fab4d84026f7e7c92f9c 100644
--- a/matlab/osr1.m
+++ b/matlab/osr1.m
@@ -101,17 +101,17 @@ i_var=unique(i_var);
%% do initial checks
[loss,info,exit_flag,vx]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
if info~=0
- print_info(info, options_.noprint, options_);
- else
- if ~options_.noprint
- fprintf('\nOSR: Initial value of the objective function: %g \n\n',loss);
- end
+ print_info(info, options_.noprint, options_);
+else
+ if ~options_.noprint
+ fprintf('\nOSR: Initial value of the objective function: %g \n\n',loss);
+ end
end
if ~options_.noprint && isinf(loss)
- fprintf('\nOSR: The initial value of the objective function is infinite.\n');
- fprintf('\nOSR: Check whether the unconditional variance of a target variable is infinite\n');
- fprintf('\nOSR: due to the presence of a unit root.\n');
- error('OSR: Initial likelihood is infinite')
+ fprintf('\nOSR: The initial value of the objective function is infinite.\n');
+ fprintf('\nOSR: Check whether the unconditional variance of a target variable is infinite\n');
+ fprintf('\nOSR: due to the presence of a unit root.\n');
+ error('OSR: Initial likelihood is infinite')
end
@@ -125,12 +125,12 @@ elseif isequal(options_.osr.opt_algo,11)
error('OSR: OSR does not support opt_algo=11.')
else
-if ~isempty(M_.osr.param_bounds) && ~(ismember(options_.osr.opt_algo,[1,2,5,9]) || ischar(options_.osr.opt_algo))
- error('OSR: OSR with bounds on parameters requires a constrained optimizer, i.e. 1,2,5, or 9.')
-end
-%%do actual optimization
-[p, f, exitflag] = dynare_minimize_objective(str2func('osr_obj'),t0,options_.osr.opt_algo,options_,M_.osr.param_bounds,cellstr(M_.param_names(i_params,:)),[],[], i_params,...
- inv_order_var(i_var),weights(i_var,i_var));
+ if ~isempty(M_.osr.param_bounds) && ~(ismember(options_.osr.opt_algo,[1,2,5,9]) || ischar(options_.osr.opt_algo))
+ error('OSR: OSR with bounds on parameters requires a constrained optimizer, i.e. 1,2,5, or 9.')
+ end
+ %%do actual optimization
+ [p, f, exitflag] = dynare_minimize_objective(str2func('osr_obj'),t0,options_.osr.opt_algo,options_,M_.osr.param_bounds,cellstr(M_.param_names(i_params,:)),[],[], i_params,...
+ inv_order_var(i_var),weights(i_var,i_var));
end
osr_res.objective_function = f;
diff --git a/matlab/osr_obj.m b/matlab/osr_obj.m
index 67efbbbc08a6a669413d4ab2d6aa9768a9af4f53..5bea8e7605ae38fd3ed1445715b6b607db4dac7b 100644
--- a/matlab/osr_obj.m
+++ b/matlab/osr_obj.m
@@ -51,8 +51,8 @@ it_ = M_.maximum_lag+1;
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
- info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
- info(1) == 81 || info(1) == 84 || info(1) == 85
+ info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+ info(1) == 81 || info(1) == 84 || info(1) == 85
loss = 1e8;
info(4)=info(2);
return
diff --git a/matlab/parallel/AnalyseComputationalEnvironment.m b/matlab/parallel/AnalyseComputationalEnvironment.m
index e5d2a87f530e2b3ef276bcbb6a52cc80bb06ff96..ff9920abfdefb56aa95b3c5994d716bf39dff7e1 100644
--- a/matlab/parallel/AnalyseComputationalEnvironment.m
+++ b/matlab/parallel/AnalyseComputationalEnvironment.m
@@ -1,657 +1,657 @@
-function [ErrorCode] = AnalyseComputationalEnvironment(DataInput, DataInputAdd)
-% PARALLEL CONTEXT
-% In a parallel context, this function is used to check the cluster defined by the user.
-% If no error happen the function returns 0. The function complies with
-% Windows/Linux operating systems and Matlab/Octave software.
-%
-%
-% INPUT/OUTPUT description:
-%
-%
-% DataInput
-% is the strcture option_.parallel, with the follow fields:
-%
-% Local 1 is on local machine, 0 remote
-% ComputerName the computer name.
-% CPUnbr the CPU's
-% UserName the user name for the ComputerName.
-% Password the password for the user name in ComputerName.
-% RemoteDrive Drive used for Remote computation (data exchange, etc): must be contain 'RemoteFolder'.
-% RemoteDirectory Folder in RemoteDrive used for Remote computation.
-% MatlabOctavePath Path to MATLAB or Octave executable.
-% DynarePath Path to matlab directory within the Dynare installation directory.
-%
-% This information is typed by the user in the DYNARE configuration file and is parsed by the preprocessor,
-% the goal of this function is to check if configuration is correct and if dynare
-% can be executed successfully in parallel mode.
-%
-%
-% DataInputAdd
-% it is the structure options_.parallel_info. Currently , only the string in the
-% field RemoteTmpFolder (the temporary directory created/destroyed on remote
-% computer) is used.
-
-if ispc,
- [tempo, MasterName]=system('hostname');
- MasterName=deblank(MasterName);
-end
-
-RemoteTmpFolder=DataInputAdd.RemoteTmpFolder;
-dynareParallelMkDir(RemoteTmpFolder,DataInput);
-
-
-% The variable ErrorCode is initialized at 0. If there are non problems with
-% Local, ComputerName connections,... in general with parallel software execution,
-% the ErrorCode is unchanged, in the others cases 1, 2 , ... The values
-% table is below.
-%
-%
-% Table for ErrorCode Values.
-%
-% ErrorCode -> 0 Initial Value -> No Error Detected!!!
-% ErrorCode -> 1 ... When an error is detected, the values 1, 2, 3... are
-% used to specify the type of error or warning.
-%
-% Value 1: The variable 'Local' has a bad value!
-%
-% Value 2: The variable 'CPUnbr' has a bad value. For more information
-% see http://www.dynare.org/DynareWiki/ParallelDynare.
-% 2.1 [warning] The user asks to use more CPU's than those available.
-% 2.2 [warning] There are unused CPU's!
-% 2.3 [error] NumberOfThreadsPerJob is not a divisor of CPUnbr
-%
-%
-% Value 3: The remote computer is unreachable!!!
-%
-% Value 4: The fields user name and/or password are/is empty!
-%
-% Value 5: Remote Drive and/or Remote Folder do not exist!
-%
-% Value 6: It is impossible write/read files on the remote computer.
-%
-% Value 7: The values user and/or passwd are incorrect or the user has
-% no permissions to execute a Matlab session. Or simply
-% Matlab path (MatlabOctavePath) is incorrect!
-%
-% Value 8: Dynare path (DynarePath) is incorrect!
-%
-% Value 9: It is impossible delete remote computational temporary files!
-%
-%
-%
-%
-% Currently when errors are detected execution simply stops and users can
-% fix configuration errors according to the error type.
-
-% Copyright (C) 2009-2013 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-ErrorCode=0;
-
-
-for Node=1:length(DataInput) % To obtain a recoursive function remove the 'for'
- % and use AnalyseComputationalEnvironment with differents input!
-
-
- % Determine the operating system or software version when necessary
- % for different command types.
-
- OScallerUnix=~ispc;
- OScallerWindows=ispc;
- OStargetUnix=strcmpi('unix',DataInput(Node).OperatingSystem);
- if isempty(DataInput(Node).OperatingSystem),
- OStargetUnix=OScallerUnix;
- end
- OStargetWindows=strcmpi('windows',DataInput(Node).OperatingSystem);
- if isempty(DataInput(Node).OperatingSystem),
- OStargetWindows=OScallerWindows;
- end
-
- Environment= (OScallerUnix || OStargetUnix);
-
- skipline(2)
- disp(['Testing computer -> ',DataInput(Node).ComputerName,' <- ...']);
- skipline(2)
-
- % The function is composed by two main blocks, determined by the 'Local'
- % variable.
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if ((DataInput(Node).Local == 0) |(DataInput(Node).Local == 1))
- % Continue it is Ok!
- disp('Check on Local Variable ..... Ok!');
- skipline()
- else
- disp('The variable "Local" has a bad value!');
- skipline()
- disp('ErrorCode 1.');
- skipline()
- ErrorCode=1;
- return
- end
-
- % %%%%%%%%%% Local (No Network) Computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- % Here only the multi-core, or multi-processor avaiable on local
- % machine are involved in parallel computing. No network
- % comunications are required!
-
-
- % In this case we need to check only the variable 'CPUnbr'.
-
- % We run the parallel code on local computer, so the others fields are automatically
- % fixed by Dynare parser. Then the user can also fill them with wrong values.
-
-
- % %%%%%%%%%% Cluster Computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- % Here we can have many computer with multi-core, or multi-processor avaiable on the
- % network and involved in parallel computing.
- % So in this case we need more sophisticated check.
-
-
- if (DataInput(Node).Local == 0)
-
- % Now we verify if it is possibile to be connected with the
- % remote computer.
-
- si1=[];
- de1=[];
-
- if Environment
- if OScallerWindows
- [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
- else
- [si1 de1]=system(['ping ', DataInput(Node).ComputerName, ' -c 4']);
- end
- else
- [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
- end
-
- if (si1)
- disp(['It is impossibile to ping to the computer with name "',DataInput(Node).ComputerName,'" using the network!'])
- skipline()
- disp('ErrorCode 3.')
- ErrorCode=3;
- skipline(2)
- else
- disp('Check on ComputerName Variable ..... Ok!')
- skipline(2)
- end
-
-
- % Now we verify if user name and password are correct and if remote
- % drive and remote folder exist on the remote computer and it is
- % possible to exchange data with them.
-
- if Environment
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if (isempty(DataInput(Node).UserName))
- disp('The fields UserName is empty!')
- skipline()
- disp('ErrorCode 4.')
- skipline(2)
- ErrorCode=4;
- return
- end
- disp('Check on UserName Variable ..... Ok!')
- skipline(2)
-
- % This check can be removed ... according to the dynare parser
- % strategy.
- if (~isempty(DataInput(Node).Password))
- disp('[WARNING] The field Password should be empty under unix or mac!');
- skipline()
- disp(['Remove the string ',DataInput(Node).Password,' from this field!'])
- skipline()
- disp('ErrorCode 4.')
- skipline(2)
- ErrorCode=4;
- else
- disp('Check on Password Variable ..... Ok!')
- skipline(2)
- end
- else
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if (isempty(DataInput(Node).UserName)) || (isempty(DataInput(Node).Password))
- disp('The fields UserName and/or Password are/is empty!');
- skipline()
- disp('ErrorCode 4.')
- skipline(2)
- ErrorCode=4;
- return
- end
- disp('Check on UserName Variable ..... Ok!');
- skipline()
- disp('Check on Password Variable ..... Ok!');
- skipline()
- end
-
- % Now we very if RemoteDrive and/or RemoteDirectory exist on remote
- % computer!
-
- if Environment
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if isempty(DataInput(Node).RemoteDirectory)
- disp('The field RemoteDirectory is empty!')
- skipline()
- disp('ErrorCode 5.')
- skipline()
- ErrorCode=5;
- return
- end
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if (~isempty(DataInput(Node).RemoteDrive))
- disp('[WARNING] The fields RemoteDrive should be empty under unix or mac!')
- skipline()
- disp(['remove the string ',DataInput(Node).RemoteDrive,' from this field!'])
- skipline()
- disp('ErrorCode 5.')
- skipline(2)
- ErrorCode=5;
- end
-
- si2=[];
- de2=[];
- if ~isempty(DataInput(Node).Port),
- ssh_token = ['-p ',DataInput(Node).Port];
- else
- ssh_token = '';
- end
-
- [si2 de2]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' ls ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder,'/']);
-
- if (si2)
- disp ('Remote Directory does not exist or is not reachable!')
- skipline()
- disp('ErrorCode 5.')
- skipline(2)
- ErrorCode=5;
- return
- end
-
- disp('Check on RemoteDirectory Variable ..... Ok!')
- skipline(2)
- disp('Check on RemoteDrive Variable ..... Ok!')
- skipline(2)
-
- else
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- if (isempty(DataInput(Node).RemoteDrive)||isempty(DataInput(Node).RemoteDirectory))
- disp('Remote RemoteDrive and/or RemoteDirectory is/are empty!')
- skipline()
- disp('ErrorCode 5.')
- skipline(2)
- ErrorCode=5;
- return
- end
-
-
- si2=[];
- de2=[];
- [si2 de2]=system(['dir \\',DataInput(Node).ComputerName,'\',DataInput(Node).RemoteDrive,'$\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder]);
-
- if (si2)
- disp ('Remote Directory does not exist or it is not reachable!')
- skipline()
- disp('ErrorCode 5.')
- skipline(2)
- ErrorCode=5;
- return
- end
-
- disp('Check on RemoteDirectory Variable ..... Ok!')
- skipline(2)
- disp('Check on RemoteDrive Variable ..... Ok!')
- skipline(2)
-
- end
-
-
- % Now we verify if it possible to exchange data with the remote
- % computer:
-
-
- % Build a command file to test the matlab execution and dynare path ...
-
- fid = fopen('Tracing.m', 'w+');
- s1=(['fT = fopen(''MatlabOctaveIsOk.txt'',''w+'');\n']);
- s2='fclose(fT);\n';
- SBS=strfind(DataInput(Node).DynarePath,'\');
- DPStr=DataInput(Node).DynarePath;
- if isempty(SBS),
- DPStrNew=DPStr;
- else
- DPStrNew=[DPStr(1:SBS(1)),'\'];
- for j=2:length(SBS),
- DPStrNew=[DPStrNew,DPStr(SBS(j-1)+1:SBS(j)),'\'];
- end
- DPStrNew=[DPStrNew,DPStr(SBS(end)+1:end)];
- end
- s3=['addpath(''',DPStrNew,'''),\n'];
- s4=['try,\n dynareroot = dynare_config();\n'];
- s41=([' fT = fopen(''DynareIsOk.txt'',''w+'');\n']);
- s42=' fclose(fT);\n';
- s5=['catch,\n'];
- s51=([' fT = fopen(''DynareFailed.txt'',''w+'');\n']);
- s52=' fclose(fT);\n';
- s6=['end,\n'];
- s7=['if ismac,\n'];
- s71=([' fT = fopen(''IsMac.txt'',''w+'');\n']);
- s72=' fclose(fT);\n';
- s8=['end,\n'];
- send='exit';
- StrCommand=([s1,s2,s3,s4,s41,s42,s5,s51,s52,s6,s7,s71,s72,s8,send]);
-
- % Mettere controllo su NbW ...
- % if isoctave
- % NbW = fprintf(fid,StrCommand, '%s');
- % else
- NbW = fprintf(fid,StrCommand, '%s');
- % end
- fclose(fid);
-
- dynareParallelSendFiles('Tracing.m', RemoteTmpFolder,DataInput(Node));
- FindTracing = dynareParallelDir('Tracing.m', RemoteTmpFolder,DataInput(Node));
-
- delete ('Tracing.m');
-
- if (isempty(FindTracing))
- disp('It is impossible to exchange data with Remote Drive and/or Remote Directory! ErrorCode 6.')
- skipline()
- disp('ErrorCode 6.')
- skipline(2)
- ErrorCode=6;
- return
- else
- disp('Check on Exchange File with Remote Computer ..... Ok!')
- skipline(2)
- end
-
-
- % Now we verify if it is possible execute a matlab/octave section on remote
- % machine when the user is .UserName with password .Password and
- % the path is MatlabOctavePath.
-
- if Environment,
- if ~isempty(DataInput(Node).Port),
- ssh_token = ['-p ',DataInput(Node).Port];
- else
- ssh_token = '';
- end
- if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' "cd ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder, '; ', DataInput(Node).MatlabOctavePath, ' Tracing.m;" &']);
- else
- system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' "cd ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder, '; ', DataInput(Node).MatlabOctavePath, ' -nosplash -nodesktop -minimize -r Tracing;" &']);
- end
- else
- if ~strcmp(DataInput(Node).ComputerName,MasterName), % run on remote machine
- if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
- else
- [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
- end
- else % run on local machine via the network: user and passwd cannot be used!
- if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
- else
- [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
- end
- end
-
- end
-
- % Timer da fissare, nei valori di attesa!
-
- t1=fix(clock);
-
- if t1(5)+1>60;
- t2=2;
- else t2=t1(5)+1;
- end
-
- Flag=0;
-
- while (1);
- if Flag==0
- disp('Try to run matlab/octave on remote machine ... ')
- skipline()
- disp('please wait ... ')
- skipline()
- Flag=1;
- end
- nt=fix(clock);
- nt(5)-t2;
-
- if (~isempty (dynareParallelDir('MatlabOctaveIsOk.txt',RemoteTmpFolder,DataInput(Node)))) || ((nt(5)-t2)>0)
- if ((nt(5)-t2)>0)
- ErrorCode=7;
- end
- break
- end
-
- end
-
- if (ErrorCode==7)
-
- disp ('It is not possible execute a matlab session on remote machine!')
- skipline()
- disp('ErrorCode 7.')
- skipline(2)
- ErrorCode=7;
- dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
- return
- else
- disp('Check on MatlabOctave Path and MatlabOctave Program Execution on remote machine ..... Ok!')
- skipline(2)
-
- % Now we verify if the DynarePath is correct ...
- disp('Check the Dynare path on remote machine ... ')
- skipline()
- disp('please wait ... ')
- skipline(2)
- pause(2)
-
- if isempty(dynareParallelDir('DynareIsOk.txt',RemoteTmpFolder,DataInput(Node)))
- ErrorCode=8;
- end
-
- if (ErrorCode==8)
- disp ('The DynarePath is incorrect!')
- skipline()
- disp('ErrorCode 8.')
- skipline(2)
- ErrorCode=8;
- dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
- return
- else
- disp('Check on Dynare Path remote machine ..... Ok!')
- if isempty(dynareParallelDir('IsMac.txt',RemoteTmpFolder,DataInput(Node)))
- RemoteEnvironment=Environment;
- else
- RemoteEnvironment=2;
- end
- skipline(2)
- end
- end
-
-
- % Now we verify if it is possible delete remote computational traces!
-
- dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
-
- si3=[];
-
- si3=dynareParallelDir('Tracing.m', RemoteTmpFolder,DataInput(Node));
-
- if (isempty(si3))
- disp ('Check on Delete Remote Computational Traces ..... Ok!')
- skipline(2)
- else
- disp ('It is impossible to delete temporary files on remote machine!')
- skipline()
- disp('ErrorCode 9.')
- skipline(2)
- ErrorCode=9;
- return
- end
-
-
- end
- % Now we check the variable 'CPUnbr'.
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
- yn=isempty(DataInput(Node).CPUnbr);
-
- if yn==1
- % The field is empty!
- disp('The field "CPUnbr" is empty!')
- skipline()
- disp('ErrorCode 2.')
- skipline(2)
- ErrorCode=2;
- return
- end
-
- % This check can be removed ... according to the dynare parser
- % strategy.
-
-
-
- % We look for the information on local computer hardware.
-
- si0=[];
- de0=[];
-
- Environment1=Environment;
- disp('Checking Hardware please wait ...');
- if (DataInput(Node).Local == 1)
- if Environment,
- if ~ismac
- [si0 de0]=system('grep processor /proc/cpuinfo');
- else
- [si0 de0]=system('sysctl -n hw.ncpu');
- Environment1=2;
- end
- else
- [si0 de0]=system(['psinfo \\']);
- end
- else
- if Environment,
- if ~isempty(DataInput(Node).Port),
- ssh_token = ['-p ',DataInput(Node).Port];
- else
- ssh_token = '';
- end
- if OStargetUnix,
- if RemoteEnvironment ==1 ,
- [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' grep processor /proc/cpuinfo']);
- else % it is MAC
- [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' sysctl -n hw.ncpu']);
- Environment1=2;
- end
- else
- [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' psinfo']);
- end
- else
- [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName,' -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password]);
- end
- end
-
-
- RealCPUnbr='';
-% keyboard;
- RealCPUnbr=GiveCPUnumber(de0,Environment1);
-
- % Questo controllo penso che si possa MIGLIORARE!!!!!
- if isempty (RealCPUnbr) && Environment1==0,
- [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName]);
- end
- RealCPUnbr=GiveCPUnumber(de0,Environment1);
-
- if isempty (RealCPUnbr)
- % An error occurred when we try to know the Cpu/Cores
- % numbers.
- disp('It is impossible determine the number of Cpu/Processor avaiable on this machine!')
- skipline()
- disp('ErrorCode 2.')
- skipline()
- if Environment
- disp('Check the command "$less /proc/cpuinfo" ... !')
- else
- disp('Check if the pstools are installed and are in machine path! And check the command "psinfo \\"')
- end
- skipline()
- ErrorCode=2;
- return
- end
-
-
- % Trasforming the input data provided in a form [n1:n2] in a single numerical
- % value.
-
-
- CPUnbrUser=length(DataInput(Node).CPUnbr);
- maxCPUnbrUser=max(DataInput(Node).CPUnbr)+1;
-
- disp(['Hardware has ', num2str(RealCPUnbr),' Cpu/Cores!'])
- disp(['User requires ',num2str(CPUnbrUser),' Cpu/Cores!'])
- if CPUnbrUser==RealCPUnbr,
- % It is Ok!
- disp('Check on CPUnbr Variable ..... Ok!')
- skipline(3)
- end
-
- if CPUnbrUser > RealCPUnbr
- disp('Warning! The user asks to use more CPU''s than those available.')
- skipline(2)
- ErrorCode=2.1;
- end
- if CPUnbrUser < RealCPUnbr
- disp('Warning! There are unused CPU''s!')
- skipline(2)
- ErrorCode=2.2;
- end
-
- if mod(length(DataInput(Node).CPUnbr),DataInput(Node).NumberOfThreadsPerJob)
- skipline()
- disp(['NumberOfThreadsPerJob = ',int2str(DataInput(Node).NumberOfThreadsPerJob),' is not an exact divisor of number of CPUs = ',int2str(DataInput(Node).CPUnbr),'!'])
- disp([' You must re-set properly NumberOfThreadsPerJob of node ' int2str(Node) ' ' DataInput(Node).ComputerName])
- disp(' in your configuration file')
- skipline()
- ErrorCode=2.3;
- end
-
- disp(['Test for Cluster computation, computer ',DataInput(Node).ComputerName, ' ..... Passed!'])
- skipline(2)
+function [ErrorCode] = AnalyseComputationalEnvironment(DataInput, DataInputAdd)
+% PARALLEL CONTEXT
+% In a parallel context, this function is used to check the cluster defined by the user.
+% If no error happen the function returns 0. The function complies with
+% Windows/Linux operating systems and Matlab/Octave software.
+%
+%
+% INPUT/OUTPUT description:
+%
+%
+% DataInput
+% is the strcture option_.parallel, with the follow fields:
+%
+% Local 1 is on local machine, 0 remote
+% ComputerName the computer name.
+% CPUnbr the CPU's
+% UserName the user name for the ComputerName.
+% Password the password for the user name in ComputerName.
+% RemoteDrive Drive used for Remote computation (data exchange, etc): must be contain 'RemoteFolder'.
+% RemoteDirectory Folder in RemoteDrive used for Remote computation.
+% MatlabOctavePath Path to MATLAB or Octave executable.
+% DynarePath Path to matlab directory within the Dynare installation directory.
+%
+% This information is typed by the user in the DYNARE configuration file and is parsed by the preprocessor,
+% the goal of this function is to check if configuration is correct and if dynare
+% can be executed successfully in parallel mode.
+%
+%
+% DataInputAdd
+% it is the structure options_.parallel_info. Currently , only the string in the
+% field RemoteTmpFolder (the temporary directory created/destroyed on remote
+% computer) is used.
+
+if ispc,
+ [tempo, MasterName]=system('hostname');
+ MasterName=deblank(MasterName);
+end
+
+RemoteTmpFolder=DataInputAdd.RemoteTmpFolder;
+dynareParallelMkDir(RemoteTmpFolder,DataInput);
+
+
+% The variable ErrorCode is initialized at 0. If there are non problems with
+% Local, ComputerName connections,... in general with parallel software execution,
+% the ErrorCode is unchanged, in the others cases 1, 2 , ... The values
+% table is below.
+%
+%
+% Table for ErrorCode Values.
+%
+% ErrorCode -> 0 Initial Value -> No Error Detected!!!
+% ErrorCode -> 1 ... When an error is detected, the values 1, 2, 3... are
+% used to specify the type of error or warning.
+%
+% Value 1: The variable 'Local' has a bad value!
+%
+% Value 2: The variable 'CPUnbr' has a bad value. For more information
+% see http://www.dynare.org/DynareWiki/ParallelDynare.
+% 2.1 [warning] The user asks to use more CPU's than those available.
+% 2.2 [warning] There are unused CPU's!
+% 2.3 [error] NumberOfThreadsPerJob is not a divisor of CPUnbr
+%
+%
+% Value 3: The remote computer is unreachable!!!
+%
+% Value 4: The fields user name and/or password are/is empty!
+%
+% Value 5: Remote Drive and/or Remote Folder do not exist!
+%
+% Value 6: It is impossible write/read files on the remote computer.
+%
+% Value 7: The values user and/or passwd are incorrect or the user has
+% no permissions to execute a Matlab session. Or simply
+% Matlab path (MatlabOctavePath) is incorrect!
+%
+% Value 8: Dynare path (DynarePath) is incorrect!
+%
+% Value 9: It is impossible delete remote computational temporary files!
+%
+%
+%
+%
+% Currently when errors are detected execution simply stops and users can
+% fix configuration errors according to the error type.
+
+% Copyright (C) 2009-2013 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+
+ErrorCode=0;
+
+
+for Node=1:length(DataInput) % To obtain a recoursive function remove the 'for'
+ % and use AnalyseComputationalEnvironment with differents input!
+
+
+ % Determine the operating system or software version when necessary
+ % for different command types.
+
+ OScallerUnix=~ispc;
+ OScallerWindows=ispc;
+ OStargetUnix=strcmpi('unix',DataInput(Node).OperatingSystem);
+ if isempty(DataInput(Node).OperatingSystem),
+ OStargetUnix=OScallerUnix;
+ end
+ OStargetWindows=strcmpi('windows',DataInput(Node).OperatingSystem);
+ if isempty(DataInput(Node).OperatingSystem),
+ OStargetWindows=OScallerWindows;
+ end
+
+ Environment= (OScallerUnix || OStargetUnix);
+
+ skipline(2)
+ disp(['Testing computer -> ',DataInput(Node).ComputerName,' <- ...']);
+ skipline(2)
+
+ % The function is composed by two main blocks, determined by the 'Local'
+ % variable.
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if ((DataInput(Node).Local == 0) |(DataInput(Node).Local == 1))
+ % Continue it is Ok!
+ disp('Check on Local Variable ..... Ok!');
+ skipline()
+ else
+ disp('The variable "Local" has a bad value!');
+ skipline()
+ disp('ErrorCode 1.');
+ skipline()
+ ErrorCode=1;
+ return
+ end
+
+ % %%%%%%%%%% Local (No Network) Computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ % Here only the multi-core, or multi-processor avaiable on local
+ % machine are involved in parallel computing. No network
+ % comunications are required!
+
+
+ % In this case we need to check only the variable 'CPUnbr'.
+
+ % We run the parallel code on local computer, so the others fields are automatically
+ % fixed by Dynare parser. Then the user can also fill them with wrong values.
+
+
+ % %%%%%%%%%% Cluster Computing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ % Here we can have many computer with multi-core, or multi-processor avaiable on the
+ % network and involved in parallel computing.
+ % So in this case we need more sophisticated check.
+
+
+ if (DataInput(Node).Local == 0)
+
+ % Now we verify if it is possibile to be connected with the
+ % remote computer.
+
+ si1=[];
+ de1=[];
+
+ if Environment
+ if OScallerWindows
+ [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
+ else
+ [si1 de1]=system(['ping ', DataInput(Node).ComputerName, ' -c 4']);
+ end
+ else
+ [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
+ end
+
+ if (si1)
+ disp(['It is impossibile to ping to the computer with name "',DataInput(Node).ComputerName,'" using the network!'])
+ skipline()
+ disp('ErrorCode 3.')
+ ErrorCode=3;
+ skipline(2)
+ else
+ disp('Check on ComputerName Variable ..... Ok!')
+ skipline(2)
+ end
+
+
+ % Now we verify if user name and password are correct and if remote
+ % drive and remote folder exist on the remote computer and it is
+ % possible to exchange data with them.
+
+ if Environment
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if (isempty(DataInput(Node).UserName))
+ disp('The fields UserName is empty!')
+ skipline()
+ disp('ErrorCode 4.')
+ skipline(2)
+ ErrorCode=4;
+ return
+ end
+ disp('Check on UserName Variable ..... Ok!')
+ skipline(2)
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+ if (~isempty(DataInput(Node).Password))
+ disp('[WARNING] The field Password should be empty under unix or mac!');
+ skipline()
+ disp(['Remove the string ',DataInput(Node).Password,' from this field!'])
+ skipline()
+ disp('ErrorCode 4.')
+ skipline(2)
+ ErrorCode=4;
+ else
+ disp('Check on Password Variable ..... Ok!')
+ skipline(2)
+ end
+ else
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if (isempty(DataInput(Node).UserName)) || (isempty(DataInput(Node).Password))
+ disp('The fields UserName and/or Password are/is empty!');
+ skipline()
+ disp('ErrorCode 4.')
+ skipline(2)
+ ErrorCode=4;
+ return
+ end
+ disp('Check on UserName Variable ..... Ok!');
+ skipline()
+ disp('Check on Password Variable ..... Ok!');
+ skipline()
+ end
+
+ % Now we very if RemoteDrive and/or RemoteDirectory exist on remote
+ % computer!
+
+ if Environment
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if isempty(DataInput(Node).RemoteDirectory)
+ disp('The field RemoteDirectory is empty!')
+ skipline()
+ disp('ErrorCode 5.')
+ skipline()
+ ErrorCode=5;
+ return
+ end
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if (~isempty(DataInput(Node).RemoteDrive))
+ disp('[WARNING] The fields RemoteDrive should be empty under unix or mac!')
+ skipline()
+ disp(['remove the string ',DataInput(Node).RemoteDrive,' from this field!'])
+ skipline()
+ disp('ErrorCode 5.')
+ skipline(2)
+ ErrorCode=5;
+ end
+
+ si2=[];
+ de2=[];
+ if ~isempty(DataInput(Node).Port),
+ ssh_token = ['-p ',DataInput(Node).Port];
+ else
+ ssh_token = '';
+ end
+
+ [si2 de2]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' ls ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder,'/']);
+
+ if (si2)
+ disp ('Remote Directory does not exist or is not reachable!')
+ skipline()
+ disp('ErrorCode 5.')
+ skipline(2)
+ ErrorCode=5;
+ return
+ end
+
+ disp('Check on RemoteDirectory Variable ..... Ok!')
+ skipline(2)
+ disp('Check on RemoteDrive Variable ..... Ok!')
+ skipline(2)
+
+ else
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ if (isempty(DataInput(Node).RemoteDrive)||isempty(DataInput(Node).RemoteDirectory))
+ disp('Remote RemoteDrive and/or RemoteDirectory is/are empty!')
+ skipline()
+ disp('ErrorCode 5.')
+ skipline(2)
+ ErrorCode=5;
+ return
+ end
+
+
+ si2=[];
+ de2=[];
+ [si2 de2]=system(['dir \\',DataInput(Node).ComputerName,'\',DataInput(Node).RemoteDrive,'$\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder]);
+
+ if (si2)
+ disp ('Remote Directory does not exist or it is not reachable!')
+ skipline()
+ disp('ErrorCode 5.')
+ skipline(2)
+ ErrorCode=5;
+ return
+ end
+
+ disp('Check on RemoteDirectory Variable ..... Ok!')
+ skipline(2)
+ disp('Check on RemoteDrive Variable ..... Ok!')
+ skipline(2)
+
+ end
+
+
+ % Now we verify if it possible to exchange data with the remote
+ % computer:
+
+
+ % Build a command file to test the matlab execution and dynare path ...
+
+ fid = fopen('Tracing.m', 'w+');
+ s1=(['fT = fopen(''MatlabOctaveIsOk.txt'',''w+'');\n']);
+ s2='fclose(fT);\n';
+ SBS=strfind(DataInput(Node).DynarePath,'\');
+ DPStr=DataInput(Node).DynarePath;
+ if isempty(SBS),
+ DPStrNew=DPStr;
+ else
+ DPStrNew=[DPStr(1:SBS(1)),'\'];
+ for j=2:length(SBS),
+ DPStrNew=[DPStrNew,DPStr(SBS(j-1)+1:SBS(j)),'\'];
+ end
+ DPStrNew=[DPStrNew,DPStr(SBS(end)+1:end)];
+ end
+ s3=['addpath(''',DPStrNew,'''),\n'];
+ s4=['try,\n dynareroot = dynare_config();\n'];
+ s41=([' fT = fopen(''DynareIsOk.txt'',''w+'');\n']);
+ s42=' fclose(fT);\n';
+ s5=['catch,\n'];
+ s51=([' fT = fopen(''DynareFailed.txt'',''w+'');\n']);
+ s52=' fclose(fT);\n';
+ s6=['end,\n'];
+ s7=['if ismac,\n'];
+ s71=([' fT = fopen(''IsMac.txt'',''w+'');\n']);
+ s72=' fclose(fT);\n';
+ s8=['end,\n'];
+ send='exit';
+ StrCommand=([s1,s2,s3,s4,s41,s42,s5,s51,s52,s6,s7,s71,s72,s8,send]);
+
+ % Mettere controllo su NbW ...
+ % if isoctave
+ % NbW = fprintf(fid,StrCommand, '%s');
+ % else
+ NbW = fprintf(fid,StrCommand, '%s');
+ % end
+ fclose(fid);
+
+ dynareParallelSendFiles('Tracing.m', RemoteTmpFolder,DataInput(Node));
+ FindTracing = dynareParallelDir('Tracing.m', RemoteTmpFolder,DataInput(Node));
+
+ delete ('Tracing.m');
+
+ if (isempty(FindTracing))
+ disp('It is impossible to exchange data with Remote Drive and/or Remote Directory! ErrorCode 6.')
+ skipline()
+ disp('ErrorCode 6.')
+ skipline(2)
+ ErrorCode=6;
+ return
+ else
+ disp('Check on Exchange File with Remote Computer ..... Ok!')
+ skipline(2)
+ end
+
+
+ % Now we verify if it is possible execute a matlab/octave section on remote
+ % machine when the user is .UserName with password .Password and
+ % the path is MatlabOctavePath.
+
+ if Environment,
+ if ~isempty(DataInput(Node).Port),
+ ssh_token = ['-p ',DataInput(Node).Port];
+ else
+ ssh_token = '';
+ end
+ if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' "cd ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder, '; ', DataInput(Node).MatlabOctavePath, ' Tracing.m;" &']);
+ else
+ system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' "cd ',DataInput(Node).RemoteDirectory,'/',RemoteTmpFolder, '; ', DataInput(Node).MatlabOctavePath, ' -nosplash -nodesktop -minimize -r Tracing;" &']);
+ end
+ else
+ if ~strcmp(DataInput(Node).ComputerName,MasterName), % run on remote machine
+ if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
+ else
+ [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
+ end
+ else % run on local machine via the network: user and passwd cannot be used!
+ if strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
+ else
+ [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
+ end
+ end
+
+ end
+
+ % Timer da fissare, nei valori di attesa!
+
+ t1=fix(clock);
+
+ if t1(5)+1>60;
+ t2=2;
+ else t2=t1(5)+1;
+ end
+
+ Flag=0;
+
+ while (1);
+ if Flag==0
+ disp('Try to run matlab/octave on remote machine ... ')
+ skipline()
+ disp('please wait ... ')
+ skipline()
+ Flag=1;
+ end
+ nt=fix(clock);
+ nt(5)-t2;
+
+ if (~isempty (dynareParallelDir('MatlabOctaveIsOk.txt',RemoteTmpFolder,DataInput(Node)))) || ((nt(5)-t2)>0)
+ if ((nt(5)-t2)>0)
+ ErrorCode=7;
+ end
+ break
+ end
+
+ end
+
+ if (ErrorCode==7)
+
+ disp ('It is not possible execute a matlab session on remote machine!')
+ skipline()
+ disp('ErrorCode 7.')
+ skipline(2)
+ ErrorCode=7;
+ dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
+ return
+ else
+ disp('Check on MatlabOctave Path and MatlabOctave Program Execution on remote machine ..... Ok!')
+ skipline(2)
+
+ % Now we verify if the DynarePath is correct ...
+ disp('Check the Dynare path on remote machine ... ')
+ skipline()
+ disp('please wait ... ')
+ skipline(2)
+ pause(2)
+
+ if isempty(dynareParallelDir('DynareIsOk.txt',RemoteTmpFolder,DataInput(Node)))
+ ErrorCode=8;
+ end
+
+ if (ErrorCode==8)
+ disp ('The DynarePath is incorrect!')
+ skipline()
+ disp('ErrorCode 8.')
+ skipline(2)
+ ErrorCode=8;
+ dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
+ return
+ else
+ disp('Check on Dynare Path remote machine ..... Ok!')
+ if isempty(dynareParallelDir('IsMac.txt',RemoteTmpFolder,DataInput(Node)))
+ RemoteEnvironment=Environment;
+ else
+ RemoteEnvironment=2;
+ end
+ skipline(2)
+ end
+ end
+
+
+ % Now we verify if it is possible delete remote computational traces!
+
+ dynareParallelRmDir(RemoteTmpFolder,DataInput(Node));
+
+ si3=[];
+
+ si3=dynareParallelDir('Tracing.m', RemoteTmpFolder,DataInput(Node));
+
+ if (isempty(si3))
+ disp ('Check on Delete Remote Computational Traces ..... Ok!')
+ skipline(2)
+ else
+ disp ('It is impossible to delete temporary files on remote machine!')
+ skipline()
+ disp('ErrorCode 9.')
+ skipline(2)
+ ErrorCode=9;
+ return
+ end
+
+
+ end
+ % Now we check the variable 'CPUnbr'.
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+ yn=isempty(DataInput(Node).CPUnbr);
+
+ if yn==1
+ % The field is empty!
+ disp('The field "CPUnbr" is empty!')
+ skipline()
+ disp('ErrorCode 2.')
+ skipline(2)
+ ErrorCode=2;
+ return
+ end
+
+ % This check can be removed ... according to the dynare parser
+ % strategy.
+
+
+
+ % We look for the information on local computer hardware.
+
+ si0=[];
+ de0=[];
+
+ Environment1=Environment;
+ disp('Checking Hardware please wait ...');
+ if (DataInput(Node).Local == 1)
+ if Environment,
+ if ~ismac
+ [si0 de0]=system('grep processor /proc/cpuinfo');
+ else
+ [si0 de0]=system('sysctl -n hw.ncpu');
+ Environment1=2;
+ end
+ else
+ [si0 de0]=system(['psinfo \\']);
+ end
+ else
+ if Environment,
+ if ~isempty(DataInput(Node).Port),
+ ssh_token = ['-p ',DataInput(Node).Port];
+ else
+ ssh_token = '';
+ end
+ if OStargetUnix,
+ if RemoteEnvironment ==1 ,
+ [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' grep processor /proc/cpuinfo']);
+ else % it is MAC
+ [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' sysctl -n hw.ncpu']);
+ Environment1=2;
+ end
+ else
+ [si0 de0]=system(['ssh ',ssh_token,' ',DataInput(Node).UserName,'@',DataInput(Node).ComputerName,' psinfo']);
+ end
+ else
+ [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName,' -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password]);
+ end
+ end
+
+
+ RealCPUnbr='';
+ % keyboard;
+ RealCPUnbr=GiveCPUnumber(de0,Environment1);
+
+ % Questo controllo penso che si possa MIGLIORARE!!!!!
+ if isempty (RealCPUnbr) && Environment1==0,
+ [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName]);
+ end
+ RealCPUnbr=GiveCPUnumber(de0,Environment1);
+
+ if isempty (RealCPUnbr)
+ % An error occurred when we try to know the Cpu/Cores
+ % numbers.
+ disp('It is impossible determine the number of Cpu/Processor avaiable on this machine!')
+ skipline()
+ disp('ErrorCode 2.')
+ skipline()
+ if Environment
+ disp('Check the command "$less /proc/cpuinfo" ... !')
+ else
+ disp('Check if the pstools are installed and are in machine path! And check the command "psinfo \\"')
+ end
+ skipline()
+ ErrorCode=2;
+ return
+ end
+
+
+ % Trasforming the input data provided in a form [n1:n2] in a single numerical
+ % value.
+
+
+ CPUnbrUser=length(DataInput(Node).CPUnbr);
+ maxCPUnbrUser=max(DataInput(Node).CPUnbr)+1;
+
+ disp(['Hardware has ', num2str(RealCPUnbr),' Cpu/Cores!'])
+ disp(['User requires ',num2str(CPUnbrUser),' Cpu/Cores!'])
+ if CPUnbrUser==RealCPUnbr,
+ % It is Ok!
+ disp('Check on CPUnbr Variable ..... Ok!')
+ skipline(3)
+ end
+
+ if CPUnbrUser > RealCPUnbr
+ disp('Warning! The user asks to use more CPU''s than those available.')
+ skipline(2)
+ ErrorCode=2.1;
+ end
+ if CPUnbrUser < RealCPUnbr
+ disp('Warning! There are unused CPU''s!')
+ skipline(2)
+ ErrorCode=2.2;
+ end
+
+ if mod(length(DataInput(Node).CPUnbr),DataInput(Node).NumberOfThreadsPerJob)
+ skipline()
+ disp(['NumberOfThreadsPerJob = ',int2str(DataInput(Node).NumberOfThreadsPerJob),' is not an exact divisor of number of CPUs = ',int2str(DataInput(Node).CPUnbr),'!'])
+ disp([' You must re-set properly NumberOfThreadsPerJob of node ' int2str(Node) ' ' DataInput(Node).ComputerName])
+ disp(' in your configuration file')
+ skipline()
+ ErrorCode=2.3;
+ end
+
+ disp(['Test for Cluster computation, computer ',DataInput(Node).ComputerName, ' ..... Passed!'])
+ skipline(2)
end
\ No newline at end of file
diff --git a/matlab/parallel/GiveCPUnumber.m b/matlab/parallel/GiveCPUnumber.m
index 00b7fc451b5dc9327865ae2df934c18289dcf027..b45d3f879e608d3f7014dd232816ce01fb9592df 100644
--- a/matlab/parallel/GiveCPUnumber.m
+++ b/matlab/parallel/GiveCPUnumber.m
@@ -1,76 +1,76 @@
-function [nCPU]= GiveCPUnumber (ComputerInformations, Environment)
-% PARALLEL CONTEXT
-% In a parallel context this function return the CPUs or cores numer avaiable
-% on the computer used for run parallel code.
-%
-% INPUTS
-% an array contained several fields that describe the hardaware
-% software enviroments of a generic computer.
-%
-% OUTPUTS
-% The CPUs or Cores numbers of computer.
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2010-2013 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-nCPU='';
-
-if nargin < 2,
-% Determine a specific operating system or software version when necessary
-% for different command (sintax, name, ...).
-Environment=~ispc;
-end
-
-switch Environment
- case 0 %WINDOWS OPERATING SYSTEM
-
- OffSet=27;
-
- SringPosition=strfind(ComputerInformations, 'Processors:');
- nCPU=ComputerInformations(SringPosition+OffSet);
-
- % We check if there are Processors/Cores more than 9.
-
-
- t0=ComputerInformations(SringPosition+OffSet+1);
- t1=str2num(t0);
- t1=isempty(t1);
-
- % if t1 is 0 the machine have more than 9 CPU.
-
- if t1==0
- nCPU=strcat(nCPU,t0);
- end
-
- nCPU=str2num(nCPU);
-
- return
- case 1 %LIKE UNIX OPERATING SYSTEM
-
- % Da generalizzare a un numero di CPu maggiore di 9!!!
-
- nCPU=str2num(ComputerInformations(length(ComputerInformations)-1))+1;
-
- case 2 %MAC-OS OPERATING SYSTEM
-
- nCPU=str2num(ComputerInformations);
-
-end
+function [nCPU]= GiveCPUnumber (ComputerInformations, Environment)
+% PARALLEL CONTEXT
+% In a parallel context this function return the CPUs or cores numer avaiable
+% on the computer used for run parallel code.
+%
+% INPUTS
+% an array contained several fields that describe the hardaware
+% software enviroments of a generic computer.
+%
+% OUTPUTS
+% The CPUs or Cores numbers of computer.
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2010-2013 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+
+nCPU='';
+
+if nargin < 2,
+ % Determine a specific operating system or software version when necessary
+ % for different command (sintax, name, ...).
+ Environment=~ispc;
+end
+
+switch Environment
+ case 0 %WINDOWS OPERATING SYSTEM
+
+ OffSet=27;
+
+ SringPosition=strfind(ComputerInformations, 'Processors:');
+ nCPU=ComputerInformations(SringPosition+OffSet);
+
+ % We check if there are Processors/Cores more than 9.
+
+
+ t0=ComputerInformations(SringPosition+OffSet+1);
+ t1=str2num(t0);
+ t1=isempty(t1);
+
+ % if t1 is 0 the machine have more than 9 CPU.
+
+ if t1==0
+ nCPU=strcat(nCPU,t0);
+ end
+
+ nCPU=str2num(nCPU);
+
+ return
+ case 1 %LIKE UNIX OPERATING SYSTEM
+
+ % Da generalizzare a un numero di CPu maggiore di 9!!!
+
+ nCPU=str2num(ComputerInformations(length(ComputerInformations)-1))+1;
+
+ case 2 %MAC-OS OPERATING SYSTEM
+
+ nCPU=str2num(ComputerInformations);
+
+end
diff --git a/matlab/parallel/closeSlave.m b/matlab/parallel/closeSlave.m
index 339d776bb2f4a8a07cc239f0804893867e35504f..03e310606588d726d2442515f102471af18476ac 100644
--- a/matlab/parallel/closeSlave.m
+++ b/matlab/parallel/closeSlave.m
@@ -45,7 +45,7 @@ if partial==1
dynareParallelSendFiles('slaveParallel_break.mat',TmpFolder,Parallel(indPC));
end
end
-% delete('slaveParallel_break')
+ % delete('slaveParallel_break')
return
end
if partial==-1
@@ -55,7 +55,7 @@ if partial==-1
dynareParallelDelete( 'slaveParallel_break.mat',TmpFolder,Parallel(indPC));
end
end
-% delete('slaveParallel_break')
+ % delete('slaveParallel_break')
return
end
@@ -64,7 +64,7 @@ for indPC=1:length(Parallel),
dynareParallelDelete( 'slaveParallel_input*.mat',TmpFolder,Parallel(indPC));
end
-
+
delete( 'slaveParallel_input*.mat');
delete( 'slaveJob*.mat');
pause(1)
@@ -86,4 +86,4 @@ while(1)
end
s=warning('on');
-
+
diff --git a/matlab/parallel/distributeJobs.m b/matlab/parallel/distributeJobs.m
index 0667d902f76240812504435977f3b4abce0897d9..65f2627a2e51ebadf6f97ef1261391ee60ff1e7a 100644
--- a/matlab/parallel/distributeJobs.m
+++ b/matlab/parallel/distributeJobs.m
@@ -92,7 +92,7 @@ for i=1:nC
% JobsForNode(i)=round(JobsForNode(i));
% JobsForNode(i)=floor(JobsForNode(i));
- JobsForNode(i)=ceil(JobsForNode(i));
+ JobsForNode(i)=ceil(JobsForNode(i));
end
@@ -151,7 +151,7 @@ for i=1:nC
% Many choices are possible:
% - ... (see above).
-
+
JobAssignedCpu=max(1,floor(JobsForNode(i)/nCPUoriginal(i)));
ChekOverFlow=0;
diff --git a/matlab/parallel/dynareParallelDeleteNewFiles.m b/matlab/parallel/dynareParallelDeleteNewFiles.m
index e28cd77da7b5ccd209e32b73bcf349da579658bf..4f12357ddda9c49931ea92e429df7aff53eb8bdf 100644
--- a/matlab/parallel/dynareParallelDeleteNewFiles.m
+++ b/matlab/parallel/dynareParallelDeleteNewFiles.m
@@ -60,11 +60,11 @@ for indPC=1:length(Parallel),
exception_flag=exception_flag+(~isempty(strfind(fileaddress{2},varargin{indexc})));
end
if exception_flag==0,
- dynareParallelDelete(fileaddress{2},[PRCDir,fS,fileaddress{1}],Parallel(indPC));
+ dynareParallelDelete(fileaddress{2},[PRCDir,fS,fileaddress{1}],Parallel(indPC));
- disp('New file deleted in remote -->');
- disp(fileaddress{2});
- disp('<--');
+ disp('New file deleted in remote -->');
+ disp(fileaddress{2});
+ disp('<--');
end
end
diff --git a/matlab/parallel/dynareParallelDir.m b/matlab/parallel/dynareParallelDir.m
index daecfe6cd49707b955f7fc1d25ca55987d4d3423..6a9dd492c917602fff4fa498bb75ca63dca048d3 100644
--- a/matlab/parallel/dynareParallelDir.m
+++ b/matlab/parallel/dynareParallelDir.m
@@ -37,10 +37,10 @@ for indPC=1:length(Parallel),
ssh_token = '';
end
if isoctave % Patch for peculiar behaviour of ssh-ls under Linux.
- % It is necessary to capture the ls warning message.
- % To do it under the ssh protocol it is necessary to redirect the ls message in a text file.
- % The file is 'OctaveStandardOutputMessage.txt' and it is
- % saved in the Model directory.
+ % It is necessary to capture the ls warning message.
+ % To do it under the ssh protocol it is necessary to redirect the ls message in a text file.
+ % The file is 'OctaveStandardOutputMessage.txt' and it is
+ % saved in the Model directory.
[check, ax]=system(['ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' ls ',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',filename, ' 2> OctaveStandardOutputMessage.txt']);
else
[check, ax]=system(['ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' ls ',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',filename]);
diff --git a/matlab/parallel/dynareParallelFindNewFiles.m b/matlab/parallel/dynareParallelFindNewFiles.m
index 4c6dabf36e3781ef039d8a916e48c9860ddf6cb5..143f2087032272e052ee2e147147c911e6e7d1a9 100644
--- a/matlab/parallel/dynareParallelFindNewFiles.m
+++ b/matlab/parallel/dynareParallelFindNewFiles.m
@@ -36,7 +36,7 @@ function [NewFilesFrom, NewFileList]=dynareParallelFindNewFiles(FileList,Paralle
NewFilesFrom={};
LfL=length(FileList);
- % The first call ...
+% The first call ...
NewFileList = dynareParallelListAllFiles('Root',PRCDir,Parallel);
diff --git a/matlab/parallel/dynareParallelGetFiles.m b/matlab/parallel/dynareParallelGetFiles.m
index 6dbf0539e4953a49a1d5d90f5c5f14e5859617f8..9ec95f386039b87192537d11e3576876706a8f0d 100644
--- a/matlab/parallel/dynareParallelGetFiles.m
+++ b/matlab/parallel/dynareParallelGetFiles.m
@@ -55,7 +55,7 @@ for indPC=1:length(Parallel),
for jfil=1:size(NamFileInput,1),
if isoctave % Patch for peculiar behaviour of ls under Linux.
- % It is necessary to manage the jolly char '*'!
+ % It is necessary to manage the jolly char '*'!
FindAst=strfind(NamFileInput{jfil,2},'comp_status_posterior_sampler_core*');
diff --git a/matlab/parallel/dynareParallelListAllFiles.m b/matlab/parallel/dynareParallelListAllFiles.m
index 53e893e15c71a65a1d2bea014d8692eb55375b1f..189908b710e5d9cb2949bb89c140276bf15a5190 100644
--- a/matlab/parallel/dynareParallelListAllFiles.m
+++ b/matlab/parallel/dynareParallelListAllFiles.m
@@ -34,7 +34,7 @@ function fileList = dynareParallelListAllFiles(dirName,PRCDir,Parallel)
if (~ispc || strcmpi('unix',Parallel.OperatingSystem))
-
+
fileList={};
currentPath=[];
if ~isempty(Parallel.Port),
@@ -43,7 +43,7 @@ if (~ispc || strcmpi('unix',Parallel.OperatingSystem))
ssh_token = '';
end
- % Get the data for the current remote directory.
+ % Get the data for the current remote directory.
[Flag fL]=system(['ssh ',ssh_token,' ',' ',Parallel.UserName,'@',Parallel.ComputerName,' ls ',Parallel.RemoteDirectory,'/',PRCDir, ' -R -p -1']);
% Format the return value fL.
@@ -60,7 +60,7 @@ if (~ispc || strcmpi('unix',Parallel.OperatingSystem))
start=nL(i)+1;
continue
end
-
+
if strfind(stringProcessed,'/')
if strfind(stringProcessed,PRCDir)
DD=strfind(stringProcessed,':');
@@ -92,7 +92,7 @@ else
% Build the path files:
if ~isempty(fileList)
fileList = cellfun(@(x) fullfile(dirName,x),...
- fileList,'UniformOutput',false);
+ fileList,'UniformOutput',false);
end
% Get a list of the subdirectories:
diff --git a/matlab/parallel/dynareParallelSendFiles.m b/matlab/parallel/dynareParallelSendFiles.m
index 6f7e645f55fa916888713603587c8b44f1a627a6..f65697286ab865bc19c338951e429f64d058fb76 100644
--- a/matlab/parallel/dynareParallelSendFiles.m
+++ b/matlab/parallel/dynareParallelSendFiles.m
@@ -68,9 +68,9 @@ for indPC=1:length(Parallel),
% It is Necessary because Octave is not able to
% create two nested directory at the same time.
- % Remove (if present) the '/' chars. Can be easily transformed
- % in a function.
-
+ % Remove (if present) the '/' chars. Can be easily transformed
+ % in a function.
+
NamFileInputTemp=NamFileInput{jfil,1};
while(1)
Bs=strfind(NamFileInputTemp,'/');
@@ -94,8 +94,8 @@ for indPC=1:length(Parallel),
% It is Necessary because Octave is not able to
% use the jolly char '*' with copyfile.
- % Remove (if present) the '/' chars. Can be easily transformed
- % in a function.
+ % Remove (if present) the '/' chars. Can be easily transformed
+ % in a function.
NamFileInputTemp=NamFileInput{jfil,1};
while(1)
diff --git a/matlab/parallel/dynareParallelSnapshot.m b/matlab/parallel/dynareParallelSnapshot.m
index 4f07b04df80e9df127ee1cfb565eb8696a272a85..ebff7a33c9d5872e391e0c557ffd4ecb72a69fd1 100644
--- a/matlab/parallel/dynareParallelSnapshot.m
+++ b/matlab/parallel/dynareParallelSnapshot.m
@@ -35,7 +35,7 @@ PRCDirSnapshot={};
for indPC=1:length(Parallel),
if Parallel(indPC).Local==0;
- % The first call ...
+ % The first call ...
PRCDirSnapshot{indPC}=dynareParallelListAllFiles('Root',PRCDir,Parallel(indPC));
end
diff --git a/matlab/parallel/masterParallel.m b/matlab/parallel/masterParallel.m
index 902b237dc3ede9cefdbd67b9ec2fa7664f30254f..f5a5b50fd208771ac1225340802b0a4102d82324 100644
--- a/matlab/parallel/masterParallel.m
+++ b/matlab/parallel/masterParallel.m
@@ -136,15 +136,15 @@ if isHybridMatlabOctave || isoctave
end
if exist('fGlobalVar','var') && ~isempty(fGlobalVar),
- fInputNames = fieldnames(fGlobalVar);
- for j=1:length(fInputNames),
- TargetVar = fGlobalVar.(fInputNames{j});
- if isa(TargetVar,'function_handle'),
- TargetVar=func2str(TargetVar);
- fGlobalVar.(fInputNames{j})=TargetVar;
+ fInputNames = fieldnames(fGlobalVar);
+ for j=1:length(fInputNames),
+ TargetVar = fGlobalVar.(fInputNames{j});
+ if isa(TargetVar,'function_handle'),
+ TargetVar=func2str(TargetVar);
+ fGlobalVar.(fInputNames{j})=TargetVar;
+ end
end
end
- end
end
if Strategy==1
@@ -165,18 +165,18 @@ end
% Save input data for use by the slaves.
switch Strategy
- case 0
- storeGlobalVars([fname,'_input.mat']);
- save([fname,'_input.mat'],'fInputVar','Parallel','-append')
-
- case 1
- if exist('fGlobalVar','var'),
- save(['temp_input.mat'],'fInputVar','fGlobalVar')
- else
- save(['temp_input.mat'],'fInputVar')
- end
- save(['temp_input.mat'],'Parallel','-append')
- closeSlave(Parallel,PRCDir,-1);
+ case 0
+ storeGlobalVars([fname,'_input.mat']);
+ save([fname,'_input.mat'],'fInputVar','Parallel','-append')
+
+ case 1
+ if exist('fGlobalVar','var'),
+ save(['temp_input.mat'],'fInputVar','fGlobalVar')
+ else
+ save(['temp_input.mat'],'fInputVar')
+ end
+ save(['temp_input.mat'],'Parallel','-append')
+ closeSlave(Parallel,PRCDir,-1);
end
@@ -329,35 +329,108 @@ for j=1:totCPU,
for jaff=2:length(affinity_range),
my_affinity = [my_affinity ',' int2str(Parallel(indPC).CPUnbr(affinity_range(jaff)))];
end
-% % % int2str(Parallel(indPC).CPUnbr(j-nCPU0))
+ % % % int2str(Parallel(indPC).CPUnbr(j-nCPU0))
% DA SINTETIZZARE:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The following 'switch - case' code is the core of this function!
switch Strategy
- case 0
+ case 0
+
+ if Parallel(indPC).Local == 1, % 0.1 Run on the local machine (localhost).
- if Parallel(indPC).Local == 1, % 0.1 Run on the local machine (localhost).
-
- if ~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem), % Hybrid computing Windows <-> Unix!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- command1=[Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')" &'];
+ if ~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem), % Hybrid computing Windows <-> Unix!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ command1=[Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')" &'];
+ else
+ command1=[Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')" &'];
+ end
+ else % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
+ command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ else
+ command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ end
+ end
+ else % 0.2 Parallel(indPC).Local==0: Run using network on remote machine or also on local machine.
+ if j==nCPU0+1,
+ dynareParallelSendFiles([fname,'_input.mat'],PRCDir,Parallel(indPC));
+ dynareParallelSendFiles(NamFileInput,PRCDir,Parallel(indPC));
+ end
+
+ if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)), % Hybrid computing Windows <-> Unix!
+ if ispc, token='start /B ';
+ else token = '';
+ end
+ if ~isempty(Parallel(indPC).Port),
+ ssh_token = ['-p ',Parallel(indPC).Port];
+ else
+ ssh_token = '';
+ end
+ % To manage the diferences in Unix/Windows OS syntax.
+ remoteFile=['remoteDynare',int2str(j)];
+ fidRemote=fopen([remoteFile,'.m'],'w+');
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave'),% Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ remoteString=['default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')'];
+ command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir, '; ',Parallel(indPC).MatlabOctavePath,' -f --eval ',remoteFile,' " &'];
+ else
+ remoteString=['addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')'];
+ command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir, '; ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r ',remoteFile,';" &'];
+ end
+ fprintf(fidRemote,'%s\n',remoteString);
+ fclose(fidRemote);
+ dynareParallelSendFiles([remoteFile,'.m'],PRCDir,Parallel(indPC));
+ delete([remoteFile,'.m']);
+ else
+ if ~strcmpi(Parallel(indPC).ComputerName,MasterName), % 0.3 Run on a remote machine!
+ % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
+ command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
else
- command1=[Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')" &'];
+
+ command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
end
- else % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ else % 0.4 Run on the local machine via the network
+ % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
- command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
else
- command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
end
end
- else % 0.2 Parallel(indPC).Local==0: Run using network on remote machine or also on local machine.
- if j==nCPU0+1,
- dynareParallelSendFiles([fname,'_input.mat'],PRCDir,Parallel(indPC));
- dynareParallelSendFiles(NamFileInput,PRCDir,Parallel(indPC));
+ end
+ end
+
+
+ case 1
+ if Parallel(indPC).Local == 1 && newInstance, % 1.1 Run on the local machine.
+ if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)), % Hybrid computing Windows <-> Unix!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
+ command1=[Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')" &'];
+ else
+ command1=[Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')" &'];
end
-
+ else % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
+ command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7'');addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
+ else
+ command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
+ end
+ end
+ elseif Parallel(indPC).Local==0, % 1.2 Run using network on remote machine or also on local machine.
+ if j==nCPU0+1,
+ dynareParallelSendFiles(NamFileInput,PRCDir,Parallel(indPC));
+ end
+ dynareParallelSendFiles(['P_',fname,'_',int2str(j),'End.txt'],PRCDir,Parallel(indPC));
+ delete(['P_',fname,'_',int2str(j),'End.txt']);
+ if newInstance,
+ dynareParallelSendFiles(['slaveJob',int2str(j),'.mat'],PRCDir,Parallel(indPC));
+ delete(['slaveJob',int2str(j),'.mat']);
+ dynareParallelSendFiles(['slaveParallel_input',int2str(j),'.mat'],PRCDir,Parallel(indPC))
if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)), % Hybrid computing Windows <-> Unix!
if ispc, token='start /B ';
else token = '';
@@ -370,128 +443,55 @@ for j=1:totCPU,
% To manage the diferences in Unix/Windows OS syntax.
remoteFile=['remoteDynare',int2str(j)];
fidRemote=fopen([remoteFile,'.m'],'w+');
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave'),% Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- remoteString=['default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')'];
- command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir, '; ',Parallel(indPC).MatlabOctavePath,' -f --eval ',remoteFile,' " &'];
+ if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
+ remoteString=['default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),');'];
+ command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir '; ',Parallel(indPC).MatlabOctavePath,' -f --eval ',remoteFile,' " &'];
else
- remoteString=['addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')'];
- command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir, '; ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r ',remoteFile,';" &'];
+ remoteString=['addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),');'];
+ command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir '; ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r ',remoteFile,';" &'];
end
fprintf(fidRemote,'%s\n',remoteString);
fclose(fidRemote);
dynareParallelSendFiles([remoteFile,'.m'],PRCDir,Parallel(indPC));
delete([remoteFile,'.m']);
else
- if ~strcmpi(Parallel(indPC).ComputerName,MasterName), % 0.3 Run on a remote machine!
- % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
+ if ~strcmpi(Parallel(indPC).ComputerName,MasterName), % 1.3 Run on a remote machine.
+ % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7'');addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
else
-
command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
end
- else % 0.4 Run on the local machine via the network
- % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
+ else % 1.4 Run on the local machine via the network.
+ % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
else
command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); fParallel(',int2str(offset+1),',',int2str(sum(nBlockPerCPU(1:j))),',',int2str(j),',',int2str(indPC),',''',fname,''')"'];
+ ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
end
end
end
- end
-
-
- case 1
- if Parallel(indPC).Local == 1 && newInstance, % 1.1 Run on the local machine.
- if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)), % Hybrid computing Windows <-> Unix!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
- command1=[Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')" &'];
- else
- command1=[Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')" &'];
- end
- else % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
- command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7'');addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- else
- command1=['psexec -d -W "',DyMo, '" -a ',my_affinity,' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- end
- end
- elseif Parallel(indPC).Local==0, % 1.2 Run using network on remote machine or also on local machine.
- if j==nCPU0+1,
- dynareParallelSendFiles(NamFileInput,PRCDir,Parallel(indPC));
- end
- dynareParallelSendFiles(['P_',fname,'_',int2str(j),'End.txt'],PRCDir,Parallel(indPC));
- delete(['P_',fname,'_',int2str(j),'End.txt']);
- if newInstance,
- dynareParallelSendFiles(['slaveJob',int2str(j),'.mat'],PRCDir,Parallel(indPC));
- delete(['slaveJob',int2str(j),'.mat']);
- dynareParallelSendFiles(['slaveParallel_input',int2str(j),'.mat'],PRCDir,Parallel(indPC))
- if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem)), % Hybrid computing Windows <-> Unix!
- if ispc, token='start /B ';
- else token = '';
- end
- if ~isempty(Parallel(indPC).Port),
- ssh_token = ['-p ',Parallel(indPC).Port];
- else
- ssh_token = '';
- end
- % To manage the diferences in Unix/Windows OS syntax.
- remoteFile=['remoteDynare',int2str(j)];
- fidRemote=fopen([remoteFile,'.m'],'w+');
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)-> Octave(Slaves) and Vice Versa!
- remoteString=['default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),');'];
- command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir '; ',Parallel(indPC).MatlabOctavePath,' -f --eval ',remoteFile,' " &'];
- else
- remoteString=['addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),');'];
- command1=[token, 'ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' "cd ',Parallel(indPC).RemoteDirectory,'/',PRCDir '; ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r ',remoteFile,';" &'];
- end
- fprintf(fidRemote,'%s\n',remoteString);
- fclose(fidRemote);
- dynareParallelSendFiles([remoteFile,'.m'],PRCDir,Parallel(indPC));
- delete([remoteFile,'.m']);
- else
- if ~strcmpi(Parallel(indPC).ComputerName,MasterName), % 1.3 Run on a remote machine.
- % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
- command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7'');addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- else
- command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -u ',Parallel(indPC).UserName,' -p ',Parallel(indPC).Password,' -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- end
- else % 1.4 Run on the local machine via the network.
- % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
- if regexpi([Parallel(indPC).MatlabOctavePath], 'octave')
- command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -f --eval "default_save_options(''-v7''); addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- else
- command1=['psexec \\',Parallel(indPC).ComputerName,' -d -e -W "',Parallel(indPC).RemoteDrive,':\',Parallel(indPC).RemoteDirectory,'\',PRCDir,'\" -a ',my_affinity, ...
- ' -low ',Parallel(indPC).MatlabOctavePath,' -nosplash -nodesktop -minimize ',compThread,' -r "addpath(''',Parallel(indPC).DynarePath,'''), dynareroot = dynare_config(); slaveParallel(',int2str(j),',',int2str(indPC),')"'];
- end
- end
- end
+ else
+ % When the user user strategy is equal to 1, you must
+ % do PRCDirSnapshot here to to avoid problems of
+ % synchronization.
+
+ if isempty(PRCDirSnapshot{indPC}),
+ PRCDirSnapshot(indPC)=dynareParallelSnapshot(PRCDir,Parallel(indPC));
+ PRCDirSnapshotInit(indPC) = PRCDirSnapshot(indPC);
else
- % When the user user strategy is equal to 1, you must
- % do PRCDirSnapshot here to to avoid problems of
- % synchronization.
-
- if isempty(PRCDirSnapshot{indPC}),
- PRCDirSnapshot(indPC)=dynareParallelSnapshot(PRCDir,Parallel(indPC));
- PRCDirSnapshotInit(indPC) = PRCDirSnapshot(indPC);
- else
- PRCDirSnapshot(indPC)=dynareParallelGetNewFiles(PRCDir,Parallel(indPC),PRCDirSnapshot(indPC));
- end
- dynareParallelSendFiles(['slaveJob',int2str(j),'.mat'],PRCDir,Parallel(indPC));
- delete(['slaveJob',int2str(j),'.mat']);
-
+ PRCDirSnapshot(indPC)=dynareParallelGetNewFiles(PRCDir,Parallel(indPC),PRCDirSnapshot(indPC));
end
+ dynareParallelSendFiles(['slaveJob',int2str(j),'.mat'],PRCDir,Parallel(indPC));
+ delete(['slaveJob',int2str(j),'.mat']);
+
end
-
+ end
+
end
fprintf(fid,'%s\n',command1);
@@ -549,18 +549,18 @@ if isoctave || options_.console_mode
end
else
hfigstatus = figure('name',['Parallel ',fname],...
- 'DockControls','off', ...
- 'IntegerHandle','off', ...
- 'Interruptible','off', ...
- 'MenuBar', 'none', ...
- 'NumberTitle','off', ...
- 'Renderer','Painters', ...
- 'Resize','off');
+ 'DockControls','off', ...
+ 'IntegerHandle','off', ...
+ 'Interruptible','off', ...
+ 'MenuBar', 'none', ...
+ 'NumberTitle','off', ...
+ 'Renderer','Painters', ...
+ 'Resize','off');
ncol = ceil(totCPU/10);
hspace = 0.9/ncol;
hstatus(1) = axes('position',[0.05/ncol 0.92 0.9/ncol 0.03], ...
- 'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
+ 'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
set(hstatus(1),'Units','pixels')
hpixel = get(hstatus(1),'Position');
hfigure = get(hfigstatus,'Position');
@@ -573,7 +573,7 @@ else
jrow = mod(j-1,10)+1;
jcol = ceil(j/10);
hstatus(j) = axes('position',[0.05/ncol+(jcol-1)/ncol vstart-vspace*(jrow-1) 0.9/ncol 0.3*vspace], ...
- 'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
+ 'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
hpat(j) = patch([0 0 0 0],[0 1 1 0],'r','EdgeColor','r');
htit(j) = title(['Initialize ...']);
@@ -667,7 +667,7 @@ while (ForEver)
try
if ~isempty(['comp_status_',fname,int2str(j),'.mat'])
load(['comp_status_',fname,int2str(j),'.mat']);
-% whoCloseAllSlaves = who(['comp_status_',fname,int2str(j),'.mat','CloseAllSlaves']);
+ % whoCloseAllSlaves = who(['comp_status_',fname,int2str(j),'.mat','CloseAllSlaves']);
if exist('CloseAllSlaves') && flag_CloseAllSlaves==0,
flag_CloseAllSlaves=1;
whoiamCloseAllSlaves=j;
@@ -687,7 +687,7 @@ while (ForEver)
status_Title{j} = waitbarTitle;
end
catch % ME
- % To define!
+ % To define!
if isoctave || options_.console_mode
if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem))
statusString = [statusString, int2str(j), ' %3.f%% done! '];
@@ -804,7 +804,7 @@ for j=1:totCPU,
end
end
for k=1:size(tmp1,1),
- dynareParallelGetFiles([OutputFileName(i,1) {tmp1(k,:)}],PRCDir,Parallel(indPC));
+ dynareParallelGetFiles([OutputFileName(i,1) {tmp1(k,:)}],PRCDir,Parallel(indPC));
end
end
% check if some output file is missing!
@@ -856,46 +856,46 @@ pause(1), % Wait for all remote diary off completed
dynareParallelGetFiles('*.log',PRCDir,Parallel(1:totSlaves));
switch Strategy
- case 0
- for indPC=1:min(find(nCPU>=totCPU)),
- if Parallel(indPC).Local == 0
- dynareParallelRmDir(PRCDir,Parallel(indPC));
- end
-
- if isempty(dir('dynareParallelLogFiles'))
- [A B C]=rmdir('dynareParallelLogFiles');
- mkdir('dynareParallelLogFiles');
- end
- try
- copyfile('*.log','dynareParallelLogFiles');
- mydelete([fname,'*.log']);
- catch
- end
-
- mydelete(['*_core*_input*.mat']);
-
+ case 0
+ for indPC=1:min(find(nCPU>=totCPU)),
+ if Parallel(indPC).Local == 0
+ dynareParallelRmDir(PRCDir,Parallel(indPC));
end
- delete ConcurrentCommand1.bat
- case 1
- delete(['temp_input.mat'])
- if newInstance,
- if isempty(dir('dynareParallelLogFiles'))
- [A B C]=rmdir('dynareParallelLogFiles');
- mkdir('dynareParallelLogFiles');
- end
+ if isempty(dir('dynareParallelLogFiles'))
+ [A B C]=rmdir('dynareParallelLogFiles');
+ mkdir('dynareParallelLogFiles');
end
- copyfile('*.log','dynareParallelLogFiles');
- if newInstance,
- delete ConcurrentCommand1.bat
+ try
+ copyfile('*.log','dynareParallelLogFiles');
+ mydelete([fname,'*.log']);
+ catch
end
- dynareParallelDelete(['comp_status_',fname,'*.mat'],PRCDir,Parallel);
- for indPC=1:min(find(nCPU>=totCPU)),
- if Parallel(indPC).Local == 0,
- dynareParallelDeleteNewFiles(PRCDir,Parallel(indPC),PRCDirSnapshotInit(indPC),'.log');
- for ifil=1:size(NamFileInput,1),
- dynareParallelDelete([NamFileInput{ifil,:}],PRCDir,Parallel(indPC));
- end
+
+ mydelete(['*_core*_input*.mat']);
+
+ end
+
+ delete ConcurrentCommand1.bat
+ case 1
+ delete(['temp_input.mat'])
+ if newInstance,
+ if isempty(dir('dynareParallelLogFiles'))
+ [A B C]=rmdir('dynareParallelLogFiles');
+ mkdir('dynareParallelLogFiles');
+ end
+ end
+ copyfile('*.log','dynareParallelLogFiles');
+ if newInstance,
+ delete ConcurrentCommand1.bat
+ end
+ dynareParallelDelete(['comp_status_',fname,'*.mat'],PRCDir,Parallel);
+ for indPC=1:min(find(nCPU>=totCPU)),
+ if Parallel(indPC).Local == 0,
+ dynareParallelDeleteNewFiles(PRCDir,Parallel(indPC),PRCDirSnapshotInit(indPC),'.log');
+ for ifil=1:size(NamFileInput,1),
+ dynareParallelDelete([NamFileInput{ifil,:}],PRCDir,Parallel(indPC));
end
end
+ end
end
\ No newline at end of file
diff --git a/matlab/parallel/slaveParallel.m b/matlab/parallel/slaveParallel.m
index d7275a4fdb10f9a07a17d81a521c96d3cec9ce4b..5de311575767419a183b6ae97f2011fbea2832cc 100644
--- a/matlab/parallel/slaveParallel.m
+++ b/matlab/parallel/slaveParallel.m
@@ -136,7 +136,7 @@ while (etime(clock,t0)<1200 && ~isempty(fslave)) || ~isempty(dir(['stayalive',in
% Save the output result.
save([ fname,'_output_',int2str(whoiam),'.mat'],'fOutputVar' );
-% keyboard,
+ % keyboard,
if isfield(fOutputVar,'CloseAllSlaves'),
CloseAllSlaves = 1;
fOutputVar = rmfield(fOutputVar,'CloseAllSlaves');
diff --git a/matlab/perfect-foresight-models/det_cond_forecast.m b/matlab/perfect-foresight-models/det_cond_forecast.m
index 38d72d34594a9b29350c0cd42c305e7645c15d60..df64df222f992da2425061f0eb4e563ca965a5d2 100644
--- a/matlab/perfect-foresight-models/det_cond_forecast.m
+++ b/matlab/perfect-foresight-models/det_cond_forecast.m
@@ -34,7 +34,7 @@ pp = 2;
initial_conditions = oo_.steady_state;
verbosity = options_.verbosity;
if options_.periods == 0
- options_.periods = 25;
+ options_.periods = 25;
end;
%We have to get an initial guess for the conditional forecast
% and terminal conditions for the non-stationary variables, we
@@ -82,7 +82,7 @@ if length(varargin) > 3
error(['Unknown exogenous variable ' controlled_varexo(i,:)]);
end
end
-
+
else
% alternative way to call: plan, dset, dates_of_frcst
plan = varargin{1};
@@ -105,7 +105,7 @@ else
sym_dset = dset(dates(-range(1)):dates(range(range.ndat)));
periods = options_.periods + M_.maximum_lag + M_.maximum_lead;
- total_periods = periods + range.ndat;
+ total_periods = periods + range.ndat;
if isfield(oo_, 'exo_simul')
if size(oo_.exo_simul, 1) ~= total_periods
oo_.exo_simul = repmat(oo_.exo_steady_state',total_periods,1);
@@ -121,14 +121,14 @@ else
if ~isempty(iy)
oo_.endo_simul(iy,1:sym_dset.nobs) = sym_dset.data(:, i);
initial_conditions(iy) = sym_dset.data(1, i);
- else
- ix = find(strcmp(strtrim(sym_dset.name{i}), strtrim(plan.exo_names)));
- if ~isempty(ix)
- oo_.exo_simul(1, ix) = sym_dset.data(1, i)';
+ else
+ ix = find(strcmp(strtrim(sym_dset.name{i}), strtrim(plan.exo_names)));
+ if ~isempty(ix)
+ oo_.exo_simul(1, ix) = sym_dset.data(1, i)';
else
%warning(['The variable ' sym_dset.name{i} ' in the dataset ' inputname(2) ' is not a endogenous neither an exogenous variable!!']);
end
- end
+ end
end
for i = 1:length(M_.aux_vars)
if M_.aux_vars(i).type == 1 %lag variable
@@ -146,8 +146,8 @@ else
%Compute the initial path using the the steady-state
% steady-state
%for jj = 2 : (options_.periods + 2)
- for jj = 2 : (range.ndat + 2)
- oo_.endo_simul(:, jj) = oo_.steady_state;
+ for jj = 2 : (range.ndat + 2)
+ oo_.endo_simul(:, jj) = oo_.steady_state;
end
missings = isnan(oo_.endo_simul(:,1));
if any(missings)
@@ -165,11 +165,11 @@ else
options_.dynatol.f = save_options_dynatol_f;
if Info == 0
- oo_.endo_simul = endo;
- oo_.exo_simul = exo;
+ oo_.endo_simul = endo;
+ oo_.exo_simul = exo;
end
endo = endo';
- endo_l = size(endo(1+M_.maximum_lag:end,:),1);
+ endo_l = size(endo(1+M_.maximum_lag:end,:),1);
jrng = dates(plan.date(1)):dates(plan.date(1)+endo_l);
data_set = dseries(nan(endo_l, dset.vobs), plan.date(1), dset.name);
for i = 1:length(dset.name)
@@ -179,7 +179,7 @@ else
else
pos = find(strcmp(dset.name{i},plan.exo_names));
if ~isempty(pos)
- data_set{dset.name{i}} = dseries(exo(1+M_.maximum_lag:end,pos), plan.date(1),dset.name{i});
+ data_set{dset.name{i}} = dseries(exo(1+M_.maximum_lag:end,pos), plan.date(1),dset.name{i});
end
end
end
@@ -209,9 +209,9 @@ else
return;
end;
else
- error('impossible case');
+ error('impossible case');
end;
-
+
else
oo_.exo_simul = repmat(oo_.exo_steady_state',options_.periods+2,1);
oo_.endo_simul = repmat(oo_.steady_state, 1, options_.periods+2);
@@ -536,19 +536,19 @@ if pf && ~surprise
end;
-% col_count = 1;
-% for j = controlled_varexo'
-% for time = time_index_constraint
-% saved = oo_.exo_simul(time,j);
-% oo_.exo_simul(time,j) = oo_.exo_simul(time,j) + eps1;
-% simul();
-% J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
-% oo_.exo_simul(time,j) = saved;
-% col_count = col_count + 1;
-% end;
-% end;
-% J1
-% sdfmlksdf;
+ % col_count = 1;
+ % for j = controlled_varexo'
+ % for time = time_index_constraint
+ % saved = oo_.exo_simul(time,j);
+ % oo_.exo_simul(time,j) = oo_.exo_simul(time,j) + eps1;
+ % simul();
+ % J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
+ % oo_.exo_simul(time,j) = saved;
+ % col_count = col_count + 1;
+ % end;
+ % end;
+ % J1
+ % sdfmlksdf;
disp(['iteration ' int2str(it) ' error = ' num2str(normr)]);
@@ -582,7 +582,7 @@ else
else
pf = length(indx_endo_solve_pf);
end;
-
+
[pos_constrained_surprise, junk] = find((1-constrained_perfect_foresight) .* is_constraint(t, :)');
indx_endo_solve_surprise = constrained_vars(pos_constrained_surprise);
@@ -601,7 +601,7 @@ else
else
b_pf = length(indx_endo_solve_pf);
end;
-
+
[pos_shock_surprise, junk] = find((1-shock_perfect_foresight) .* is_shock(t, :)');
indx_endo_solve_surprise = shock_vars(pos_shock_surprise);
@@ -822,34 +822,34 @@ else
end;
-% % Numerical computation of the derivatives in the second systme
-% J1 = [];
-% col_count = 1;
-% for j = constraint_index_t
-% j_pos = controlled_varexo(j);
-% if constrained_perfect_foresight(j)
-% for time = time_index_constraint
-% saved = oo_.exo_simul(time,j_pos);
-% oo_.exo_simul(time,j_pos) = oo_.exo_simul(time,j_pos) + eps1;
-% simul();
-% J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
-% oo_.exo_simul(time,j_pos) = saved;
-% col_count = col_count + 1;
-% end;
-% else
-% saved = oo_.exo_simul(maximum_lag+1,j_pos);
-% oo_.exo_simul(maximum_lag+1,j_pos) = oo_.exo_simul(maximum_lag+1,j_pos) + eps1;
-% simul();
-% % indx_endo
-% J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
-% % J(:,col_count) = (oo_.endo_simul((pp - 1) * M_.endo_nbr + 1: pp * M_.endo_nbr) - ys) / eps1;
-% oo_.exo_simul(maximum_lag+1,j_pos) = saved;
-% col_count = col_count + 1;
-% end;
-% end;
-% disp('J1');
-% disp(full(J1));
-% sdfmlk;
+ % % Numerical computation of the derivatives in the second systme
+ % J1 = [];
+ % col_count = 1;
+ % for j = constraint_index_t
+ % j_pos = controlled_varexo(j);
+ % if constrained_perfect_foresight(j)
+ % for time = time_index_constraint
+ % saved = oo_.exo_simul(time,j_pos);
+ % oo_.exo_simul(time,j_pos) = oo_.exo_simul(time,j_pos) + eps1;
+ % simul();
+ % J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
+ % oo_.exo_simul(time,j_pos) = saved;
+ % col_count = col_count + 1;
+ % end;
+ % else
+ % saved = oo_.exo_simul(maximum_lag+1,j_pos);
+ % oo_.exo_simul(maximum_lag+1,j_pos) = oo_.exo_simul(maximum_lag+1,j_pos) + eps1;
+ % simul();
+ % % indx_endo
+ % J1(:,col_count) = (oo_.endo_simul(indx_endo) - ys) / eps1;
+ % % J(:,col_count) = (oo_.endo_simul((pp - 1) * M_.endo_nbr + 1: pp * M_.endo_nbr) - ys) / eps1;
+ % oo_.exo_simul(maximum_lag+1,j_pos) = saved;
+ % col_count = col_count + 1;
+ % end;
+ % end;
+ % disp('J1');
+ % disp(full(J1));
+ % sdfmlk;
normr = norm(r, 1);
@@ -862,7 +862,7 @@ else
else
% Newton update on exogenous shocks
try
- D_exo = - J \ r;
+ D_exo = - J \ r;
catch
[V, D] = eig(full(J));
ev = diag(D);
diff --git a/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m b/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
index 306960b4c87d4046b7826cfa053cb5659d67b165..b8f710bd41efd3f50dff34146dbd8e5ae63722ae 100644
--- a/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
+++ b/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
@@ -1,8 +1,8 @@
-function [residuals,JJacobian] = perfect_foresight_problem(y, dynamicjacobian, Y0, YT, ...
- exo_simul, params, steady_state, ...
- maximum_lag, T, ny, i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, ...
- i_cols_j,nnzJ,jendo,jexog)
+function [residuals,JJacobian] = linear_perfect_foresight_problem(y, dynamicjacobian, Y0, YT, ...
+ exo_simul, params, steady_state, ...
+ maximum_lag, T, ny, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, ...
+ i_cols_j,nnzJ,jendo,jexog)
% function [residuals,JJacobian] = perfect_foresight_problem(x, model_dynamic, Y0, YT,exo_simul,
% params, steady_state, maximum_lag, periods, ny, i_cols,i_cols_J1, i_cols_1,
% i_cols_T, i_cols_j, nnzA)
diff --git a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
index d4b419308659cec1ac240f50657f66cbc3f6f9f1..ed6553201c4a6c3e2e5bfd7baaedcf50f27df2d6 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
@@ -1,8 +1,8 @@
function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_function, Y0, YT, ...
- exo_simul, params, steady_state, ...
- maximum_lag, T, ny, i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, ...
- i_cols_j,nnzJ,eq_index)
+ exo_simul, params, steady_state, ...
+ maximum_lag, T, ny, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, ...
+ i_cols_j,nnzJ,eq_index)
% function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_function, Y0, YT, ...
% exo_simul, params, steady_state, ...
% maximum_lag, T, ny, i_cols, ...
@@ -77,11 +77,11 @@ i_cols_J = i_cols;
for it = 2:(T+1)
if nargout == 1
res = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
+ steady_state,it);
residuals(i_rows) = res(eq_index);
elseif nargout == 2
[res,jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
+ steady_state,it);
residuals(i_rows) = res(eq_index);
if it == 2
[rows,cols,vals] = find(jacobian(eq_index,i_cols_1));
@@ -104,5 +104,5 @@ end
if nargout == 2
iJacobian = cat(1,iJacobian{:});
JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
- ny,T*ny);
+ ny,T*ny);
end
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/perfect_foresight_problem.m b/matlab/perfect-foresight-models/perfect_foresight_problem.m
index 95611909453fa57a6652972ecb50ed3f96bcc697..2f4cee6fd3ddd30a22c7f0e9d55a8f10ae1f9766 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_problem.m
@@ -1,8 +1,8 @@
function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function, Y0, YT, ...
- exo_simul, params, steady_state, ...
- maximum_lag, T, ny, i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, ...
- i_cols_j,nnzJ)
+ exo_simul, params, steady_state, ...
+ maximum_lag, T, ny, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, ...
+ i_cols_j,nnzJ)
% function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function, Y0, YT, ...
% exo_simul, params, steady_state, ...
% maximum_lag, T, ny, i_cols, ...
@@ -60,44 +60,44 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- YY = [Y0; y; YT];
-
- residuals = zeros(T*ny,1);
- if nargout == 2
- iJacobian = cell(T,1);
- end
+YY = [Y0; y; YT];
- i_rows = 1:ny;
- i_cols_J = i_cols;
- offset = 0;
-
- for it = 2:(T+1)
- if nargout == 1
- residuals(i_rows) = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
- elseif nargout == 2
- [residuals(i_rows),jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
- steady_state,it);
- if it == 2
- [rows,cols,vals] = find(jacobian(:,i_cols_1));
- iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
- elseif it == T + 1
- [rows,cols,vals] = find(jacobian(:,i_cols_T));
- iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
- else
- [rows,cols,vals] = find(jacobian(:,i_cols_j));
- iJacobian{it-1} = [offset+rows, i_cols_J(cols), vals];
- i_cols_J = i_cols_J + ny;
- end
- offset = offset + ny;
- end
+residuals = zeros(T*ny,1);
+if nargout == 2
+ iJacobian = cell(T,1);
+end
+
+i_rows = 1:ny;
+i_cols_J = i_cols;
+offset = 0;
- i_rows = i_rows + ny;
- i_cols = i_cols + ny;
+for it = 2:(T+1)
+ if nargout == 1
+ residuals(i_rows) = dynamic_function(YY(i_cols),exo_simul, params, ...
+ steady_state,it);
+ elseif nargout == 2
+ [residuals(i_rows),jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
+ steady_state,it);
+ if it == 2
+ [rows,cols,vals] = find(jacobian(:,i_cols_1));
+ iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
+ elseif it == T + 1
+ [rows,cols,vals] = find(jacobian(:,i_cols_T));
+ iJacobian{T} = [offset+rows, i_cols_J(i_cols_T(cols)), vals];
+ else
+ [rows,cols,vals] = find(jacobian(:,i_cols_j));
+ iJacobian{it-1} = [offset+rows, i_cols_J(cols), vals];
+ i_cols_J = i_cols_J + ny;
+ end
+ offset = offset + ny;
end
- if nargout == 2
- iJacobian = cat(1,iJacobian{:});
- JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
- ny,T*ny);
- end
\ No newline at end of file
+ i_rows = i_rows + ny;
+ i_cols = i_cols + ny;
+end
+
+if nargout == 2
+ iJacobian = cat(1,iJacobian{:});
+ JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
+ ny,T*ny);
+end
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/perfect_foresight_setup.m b/matlab/perfect-foresight-models/perfect_foresight_setup.m
index 3b28254326f2642f46cfa098ec61b210e9336f8d..7d7312fc03fd4112701d9e9d65afc7eff50ad51b 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_setup.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_setup.m
@@ -35,14 +35,14 @@ test_for_deep_parameters_calibration(M_);
if size(M_.lead_lag_incidence,2)-nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)) > 0
mess = ['PERFECT_FORESIGHT_SETUP: error in model specification : the variable(s) '];
- var_list=M_.endo_names(find(M_.lead_lag_incidence(M_.maximum_lag+1,:)==0),:);
- for i=1:size(var_list,1)
- if i<size(var_list,1)
- mess = [mess, deblank(var_list(i,:)) ', '];
- else
- mess = [mess, deblank(var_list(i,:)) ];
- end
+ var_list=M_.endo_names(find(M_.lead_lag_incidence(M_.maximum_lag+1,:)==0),:);
+ for i=1:size(var_list,1)
+ if i<size(var_list,1)
+ mess = [mess, deblank(var_list(i,:)) ', '];
+ else
+ mess = [mess, deblank(var_list(i,:)) ];
end
+ end
mess = [mess ' don''t appear as current period variables.'];
error(mess)
end
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver.m b/matlab/perfect-foresight-models/perfect_foresight_solver.m
index b5fc7e4f21402b04c7fdbbb23c7bd55838662958..b159f2aea7b4779e3cace44d365858bb98e14071 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver.m
@@ -184,10 +184,10 @@ if ~isreal(oo_.endo_simul(:)) %can only happen without bytecode
[i_cols_J1,junk,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '_dynamic']), y0, yT, ...
- oo_.exo_simul,M_.params,oo_.steady_state, ...
- M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
- M_.NNZDerivatives(1));
+ oo_.exo_simul,M_.params,oo_.steady_state, ...
+ M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
+ M_.NNZDerivatives(1));
if max(abs(residuals))< options_.dynatol.f
oo_.deterministic_simulation.status = 1;
oo_.endo_simul=real(oo_.endo_simul);
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
index da267b7dae697716ccffc3b4e8537c01375d33d4..c01333f0bc18840c50010550aa08389c7bfdb123 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
@@ -81,8 +81,8 @@ else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
elseif M_.maximum_endo_lag == 0 % Purely forward model
- [oo_.endo_simul, oo_.deterministic_simulation] = ...
- sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
+ [oo_.endo_simul, oo_.deterministic_simulation] = ...
+ sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
else % General case
switch options_.stack_solve_algo
case 0
diff --git a/matlab/perfect-foresight-models/private/initialize_stacked_problem.m b/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
index 64beba34e2734456a69da08bcee6a4ecea70b88c..3f4ead4d12632b378e6494de59ab2f076545991f 100644
--- a/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
+++ b/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
@@ -43,7 +43,7 @@ function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, .
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
periods = options.periods;
if (options.solve_algo == 10)
if ~isfield(options.lmmcp,'lb')
diff --git a/matlab/perfect-foresight-models/private/simulation_core.m b/matlab/perfect-foresight-models/private/simulation_core.m
index b52ecad172b4fc167e7dd48a00f7536532ece602..1b8b1d51ecc5fd9e4aed943b19cc3bffbece95bd 100644
--- a/matlab/perfect-foresight-models/private/simulation_core.m
+++ b/matlab/perfect-foresight-models/private/simulation_core.m
@@ -66,8 +66,8 @@ else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
elseif M_.maximum_endo_lag == 0 % Purely forward model
- [oo_.endo_simul, oo_.deterministic_simulation] = ...
- sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
+ [oo_.endo_simul, oo_.deterministic_simulation] = ...
+ sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
else % General case
if options_.stack_solve_algo == 0
if options_.linear_approximation
diff --git a/matlab/perfect-foresight-models/sim1.m b/matlab/perfect-foresight-models/sim1.m
index 1a173b2115580e422a6bcb67e74bf254a8f2ba51..f00bb43f8c95ff36185dacf642dc0f3ecb97a686 100644
--- a/matlab/perfect-foresight-models/sim1.m
+++ b/matlab/perfect-foresight-models/sim1.m
@@ -228,108 +228,107 @@ if verbose
end
function x = lin_solve( A, b,verbose)
- if norm( b ) < sqrt( eps ) % then x = 0 is a solution
- x = 0;
- return
- end
-
- x = A\b;
- x( ~isfinite( x ) ) = 0;
- relres = norm( b - A * x ) / norm( b );
- if relres > 1e-6 && verbose
- fprintf( 'WARNING : Failed to find a solution to the linear system.\n' );
- end
-
+if norm( b ) < sqrt( eps ) % then x = 0 is a solution
+ x = 0;
+ return
+end
+
+x = A\b;
+x( ~isfinite( x ) ) = 0;
+relres = norm( b - A * x ) / norm( b );
+if relres > 1e-6 && verbose
+ fprintf( 'WARNING : Failed to find a solution to the linear system.\n' );
+end
+
function [ x, flag, relres ] = lin_solve_robust( A, b , verbose)
- if norm( b ) < sqrt( eps ) % then x = 0 is a solution
- x = 0;
- flag = 0;
- relres = 0;
- return
- end
-
- x = A\b;
- x( ~isfinite( x ) ) = 0;
- [ x, flag, relres ] = bicgstab( A, b, [], [], [], [], x ); % returns immediately if x is a solution
- if flag == 0
- return
- end
+if norm( b ) < sqrt( eps ) % then x = 0 is a solution
+ x = 0;
+ flag = 0;
+ relres = 0;
+ return
+end
- disp( relres );
+x = A\b;
+x( ~isfinite( x ) ) = 0;
+[ x, flag, relres ] = bicgstab( A, b, [], [], [], [], x ); % returns immediately if x is a solution
+if flag == 0
+ return
+end
- if verbose
- fprintf( 'Initial bicgstab failed, trying alternative start point.\n' );
- end
- old_x = x;
- old_relres = relres;
- [ x, flag, relres ] = bicgstab( A, b );
- if flag == 0
- return
- end
+disp( relres );
- if verbose
- fprintf( 'Alternative start point also failed with bicgstab, trying gmres.\n' );
- end
- if old_relres < relres
- x = old_x;
- end
- [ x, flag, relres ] = gmres( A, b, [], [], [], [], [], x );
- if flag == 0
- return
- end
+if verbose
+ fprintf( 'Initial bicgstab failed, trying alternative start point.\n' );
+end
+old_x = x;
+old_relres = relres;
+[ x, flag, relres ] = bicgstab( A, b );
+if flag == 0
+ return
+end
- if verbose
- fprintf( 'Initial gmres failed, trying alternative start point.\n' );
- end
- old_x = x;
- old_relres = relres;
- [ x, flag, relres ] = gmres( A, b );
- if flag == 0
- return
- end
+if verbose
+ fprintf( 'Alternative start point also failed with bicgstab, trying gmres.\n' );
+end
+if old_relres < relres
+ x = old_x;
+end
+[ x, flag, relres ] = gmres( A, b, [], [], [], [], [], x );
+if flag == 0
+ return
+end
+
+if verbose
+ fprintf( 'Initial gmres failed, trying alternative start point.\n' );
+end
+old_x = x;
+old_relres = relres;
+[ x, flag, relres ] = gmres( A, b );
+if flag == 0
+ return
+end
+
+if verbose
+ fprintf( 'Alternative start point also failed with gmres, using the (SLOW) Moore-Penrose Pseudo-Inverse.\n' );
+end
+if old_relres < relres
+ x = old_x;
+ relres = old_relres;
+end
+old_x = x;
+old_relres = relres;
+x = pinv( full( A ) ) * b;
+relres = norm( b - A * x ) / norm( b );
+if old_relres < relres
+ x = old_x;
+ relres = old_relres;
+end
+flag = relres > 1e-6;
+if flag ~= 0 && verbose
+ fprintf( 'WARNING : Failed to find a solution to the linear system\n' );
+end
- if verbose
- fprintf( 'Alternative start point also failed with gmres, using the (SLOW) Moore-Penrose Pseudo-Inverse.\n' );
- end
- if old_relres < relres
- x = old_x;
- relres = old_relres;
- end
- old_x = x;
- old_relres = relres;
- x = pinv( full( A ) ) * b;
- relres = norm( b - A * x ) / norm( b );
- if old_relres < relres
- x = old_x;
- relres = old_relres;
- end
- flag = relres > 1e-6;
- if flag ~= 0 && verbose
- fprintf( 'WARNING : Failed to find a solution to the linear system\n' );
- end
-
function display_critical_variables(dyy, M)
- if any(isnan(dyy))
- indx = find(any(isnan(dyy)));
- endo_names=cellstr(M.endo_names(indx,:));
- disp('Last iteration provided NaN for the following variables:')
- fprintf('%s, ',endo_names{:}),
- fprintf('\n'),
- end
- if any(isinf(dyy))
- indx = find(any(isinf(dyy)));
- endo_names=cellstr(M.endo_names(indx,:));
- disp('Last iteration diverged (Inf) for the following variables:')
- fprintf('%s, ',endo_names{:}),
- fprintf('\n'),
- end
- if any(~isreal(dyy))
- indx = find(any(~isreal(dyy)));
- endo_names=cellstr(M.endo_names(indx,:));
- disp('Last iteration provided complex number for the following variables:')
- fprintf('%s, ',endo_names{:}),
- fprintf('\n'),
- end
+if any(isnan(dyy))
+ indx = find(any(isnan(dyy)));
+ endo_names=cellstr(M.endo_names(indx,:));
+ disp('Last iteration provided NaN for the following variables:')
+ fprintf('%s, ',endo_names{:}),
+ fprintf('\n'),
+end
+if any(isinf(dyy))
+ indx = find(any(isinf(dyy)));
+ endo_names=cellstr(M.endo_names(indx,:));
+ disp('Last iteration diverged (Inf) for the following variables:')
+ fprintf('%s, ',endo_names{:}),
+ fprintf('\n'),
+end
+if any(~isreal(dyy))
+ indx = find(any(~isreal(dyy)));
+ endo_names=cellstr(M.endo_names(indx,:));
+ disp('Last iteration provided complex number for the following variables:')
+ fprintf('%s, ',endo_names{:}),
+ fprintf('\n'),
+end
-
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/sim1_purely_backward.m b/matlab/perfect-foresight-models/sim1_purely_backward.m
index abc871a6852d76861d1f749e9c0023a8aef33970..b4f1decd447546ceea25a1b9098f426c29114301 100644
--- a/matlab/perfect-foresight-models/sim1_purely_backward.m
+++ b/matlab/perfect-foresight-models/sim1_purely_backward.m
@@ -28,12 +28,12 @@ else
nyb = 0;
iyb = [];
end
-
+
if ny0 ~= M.endo_nbr
error('All endogenous variables must appear at the current period!')
end
-
+
dynamicmodel = str2func([M.fname,'_dynamic']);
info.status = 1;
diff --git a/matlab/perfect-foresight-models/sim1_purely_forward.m b/matlab/perfect-foresight-models/sim1_purely_forward.m
index ca2b2b5b2770a88af1e9fb388902788546ceb317..5032e0498808f89e4d86bf4b3a9ce98658fc1b96 100644
--- a/matlab/perfect-foresight-models/sim1_purely_forward.m
+++ b/matlab/perfect-foresight-models/sim1_purely_forward.m
@@ -24,7 +24,7 @@ iyf = find(M.lead_lag_incidence(2,:)>0); % Indices of variables at next period
if ny0 ~= M.endo_nbr
error('All endogenous variables must appear at the current period!')
end
-
+
dynamicmodel = str2func([M.fname,'_dynamic']);
info.status = 1;
diff --git a/matlab/perfect-foresight-models/solve_stacked_linear_problem.m b/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
index 52a2efe48c55598a37ba1cd771813167fbdabfbb..cd50c1cbeb6b06ccca89c6f6be74789674fff792 100644
--- a/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
@@ -42,11 +42,11 @@ z = bsxfun(@minus, z, steadystate_y);
x = bsxfun(@minus, exogenousvariables, steadystate_x');
[y, check] = dynare_solve(@linear_perfect_foresight_problem,z(:), options, ...
- jacobian, y0-steadystate_y, yT-steadystate_y, ...
- x, M.params, steadystate_y, ...
- M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
- i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
- M.NNZDerivatives(1), jendo, jexog);
+ jacobian, y0-steadystate_y, yT-steadystate_y, ...
+ x, M.params, steadystate_y, ...
+ M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
+ M.NNZDerivatives(1), jendo, jexog);
if all(imag(y)<.1*options.dynatol.x)
if ~isreal(y)
diff --git a/matlab/perfect-foresight-models/solve_stacked_problem.m b/matlab/perfect-foresight-models/solve_stacked_problem.m
index 7a1835ba6bc52bd0e19f9b150d4e29be86e1334a..1c29f565ef81f5f54043e1db9ef3e1dfd357f998 100644
--- a/matlab/perfect-foresight-models/solve_stacked_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_problem.m
@@ -29,7 +29,7 @@ function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
[options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, dynamicmodel] = ...
initialize_stacked_problem(endogenousvariables, options, M, steadystate);
diff --git a/matlab/plot_identification.m b/matlab/plot_identification.m
index f699d2e424d0f112c53d5e111d1a5f84d5a3c27d..0c1f48b2cc63c06e2f65bb6f26b8d3b26b28e931 100644
--- a/matlab/plot_identification.m
+++ b/matlab/plot_identification.m
@@ -41,7 +41,7 @@ global M_ options_
if nargin <10 || isempty(tit_TeX)
tit_TeX=tittxt;
end
-
+
if nargin <11
name_TeX=name;
end
@@ -169,9 +169,9 @@ if SampleSize == 1,
% identificaton patterns
for j=1:size(idemoments.cosnJ,2),
pax=NaN(nparam,nparam);
-% fprintf('\n')
-% disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
-% fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
+ % fprintf('\n')
+ % disp(['Collinearity patterns with ', int2str(j) ,' parameter(s)'])
+ % fprintf('%-15s [%-*s] %10s\n','Parameter',(15+1)*j,' Expl. params ','cosn')
for i=1:nparam,
namx='';
for in=1:j,
@@ -183,7 +183,7 @@ if SampleSize == 1,
pax(i,dumpindx)=idemoments.cosnJ(i,j);
end
end
-% fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments.cosnJ(i,j))
+ % fprintf('%-15s [%s] %10.3f\n',name{i},namx,idemoments.cosnJ(i,j))
end
hh = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Collinearity patterns with ', int2str(j) ,' parameter(s)']);
imagesc(pax,[0 1]);
@@ -234,8 +234,8 @@ if SampleSize == 1,
tex_tit_2=[tittxt,' - Identification patterns (Information matrix): HIGHEST SV'];
end
else
-% S = idemoments.S;
-% V = idemoments.V;
+ % S = idemoments.S;
+ % V = idemoments.V;
if nparam<5,
f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - Identification patterns (moments Information matrix)']);
tex_tit_1=[tittxt,' - Identification patterns (moments Information matrix)'];
@@ -317,7 +317,7 @@ else
mmm1 = [NaN(offset,1); mmm1./max(mmm1)];
mmm=[mmm mmm1];
end
-
+
bar(mmm(is,:))
set(gca,'xlim',[0 nparam+1])
set(gca,'xticklabel','')
@@ -349,7 +349,7 @@ else
skipline()
disp('Press ENTER to display advanced diagnostics'), pause(5),
end
-% options_.nograph=1;
+ % options_.nograph=1;
hh = dyn_figure(options_.nodisplay,'Name','MC Condition Number');
subplot(221)
hist(log10(idemodel.cond))
@@ -386,16 +386,16 @@ else
options_mcf.title = 'MC Highest Condition Number Model Moments';
[dum,is]=sort(idemoments.cond);
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, options_);
-% [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
-% for j=1:nparam,
-% % ibeh=find(idemoments.Mco(j,:)<0.9);
-% % inonbeh=find(idemoments.Mco(j,:)>=0.9);
-% % if ~isempty(ibeh) && ~isempty(inonbeh)
-% % [proba, dproba] = stab_map_1(params, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
-% % end
-% [~,is]=sort(idemoments.Mco(:,j));
-% [proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), ['MC_HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName, 0.15);
-% end
+ % [proba, dproba] = stab_map_1(idemoments.Mco', is(1:ncut), is(ncut+1:end), 'HighestCondNumberMoments_vs_Mco', 1, [], IdentifDirectoryName);
+ % for j=1:nparam,
+ % % ibeh=find(idemoments.Mco(j,:)<0.9);
+ % % inonbeh=find(idemoments.Mco(j,:)>=0.9);
+ % % if ~isempty(ibeh) && ~isempty(inonbeh)
+ % % [proba, dproba] = stab_map_1(params, ibeh, inonbeh, ['HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName);
+ % % end
+ % [~,is]=sort(idemoments.Mco(:,j));
+ % [proba, dproba] = stab_map_1(params, is(1:ncut), is(ncut+1:end), ['MC_HighestMultiCollinearity_',name{j}], 1, [], IdentifDirectoryName, 0.15);
+ % end
if nparam<5,
f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
diff --git a/matlab/plot_shock_decomposition.m b/matlab/plot_shock_decomposition.m
index 15c97506ac95e6c16cad20f429809ca83ed05a67..81602d0590cc05cb95435bc2b73cb1807bcbbdb0 100644
--- a/matlab/plot_shock_decomposition.m
+++ b/matlab/plot_shock_decomposition.m
@@ -76,7 +76,7 @@ if vintage_,
end
initial_date = options_.initial_date;
-
+
if isfield(options_.plot_shock_decomp,'q2a'), % private trap for aoa calls
q2a=options_.plot_shock_decomp.q2a;
if isstruct(q2a) && isempty(fieldnames(q2a)),
@@ -88,59 +88,59 @@ end
switch realtime_
- case 0
- z = oo_.shock_decomposition;
- fig_name1=fig_name;
+ case 0
+ z = oo_.shock_decomposition;
+ fig_name1=fig_name;
- case 1 % realtime
- if vintage_
- z = oo_.realtime_shock_decomposition.(['time_' int2str(vintage_)]);
- fig_name1=[fig_name ' realtime (vintage ' char(initial_date+vintage_-1) ')'];
- else
- z = oo_.realtime_shock_decomposition.pool;
- fig_name1=[fig_name ' realtime (rolling)'];
- end
+ case 1 % realtime
+ if vintage_
+ z = oo_.realtime_shock_decomposition.(['time_' int2str(vintage_)]);
+ fig_name1=[fig_name ' realtime (vintage ' char(initial_date+vintage_-1) ')'];
+ else
+ z = oo_.realtime_shock_decomposition.pool;
+ fig_name1=[fig_name ' realtime (rolling)'];
+ end
- case 2 % conditional
- if vintage_
- z = oo_.realtime_conditional_shock_decomposition.(['time_' int2str(vintage_)]);
- initial_date = options_.initial_date+vintage_-1;
- fig_name1=[fig_name ' ' int2str(forecast_) '-step ahead conditional forecast (given ' char(initial_date) ')'];
- else
- z = oo_.conditional_shock_decomposition.pool;
- fig_name1=[fig_name ' 1-step ahead conditional forecast (rolling)'];
- end
-
- case 3 % forecast
- if vintage_
- z = oo_.realtime_forecast_shock_decomposition.(['time_' int2str(vintage_)]);
- initial_date = options_.initial_date+vintage_-1;
- fig_name1=[fig_name ' ' int2str(forecast_) '-step ahead forecast (given ' char(initial_date) ')'];
- else
- z = oo_.realtime_forecast_shock_decomposition.pool;
- fig_name1=[fig_name ' 1-step ahead forecast (rolling)'];
- end
+ case 2 % conditional
+ if vintage_
+ z = oo_.realtime_conditional_shock_decomposition.(['time_' int2str(vintage_)]);
+ initial_date = options_.initial_date+vintage_-1;
+ fig_name1=[fig_name ' ' int2str(forecast_) '-step ahead conditional forecast (given ' char(initial_date) ')'];
+ else
+ z = oo_.conditional_shock_decomposition.pool;
+ fig_name1=[fig_name ' 1-step ahead conditional forecast (rolling)'];
+ end
+
+ case 3 % forecast
+ if vintage_
+ z = oo_.realtime_forecast_shock_decomposition.(['time_' int2str(vintage_)]);
+ initial_date = options_.initial_date+vintage_-1;
+ fig_name1=[fig_name ' ' int2str(forecast_) '-step ahead forecast (given ' char(initial_date) ')'];
+ else
+ z = oo_.realtime_forecast_shock_decomposition.pool;
+ fig_name1=[fig_name ' 1-step ahead forecast (rolling)'];
+ end
end
steady_state = oo_.steady_state;
if isequal(type,'aoa') && isstruct(q2a) && realtime_
- if isempty(initial_date),
+ if isempty(initial_date),
+ t0=1;
+ initial_date = dates('1Y');
+ else
+ initial_date0 = dates([int2str(initial_date.time(1)) 'Y']);
+ if initial_date.time(2)==1,
t0=1;
- initial_date = dates('1Y');
+ initial_date1=initial_date0;
else
- initial_date0 = dates([int2str(initial_date.time(1)) 'Y']);
- if initial_date.time(2)==1,
- t0=1;
- initial_date1=initial_date0;
- else
- t0=(4-initial_date.time(2)+2);
- initial_date1=initial_date0+1;
- end
+ t0=(4-initial_date.time(2)+2);
+ initial_date1=initial_date0+1;
end
- t0=min(options_.plot_shock_decomp.save_realtime);
- ini1 = initial_date+t0-1;
- t0=t0+(4-ini1.time(2));
+ end
+ t0=min(options_.plot_shock_decomp.save_realtime);
+ ini1 = initial_date+t0-1;
+ t0=t0+(4-ini1.time(2));
if ~isfield(q2a,'var_type'), % private trap for aoa calls
q2a.var_type=1;
end
@@ -160,20 +160,20 @@ if isequal(type,'aoa') && isstruct(q2a) && realtime_
q2a.plot=1; % growth rate
end
-% if isstruct(q2a.aux) && ischar(q2a.aux.y)
-% opts=options_;
-% opts.plot_shock_decomp.type='qoq';
-% [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,opts,q2a.aux.y);
-% q2a.aux.y=y_aux;
-% q2a.aux.yss=steady_state_aux;
-% end
+ % if isstruct(q2a.aux) && ischar(q2a.aux.y)
+ % opts=options_;
+ % opts.plot_shock_decomp.type='qoq';
+ % [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,opts,q2a.aux.y);
+ % q2a.aux.y=y_aux;
+ % q2a.aux.yss=steady_state_aux;
+ % end
[za, endo_names, endo_names_tex, steady_state, i_var, oo_] = ...
annualized_shock_decomposition(oo_,M_, options_, i_var, t0, options_.nobs, realtime_, vintage_, steady_state,q2a);
-% if realtime_<2
-% initial_date = initial_date1;
-% else
-% initial_date = initial_date0;
-% end
+ % if realtime_<2
+ % initial_date = initial_date1;
+ % else
+ % initial_date = initial_date0;
+ % end
end
@@ -189,7 +189,7 @@ if options_.plot_shock_decomp.use_shock_groups
fig_name=[fig_name ' group ' options_.plot_shock_decomp.use_shock_groups];
shock_names = shock_ind;
for i=1:ngroups,
- shock_names{i} = (shock_groups.(shock_ind{i}).label);
+ shock_names{i} = (shock_groups.(shock_ind{i}).label);
end
zz = zeros(endo_nbr,ngroups+2,gend);
kcum=[];
@@ -215,10 +215,10 @@ else
shock_names = M_.exo_names;
end
- func = @(x) colorspace('RGB->Lab',x);
- MAP = distinguishable_colors(size(z,2)-1,'w',func);
+func = @(x) colorspace('RGB->Lab',x);
+MAP = distinguishable_colors(size(z,2)-1,'w',func);
% MAP = [MAP; MAP(end,:)];
- MAP(end,:) = [0.7 0.7 0.7];
+MAP(end,:) = [0.7 0.7 0.7];
% MAP = [MAP; [0.7 0.7 0.7]; [0.3 0.3 0.3]];
if isempty(options_.plot_shock_decomp.colormap),
@@ -227,36 +227,36 @@ end
switch type
- case '' % default
+ case '' % default
- case 'qoq'
+ case 'qoq'
- case 'yoy'
- z=z(:,:,1:end-3)+z(:,:,2:end-2)+z(:,:,3:end-1)+z(:,:,4:end);
- if ~isempty(initial_date),
- initial_date = initial_date+3;
- else
- initial_date = dates('1Q4');
- end
- steady_state = 4*steady_state;
-
- case 'aoa'
+ case 'yoy'
+ z=z(:,:,1:end-3)+z(:,:,2:end-2)+z(:,:,3:end-1)+z(:,:,4:end);
+ if ~isempty(initial_date),
+ initial_date = initial_date+3;
+ else
+ initial_date = dates('1Q4');
+ end
+ steady_state = 4*steady_state;
+
+ case 'aoa'
- if isempty(initial_date),
- t0=4;
- initial_date = dates('1Y');
+ if isempty(initial_date),
+ t0=4;
+ initial_date = dates('1Y');
+ else
+ initial_date0 = dates([int2str(initial_date.time(1)) 'Y']);
+ if initial_date.time(2)==1,
+ t0=1;
+ initial_date1=initial_date0;
else
- initial_date0 = dates([int2str(initial_date.time(1)) 'Y']);
- if initial_date.time(2)==1,
- t0=1;
- initial_date1=initial_date0;
- else
- t0=(4-initial_date.time(2)+2);
- initial_date1=initial_date0+1;
- end
+ t0=(4-initial_date.time(2)+2);
+ initial_date1=initial_date0+1;
end
- if isstruct(q2a)
- if realtime_ == 0
+ end
+ if isstruct(q2a)
+ if realtime_ == 0
if ~isfield(q2a,'var_type'), % private trap for aoa calls
q2a.var_type=1;
end
@@ -285,33 +285,33 @@ switch type
end
[za, endo_names, endo_names_tex, steady_state, i_var, oo_] = ...
annualized_shock_decomposition(z,M_, options_, i_var, t0, options_.nobs, realtime_, vintage_, steady_state,q2a);
- end
- z = za;
- M_.endo_names = endo_names;
- M_.endo_names_tex = endo_names_tex;
-% endo_nbr = size(z,1);
- if realtime_<2
- initial_date = initial_date1;
- else
- initial_date = initial_date0;
- end
+ end
+ z = za;
+ M_.endo_names = endo_names;
+ M_.endo_names_tex = endo_names_tex;
+ % endo_nbr = size(z,1);
+ if realtime_<2
+ initial_date = initial_date1;
else
- t0=4-initial_date.time(2)+1;
initial_date = initial_date0;
- z=z(:,:,t0:4:end);
- end
-
- if ~isempty(options_.plot_shock_decomp.plot_init_date)
- options_.plot_shock_decomp.plot_init_date = dates([int2str(options_.plot_shock_decomp.plot_init_date.time(1)) 'Y']);
end
- if ~isempty(options_.plot_shock_decomp.plot_end_date)
- options_.plot_shock_decomp.plot_end_date = dates([int2str(options_.plot_shock_decomp.plot_end_date.time(1)) 'Y']);
- end
-
-
- otherwise
+ else
+ t0=4-initial_date.time(2)+1;
+ initial_date = initial_date0;
+ z=z(:,:,t0:4:end);
+ end
+
+ if ~isempty(options_.plot_shock_decomp.plot_init_date)
+ options_.plot_shock_decomp.plot_init_date = dates([int2str(options_.plot_shock_decomp.plot_init_date.time(1)) 'Y']);
+ end
+ if ~isempty(options_.plot_shock_decomp.plot_end_date)
+ options_.plot_shock_decomp.plot_end_date = dates([int2str(options_.plot_shock_decomp.plot_end_date.time(1)) 'Y']);
+ end
+
+
+ otherwise
- error('plot_shock_decomposition:: Wrong type')
+ error('plot_shock_decomposition:: Wrong type')
end
if steadystate
diff --git a/matlab/pm3.m b/matlab/pm3.m
index ae0f474d16b03363ce7ca1fd9a09168c2b6caf7c..d5502d29bd0ae3c4617b629ed26741a18f9c5f66 100644
--- a/matlab/pm3.m
+++ b/matlab/pm3.m
@@ -63,7 +63,7 @@ if options_.TeX
if isempty(tit_tex),
tit_tex=names1;
end
-
+
varlist_TeX = [];
for i=1:nvar
if i==1
@@ -289,112 +289,112 @@ if strcmp(var_type,'_trend_coeff') || max(max(abs(Mean(:,:))))<=10^(-6) || all(a
return %not do plots
end
%%
-%% Finally I build the plots.
+%% Finally I build the plots.
%%
if ~options_.nograph && ~options_.no_graph.posterior
-% Block of code executed in parallel, with the exception of file
-% .tex generation always run sequentially. This portion of code is execute in parallel by
-% pm3_core1.m function.
+ % Block of code executed in parallel, with the exception of file
+ % .tex generation always run sequentially. This portion of code is execute in parallel by
+ % pm3_core1.m function.
-% %%%%%%%%% PARALLEL BLOCK % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%
-% %%% The file .TeX! are not saved in parallel.
+ % %%%%%%%%% PARALLEL BLOCK % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ %
+ % %%% The file .TeX! are not saved in parallel.
-% Store the variable mandatory for local/remote parallel computing.
+ % Store the variable mandatory for local/remote parallel computing.
-localVars=[];
+ localVars=[];
-localVars.tit1=tit1;
-localVars.nn=nn;
-localVars.n2=n2;
-localVars.Distrib=Distrib;
-localVars.varlist=varlist;
-localVars.MaxNumberOfPlotsPerFigure=MaxNumberOfPlotsPerFigure;
-localVars.name3=name3;
-localVars.tit3=tit3;
-localVars.Mean=Mean;
-% Like sequential execution!
-nvar0=nvar;
+ localVars.tit1=tit1;
+ localVars.nn=nn;
+ localVars.n2=n2;
+ localVars.Distrib=Distrib;
+ localVars.varlist=varlist;
+ localVars.MaxNumberOfPlotsPerFigure=MaxNumberOfPlotsPerFigure;
+ localVars.name3=name3;
+ localVars.tit3=tit3;
+ localVars.Mean=Mean;
+ % Like sequential execution!
+ nvar0=nvar;
-if ~isoctave
- % Commenting for testing!
- if isnumeric(options_.parallel) || ceil(size(varlist,1)/MaxNumberOfPlotsPerFigure)<4,
- fout = pm3_core(localVars,1,nvar,0);
-
- % Parallel execution!
- else
- isRemoteOctave = 0;
- for indPC=1:length(options_.parallel),
- isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
- end
- if isRemoteOctave
+ if ~isoctave
+ % Commenting for testing!
+ if isnumeric(options_.parallel) || ceil(size(varlist,1)/MaxNumberOfPlotsPerFigure)<4,
fout = pm3_core(localVars,1,nvar,0);
+
+ % Parallel execution!
else
- globalVars = struct('M_',M_, ...
- 'options_', options_, ...
- 'oo_', oo_);
- [fout, nvar0, totCPU] = masterParallel(options_.parallel, 1, nvar, [],'pm3_core', localVars,globalVars, options_.parallel_info);
+ isRemoteOctave = 0;
+ for indPC=1:length(options_.parallel),
+ isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
+ end
+ if isRemoteOctave
+ fout = pm3_core(localVars,1,nvar,0);
+ else
+ globalVars = struct('M_',M_, ...
+ 'options_', options_, ...
+ 'oo_', oo_);
+ [fout, nvar0, totCPU] = masterParallel(options_.parallel, 1, nvar, [],'pm3_core', localVars,globalVars, options_.parallel_info);
+ end
end
+ else
+ % For the time being in Octave enviroment the pm3.m is executed only in
+ % serial modality, to avoid problem with the plots.
+
+ fout = pm3_core(localVars,1,nvar,0);
end
-else
- % For the time being in Octave enviroment the pm3.m is executed only in
- % serial modality, to avoid problem with the plots.
-
- fout = pm3_core(localVars,1,nvar,0);
-end
-subplotnum = 0;
+ subplotnum = 0;
-if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
- fidTeX = fopen([M_.dname '/Output/' M_.fname '_' name3 '.tex'],'w');
- fprintf(fidTeX,'%% TeX eps-loader file generated by Dynare.\n');
- fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
- fprintf(fidTeX,' \n');
- nvar0=cumsum(nvar0);
+ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+ fidTeX = fopen([M_.dname '/Output/' M_.fname '_' name3 '.tex'],'w');
+ fprintf(fidTeX,'%% TeX eps-loader file generated by Dynare.\n');
+ fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
+ fprintf(fidTeX,' \n');
+ nvar0=cumsum(nvar0);
- i=0;
- for j=1:length(nvar0),
-
- NAMES = [];
- TEXNAMES = [];
- nvar=nvar0(j);
- while i<nvar,
- i=i+1;
- if max(abs(Mean(:,i))) > 10^(-6)
- subplotnum = subplotnum+1;
- name = deblank(varlist(i,:));
- texname = deblank(varlist_TeX(i,:));
- if subplotnum==1
- NAMES = name;
- TEXNAMES = ['$' texname '$'];
- else
- NAMES = char(NAMES,name);
- TEXNAMES = char(TEXNAMES,['$' texname '$']);
- end
- end
- if subplotnum == MaxNumberOfPlotsPerFigure || i == nvar
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- for jj = 1:size(TEXNAMES,1)
- fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
- end
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,['\\includegraphics[width=%2.2f\\textwidth]{%s/Output/%s_' name3 '_%s}\n'],options_.figures.textwidth*min(subplotnum/nn,1),M_.dname,M_.fname,deblank(tit3(i,:)));
- fprintf(fidTeX,'\\label{Fig:%s:%s}\n',name3,deblank(tit3(i,:)));
- fprintf(fidTeX,'\\caption{%s}\n',tit1);
- fprintf(fidTeX,'\\end{figure}\n');
- fprintf(fidTeX,' \n');
- subplotnum = 0;
+ i=0;
+ for j=1:length(nvar0),
+
NAMES = [];
TEXNAMES = [];
+ nvar=nvar0(j);
+ while i<nvar,
+ i=i+1;
+ if max(abs(Mean(:,i))) > 10^(-6)
+ subplotnum = subplotnum+1;
+ name = deblank(varlist(i,:));
+ texname = deblank(varlist_TeX(i,:));
+ if subplotnum==1
+ NAMES = name;
+ TEXNAMES = ['$' texname '$'];
+ else
+ NAMES = char(NAMES,name);
+ TEXNAMES = char(TEXNAMES,['$' texname '$']);
+ end
+ end
+ if subplotnum == MaxNumberOfPlotsPerFigure || i == nvar
+ fprintf(fidTeX,'\\begin{figure}[H]\n');
+ for jj = 1:size(TEXNAMES,1)
+ fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
+ end
+ fprintf(fidTeX,'\\centering \n');
+ fprintf(fidTeX,['\\includegraphics[width=%2.2f\\textwidth]{%s/Output/%s_' name3 '_%s}\n'],options_.figures.textwidth*min(subplotnum/nn,1),M_.dname,M_.fname,deblank(tit3(i,:)));
+ fprintf(fidTeX,'\\label{Fig:%s:%s}\n',name3,deblank(tit3(i,:)));
+ fprintf(fidTeX,'\\caption{%s}\n',tit1);
+ fprintf(fidTeX,'\\end{figure}\n');
+ fprintf(fidTeX,' \n');
+ subplotnum = 0;
+ NAMES = [];
+ TEXNAMES = [];
+ end
+ end
end
+ fprintf(fidTeX,'%% End of TeX file.\n');
+ fclose(fidTeX);
end
- end
- fprintf(fidTeX,'%% End of TeX file.\n');
- fclose(fidTeX);
-end
end
fprintf(['Estimation::mcmc: ' tit1 ', done!\n']);
diff --git a/matlab/pm3_core.m b/matlab/pm3_core.m
index 32052f83c48ea265709c4983da996d442b5f020a..2f925e5e373c246f2d029c4a8026ba97978c1933 100644
--- a/matlab/pm3_core.m
+++ b/matlab/pm3_core.m
@@ -120,8 +120,8 @@ for i=fpar:nvar
end
if whoiam,
-% waitbarString = [ 'Variable ' int2str(i) '/' int2str(nvar) ' done.'];
-% fMessageStatus((i-fpar+1)/(nvar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
+ % waitbarString = [ 'Variable ' int2str(i) '/' int2str(nvar) ' done.'];
+ % fMessageStatus((i-fpar+1)/(nvar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
dyn_waitbar((i-fpar+1)/(nvar-fpar+1),h);
end
diff --git a/matlab/posterior_sampler_initialization.m b/matlab/posterior_sampler_initialization.m
index f7927a173531a09c77c6749bad65344c04d0261c..0292b010d9877caa5a5e26fb808537dde313555a 100644
--- a/matlab/posterior_sampler_initialization.m
+++ b/matlab/posterior_sampler_initialization.m
@@ -347,9 +347,9 @@ elseif options_.mh_recover
if OldMhExists
LastLineNumberInThePreviousMh = record.MhDraws(end-1,3);% Number of lines in the last mh files of the previous session.
LastFileNumberInThePreviousMh = sum(record.MhDraws(1:end-1,2),1);% Number of mh files in the the previous sessions.
- %Test if the last mh files of the previous session were not full yet
+ %Test if the last mh files of the previous session were not full yet
if LastLineNumberInThePreviousMh < MAX_nruns%not full
- %store starting point if whole chain needs to be redone
+ %store starting point if whole chain needs to be redone
NewFile = ones(NumberOfBlocks,1)*LastFileNumberInThePreviousMh;
FirstLine = ones(NumberOfBlocks,1)*(LastLineNumberInThePreviousMh+1);
LastFileFullIndicator=0;
@@ -406,7 +406,7 @@ elseif options_.mh_recover
% How many mh-files are saved in this block?
NumberOfSavedMhFilesInTheCrashedBlck = NumberOfMhFilesPerBlock(FirstBlock);
ExistingDrawsInLastMCFile=0; %initialize: no MCMC draws of current MCMC are in file from last run
- % Check whether last present file is a file included in the last MCMC run
+ % Check whether last present file is a file included in the last MCMC run
if ~LastFileFullIndicator
if NumberOfSavedMhFilesInTheCrashedBlck==NewFile(FirstBlock) %only that last file exists, but no files from current MCMC
loaded_results=load([BaseName '_mh' int2str(NewFile(FirstBlock)) '_blck' int2str(FirstBlock) '.mat']);
@@ -425,13 +425,13 @@ elseif options_.mh_recover
NewFile(FirstBlock)=NewFile(FirstBlock)+1; %set first file to be created to next one
end
end
-% % Correct the number of saved mh files if the crashed Metropolis was not the first session (so
-% % that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
-% % of the current session).
-% if OldMhExists
-% NumberOfSavedMhFilesInTheCrashedBlck = NumberOfSavedMhFilesInTheCrashedBlck - LastFileNumberInThePreviousMh;
-% end
-% NumberOfSavedMhFiles = NumberOfSavedMhFilesInTheCrashedBlck+LastFileNumberInThePreviousMh;
+ % % Correct the number of saved mh files if the crashed Metropolis was not the first session (so
+ % % that NumberOfSavedMhFilesInTheCrashedBlck is the number of saved mh files in the crashed chain
+ % % of the current session).
+ % if OldMhExists
+ % NumberOfSavedMhFilesInTheCrashedBlck = NumberOfSavedMhFilesInTheCrashedBlck - LastFileNumberInThePreviousMh;
+ % end
+ % NumberOfSavedMhFiles = NumberOfSavedMhFilesInTheCrashedBlck+LastFileNumberInThePreviousMh;
% Correct initial conditions.
if NumberOfSavedMhFilesInTheCrashedBlck<ExpectedNumberOfMhFilesPerBlock
@@ -452,19 +452,19 @@ elseif options_.mh_recover
end
function [d,bayestopt_]=set_proposal_density_to_previous_value(record,options_,bayestopt_,d)
- if isfield(record,'ProposalCovariance') && isfield(record,'ProposalCovariance')
- if isfield(record,'MCMC_sampler')
- if ~strcmp(record.MCMC_sampler,options_.posterior_sampler_options.posterior_sampling_method)
- error(fprintf('Estimation::mcmc: The posterior_sampling_method differs from the one of the original chain. Please reset it to %s',record.MCMC_sampler))
- end
- end
- fprintf('Estimation::mcmc: Recovering the previous proposal density.\n')
- d=record.ProposalCovariance;
- bayestopt_.jscale=record.ProposalScaleVec;
- else
- if options_.mode_compute~=0
- fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by mode_compute\n.');
- elseif ~isempty(options_.mode_file)
- fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by the mode_file\n.');
- end
+if isfield(record,'ProposalCovariance') && isfield(record,'ProposalCovariance')
+ if isfield(record,'MCMC_sampler')
+ if ~strcmp(record.MCMC_sampler,options_.posterior_sampler_options.posterior_sampling_method)
+ error(fprintf('Estimation::mcmc: The posterior_sampling_method differs from the one of the original chain. Please reset it to %s',record.MCMC_sampler))
+ end
+ end
+ fprintf('Estimation::mcmc: Recovering the previous proposal density.\n')
+ d=record.ProposalCovariance;
+ bayestopt_.jscale=record.ProposalScaleVec;
+else
+ if options_.mode_compute~=0
+ fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by mode_compute\n.');
+ elseif ~isempty(options_.mode_file)
+ fprintf('Estimation::mcmc: No stored previous proposal density found, continuing with the one implied by the mode_file\n.');
end
+end
diff --git a/matlab/posterior_sampler_iteration.m b/matlab/posterior_sampler_iteration.m
index 09e5d8851060d425cacd040ff90e1b19232b0de9..14dc11b923beabf9b60fae937015c3f007fd6b85 100644
--- a/matlab/posterior_sampler_iteration.m
+++ b/matlab/posterior_sampler_iteration.m
@@ -1,173 +1,173 @@
-function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,varargin)
-
-% function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
-% posterior samplers
-%
-% INPUTS
-% posterior_sampler_options: posterior sampler options
-% options_: structure storing the options
-
-% OUTPUTS
-% posterior_sampler_options: checked posterior sampler options
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2015-16 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-
-posterior_sampling_method = sampler_options.posterior_sampling_method;
-mh_bounds = sampler_options.bounds;
-
-switch posterior_sampling_method
- case 'slice'
-
- [par, logpost, neval] = slice_sampler(TargetFun,last_draw, [mh_bounds.lb mh_bounds.ub], sampler_options,varargin{:});
- accepted = 1;
- case 'random_walk_metropolis_hastings'
- neval = 1;
- ProposalFun = sampler_options.proposal_distribution;
- proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
- n = sampler_options.n;
-
- par = feval(ProposalFun, last_draw, proposal_covariance_Cholesky_decomposition, n);
- if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
- try
- logpost = - feval(TargetFun, par(:),varargin{:});
- catch
- logpost = -inf;
- end
- else
- logpost = -inf;
- end
- r = logpost-last_posterior;
- if (logpost > -inf) && (log(rand) < r)
- accepted = 1;
- else
- accepted = 0;
- par = last_draw;
- logpost = last_posterior;
- end
- case 'tailored_random_block_metropolis_hastings'
- options_=varargin{3};
- bayestopt_=varargin{6};
- npar=length(last_draw);
- %% randomize indices for blocking in this iteration
- indices=randperm(npar)';
- blocks=[1; (1+cumsum((rand(length(indices)-1,1)>(1-sampler_options.new_block_probability))))];
- nblocks=blocks(end,1); %get number of blocks this iteration
- current_draw=last_draw'; %get starting point for current draw for updating
- blocked_draws_counter=0;
- accepted_draws_counter=0;
- for block_iter=1:nblocks
- blocked_draws_counter=blocked_draws_counter+1;
- nxopt=length(indices(blocks==block_iter,1)); %get size of current block
- par_start_current_block=current_draw(indices(blocks==block_iter,1));
- [xopt_current_block, fval, exitflag, hess_mat_optimizer, options_, Scale] = dynare_minimize_objective(@TaRB_optimizer_wrapper,par_start_current_block,sampler_options.mode_compute,options_,[mh_bounds.lb(indices(blocks==block_iter,1),1) mh_bounds.ub(indices(blocks==block_iter,1),1)],bayestopt_.name,bayestopt_,[],...
- current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
- varargin{:}); %inputs for objective
- %% covariance for proposal density
- hessian_mat = reshape(hessian('TaRB_optimizer_wrapper',xopt_current_block, ...
- options_.gstep,...
- current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
- varargin{:}),nxopt,nxopt);
-
- if any(any(isnan(hessian_mat))) || any(any(isinf(hessian_mat)))
- inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
- else
- inverse_hessian_mat=inv(hessian_mat); %get inverse Hessian
- if any(any((isnan(inverse_hessian_mat)))) || any(any((isinf(inverse_hessian_mat))))
- inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
- end
- end
- [proposal_covariance_Cholesky_decomposition_upper,negeigenvalues]=chol(inverse_hessian_mat);
- %if not positive definite, use generalized Cholesky of Eskow/Schnabel
- if negeigenvalues~=0
- proposal_covariance_Cholesky_decomposition_upper=chol_SE(inverse_hessian_mat,0);
- end
- proposal_covariance_Cholesky_decomposition_upper=proposal_covariance_Cholesky_decomposition_upper*diag(bayestopt_.jscale(indices(blocks==block_iter,1),:));
- %get proposal draw
- if strcmpi(sampler_options.proposal_distribution,'rand_multivariate_normal')
- n = nxopt;
- elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
- n = options_.student_degrees_of_freedom;
- end
-
- proposed_par = feval(sampler_options.proposal_distribution, xopt_current_block', proposal_covariance_Cholesky_decomposition_upper, n);
- % check whether draw is valid and compute posterior
- if all( proposed_par(:) > mh_bounds.lb(indices(blocks==block_iter,1),:) ) && all( proposed_par(:) < mh_bounds.ub(indices(blocks==block_iter,1),:) )
- try
- logpost = - feval('TaRB_optimizer_wrapper', proposed_par(:),...
- current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
- varargin{:});
- catch
- logpost = -inf;
- end
- else
- logpost = -inf;
- end
- %get ratio of proposal densities, required because proposal depends
- %on current mode via Hessian and is thus not symmetric anymore
- if strcmpi(sampler_options.proposal_distribution,'rand_multivariate_normal')
- proposal_density_proposed_move_forward=multivariate_normal_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
- proposal_density_proposed_move_backward=multivariate_normal_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
- elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
- proposal_density_proposed_move_forward=multivariate_student_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
- proposal_density_proposed_move_backward=multivariate_student_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
- end
- accprob=logpost-last_posterior+ log(proposal_density_proposed_move_backward)-log(proposal_density_proposed_move_forward); %Formula (6), Chib/Ramamurthy
-
- if (logpost > -inf) && (log(rand) < accprob)
- current_draw(indices(blocks==block_iter,1))=proposed_par;
- last_posterior=logpost;
- accepted_draws_counter =accepted_draws_counter +1;
- else %no updating
- %do nothing, keep old value
- end
- end
- accepted=accepted_draws_counter/blocked_draws_counter;
- par = current_draw;
- neval=1;
- case 'independent_metropolis_hastings'
- neval = 1;
- ProposalFun = sampler_options.proposal_distribution;
- ProposalDensity = sampler_options.ProposalDensity;
- proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
- n = sampler_options.n;
- xparam1 = sampler_options.xparam1';
- par = feval(ProposalFun, xparam1, proposal_covariance_Cholesky_decomposition, n);
- if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
- try
- logpost = - feval(TargetFun, par(:),varargin{:});
- catch
- logpost = -inf;
- end
- else
- logpost = -inf;
- end
- r = logpost - last_posterior + ...
- log(feval(ProposalDensity, last_draw, xparam1, proposal_covariance_Cholesky_decomposition, n)) - ...
- log(feval(ProposalDensity, par, xparam1, proposal_covariance_Cholesky_decomposition, n));
- if (logpost > -inf) && (log(rand) < r)
- accepted = 1;
- else
- accepted = 0;
- par = last_draw;
- logpost = last_posterior;
- end
+function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,varargin)
+
+% function [par, logpost, accepted, neval] = posterior_sampler_iteration(TargetFun,last_draw, last_posterior, sampler_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
+% posterior samplers
+%
+% INPUTS
+% posterior_sampler_options: posterior sampler options
+% options_: structure storing the options
+
+% OUTPUTS
+% posterior_sampler_options: checked posterior sampler options
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2015-16 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+
+posterior_sampling_method = sampler_options.posterior_sampling_method;
+mh_bounds = sampler_options.bounds;
+
+switch posterior_sampling_method
+ case 'slice'
+
+ [par, logpost, neval] = slice_sampler(TargetFun,last_draw, [mh_bounds.lb mh_bounds.ub], sampler_options,varargin{:});
+ accepted = 1;
+ case 'random_walk_metropolis_hastings'
+ neval = 1;
+ ProposalFun = sampler_options.proposal_distribution;
+ proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
+ n = sampler_options.n;
+
+ par = feval(ProposalFun, last_draw, proposal_covariance_Cholesky_decomposition, n);
+ if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
+ try
+ logpost = - feval(TargetFun, par(:),varargin{:});
+ catch
+ logpost = -inf;
+ end
+ else
+ logpost = -inf;
+ end
+ r = logpost-last_posterior;
+ if (logpost > -inf) && (log(rand) < r)
+ accepted = 1;
+ else
+ accepted = 0;
+ par = last_draw;
+ logpost = last_posterior;
+ end
+ case 'tailored_random_block_metropolis_hastings'
+ options_=varargin{3};
+ bayestopt_=varargin{6};
+ npar=length(last_draw);
+ %% randomize indices for blocking in this iteration
+ indices=randperm(npar)';
+ blocks=[1; (1+cumsum((rand(length(indices)-1,1)>(1-sampler_options.new_block_probability))))];
+ nblocks=blocks(end,1); %get number of blocks this iteration
+ current_draw=last_draw'; %get starting point for current draw for updating
+ blocked_draws_counter=0;
+ accepted_draws_counter=0;
+ for block_iter=1:nblocks
+ blocked_draws_counter=blocked_draws_counter+1;
+ nxopt=length(indices(blocks==block_iter,1)); %get size of current block
+ par_start_current_block=current_draw(indices(blocks==block_iter,1));
+ [xopt_current_block, fval, exitflag, hess_mat_optimizer, options_, Scale] = dynare_minimize_objective(@TaRB_optimizer_wrapper,par_start_current_block,sampler_options.mode_compute,options_,[mh_bounds.lb(indices(blocks==block_iter,1),1) mh_bounds.ub(indices(blocks==block_iter,1),1)],bayestopt_.name,bayestopt_,[],...
+ current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
+ varargin{:}); %inputs for objective
+ %% covariance for proposal density
+ hessian_mat = reshape(hessian('TaRB_optimizer_wrapper',xopt_current_block, ...
+ options_.gstep,...
+ current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
+ varargin{:}),nxopt,nxopt);
+
+ if any(any(isnan(hessian_mat))) || any(any(isinf(hessian_mat)))
+ inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
+ else
+ inverse_hessian_mat=inv(hessian_mat); %get inverse Hessian
+ if any(any((isnan(inverse_hessian_mat)))) || any(any((isinf(inverse_hessian_mat))))
+ inverse_hessian_mat=eye(nxopt)*1e-4; %use diagonal
+ end
+ end
+ [proposal_covariance_Cholesky_decomposition_upper,negeigenvalues]=chol(inverse_hessian_mat);
+ %if not positive definite, use generalized Cholesky of Eskow/Schnabel
+ if negeigenvalues~=0
+ proposal_covariance_Cholesky_decomposition_upper=chol_SE(inverse_hessian_mat,0);
+ end
+ proposal_covariance_Cholesky_decomposition_upper=proposal_covariance_Cholesky_decomposition_upper*diag(bayestopt_.jscale(indices(blocks==block_iter,1),:));
+ %get proposal draw
+ if strcmpi(sampler_options.proposal_distribution,'rand_multivariate_normal')
+ n = nxopt;
+ elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
+ n = options_.student_degrees_of_freedom;
+ end
+
+ proposed_par = feval(sampler_options.proposal_distribution, xopt_current_block', proposal_covariance_Cholesky_decomposition_upper, n);
+ % check whether draw is valid and compute posterior
+ if all( proposed_par(:) > mh_bounds.lb(indices(blocks==block_iter,1),:) ) && all( proposed_par(:) < mh_bounds.ub(indices(blocks==block_iter,1),:) )
+ try
+ logpost = - feval('TaRB_optimizer_wrapper', proposed_par(:),...
+ current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
+ varargin{:});
+ catch
+ logpost = -inf;
+ end
+ else
+ logpost = -inf;
+ end
+ %get ratio of proposal densities, required because proposal depends
+ %on current mode via Hessian and is thus not symmetric anymore
+ if strcmpi(sampler_options.proposal_distribution,'rand_multivariate_normal')
+ proposal_density_proposed_move_forward=multivariate_normal_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
+ proposal_density_proposed_move_backward=multivariate_normal_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
+ elseif strcmpi(sampler_options.proposal_distribution,'rand_multivariate_student')
+ proposal_density_proposed_move_forward=multivariate_student_pdf(proposed_par,xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
+ proposal_density_proposed_move_backward=multivariate_student_pdf(par_start_current_block',xopt_current_block',proposal_covariance_Cholesky_decomposition_upper,n);
+ end
+ accprob=logpost-last_posterior+ log(proposal_density_proposed_move_backward)-log(proposal_density_proposed_move_forward); %Formula (6), Chib/Ramamurthy
+
+ if (logpost > -inf) && (log(rand) < accprob)
+ current_draw(indices(blocks==block_iter,1))=proposed_par;
+ last_posterior=logpost;
+ accepted_draws_counter =accepted_draws_counter +1;
+ else %no updating
+ %do nothing, keep old value
+ end
+ end
+ accepted=accepted_draws_counter/blocked_draws_counter;
+ par = current_draw;
+ neval=1;
+ case 'independent_metropolis_hastings'
+ neval = 1;
+ ProposalFun = sampler_options.proposal_distribution;
+ ProposalDensity = sampler_options.ProposalDensity;
+ proposal_covariance_Cholesky_decomposition = sampler_options.proposal_covariance_Cholesky_decomposition;
+ n = sampler_options.n;
+ xparam1 = sampler_options.xparam1';
+ par = feval(ProposalFun, xparam1, proposal_covariance_Cholesky_decomposition, n);
+ if all( par(:) > mh_bounds.lb ) && all( par(:) < mh_bounds.ub )
+ try
+ logpost = - feval(TargetFun, par(:),varargin{:});
+ catch
+ logpost = -inf;
+ end
+ else
+ logpost = -inf;
+ end
+ r = logpost - last_posterior + ...
+ log(feval(ProposalDensity, last_draw, xparam1, proposal_covariance_Cholesky_decomposition, n)) - ...
+ log(feval(ProposalDensity, par, xparam1, proposal_covariance_Cholesky_decomposition, n));
+ if (logpost > -inf) && (log(rand) < r)
+ accepted = 1;
+ else
+ accepted = 0;
+ par = last_draw;
+ logpost = last_posterior;
+ end
end
\ No newline at end of file
diff --git a/matlab/print_info.m b/matlab/print_info.m
index e66b2e82a0983f82d5137da1ee6c6d56d8147093..3185e16bf80835cf19a58e72ab1231e5ef5588b5 100644
--- a/matlab/print_info.m
+++ b/matlab/print_info.m
@@ -54,15 +54,15 @@ if ~noprint
error('One of the eigenvalues is close to 0/0 (the absolute value of numerator and denominator is smaller than %s!\n If you believe that the model has a unique solution you can try to reduce the value of qz_zero_threshold.',num2str(DynareOptions.qz_zero_threshold))
case 8
if size(info,2)>=2
- global M_;
+ global M_;
disp_string=deblank(M_.param_names(info(2),:));
- for ii=1:length(info)-2
- disp_string=[disp_string,', ',deblank(M_.param_names(info(2+ii),:))];
- end
- error(['The Jacobian contains NaNs because the following parameters are NaN: '...
- disp_string])
+ for ii=1:length(info)-2
+ disp_string=[disp_string,', ',deblank(M_.param_names(info(2+ii),:))];
+ end
+ error(['The Jacobian contains NaNs because the following parameters are NaN: '...
+ disp_string])
else
- error(['The Jacobian contains NaNs. For more information, use options_.debug.'])
+ error(['The Jacobian contains NaNs. For more information, use options_.debug.'])
end
case 9
error(['k_order_pert was unable to compute the solution'])
diff --git a/matlab/print_table_prior.m b/matlab/print_table_prior.m
index 605adfabf7ff8aad771e4b829711a7a998b63b61..d84e69a9cc04e8bffaab6b1383236f62f7f829c1 100644
--- a/matlab/print_table_prior.m
+++ b/matlab/print_table_prior.m
@@ -134,25 +134,25 @@ skipline(2)
function format_string = build_format_string(PriorMode,PriorStandardDeviation,LowerBound,UpperBound)
- format_string = ['%s \t %6.4f \t'];
- if isnan(PriorMode)
- format_string = [ format_string , ' %s \t'];
- else
- format_string = [ format_string , ' %6.4f \t'];
- end
- if ~isnumeric(PriorStandardDeviation)
- format_string = [ format_string , ' %s \t'];
- else
- format_string = [ format_string , ' %6.4f \t'];
- end
- if ~isnumeric(LowerBound)
- format_string = [ format_string , ' %s \t'];
- else
- format_string = [ format_string , ' %6.4f \t'];
- end
- if ~isnumeric(UpperBound)
- format_string = [ format_string , ' %s \t'];
- else
- format_string = [ format_string , ' %6.4f \t'];
- end
- format_string = [ format_string , ' %6.4f \t %6.4f'];
\ No newline at end of file
+format_string = ['%s \t %6.4f \t'];
+if isnan(PriorMode)
+ format_string = [ format_string , ' %s \t'];
+else
+ format_string = [ format_string , ' %6.4f \t'];
+end
+if ~isnumeric(PriorStandardDeviation)
+ format_string = [ format_string , ' %s \t'];
+else
+ format_string = [ format_string , ' %6.4f \t'];
+end
+if ~isnumeric(LowerBound)
+ format_string = [ format_string , ' %s \t'];
+else
+ format_string = [ format_string , ' %6.4f \t'];
+end
+if ~isnumeric(UpperBound)
+ format_string = [ format_string , ' %s \t'];
+else
+ format_string = [ format_string , ' %6.4f \t'];
+end
+format_string = [ format_string , ' %6.4f \t %6.4f'];
\ No newline at end of file
diff --git a/matlab/prior_posterior_statistics.m b/matlab/prior_posterior_statistics.m
index b3cfdc933b37cbef6a77763469add7725cb02018..cf5d18e14e4dab581057ef4cb00b0539f7713d32 100644
--- a/matlab/prior_posterior_statistics.m
+++ b/matlab/prior_posterior_statistics.m
@@ -331,7 +331,7 @@ if options_.smoother
'',M_.exo_names,M_.exo_names_tex,M_.exo_names,...
M_.exo_names,'SmoothedShocks',DirectoryName,'_inno');
pm3(endo_nbr,1,ifil(9),B,'Trend_coefficients',...
- '',varlist,M_.endo_names_tex,M_.endo_names,...
+ '',varlist,M_.endo_names_tex,M_.endo_names,...
varlist,'TrendCoeff',DirectoryName,'_trend_coeff');
pm3(endo_nbr,gend,ifil(10),B,'Smoothed constant',...
'',varlist,M_.endo_names_tex,M_.endo_names,...
@@ -357,8 +357,8 @@ if options_.smoother
meas_error_names=char(meas_error_names);
texnames=char(texnames);
pm3(meas_err_nbr,gend,ifil(3),B,'Smoothed measurement errors',...
- '',meas_error_names,texnames,meas_error_names,...
- meas_error_names,'SmoothedMeasurementErrors',DirectoryName,'_error')
+ '',meas_error_names,texnames,meas_error_names,...
+ meas_error_names,'SmoothedMeasurementErrors',DirectoryName,'_error')
end
end
@@ -385,8 +385,8 @@ if options_.forecast
texnames=char(texnames);
varlist_forecast_ME=intersect(options_.varobs,varlist);
pm3(meas_err_nbr,horizon,ifil(12),B,'Forecasted variables (point) with ME',...
- '',char(varlist_forecast_ME),texnames,obs_names,...
- char(varlist_forecast_ME),'PointForecastME',DirectoryName,'_forc_point_ME')
+ '',char(varlist_forecast_ME),texnames,obs_names,...
+ char(varlist_forecast_ME),'PointForecastME',DirectoryName,'_forc_point_ME')
end
end
diff --git a/matlab/prior_posterior_statistics_core.m b/matlab/prior_posterior_statistics_core.m
index 3e8e314366d51a15e8694867a1d0a83bad947aaf..bb21031ea64625333aea7da666c888b2e06da2ce 100644
--- a/matlab/prior_posterior_statistics_core.m
+++ b/matlab/prior_posterior_statistics_core.m
@@ -157,22 +157,22 @@ end
%initialize arrays
if run_smoother
- stock_smooth=NaN(endo_nbr,gend,MAX_nsmoo);
- stock_update=NaN(endo_nbr,gend,MAX_nsmoo);
- stock_innov=NaN(M_.exo_nbr,gend,MAX_ninno);
- stock_smoothed_constant=NaN(endo_nbr,gend,MAX_n_smoothed_constant);
- stock_smoothed_trend=NaN(endo_nbr,gend,MAX_n_smoothed_trend);
- stock_trend_coeff = zeros(endo_nbr,MAX_n_trend_coeff);
- if horizon
- stock_forcst_mean= NaN(endo_nbr,horizon,MAX_nforc1);
- stock_forcst_point = NaN(endo_nbr,horizon,MAX_nforc2);
- if ~isequal(M_.H,0)
- stock_forcst_point_ME = NaN(length(varobs),horizon,MAX_nforc_ME);
- end
- end
+ stock_smooth=NaN(endo_nbr,gend,MAX_nsmoo);
+ stock_update=NaN(endo_nbr,gend,MAX_nsmoo);
+ stock_innov=NaN(M_.exo_nbr,gend,MAX_ninno);
+ stock_smoothed_constant=NaN(endo_nbr,gend,MAX_n_smoothed_constant);
+ stock_smoothed_trend=NaN(endo_nbr,gend,MAX_n_smoothed_trend);
+ stock_trend_coeff = zeros(endo_nbr,MAX_n_trend_coeff);
+ if horizon
+ stock_forcst_mean= NaN(endo_nbr,horizon,MAX_nforc1);
+ stock_forcst_point = NaN(endo_nbr,horizon,MAX_nforc2);
+ if ~isequal(M_.H,0)
+ stock_forcst_point_ME = NaN(length(varobs),horizon,MAX_nforc_ME);
+ end
+ end
end
if nvn
- stock_error = NaN(length(varobs),gend,MAX_nerro);
+ stock_error = NaN(length(varobs),gend,MAX_nerro);
end
if naK
stock_filter_step_ahead =NaN(length(options_.filter_step_ahead),endo_nbr,gend+max(options_.filter_step_ahead),MAX_naK);
@@ -286,10 +286,10 @@ for b=fpar:B
horizon+maxlag,1);
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
yf(:,IdObs) = yf(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff'-...
- repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
+ repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
else
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
- yf(:,IdObs) = yf(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff';
+ yf(:,IdObs) = yf(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff';
end
if options_.loglinear
yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
@@ -303,10 +303,10 @@ for b=fpar:B
repmat(mean_varobs,[horizon+maxlag,1,1]);
% add trend, taking into account that last point of sample is still included in forecasts and only cut off later
yf1(:,IdObs) = yf1(:,IdObs)+((options_.first_obs-1)+gend+[1-maxlag:horizon]')*trend_coeff'-...
- repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
+ repmat(mean(trend_coeff*[options_.first_obs:options_.first_obs+gend-1],2)',length(1-maxlag:horizon),1); %center trend
else
- % add trend, taking into account that last point of sample is still included in forecasts and only cut off later
- yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat(((options_.first_obs-1)+gend+[1-maxlag:horizon]')* ...
+ % add trend, taking into account that last point of sample is still included in forecasts and only cut off later
+ yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat(((options_.first_obs-1)+gend+[1-maxlag:horizon]')* ...
trend_coeff',[1,1,1]);
end
if options_.loglinear
diff --git a/matlab/quadratic_matrix_equation_solver.m b/matlab/quadratic_matrix_equation_solver.m
index f222df843f9aa5cf76bf61ef401d269f23a9ec31..d96ea0d9d52aa7dd3e7a242143d3d8589c1ba926 100644
--- a/matlab/quadratic_matrix_equation_solver.m
+++ b/matlab/quadratic_matrix_equation_solver.m
@@ -106,34 +106,34 @@ end
function f = eval_quadratic_matrix_equation(A,B,C,X)
- f = C + (B + A*X)*X;
+f = C + (B + A*X)*X;
function [p0,p1] = merit_polynomial(A,H,F)
- AHH = A*H*H;
- gamma = norm(AHH,'fro')^2;
- alpha = norm(F,'fro')^2;
- beta = trace(F*AHH*AHH*F);
- p0 = [gamma, -beta, alpha+beta, -2*alpha, alpha];
- p1 = [4*gamma, -3*beta, 2*(alpha+beta), -2*alpha];
+AHH = A*H*H;
+gamma = norm(AHH,'fro')^2;
+alpha = norm(F,'fro')^2;
+beta = trace(F*AHH*AHH*F);
+p0 = [gamma, -beta, alpha+beta, -2*alpha, alpha];
+p1 = [4*gamma, -3*beta, 2*(alpha+beta), -2*alpha];
function t = line_search(A,H,F)
- [p0,p1] = merit_polynomial(A,H,F);
- if any(isnan(p0)) || any(isinf(p0))
- t = 1.0;
- return
- end
- r = roots(p1);
- s = [Inf(3,1),r];
- for i = 1:3
- if isreal(r(i))
- s(i,1) = p0(1)*r(i)^4 + p0(2)*r(i)^3 + p0(3)*r(i)^2 + p0(4)*r(i) + p0(5);
- end
- end
- s = sortrows(s,1);
- t = s(1,2);
- if t<=1e-12 || t>=2
- t = 1;
+[p0,p1] = merit_polynomial(A,H,F);
+if any(isnan(p0)) || any(isinf(p0))
+ t = 1.0;
+ return
+end
+r = roots(p1);
+s = [Inf(3,1),r];
+for i = 1:3
+ if isreal(r(i))
+ s(i,1) = p0(1)*r(i)^4 + p0(2)*r(i)^3 + p0(3)*r(i)^2 + p0(4)*r(i) + p0(5);
end
+end
+s = sortrows(s,1);
+t = s(1,2);
+if t<=1e-12 || t>=2
+ t = 1;
+end
%@test:1
%$ addpath ../matlab
diff --git a/matlab/ramsey_policy.m b/matlab/ramsey_policy.m
index 9766a5519cafa7940871568bd864e6869faaa106..ccc50fb040074b6b4f5b4cffca086cbfe91c0a21 100644
--- a/matlab/ramsey_policy.m
+++ b/matlab/ramsey_policy.m
@@ -38,7 +38,7 @@ else
error('You have specified a steady state file, but not provided an instrument. Either delete the steady state file or provide an instrument')
end
end
-
+
info = stoch_simul(var_list);
oo_.steady_state = oo_.dr.ys;
diff --git a/matlab/read_key_value_string.m b/matlab/read_key_value_string.m
index d6b7124ed639144f02b234b61a18cea1d8deca4e..d92fb269858b2bf6fc9ebb9c5e56efbfd83f6291 100644
--- a/matlab/read_key_value_string.m
+++ b/matlab/read_key_value_string.m
@@ -40,7 +40,7 @@ iComma = strfind(s,',');
%delete commata in sublists from further checks
for sublist_iter=length(i_begin_sublist):-1:1
- iComma(iComma>=i_begin_sublist(sublist_iter) & iComma<=i_end_sublist(sublist_iter))=[];
+ iComma(iComma>=i_begin_sublist(sublist_iter) & iComma<=i_end_sublist(sublist_iter))=[];
end
nComma = length(iComma);
@@ -85,10 +85,10 @@ end
function j = comma2opt(i)
- if isodd(i)
- % The comma is a separator between a Key and a Value (returned j is minus the option number).
- j = - (i+1)/2;
- else
- % The comma is a separator between two options (returned j is the option number).
- j = i/2;
- end
\ No newline at end of file
+if isodd(i)
+ % The comma is a separator between a Key and a Value (returned j is minus the option number).
+ j = - (i+1)/2;
+else
+ % The comma is a separator between two options (returned j is the option number).
+ j = i/2;
+end
\ No newline at end of file
diff --git a/matlab/read_variables.m b/matlab/read_variables.m
index 6859a679574c2c557934ac7ab37c7f42e58db9a0..a96e8d013cb602be282ad1cefe7699f33c77f07f 100644
--- a/matlab/read_variables.m
+++ b/matlab/read_variables.m
@@ -68,61 +68,61 @@ if ~exist(fullname)
end
switch (extension)
- case '.m'
- eval(basename);
- for dyn_i_01=1:var_size_01
- dyn_tmp_01 = eval(var_names_01{dyn_i_01});
+ case '.m'
+ eval(basename);
+ for dyn_i_01=1:var_size_01
+ dyn_tmp_01 = eval(var_names_01{dyn_i_01});
+ if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
+ cd(old_pwd)
+ error('data size is too large')
+ end
+ dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
+ end
+ case '.mat'
+ s = load(basename);
+ for dyn_i_01=1:var_size_01
+ dyn_tmp_01 = s.(var_names_01{dyn_i_01});
+ if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
+ cd(old_pwd)
+ error('data size is too large')
+ end
+ dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
+ end
+ case { '.xls', '.xlsx' }
+ [freq,init,data,varlist] = load_xls_file_data(fullname,xls_sheet,xls_range);
+ for dyn_i_01=1:var_size_01
+ iv = strmatch(strtrim(var_names_01(dyn_i_01,:)),varlist,'exact');
+ if ~isempty(iv)
+ dyn_tmp_01 = [data(:,iv)]';
if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
cd(old_pwd)
error('data size is too large')
end
dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
+ else
+ cd(old_pwd)
+ error([strtrim(var_names_01(dyn_i_01,:)) ' not found in ' fullname])
end
- case '.mat'
- s = load(basename);
- for dyn_i_01=1:var_size_01
- dyn_tmp_01 = s.(var_names_01{dyn_i_01});
+ end
+ case '.csv'
+ [freq,init,data,varlist] = load_csv_file_data(fullname);
+ for dyn_i_01=1:var_size_01
+ iv = strmatch(var_names_01{dyn_i_01},varlist,'exact');
+ if ~isempty(iv)
+ dyn_tmp_01 = [data(:,iv)]';
if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
cd(old_pwd)
error('data size is too large')
end
dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
+ else
+ cd(old_pwd)
+ error([var_names_01{dyn_i_01} ' not found in ' fullname])
end
- case { '.xls', '.xlsx' }
- [freq,init,data,varlist] = load_xls_file_data(fullname,xls_sheet,xls_range);
- for dyn_i_01=1:var_size_01
- iv = strmatch(strtrim(var_names_01(dyn_i_01,:)),varlist,'exact');
- if ~isempty(iv)
- dyn_tmp_01 = [data(:,iv)]';
- if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
- cd(old_pwd)
- error('data size is too large')
- end
- dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
- else
- cd(old_pwd)
- error([strtrim(var_names_01(dyn_i_01,:)) ' not found in ' fullname])
- end
- end
- case '.csv'
- [freq,init,data,varlist] = load_csv_file_data(fullname);
- for dyn_i_01=1:var_size_01
- iv = strmatch(var_names_01{dyn_i_01},varlist,'exact');
- if ~isempty(iv)
- dyn_tmp_01 = [data(:,iv)]';
- if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
- cd(old_pwd)
- error('data size is too large')
- end
- dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
- else
- cd(old_pwd)
- error([var_names_01{dyn_i_01} ' not found in ' fullname])
- end
- end
- otherwise
- cd(old_pwd)
- error(['Unsupported extension for datafile: ' extension])
+ end
+ otherwise
+ cd(old_pwd)
+ error(['Unsupported extension for datafile: ' extension])
end
cd(old_pwd)
diff --git a/matlab/realtime_shock_decomposition.m b/matlab/realtime_shock_decomposition.m
index 2e47066e15f3a503c180c979cbc328ef04547c6d..72b1bde3ef8206005016fffeb5bc2c050a05f195 100644
--- a/matlab/realtime_shock_decomposition.m
+++ b/matlab/realtime_shock_decomposition.m
@@ -63,7 +63,7 @@ if isempty(parameter_set)
parameter_set = 'posterior_mode';
else
error(['realtime_shock_decomposition: option parameter_set is not specified ' ...
- 'and posterior mode is not available'])
+ 'and posterior mode is not available'])
end
end
@@ -103,7 +103,7 @@ if forecast_ && any(forecast_params),
end
for j=presample+1:nobs,
-% evalin('base',['options_.nobs=' int2str(j) ';'])
+ % evalin('base',['options_.nobs=' int2str(j) ';'])
options_.nobs=j;
[oo, M_, junk2, junk3, Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
@@ -165,7 +165,7 @@ for j=presample+1:nobs,
z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
end
-% z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
+ % z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
z(:,nshocks+1,i) = z(:,nshocks+2,i) - sum(z(:,1:nshocks,i),2);
end
@@ -197,13 +197,13 @@ for j=presample+1:nobs,
zn(:,1:nshocks,i) = zn(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i+gend-forecast_-1)',endo_nbr,1);
end
-% zn(:,1:nshocks,i) = zn(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i+gend-forecast_-1)',endo_nbr,1);
+ % zn(:,1:nshocks,i) = zn(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i+gend-forecast_-1)',endo_nbr,1);
zn(:,nshocks+1,i) = zn(:,nshocks+2,i) - sum(zn(:,1:nshocks,i),2);
end
oo_.conditional_shock_decomposition.(['time_' int2str(j-forecast_)])=zn;
end
%%
-
+
if init,
zreal(:,:,1:j) = z(:,:,1:j);
else
@@ -218,7 +218,7 @@ for j=presample+1:nobs,
zfrcst(:,:,j+1) = z(:,:,gend+1);
oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j)])=z(:,:,gend:end);
if j>forecast_+presample
- %% realtime conditional shock decomp k step ahead
+ %% realtime conditional shock decomp k step ahead
oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-forecast_)]) = ...
zreal(:,:,j-forecast_:j) - ...
oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j-forecast_)]);
@@ -226,7 +226,7 @@ for j=presample+1:nobs,
oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j-forecast_)])(:,end,:);
oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-forecast_)])(:,end,:) = ...
zreal(:,end,j-forecast_:j);
-
+
if j==nobs
for my_forecast_=(forecast_-1):-1:1,
oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-my_forecast_)]) = ...
diff --git a/matlab/reduced_rank_cholesky.m b/matlab/reduced_rank_cholesky.m
index 1af9ec6090e2838d9f9131275d22b90975885032..f2b6ff74c2208391e41bf2b987d96255d1cc3415 100644
--- a/matlab/reduced_rank_cholesky.m
+++ b/matlab/reduced_rank_cholesky.m
@@ -52,7 +52,7 @@ function T = reduced_rank_cholesky(X)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
[T,X_is_not_positive_definite] = chol(X);
if X_is_not_positive_definite
diff --git a/matlab/resid.m b/matlab/resid.m
index 7383c1944359fa29fd282bbac546b1bf845a50a2..71e0f6619466814790ced622d37365694aa3e383 100644
--- a/matlab/resid.m
+++ b/matlab/resid.m
@@ -60,7 +60,7 @@ if any(imag(oo_.steady_state))
end
end
end
-
+
if options_.steadystate_flag
[oo_.steady_state,M_.params,info] = ...
evaluate_steady_state(oo_.steady_state,M_,options_,oo_,0);
diff --git a/matlab/rotated_slice_sampler.m b/matlab/rotated_slice_sampler.m
index 66908d77d44264a54693061b5a7cb738bb64231a..d5e53e384ea8cf8f4200db82bce304a90cc68854 100644
--- a/matlab/rotated_slice_sampler.m
+++ b/matlab/rotated_slice_sampler.m
@@ -1,184 +1,184 @@
-function [theta, fxsim, neval] = rotated_slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
-% ----------------------------------------------------------
-% ROTATED SLICE SAMPLER - with stepping out (Neal, 2003)
-% extension of the orthogonal univarite sampler (slice_sampler.m)
-% copyright M. Ratto (European Commission)
-%
-% objective_function(theta,varargin): -log of any unnormalized pdf
-% with varargin (optional) a vector of auxiliaty parameters
-% to be passed to f( ).
-% ----------------------------------------------------------
-%
-% INPUTS
-% objective_function: objective function (expressed as minus the log of a density)
-% theta: last value of theta
-% thetaprior: bounds of the theta space
-% sampler_options: posterior sampler options
-% varargin: optional input arguments to objective function
-%
-% OUTPUTS
-% theta: new theta sample
-% fxsim: value of the objective function for the new sample
-% neval: number of function evaluations
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2015 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-theta=theta(:);
-npar = length(theta);
-neval = zeros(npar,1);
-W1=[];
-if isfield(sampler_options,'WR'),
- W1 = sampler_options.WR;
-end
-if ~isempty(sampler_options.mode),
- mm = sampler_options.mode;
- n = length(mm);
- for j=1:n,
- distance(j)=sqrt(sum((theta-mm(j).m).^2));
- end
- [m, im] = min(distance);
-
- r=im;
- V1 = mm(r).m;
- jj=0;
- for j=1:n,
- if j~=r,
- jj=jj+1;
- tmp=mm(j).m-mm(r).m;
- %tmp=mm(j).m-theta;
- V1(:,jj)=tmp/norm(tmp);
- end
- end
- resul=randperm(n-1,n-1);
- V1 = V1(:,resul);
-
- %V1 = V1(:, randperm(n-1));
-% %d = chol(mm(r).invhess);
-% %V1 = transpose(feval(sampler_options.proposal_distribution, transpose(mm(r).m), d, npar));
-%
-% V1=eye(npar);
-% V1=V1(:,randperm(npar));
-% for j=1:2,
-% V1(:,j)=mm(r(j)).m-theta;
-% V1(:,j)=V1(:,j)/norm(V1(:,j));
-% end
-% % Gram-Schmidt
-% for j=2:npar,
-% for k=1:j-1,
-% V1(:,j)=V1(:,j)-V1(:,k)'*V1(:,j)*V1(:,k);
-% end
-% V1(:,j)=V1(:,j)/norm(V1(:,j));
-% end
-% for j=1:n,
-% distance(j)=sqrt(sum((theta-mm(j).m).^2));
-% end
-% [m, im] = min(distance);
-% if im==r,
-% fxsim=[];
-% return,
-% else
-% theta1=theta;
-% end
-else
- V1 = sampler_options.V1;
-end
-npar=size(V1,2);
-
-for it=1:npar,
- theta0 = theta;
- neval(it) = 0;
- xold = 0;
- % XLB = thetaprior(3);
- % XUB = thetaprior(4);
- tb=sort([(thetaprior(:,1)-theta)./V1(:,it) (thetaprior(:,2)-theta)./V1(:,it)],2);
- XLB=max(tb(:,1));
- XUB=min(tb(:,2));
- if isempty(W1),
- W = (XUB-XLB); %*0.8;
- else
- W = W1(it);
- end
-
- % -------------------------------------------------------
- % 1. DRAW Z = ln[f(X0)] - EXP(1) where EXP(1)=-ln(U(0,1))
- % THIS DEFINES THE SLICE S={x: z < ln(f(x))}
- % -------------------------------------------------------
-
- fxold = -feval(objective_function,theta,varargin{:});
- %I have to be sure that the rotation is for L,R or for Fxold, theta(it)
- neval(it) = neval(it) + 1;
- Z = fxold + log(rand(1,1));
- % -------------------------------------------------------------
- % 2. FIND I=(L,R) AROUND X0 THAT CONTAINS S AS MUCH AS POSSIBLE
- % STEPPING-OUT PROCEDURE
- % -------------------------------------------------------------
- u = rand(1,1);
- L = max(XLB,xold-W*u);
- R = min(XUB,L+W);
-
- %[L R]=slice_rotation(L, R, alpha);
- while(L > XLB)
- xsim = L;
- theta = theta0+xsim*V1(:,it);
- fxl = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (fxl <= Z)
- break;
- end
- L = max(XLB,L-W);
- end
- while(R < XUB)
- xsim = R;
- theta = theta0+xsim*V1(:,it);
- fxr = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (fxr <= Z)
- break;
- end
- R = min(XUB,R+W);
- end
- % ------------------------------------------------------
- % 3. SAMPLING FROM THE SET A = (I INTERSECT S) = (LA,RA)
- % ------------------------------------------------------
- fxsim = Z-1;
- while (fxsim < Z)
- u = rand(1,1);
- xsim = L + u*(R - L);
- theta = theta0+xsim*V1(:,it);
- fxsim = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (xsim > xold)
- R = xsim;
- else
- L = xsim;
- end
- end
-end
-
-% if ~isempty(sampler_options.mode),
-% dist1=sqrt(sum((theta-mm(r).m).^2));
-% if dist1>distance(r),
-% theta=theta1;
-% fxsim=[];
-% end
-% end
-end
-
+function [theta, fxsim, neval] = rotated_slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
+% ----------------------------------------------------------
+% ROTATED SLICE SAMPLER - with stepping out (Neal, 2003)
+% extension of the orthogonal univarite sampler (slice_sampler.m)
+% copyright M. Ratto (European Commission)
+%
+% objective_function(theta,varargin): -log of any unnormalized pdf
+% with varargin (optional) a vector of auxiliaty parameters
+% to be passed to f( ).
+% ----------------------------------------------------------
+%
+% INPUTS
+% objective_function: objective function (expressed as minus the log of a density)
+% theta: last value of theta
+% thetaprior: bounds of the theta space
+% sampler_options: posterior sampler options
+% varargin: optional input arguments to objective function
+%
+% OUTPUTS
+% theta: new theta sample
+% fxsim: value of the objective function for the new sample
+% neval: number of function evaluations
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2015 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+theta=theta(:);
+npar = length(theta);
+neval = zeros(npar,1);
+W1=[];
+if isfield(sampler_options,'WR'),
+ W1 = sampler_options.WR;
+end
+if ~isempty(sampler_options.mode),
+ mm = sampler_options.mode;
+ n = length(mm);
+ for j=1:n,
+ distance(j)=sqrt(sum((theta-mm(j).m).^2));
+ end
+ [m, im] = min(distance);
+
+ r=im;
+ V1 = mm(r).m;
+ jj=0;
+ for j=1:n,
+ if j~=r,
+ jj=jj+1;
+ tmp=mm(j).m-mm(r).m;
+ %tmp=mm(j).m-theta;
+ V1(:,jj)=tmp/norm(tmp);
+ end
+ end
+ resul=randperm(n-1,n-1);
+ V1 = V1(:,resul);
+
+ %V1 = V1(:, randperm(n-1));
+ % %d = chol(mm(r).invhess);
+ % %V1 = transpose(feval(sampler_options.proposal_distribution, transpose(mm(r).m), d, npar));
+ %
+ % V1=eye(npar);
+ % V1=V1(:,randperm(npar));
+ % for j=1:2,
+ % V1(:,j)=mm(r(j)).m-theta;
+ % V1(:,j)=V1(:,j)/norm(V1(:,j));
+ % end
+ % % Gram-Schmidt
+ % for j=2:npar,
+ % for k=1:j-1,
+ % V1(:,j)=V1(:,j)-V1(:,k)'*V1(:,j)*V1(:,k);
+ % end
+ % V1(:,j)=V1(:,j)/norm(V1(:,j));
+ % end
+ % for j=1:n,
+ % distance(j)=sqrt(sum((theta-mm(j).m).^2));
+ % end
+ % [m, im] = min(distance);
+ % if im==r,
+ % fxsim=[];
+ % return,
+ % else
+ % theta1=theta;
+ % end
+else
+ V1 = sampler_options.V1;
+end
+npar=size(V1,2);
+
+for it=1:npar,
+ theta0 = theta;
+ neval(it) = 0;
+ xold = 0;
+ % XLB = thetaprior(3);
+ % XUB = thetaprior(4);
+ tb=sort([(thetaprior(:,1)-theta)./V1(:,it) (thetaprior(:,2)-theta)./V1(:,it)],2);
+ XLB=max(tb(:,1));
+ XUB=min(tb(:,2));
+ if isempty(W1),
+ W = (XUB-XLB); %*0.8;
+ else
+ W = W1(it);
+ end
+
+ % -------------------------------------------------------
+ % 1. DRAW Z = ln[f(X0)] - EXP(1) where EXP(1)=-ln(U(0,1))
+ % THIS DEFINES THE SLICE S={x: z < ln(f(x))}
+ % -------------------------------------------------------
+
+ fxold = -feval(objective_function,theta,varargin{:});
+ %I have to be sure that the rotation is for L,R or for Fxold, theta(it)
+ neval(it) = neval(it) + 1;
+ Z = fxold + log(rand(1,1));
+ % -------------------------------------------------------------
+ % 2. FIND I=(L,R) AROUND X0 THAT CONTAINS S AS MUCH AS POSSIBLE
+ % STEPPING-OUT PROCEDURE
+ % -------------------------------------------------------------
+ u = rand(1,1);
+ L = max(XLB,xold-W*u);
+ R = min(XUB,L+W);
+
+ %[L R]=slice_rotation(L, R, alpha);
+ while(L > XLB)
+ xsim = L;
+ theta = theta0+xsim*V1(:,it);
+ fxl = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (fxl <= Z)
+ break;
+ end
+ L = max(XLB,L-W);
+ end
+ while(R < XUB)
+ xsim = R;
+ theta = theta0+xsim*V1(:,it);
+ fxr = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (fxr <= Z)
+ break;
+ end
+ R = min(XUB,R+W);
+ end
+ % ------------------------------------------------------
+ % 3. SAMPLING FROM THE SET A = (I INTERSECT S) = (LA,RA)
+ % ------------------------------------------------------
+ fxsim = Z-1;
+ while (fxsim < Z)
+ u = rand(1,1);
+ xsim = L + u*(R - L);
+ theta = theta0+xsim*V1(:,it);
+ fxsim = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (xsim > xold)
+ R = xsim;
+ else
+ L = xsim;
+ end
+ end
+end
+
+% if ~isempty(sampler_options.mode),
+% dist1=sqrt(sum((theta-mm(r).m).^2));
+% if dist1>distance(r),
+% theta=theta1;
+% fxsim=[];
+% end
+% end
+end
+
diff --git a/matlab/rplot.m b/matlab/rplot.m
index 5f190189411d5cc021eea821f4def8f3d20a0efb..b77bbdd2fa03070f606e216833b98980d591596e 100644
--- a/matlab/rplot.m
+++ b/matlab/rplot.m
@@ -140,16 +140,16 @@ if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
end
function []=create_TeX_loader(fidTeX,options,figpath,caption,label_name,label_type,scale_factor)
- if nargin<6
- scale_factor=1;
- end
- fprintf(fidTeX,' \n');
- fprintf(fidTeX,'\\begin{figure}[H]\n');
- fprintf(fidTeX,'\\centering \n');
- fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s}\n',0.8*scale_factor,strrep(figpath,'\','/'));
- fprintf(fidTeX,'\\caption{%s.}',caption);
- fprintf(fidTeX,'\\label{Fig:%s:%s}\n',label_name,label_type);
- fprintf(fidTeX,'\\end{figure}\n\n');
+if nargin<6
+ scale_factor=1;
+end
+fprintf(fidTeX,' \n');
+fprintf(fidTeX,'\\begin{figure}[H]\n');
+fprintf(fidTeX,'\\centering \n');
+fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s}\n',0.8*scale_factor,strrep(figpath,'\','/'));
+fprintf(fidTeX,'\\caption{%s.}',caption);
+fprintf(fidTeX,'\\label{Fig:%s:%s}\n',label_name,label_type);
+fprintf(fidTeX,'\\end{figure}\n\n');
% 02/28/01 MJ replaced bseastr by MATLAB's strmatch
% 06/19/01 MJ added 'exact' to strmatch calls
diff --git a/matlab/score.m b/matlab/score.m
index 273ca639537cc49807369e58f71156023eac6afe..f9d418bfec30b1e430534d6f88329bbe2c0ab801 100644
--- a/matlab/score.m
+++ b/matlab/score.m
@@ -25,92 +25,92 @@ function [DLIK] = score(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,start,mf,kalman_tol,riccat
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licen
- k = size(DT,3); % number of structural parameters
- smpl = size(Y,2); % Sample size.
- mm = size(T,2); % Number of state variables.
- a = zeros(mm,1); % State vector.
- Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
- t = 0; % Initialization of the time index.
- oldK = 0;
- notsteady = 1; % Steady state flag.
- F_singular = 1;
-
- DLIK = zeros(k,1); % Initialization of the score.
- Da = zeros(mm,k); % State vector.
- Dv = zeros(length(mf),k); % observation vector.
-
+k = size(DT,3); % number of structural parameters
+smpl = size(Y,2); % Sample size.
+mm = size(T,2); % Number of state variables.
+a = zeros(mm,1); % State vector.
+Om = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
+t = 0; % Initialization of the time index.
+oldK = 0;
+notsteady = 1; % Steady state flag.
+F_singular = 1;
+
+DLIK = zeros(k,1); % Initialization of the score.
+Da = zeros(mm,k); % State vector.
+Dv = zeros(length(mf),k); % observation vector.
+
% for ii = 1:k
% DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
% end
-
- while notsteady & t<smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- F = P(mf,mf) + H;
- if rcond(F) < kalman_tol
- if ~all(abs(F(:))<kalman_tol)
- return
- else
- a = T*a;
- P = T*P*transpose(T)+Om;
- end
+
+while notsteady & t<smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
+ F = P(mf,mf) + H;
+ if rcond(F) < kalman_tol
+ if ~all(abs(F(:))<kalman_tol)
+ return
else
- F_singular = 0;
- iF = inv(F);
- K = P(:,mf)*iF;
-
- [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
- Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
- if t>=start
- DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
- end
+ a = T*a;
+ P = T*P*transpose(T)+Om;
+ end
+ else
+ F_singular = 0;
+ iF = inv(F);
+ K = P(:,mf)*iF;
+
+ [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
+ for ii = 1:k
+ Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
+ Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
+ if t>=start
+ DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
end
- a = T*(a+K*v);
- P = T*(P-K*P(mf,:))*transpose(T)+Om;
- DP = DP1;
end
- notsteady = max(max(abs(K-oldK))) > riccati_tol;
- oldK = K;
+ a = T*(a+K*v);
+ P = T*(P-K*P(mf,:))*transpose(T)+Om;
+ DP = DP1;
end
+ notsteady = max(max(abs(K-oldK))) > riccati_tol;
+ oldK = K;
+end
- if F_singular
- error('The variance of the forecast error remains singular until the end of the sample')
- end
+if F_singular
+ error('The variance of the forecast error remains singular until the end of the sample')
+end
- for ii = 1:k
- tmp0(:,:,ii) = iF*DF(:,:,ii)*iF;
- end
-
- if t < smpl
- t0 = t+1;
- while t < smpl
- t = t+1;
- v = Y(:,t)-a(mf);
- for ii = 1:k
- Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
- Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
- if t>=start
- DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
- end
- end
- a = T*(a+K*v);
- end
+for ii = 1:k
+ tmp0(:,:,ii) = iF*DF(:,:,ii)*iF;
+end
+
+if t < smpl
+ t0 = t+1;
+ while t < smpl
+ t = t+1;
+ v = Y(:,t)-a(mf);
for ii = 1:k
-% DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
+ Dv(:,ii) = -Da(mf,ii)-DYss(mf,ii);
+ Da(:,ii) = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
+ if t>=start
+ DLIK(ii,1) = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
+ end
end
-
- end
-
- DLIK = DLIK/2;
+ a = T*(a+K*v);
+ end
+ for ii = 1:k
+ % DLIK(ii,1) = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
+ end
+end
+
+DLIK = DLIK/2;
+
% end of main function
-
+
function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
- k = size(DT,3);
- tmp = P-K*P(mf,:);
+k = size(DT,3);
+tmp = P-K*P(mf,:);
for ii = 1:k
DF(:,:,ii) = DP(mf,mf,ii) + DH(:,:,ii);
@@ -123,4 +123,3 @@ end
% end of computeDKalman
-
\ No newline at end of file
diff --git a/matlab/set_dynare_random_generator_state.m b/matlab/set_dynare_random_generator_state.m
index f16ce13aed7eb52402aa00a635add310bca09326..209965124bda28dc3f593c69174a9a80f5660b19 100644
--- a/matlab/set_dynare_random_generator_state.m
+++ b/matlab/set_dynare_random_generator_state.m
@@ -23,37 +23,37 @@ function [state_u,state_n] = set_dynare_random_generator_state(state_u,state_n)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- matlab_random_streams = ~(isoctave || matlab_ver_less_than('7.7'));
+matlab_random_streams = ~(isoctave || matlab_ver_less_than('7.7'));
- if matlab_random_streams% Use new matlab interface.
+if matlab_random_streams% Use new matlab interface.
+ if matlab_ver_less_than('7.12')
+ s = RandStream.getDefaultStream();
+ else
+ s = RandStream.getGlobalStream();
+ end
+ if isequal(s.Type,'legacy')
+ rand('state',state_u);
+ randn('state',state_n);
+ else
+ if ~isequal(state_u,state_n)
+ error(['You are using the new Matlab RandStream mechanism ' ...
+ 'with a single random generator, but the values ' ...
+ 'of the state of the uniformly ' ...
+ 'distributed numbers and of the state of the ' ...
+ 'normally distributed numbers are different. Something must be ' ...
+ 'wrong, such as reloading old Metropolis runs, ' ...
+ 'computed on a different version of Matlab. If you ' ...
+ 'don''t understand the origin of the problem, ' ...
+ 'please, contact Dynare''s development team.'])
+ end
+ s.State = state_u;
if matlab_ver_less_than('7.12')
- s = RandStream.getDefaultStream();
+ RandStream.setDefaultStream(s);
else
- s = RandStream.getGlobalStream();
+ RandStream.setGlobalStream(s);
end
- if isequal(s.Type,'legacy')
- rand('state',state_u);
- randn('state',state_n);
- else
- if ~isequal(state_u,state_n)
- error(['You are using the new Matlab RandStream mechanism ' ...
- 'with a single random generator, but the values ' ...
- 'of the state of the uniformly ' ...
- 'distributed numbers and of the state of the ' ...
- 'normally distributed numbers are different. Something must be ' ...
- 'wrong, such as reloading old Metropolis runs, ' ...
- 'computed on a different version of Matlab. If you ' ...
- 'don''t understand the origin of the problem, ' ...
- 'please, contact Dynare''s development team.'])
- end
- s.State = state_u;
- if matlab_ver_less_than('7.12')
- RandStream.setDefaultStream(s);
- else
- RandStream.setGlobalStream(s);
- end
- end
- else% Use old matlab interface.
- rand('state',state_u);
- randn('state',state_n);
- end
\ No newline at end of file
+ end
+else% Use old matlab interface.
+ rand('state',state_u);
+ randn('state',state_n);
+end
\ No newline at end of file
diff --git a/matlab/set_prior.m b/matlab/set_prior.m
index 4c9e604117bf529ed5f37f8016c31d685dd2b559..bb4924c4c0b991c526ca5bfd45eae7de97f4af92 100644
--- a/matlab/set_prior.m
+++ b/matlab/set_prior.m
@@ -158,7 +158,7 @@ bayestopt_.p7 = bayestopt_.p6 ;
%% check for point priors and disallow them as they do not work with MCMC
if any(bayestopt_.p2 ==0)
error(sprintf(['Error in prior for %s: you cannot use a point prior in estimation. Either increase the prior standard deviation',...
- ' or fix the parameter completely.'], bayestopt_.name{bayestopt_.p2 ==0}))
+ ' or fix the parameter completely.'], bayestopt_.name{bayestopt_.p2 ==0}))
end
% generalized location parameters by default for beta distribution
diff --git a/matlab/shock_decomposition.m b/matlab/shock_decomposition.m
index 9c5144110cd977159bf3b84b4cf89b5f100ee2c7..c63facfe7813516b6a68786673a5f201a722575e 100644
--- a/matlab/shock_decomposition.m
+++ b/matlab/shock_decomposition.m
@@ -114,7 +114,7 @@ for i=1:gend
end
if i > options_.shock_decomp.init_state
- z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
+ z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
end
z(:,nshocks+1,i) = z(:,nshocks+2,i) - sum(z(:,1:nshocks,i),2);
end
diff --git a/matlab/simulated_moments_estimation.m b/matlab/simulated_moments_estimation.m
index 314122d835c145c75adb8d236dbf6469c130c40e..0946e3117ae28b4185d24c005c28f6b1b559eb5a 100644
--- a/matlab/simulated_moments_estimation.m
+++ b/matlab/simulated_moments_estimation.m
@@ -270,9 +270,9 @@ fprintf(fid,'M_.Sigma_e = diag(tmp);')
fprintf(fid,['stream=RandStream(''mt19937ar'',''Seed'',' int2str(slave_number) ');\n']);
if matlab_ver_less_than('7.12')
- fprintf(fid,['RandStream.setDefaultStream(stream);\n\n']);
+ fprintf(fid,['RandStream.setDefaultStream(stream);\n\n']);
else
- fprintf(fid,['RandStream.setGlobalStream(stream);\n\n']);
+ fprintf(fid,['RandStream.setGlobalStream(stream);\n\n']);
end
fprintf(fid,['maxNumCompThreads(' int2str(threads_per_job) ');\n\n']);
diff --git a/matlab/simult_.m b/matlab/simult_.m
index 31daa3a6ccebe33068ead81daf6916721b87395b..6113cbb370cc204fc246efab841bc2dfb6d93e01 100644
--- a/matlab/simult_.m
+++ b/matlab/simult_.m
@@ -134,7 +134,7 @@ else
y_(dr.order_var,i) = constant + dr.ghx*yhat + dr.ghu*epsilon ...
+ abcOut1 + abcOut2 + abcOut3;
end
- end
+ end
case 3
% only with pruning
% the third moments of the shocks are assumed null. We don't have
diff --git a/matlab/simultxdet.m b/matlab/simultxdet.m
index 032f20588ceb4b2c0ee25af6716ff6ace802db74..96b1f4c442d8e2fca3cfa5f461f5c7ccb7fa9306 100644
--- a/matlab/simultxdet.m
+++ b/matlab/simultxdet.m
@@ -153,7 +153,7 @@ end
int_width = zeros(iter,nvar);
for i=1:nvar
int_width(:,i) = fact*sqrt(var_yf(:,i));
- if nargout==3
+ if nargout==3
int_width_ME(:,i) = -fact*sqrt(var_yf_ME(:,i));
- end
+ end
end
diff --git a/matlab/slice_sampler.m b/matlab/slice_sampler.m
index e454d59365da8030f5b871d5d8ee780c9e7628b4..1505a5f28276b3c0b8d3964db2553ee4ec493e0c 100644
--- a/matlab/slice_sampler.m
+++ b/matlab/slice_sampler.m
@@ -1,123 +1,123 @@
-function [theta, fxsim, neval] = slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
-% function [theta, fxsim, neval] = slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
-% ----------------------------------------------------------
-% UNIVARIATE SLICE SAMPLER - stepping out (Neal, 2003)
-% W: optimal value in the range (3,10)*std(x)
-% - see C.Planas and A.Rossi (2014)
-% objective_function(theta,varargin): -log of any unnormalized pdf
-% with varargin (optional) a vector of auxiliaty parameters
-% to be passed to f( ).
-% ----------------------------------------------------------
-%
-% INPUTS
-% objective_function: objective function (expressed as minus the log of a density)
-% theta: last value of theta
-% thetaprior: bounds of the theta space
-% sampler_options: posterior sampler options
-% varargin: optional input arguments to objective function
-%
-% OUTPUTS
-% theta: new theta sample
-% fxsim: value of the objective function for the new sample
-% neval: number of function evaluations
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2015 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if sampler_options.rotated %&& ~isempty(sampler_options.V1),
- [theta, fxsim, neval] = rotated_slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin{:});
- if isempty(sampler_options.mode), % jumping
- return,
- else
- nevalR=sum(neval);
- end
-end
-
-theta=theta(:);
-npar = length(theta);
-W1 = sampler_options.W1;
-neval = zeros(npar,1);
-
-for it=1:npar,
- neval(it) = 0;
- W = W1(it);
- xold = theta(it);
- % XLB = thetaprior(3);
- % XUB = thetaprior(4);
- XLB = thetaprior(it,1);
- XUB = thetaprior(it,2);
-
-
- % -------------------------------------------------------
- % 1. DRAW Z = ln[f(X0)] - EXP(1) where EXP(1)=-ln(U(0,1))
- % THIS DEFINES THE SLICE S={x: z < ln(f(x))}
- % -------------------------------------------------------
- fxold = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- Z = fxold + log(rand(1,1));
- % -------------------------------------------------------------
- % 2. FIND I=(L,R) AROUND X0 THAT CONTAINS S AS MUCH AS POSSIBLE
- % STEPPING-OUT PROCEDURE
- % -------------------------------------------------------------
- u = rand(1,1);
- L = max(XLB,xold-W*u);
- R = min(XUB,L+W);
- while(L > XLB)
- xsim = L;
- theta(it) = xsim;
- fxl = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (fxl <= Z)
- break;
- end
- L = max(XLB,L-W);
- end
- while(R < XUB)
- xsim = R;
- theta(it) = xsim;
- fxr = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (fxr <= Z)
- break;
- end
- R = min(XUB,R+W);
- end
- % ------------------------------------------------------
- % 3. SAMPLING FROM THE SET A = (I INTERSECT S) = (LA,RA)
- % ------------------------------------------------------
- fxsim = Z-1;
- while (fxsim < Z)
- u = rand(1,1);
- xsim = L + u*(R - L);
- theta(it) = xsim;
- fxsim = -feval(objective_function,theta,varargin{:});
- neval(it) = neval(it) + 1;
- if (xsim > xold)
- R = xsim;
- else
- L = xsim;
- end
- end
-
-end
-
-if sampler_options.rotated && ~isempty(sampler_options.mode), % jumping
- neval=sum(neval)+nevalR;
-end
+function [theta, fxsim, neval] = slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
+% function [theta, fxsim, neval] = slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin)
+% ----------------------------------------------------------
+% UNIVARIATE SLICE SAMPLER - stepping out (Neal, 2003)
+% W: optimal value in the range (3,10)*std(x)
+% - see C.Planas and A.Rossi (2014)
+% objective_function(theta,varargin): -log of any unnormalized pdf
+% with varargin (optional) a vector of auxiliaty parameters
+% to be passed to f( ).
+% ----------------------------------------------------------
+%
+% INPUTS
+% objective_function: objective function (expressed as minus the log of a density)
+% theta: last value of theta
+% thetaprior: bounds of the theta space
+% sampler_options: posterior sampler options
+% varargin: optional input arguments to objective function
+%
+% OUTPUTS
+% theta: new theta sample
+% fxsim: value of the objective function for the new sample
+% neval: number of function evaluations
+%
+% SPECIAL REQUIREMENTS
+% none
+
+% Copyright (C) 2015 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+if sampler_options.rotated %&& ~isempty(sampler_options.V1),
+ [theta, fxsim, neval] = rotated_slice_sampler(objective_function,theta,thetaprior,sampler_options,varargin{:});
+ if isempty(sampler_options.mode), % jumping
+ return,
+ else
+ nevalR=sum(neval);
+ end
+end
+
+theta=theta(:);
+npar = length(theta);
+W1 = sampler_options.W1;
+neval = zeros(npar,1);
+
+for it=1:npar,
+ neval(it) = 0;
+ W = W1(it);
+ xold = theta(it);
+ % XLB = thetaprior(3);
+ % XUB = thetaprior(4);
+ XLB = thetaprior(it,1);
+ XUB = thetaprior(it,2);
+
+
+ % -------------------------------------------------------
+ % 1. DRAW Z = ln[f(X0)] - EXP(1) where EXP(1)=-ln(U(0,1))
+ % THIS DEFINES THE SLICE S={x: z < ln(f(x))}
+ % -------------------------------------------------------
+ fxold = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ Z = fxold + log(rand(1,1));
+ % -------------------------------------------------------------
+ % 2. FIND I=(L,R) AROUND X0 THAT CONTAINS S AS MUCH AS POSSIBLE
+ % STEPPING-OUT PROCEDURE
+ % -------------------------------------------------------------
+ u = rand(1,1);
+ L = max(XLB,xold-W*u);
+ R = min(XUB,L+W);
+ while(L > XLB)
+ xsim = L;
+ theta(it) = xsim;
+ fxl = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (fxl <= Z)
+ break;
+ end
+ L = max(XLB,L-W);
+ end
+ while(R < XUB)
+ xsim = R;
+ theta(it) = xsim;
+ fxr = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (fxr <= Z)
+ break;
+ end
+ R = min(XUB,R+W);
+ end
+ % ------------------------------------------------------
+ % 3. SAMPLING FROM THE SET A = (I INTERSECT S) = (LA,RA)
+ % ------------------------------------------------------
+ fxsim = Z-1;
+ while (fxsim < Z)
+ u = rand(1,1);
+ xsim = L + u*(R - L);
+ theta(it) = xsim;
+ fxsim = -feval(objective_function,theta,varargin{:});
+ neval(it) = neval(it) + 1;
+ if (xsim > xold)
+ R = xsim;
+ else
+ L = xsim;
+ end
+ end
+
+end
+
+if sampler_options.rotated && ~isempty(sampler_options.mode), % jumping
+ neval=sum(neval)+nevalR;
+end
diff --git a/matlab/solve_perfect_foresight_model.m b/matlab/solve_perfect_foresight_model.m
index 5de112ad8a6dd3d65ca8da7c93f94816aebef796..89d39fac659809abef508f06f8869ce010bb4ab1 100644
--- a/matlab/solve_perfect_foresight_model.m
+++ b/matlab/solve_perfect_foresight_model.m
@@ -17,107 +17,107 @@ function [flag,endo_simul,err] = solve_perfect_foresight_model(endo_simul,exo_si
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- flag = 0;
- err = 0;
- stop = 0;
- nan_flag = 0;
+flag = 0;
+err = 0;
+stop = 0;
+nan_flag = 0;
- model_dynamic = pfm.dynamic_model;
+model_dynamic = pfm.dynamic_model;
- Y = endo_simul(:);
+Y = endo_simul(:);
- if pfm.verbose
- disp (['-----------------------------------------------------']) ;
- disp (['MODEL SIMULATION :']) ;
- fprintf('\n') ;
- end
+if pfm.verbose
+ disp (['-----------------------------------------------------']) ;
+ disp (['MODEL SIMULATION :']) ;
+ fprintf('\n') ;
+end
- if pfm.use_bytecode
- [flag, endo_simul]=bytecode(Y, exo_simul, pfm.params);
- return;
- end
+if pfm.use_bytecode
+ [flag, endo_simul]=bytecode(Y, exo_simul, pfm.params);
+ return;
+end
- z = Y(find(pfm.lead_lag_incidence'));
- [d1,jacobian] = model_dynamic(z,exo_simul,pfm.params,pfm.steady_state,2);
+z = Y(find(pfm.lead_lag_incidence'));
+[d1,jacobian] = model_dynamic(z,exo_simul,pfm.params,pfm.steady_state,2);
- % Initialization of the jacobian of the stacked model.
- A = sparse([],[],[],pfm.periods*pfm.ny,pfm.periods*pfm.ny,pfm.periods*nnz(jacobian));
+% Initialization of the jacobian of the stacked model.
+A = sparse([],[],[],pfm.periods*pfm.ny,pfm.periods*pfm.ny,pfm.periods*nnz(jacobian));
- % Initialization of the Newton residuals.
- res = zeros(pfm.periods*pfm.ny,1);
+% Initialization of the Newton residuals.
+res = zeros(pfm.periods*pfm.ny,1);
- h1 = clock;
-
- % Newton loop.
- for iter = 1:pfm.maxit_
- h2 = clock;
- i_rows = 1:pfm.ny;
- i_cols = find(pfm.lead_lag_incidence');
- i_cols_A = i_cols;
- % Fill the jacobian of the stacked model.
- for it = 2:(pfm.periods+1)
- [d1,jacobian] = model_dynamic(Y(i_cols),exo_simul,pfm.params,pfm.steady_state,it);
- if it == 2
- A(i_rows,pfm.i_cols_A1) = jacobian(:,pfm.i_cols_1);
- elseif it == pfm.periods+1
- A(i_rows,i_cols_A(pfm.i_cols_T)) = jacobian(:,pfm.i_cols_T);
- else
- A(i_rows,i_cols_A) = jacobian(:,pfm.i_cols_j);
- end
- res(i_rows) = d1;
- i_rows = i_rows + pfm.ny;
- i_cols = i_cols + pfm.ny;
- if it > 2
- i_cols_A = i_cols_A + pfm.ny;
- end
- end
- % Stop if Newton residuals are zero.
- err = max(abs(res));
- if err < pfm.tolerance
- stop = 1 ;
- if pfm.verbose
- fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
- fprintf('\n') ;
- disp([' Convergency obtained.']) ;
- fprintf('\n') ;
- end
- flag = 0;% Convergency obtained.
- endo_simul = reshape(Y,pfm.ny,pfm.periods+2);
- break
+h1 = clock;
+
+% Newton loop.
+for iter = 1:pfm.maxit_
+ h2 = clock;
+ i_rows = 1:pfm.ny;
+ i_cols = find(pfm.lead_lag_incidence');
+ i_cols_A = i_cols;
+ % Fill the jacobian of the stacked model.
+ for it = 2:(pfm.periods+1)
+ [d1,jacobian] = model_dynamic(Y(i_cols),exo_simul,pfm.params,pfm.steady_state,it);
+ if it == 2
+ A(i_rows,pfm.i_cols_A1) = jacobian(:,pfm.i_cols_1);
+ elseif it == pfm.periods+1
+ A(i_rows,i_cols_A(pfm.i_cols_T)) = jacobian(:,pfm.i_cols_T);
+ else
+ A(i_rows,i_cols_A) = jacobian(:,pfm.i_cols_j);
end
- % Compute the Newton step.
- dy = -A\res;
- if any(isnan(dy))
- nan_flag = 1;
- break
+ res(i_rows) = d1;
+ i_rows = i_rows + pfm.ny;
+ i_cols = i_cols + pfm.ny;
+ if it > 2
+ i_cols_A = i_cols_A + pfm.ny;
end
- % Update the endogenous variables paths.
- Y(pfm.i_upd) = Y(pfm.i_upd) + dy;
end
-
- if ~stop
+ % Stop if Newton residuals are zero.
+ err = max(abs(res));
+ if err < pfm.tolerance
+ stop = 1 ;
if pfm.verbose
fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
fprintf('\n') ;
- disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
+ disp([' Convergency obtained.']) ;
fprintf('\n') ;
end
- flag = 1;% more iterations are needed.
- endo_simul = 1;
+ flag = 0;% Convergency obtained.
+ endo_simul = reshape(Y,pfm.ny,pfm.periods+2);
+ break
end
- if nan_flag
- if pfm.verbose
- fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
- fprintf('\n') ;
- disp(['WARNING : NaNs!']) ;
- fprintf('\n') ;
- end
- flag = 1;
- endo_simul = 1;
+ % Compute the Newton step.
+ dy = -A\res;
+ if any(isnan(dy))
+ nan_flag = 1;
+ break
end
+ % Update the endogenous variables paths.
+ Y(pfm.i_upd) = Y(pfm.i_upd) + dy;
+end
+
+if ~stop
if pfm.verbose
- disp (['-----------------------------------------------------']) ;
- end
\ No newline at end of file
+ fprintf('\n') ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ fprintf('\n') ;
+ disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
+ fprintf('\n') ;
+ end
+ flag = 1;% more iterations are needed.
+ endo_simul = 1;
+end
+if nan_flag
+ if pfm.verbose
+ fprintf('\n') ;
+ disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
+ fprintf('\n') ;
+ disp(['WARNING : NaNs!']) ;
+ fprintf('\n') ;
+ end
+ flag = 1;
+ endo_simul = 1;
+end
+if pfm.verbose
+ disp (['-----------------------------------------------------']) ;
+end
\ No newline at end of file
diff --git a/matlab/steady.m b/matlab/steady.m
index 760fb1d3f5f9aee4d51f3efcbcbf02a1ed9ede52..5f11f73c83e98dce0dad78bbdbc8f444e3c8b91f 100644
--- a/matlab/steady.m
+++ b/matlab/steady.m
@@ -77,7 +77,7 @@ if info(1)
error('Homotopy step failed')
end
end
-
+
[steady_state,M_.params,info] = steady_(M_,options_,oo_);
oo_.steady_state = steady_state;
diff --git a/matlab/step_length_correction.m b/matlab/step_length_correction.m
index 8950bc7649d387190c47bddc703e1ebc76f09e18..5af7dcaee870f9ba81255b6026cb3f906869a6d0 100644
--- a/matlab/step_length_correction.m
+++ b/matlab/step_length_correction.m
@@ -17,8 +17,8 @@ function c = step_length_correction(x,scale,i)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- if isempty(scale)
- c = 10^round(log10(abs(x)));
- else
- c = scale(i);
- end
+if isempty(scale)
+ c = 10^round(log10(abs(x)));
+else
+ c = scale(i);
+end
diff --git a/matlab/stoch_simul.m b/matlab/stoch_simul.m
index d187a45a530ae59e7947ea821046561d47860740..5e7decfff752ddb3a601d00d569d1621d063a1ad 100644
--- a/matlab/stoch_simul.m
+++ b/matlab/stoch_simul.m
@@ -159,7 +159,7 @@ if options_.periods > 0 && ~PI_PCL_solver
[ys, oo_] = simult(y0,oo_.dr,M_,options_,oo_);
oo_.endo_simul = ys;
if ~options_.minimal_workspace
- dyn2vec;
+ dyn2vec;
end
end
diff --git a/matlab/stochastic_solvers.m b/matlab/stochastic_solvers.m
index 7332654a8d5ddc79cfb0991e45040ba24b78a498..1da71109ddd4f84286e3c40473a44194bddc0daf 100644
--- a/matlab/stochastic_solvers.m
+++ b/matlab/stochastic_solvers.m
@@ -59,16 +59,16 @@ if M_.hessian_eq_zero && local_order~=1
end
if (options_.aim_solver == 1) && (local_order > 1)
- error('Option "aim_solver" is incompatible with order >= 2')
+ error('Option "aim_solver" is incompatible with order >= 2')
end
if M_.maximum_endo_lag == 0
if local_order >= 2
- fprintf('\nSTOCHASTIC_SOLVER: Dynare does not solve purely forward models at higher order.\n')
- fprintf('STOCHASTIC_SOLVER: To circumvent this restriction, you can add a backward-looking dummy equation of the form:\n')
- fprintf('STOCHASTIC_SOLVER: junk=0.9*junk(-1);\n')
- error(['2nd and 3rd order approximation not implemented for purely ' ...
- 'forward models'])
+ fprintf('\nSTOCHASTIC_SOLVER: Dynare does not solve purely forward models at higher order.\n')
+ fprintf('STOCHASTIC_SOLVER: To circumvent this restriction, you can add a backward-looking dummy equation of the form:\n')
+ fprintf('STOCHASTIC_SOLVER: junk=0.9*junk(-1);\n')
+ error(['2nd and 3rd order approximation not implemented for purely ' ...
+ 'forward models'])
end
if M_.exo_det_nbr~=0
fprintf('\nSTOCHASTIC_SOLVER: Dynare does not solve purely forward models with var_exo_det.\n')
@@ -88,7 +88,7 @@ end
if options_.k_order_solver;
if options_.risky_steadystate
[dr,info] = dyn_risky_steadystate_solver(oo_.steady_state,M_,dr, ...
- options_,oo_);
+ options_,oo_);
else
orig_order = options_.order;
options_.order = local_order;
@@ -119,12 +119,12 @@ if local_order == 1
jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
else
[junk,jacobia_] = feval([M_.fname '_dynamic'],z(iyr0),exo_simul, ...
- M_.params, dr.ys, it_);
+ M_.params, dr.ys, it_);
end;
elseif local_order == 2
if (options_.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
- M_.params, dr.ys, 1);
+ M_.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x];
else
[junk,jacobia_,hessian1] = feval([M_.fname '_dynamic'],z(iyr0),...
@@ -139,9 +139,9 @@ elseif local_order == 2
[infrow,infcol]=find(isinf(hessian1));
if options_.debug
if ~isempty(infrow)
- fprintf('\nSTOCHASTIC_SOLVER: The Hessian of the dynamic model contains Inf.\n')
- fprintf('STOCHASTIC_SOLVER: Try running model_diagnostics to find the source of the problem.\n')
- save([M_.fname '_debug.mat'],'hessian1')
+ fprintf('\nSTOCHASTIC_SOLVER: The Hessian of the dynamic model contains Inf.\n')
+ fprintf('STOCHASTIC_SOLVER: Try running model_diagnostics to find the source of the problem.\n')
+ save([M_.fname '_debug.mat'],'hessian1')
end
end
if ~isempty(infrow)
@@ -163,12 +163,12 @@ elseif local_order == 2
end
[infrow,infcol]=find(isinf(jacobia_));
-
+
if options_.debug
if ~isempty(infrow)
- fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains Inf. The problem is associated with:\n\n')
- display_problematic_vars_Jacobian(infrow,infcol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
- save([M_.fname '_debug.mat'],'jacobia_')
+ fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains Inf. The problem is associated with:\n\n')
+ display_problematic_vars_Jacobian(infrow,infcol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
+ save([M_.fname '_debug.mat'],'jacobia_')
end
end
@@ -195,17 +195,17 @@ end
[nanrow,nancol]=find(isnan(jacobia_));
if options_.debug
if ~isempty(nanrow)
- fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains NaN. The problem is associated with:\n\n')
- display_problematic_vars_Jacobian(nanrow,nancol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
- save([M_.fname '_debug.mat'],'jacobia_')
+ fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains NaN. The problem is associated with:\n\n')
+ display_problematic_vars_Jacobian(nanrow,nancol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
+ save([M_.fname '_debug.mat'],'jacobia_')
end
end
if ~isempty(nanrow)
- info(1) = 8;
- NaN_params=find(isnan(M_.params));
- info(2:length(NaN_params)+1) = NaN_params;
- return
+ info(1) = 8;
+ NaN_params=find(isnan(M_.params));
+ info(2:length(NaN_params)+1) = NaN_params;
+ return
end
kstate = dr.kstate;
@@ -272,7 +272,7 @@ else
dr = dyn_second_order_solver(jacobia_,hessian1,dr,M_,...
options_.threads.kronecker.A_times_B_kronecker_C,...
options_.threads.kronecker.sparse_hessian_times_B_kronecker_C);
-
+
% reordering second order derivatives, used for deterministic
% variables below
k1 = nonzeros(M_.lead_lag_incidence(:,order_var)');
@@ -306,7 +306,7 @@ if M_.exo_det_nbr > 0
hu = dr.ghu(nstatic+[1:nspred],:);
hud = dr.ghud{1}(nstatic+1:nstatic+nspred,:);
zx = [eye(nspred);dr.ghx(k0,:);gx*dr.Gy;zeros(M_.exo_nbr+M_.exo_det_nbr, ...
- nspred)];
+ nspred)];
zu = [zeros(nspred,M_.exo_nbr); dr.ghu(k0,:); gx*hu; zeros(M_.exo_nbr+M_.exo_det_nbr, ...
M_.exo_nbr)];
zud=[zeros(nspred,M_.exo_det_nbr);dr.ghud{1};gx(:,1:nspred)*hud;zeros(M_.exo_nbr,M_.exo_det_nbr);eye(M_.exo_det_nbr)];
@@ -377,7 +377,7 @@ if options_.loglinear
length(il),1);
end
if local_order > 1
- error('Loglinear options currently only works at order 1')
+ error('Loglinear options currently only works at order 1')
end
end
end
diff --git a/matlab/store_smoother_results.m b/matlab/store_smoother_results.m
index 98882dde376f04e1b00e8ece05e0b31bf15d0830..d8fcbbd069859d01a6a4ccd699ff7291aba64a64 100644
--- a/matlab/store_smoother_results.m
+++ b/matlab/store_smoother_results.m
@@ -167,7 +167,7 @@ for i_endo_in_bayestopt_smoother_varlist=bayestopt_.smoother_saved_var_list'
end
oo_.UpdatedVariables.(deblank(M_.endo_names(i_endo_declaration_order,:)))=updated_variables(i_endo_in_dr,:)'+constant_current_variable;
end
-
+
%% Add trend and constant for observed variables
for pos_iter=1:length(bayestopt_.mf)
oo_.Smoother.Constant.(deblank(M_.endo_names(bayestopt_.mfys(pos_iter),:)))=constant_part(pos_iter,:)';
@@ -229,8 +229,8 @@ end
%% Smoothed measurement errors
if ~isequal(M_.H,0)
-% measurement_error_indices=find(diag(M_.H)~=0);
+ % measurement_error_indices=find(diag(M_.H)~=0);
for meas_error_iter=1:length(options_.varobs)
- oo_.SmoothedMeasurementErrors.(options_.varobs{meas_error_iter})= measurement_error(meas_error_iter,:)';
+ oo_.SmoothedMeasurementErrors.(options_.varobs{meas_error_iter})= measurement_error(meas_error_iter,:)';
end
end
diff --git a/matlab/subset.m b/matlab/subset.m
index dedbdbfba2ec4a5997c1b80b6b0444199ab6ce67..95ad2364579763f40e9590cf3053361cbe29902f 100644
--- a/matlab/subset.m
+++ b/matlab/subset.m
@@ -54,8 +54,8 @@ else
for i = 1:length(ExcludedParamNames)
tmp = strmatch(ExcludedParamNames{i},M_.exo_names);
if ~isempty(tmp) && ( strcmpi(info,'All') || strcmpi(info,'StructuralShocks') || ...
- strcmpi(info,'StructuralShocksWithoutCorrelations') || ...
- strcmpi(info,'AllWithoutMeasurementErrors') )
+ strcmpi(info,'StructuralShocksWithoutCorrelations') || ...
+ strcmpi(info,'AllWithoutMeasurementErrors') )
% The parameter the user wants to exclude is related to the size of the structural innovations.
if ncx
disp(['I cannot exclude some of the structural variances if the'])
@@ -66,7 +66,7 @@ else
elseif isempty(tmp) && nvn
tmp = strmatch(ExcludedParamNames{i},options_.varobs);
if ~isempty(tmp) && ( strcmpi(info,'All') || strcmpi(info,'MeasurementErrors') || ...
- strcmpi(info,'MeasurementErrorsWithoutCorrelations') )
+ strcmpi(info,'MeasurementErrorsWithoutCorrelations') )
% The parameter the user wants to exclude is related to the size of the measurement errors variances.
tmp = nvx+tmp;
if ncn
diff --git a/matlab/th_autocovariances.m b/matlab/th_autocovariances.m
index 966ece83415ef7f71a5c7a92adf231a7d5ed33f6..0f08c18cec1c4de85520408ea41a59bc184cd8d5 100644
--- a/matlab/th_autocovariances.m
+++ b/matlab/th_autocovariances.m
@@ -237,7 +237,7 @@ else% ==> Theoretical filters.
f_hp = zeros(length(ivar),length(ivar));
else
f_omega =(1/(2*pi))*([(IA-A*tneg(ig))\ghu1;IE]...
- *M_.Sigma_e*[ghu1'/(IA-A'*tpos(ig)) IE]); % spectral density of state variables; top formula Uhlig (2001), p. 20 with N=0
+ *M_.Sigma_e*[ghu1'/(IA-A'*tpos(ig)) IE]); % spectral density of state variables; top formula Uhlig (2001), p. 20 with N=0
g_omega = [aa*tneg(ig) bb]*f_omega*[aa'*tpos(ig); bb']; % spectral density of selected variables; middle formula Uhlig (2001), p. 20; only middle block, i.e. y_t'
f_hp = filter_gain(ig)^2*g_omega; % spectral density of selected filtered series; top formula Uhlig (2001), p. 21;
end
diff --git a/matlab/thet2tau.m b/matlab/thet2tau.m
index 929193ca44d4b440aa5c4c0d4a4b2c5dccd03b6b..54673e5420b1016bfd62d279c2fe49a97e57eab9 100644
--- a/matlab/thet2tau.m
+++ b/matlab/thet2tau.m
@@ -51,7 +51,7 @@ elseif flagmoments==-1
[I,J]=find(M_.lead_lag_incidence');
yy0=oo_.dr.ys(I);
[residual, g1] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
- M_.params, oo_.dr.ys, 1);
+ M_.params, oo_.dr.ys, 1);
tau=[oo_.dr.ys(oo_.dr.order_var); g1(:)];
else
diff --git a/matlab/user_has_matlab_license.m b/matlab/user_has_matlab_license.m
index 86b8c94bd688133738578f3aa18294fd8631ed42..51d7cf0b6761337d295466d2495a250d6b1cf011 100644
--- a/matlab/user_has_matlab_license.m
+++ b/matlab/user_has_matlab_license.m
@@ -38,14 +38,14 @@ if ~hasLicense
end
switch toolbox
%use function unique to toolbox
- case 'statistics_toolbox'
- n = 'gppdf';
- case 'optimization_toolbox'
- n='fsolve';
- case 'GADS_Toolbox'
- n='simulannealbnd';
- case 'control_toolbox'
- n='dlyap';
+ case 'statistics_toolbox'
+ n = 'gppdf';
+ case 'optimization_toolbox'
+ n='fsolve';
+ case 'GADS_Toolbox'
+ n='simulannealbnd';
+ case 'control_toolbox'
+ n='dlyap';
end
hasInstallation=check_toolbox_installation(n);
if ~hasInstallation
diff --git a/matlab/utilities/dataset/initialize_dataset.m b/matlab/utilities/dataset/initialize_dataset.m
index 5d992a288955d728734b7b5f216fe03f371ef0fc..0b00df4dcf72d5abebac1a0f60cb3b0a088c564c 100644
--- a/matlab/utilities/dataset/initialize_dataset.m
+++ b/matlab/utilities/dataset/initialize_dataset.m
@@ -55,7 +55,7 @@ if dataset_.info.nvobs-size(rawdata,2)
end
if size(rawdata,1)~=dataset_.info.ntobs
- fprintf('Restricting the sample to observations %d to %d. Using in total %d observations. \n',first,first+dataset_.info.ntobs-1,dataset_.info.ntobs)
+ fprintf('Restricting the sample to observations %d to %d. Using in total %d observations. \n',first,first+dataset_.info.ntobs-1,dataset_.info.ntobs)
end
rawdata = rawdata(first:(first+dataset_.info.ntobs-1),:);
diff --git a/matlab/utilities/dataset/lagged.m b/matlab/utilities/dataset/lagged.m
index 31a58909ac117db57b9c1197b84cad313a6e8daf..87a7c64a440c5d1333d5dbad697acb6038ad3fa2 100644
--- a/matlab/utilities/dataset/lagged.m
+++ b/matlab/utilities/dataset/lagged.m
@@ -1,32 +1,32 @@
-function xlag = lagged(x, n);
-% xlag = lagged(x, n);
-% applies n-lags backward shift operator to x
-%
-% INPUTS
-% x = time series
-% n = number of backward shifts [DEFAULT=1]
-%
-% OUTPUT
-% xlag = backward shifted series
-
-% Copyright (C) 2017 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if nargin==1, n=1; end
-
-x=x(:);
-xlag=[NaN(n,1); x(1:end-n)];
+function xlag = lagged(x, n);
+% xlag = lagged(x, n);
+% applies n-lags backward shift operator to x
+%
+% INPUTS
+% x = time series
+% n = number of backward shifts [DEFAULT=1]
+%
+% OUTPUT
+% xlag = backward shifted series
+
+% Copyright (C) 2017 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+if nargin==1, n=1; end
+
+x=x(:);
+xlag=[NaN(n,1); x(1:end-n)];
diff --git a/matlab/utilities/dataset/makedataset.m b/matlab/utilities/dataset/makedataset.m
index 6ac4035c838a36e49990cb61e9f135bdb19d55da..e46f7aa76d6ebced0455258f20aa5b140a3e2c12 100644
--- a/matlab/utilities/dataset/makedataset.m
+++ b/matlab/utilities/dataset/makedataset.m
@@ -37,7 +37,7 @@ if isempty(DynareOptions.datafile) && isempty(DynareOptions.dataset.file) && ise
if gsa_flag
DynareDataset = dseries();
DatasetInfo = struct('missing', struct('state', 0, 'aindex', [], 'vindex', [], 'number_of_observations', NaN, 'no_more_missing_observations', NaN), ...
- 'descriptive', struct('mean', [], 'covariance', [], 'correlation', [], 'autocovariance', []));
+ 'descriptive', struct('mean', [], 'covariance', [], 'correlation', [], 'autocovariance', []));
newdatainterface=0;
return
else
diff --git a/matlab/utilities/dataset/quarterly2annual.m b/matlab/utilities/dataset/quarterly2annual.m
index cc655f1e0d31c35de3b973a03d24cc68a2e358f7..4a01941bda1f51b409c57b36ca1c8c840d5f05c3 100644
--- a/matlab/utilities/dataset/quarterly2annual.m
+++ b/matlab/utilities/dataset/quarterly2annual.m
@@ -1,144 +1,144 @@
-function [ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog,aux)
-% function [ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog,aux)
-% transforms quarterly (log-)level time series to annual level and growth rate
-% it accounts for stock/flow/deflator series.
-%
-% INPUTS
-% y quarterly time series
-% yss steady state of y
-% GYTREND0 growth rate of y
-% type 1 sum (default)
-% 2 average
-% 3 last period (Q4)
-% 4 geometric average
-% 5 annual price as quantity weighted average
-% 6 annual quantity from average price
-% 7 annual nominal from Q real and deflator
-% islog 0 level (default)
-% 1 log-level
-% 2 growth rate Q frequency
-% aux optional input used when type>4
-%
-%
-% OUTPUTS
-% ya annual (log-)level
-% yass annual steadystate (log-)level
-% gya annual growth rate
-% gyass annual growth rate steadystate
-
-% Copyright (C) 2017 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
-if nargin ==0,
- disp('[ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog);')
- return
-end
-
-if nargin<4 || isempty(type),
- type=1;
-end
-if nargin<5 || isempty(islog),
- islog=0;
-end
-if isstruct(aux),
- yaux=aux.y;
- yauxss=aux.yss;
- islogaux=aux.islog;
- GYTREND0aux=aux.GYTREND0;
- typeaux=aux.type;
- if islogaux
- yaux=exp(yaux+yauxss);
- yauxss=exp(yauxss);
- yaux=yaux-yauxss;
- end
-elseif type > 4,
- error('TYPE>4 requires auxiliary variable!')
-end
-if islog == 2,
- % construct loglevel out of growth rate
- y = cumsum(y);
- yss=0;
- islog=1;
-end
-if islog == 1
- y=exp(y+yss);
- yss=exp(yss);
- y=y-yss;
-end
-switch type
-
- case 1
- yass = yss*(exp(-GYTREND0*3)+exp(-GYTREND0*2)+exp(-GYTREND0)+1);
- tmp = lagged(y,3)*exp(-GYTREND0*3)+lagged(y,2)*exp(-GYTREND0*2)+lagged(y,1)*exp(-GYTREND0)+y; % annualized level
- ya = tmp(4:4:end);
-
- case 2
- yass = yss*(exp(-GYTREND0*3)+exp(-GYTREND0*2)+exp(-GYTREND0)+1)/4;
- tmp = (lagged(y,3)*exp(-GYTREND0*3)+lagged(y,2)*exp(-GYTREND0*2)+lagged(y,1)*exp(-GYTREND0)+y)/4; % annualized level
- ya = tmp(4:4:end);
-
- case 3
- yass=yss;
- tmp = y;
- ya = tmp(4:4:end);
-
- case 4
- yass = yss*(exp(-GYTREND0*3/2));
- tmp = (lagged(y+yss,3)*exp(-GYTREND0*3).*lagged(y+yss,2)*exp(-GYTREND0*2).*lagged(y+yss,1)*exp(-GYTREND0).*(y+yss)).^(1/4); % annualized level
- tmp = tmp - yass;
- ya = tmp(4:4:end);
-
- case 5
- % nominal series
- yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
- [yna, ynass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
- % real series
- [yra, yrass] = quarterly2annual(yaux,yauxss,GYTREND0aux,typeaux,0,0);
- % deflator
- yass = ynass/yrass;
- ya = (yna+ynass)./(yr+yrass)-yass;
-
- case 6
- % nominal series
- yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
- [yna, ynass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
- % deflator
- [pa, pass] = quarterly2annual(yaux,yauxss,GYTREND0aux,2,0,0);
- % real series
- yass = ynass/pass;
- ya = (yna+ynass)./(pa+pass)-yass;
-
- case 7
- % nominal series
- yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
- [ya, yass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
- GYTREND0=GYTREND0+GYTREND0aux;
-
- otherwise
- error('Wrong type input')
-end
-
-% annual growth rate
-gyass = GYTREND0*4;
-gya = (ya+yass)./(lagged(ya,1)+yass).*exp(4*GYTREND0)-1-gyass;
-
-if islog
- ya=log(ya+yass);
- yass=log(yass);
- ya=ya-yass;
-end
-
+function [ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog,aux)
+% function [ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog,aux)
+% transforms quarterly (log-)level time series to annual level and growth rate
+% it accounts for stock/flow/deflator series.
+%
+% INPUTS
+% y quarterly time series
+% yss steady state of y
+% GYTREND0 growth rate of y
+% type 1 sum (default)
+% 2 average
+% 3 last period (Q4)
+% 4 geometric average
+% 5 annual price as quantity weighted average
+% 6 annual quantity from average price
+% 7 annual nominal from Q real and deflator
+% islog 0 level (default)
+% 1 log-level
+% 2 growth rate Q frequency
+% aux optional input used when type>4
+%
+%
+% OUTPUTS
+% ya annual (log-)level
+% yass annual steadystate (log-)level
+% gya annual growth rate
+% gyass annual growth rate steadystate
+
+% Copyright (C) 2017 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+if nargin ==0,
+ disp('[ya, yass, gya, gyass] = quarterly2annual(y,yss,GYTREND0,type,islog);')
+ return
+end
+
+if nargin<4 || isempty(type),
+ type=1;
+end
+if nargin<5 || isempty(islog),
+ islog=0;
+end
+if isstruct(aux),
+ yaux=aux.y;
+ yauxss=aux.yss;
+ islogaux=aux.islog;
+ GYTREND0aux=aux.GYTREND0;
+ typeaux=aux.type;
+ if islogaux
+ yaux=exp(yaux+yauxss);
+ yauxss=exp(yauxss);
+ yaux=yaux-yauxss;
+ end
+elseif type > 4,
+ error('TYPE>4 requires auxiliary variable!')
+end
+if islog == 2,
+ % construct loglevel out of growth rate
+ y = cumsum(y);
+ yss=0;
+ islog=1;
+end
+if islog == 1
+ y=exp(y+yss);
+ yss=exp(yss);
+ y=y-yss;
+end
+switch type
+
+ case 1
+ yass = yss*(exp(-GYTREND0*3)+exp(-GYTREND0*2)+exp(-GYTREND0)+1);
+ tmp = lagged(y,3)*exp(-GYTREND0*3)+lagged(y,2)*exp(-GYTREND0*2)+lagged(y,1)*exp(-GYTREND0)+y; % annualized level
+ ya = tmp(4:4:end);
+
+ case 2
+ yass = yss*(exp(-GYTREND0*3)+exp(-GYTREND0*2)+exp(-GYTREND0)+1)/4;
+ tmp = (lagged(y,3)*exp(-GYTREND0*3)+lagged(y,2)*exp(-GYTREND0*2)+lagged(y,1)*exp(-GYTREND0)+y)/4; % annualized level
+ ya = tmp(4:4:end);
+
+ case 3
+ yass=yss;
+ tmp = y;
+ ya = tmp(4:4:end);
+
+ case 4
+ yass = yss*(exp(-GYTREND0*3/2));
+ tmp = (lagged(y+yss,3)*exp(-GYTREND0*3).*lagged(y+yss,2)*exp(-GYTREND0*2).*lagged(y+yss,1)*exp(-GYTREND0).*(y+yss)).^(1/4); % annualized level
+ tmp = tmp - yass;
+ ya = tmp(4:4:end);
+
+ case 5
+ % nominal series
+ yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
+ [yna, ynass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
+ % real series
+ [yra, yrass] = quarterly2annual(yaux,yauxss,GYTREND0aux,typeaux,0,0);
+ % deflator
+ yass = ynass/yrass;
+ ya = (yna+ynass)./(yr+yrass)-yass;
+
+ case 6
+ % nominal series
+ yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
+ [yna, ynass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
+ % deflator
+ [pa, pass] = quarterly2annual(yaux,yauxss,GYTREND0aux,2,0,0);
+ % real series
+ yass = ynass/pass;
+ ya = (yna+ynass)./(pa+pass)-yass;
+
+ case 7
+ % nominal series
+ yn = (y+yss).*(yaux+yauxss) - yss.*yauxss;
+ [ya, yass] = quarterly2annual(yn,yss.*yauxss,GYTREND0+GYTREND0aux,typeaux,0,0);
+ GYTREND0=GYTREND0+GYTREND0aux;
+
+ otherwise
+ error('Wrong type input')
+end
+
+% annual growth rate
+gyass = GYTREND0*4;
+gya = (ya+yass)./(lagged(ya,1)+yass).*exp(4*GYTREND0)-1-gyass;
+
+if islog
+ ya=log(ya+yass);
+ yass=log(yass);
+ ya=ya-yass;
+end
+
diff --git a/matlab/utilities/doc/get_internal_doc_block.m b/matlab/utilities/doc/get_internal_doc_block.m
index 718b2a28d94f3802a21a0a1037bc62f89fff5098..77e4f6625b45fba5661fbe507edc40f75a91ac1a 100644
--- a/matlab/utilities/doc/get_internal_doc_block.m
+++ b/matlab/utilities/doc/get_internal_doc_block.m
@@ -1,6 +1,6 @@
function block = get_internal_doc_block(fname,fpath)
% Extract doc sections from matlab's routine.
-
+
% Copyright (C) 2011-2012 Dynare Team
%
% This file is part of Dynare.
@@ -22,7 +22,7 @@ function block = get_internal_doc_block(fname,fpath)
% Default output
block = [];
-
+
% Open the matlab file.
mid = fopen([fpath '/' fname '.m'],'r');
@@ -43,11 +43,11 @@ if ( isempty(b1) && isempty(b2) && isempty(b) )
return
else
if ( (~isempty(b1) && isempty(b2) && isempty(b)) || ...
- (isempty(b1) && ~isempty(b2) && isempty(b)) || ...
- (isempty(b1) && isempty(b2) && ~isempty(b)) || ...
- (isempty(b1) && ~isempty(b2) && ~isempty(b)) || ...
- (~isempty(b1) && isempty(b2) && ~isempty(b)) || ...
- (~isempty(b1) && ~isempty(b2) && isempty(b)) )
+ (isempty(b1) && ~isempty(b2) && isempty(b)) || ...
+ (isempty(b1) && isempty(b2) && ~isempty(b)) || ...
+ (isempty(b1) && ~isempty(b2) && ~isempty(b)) || ...
+ (~isempty(b1) && isempty(b2) && ~isempty(b)) || ...
+ (~isempty(b1) && ~isempty(b2) && isempty(b)) )
error('get_internal_doc_block:: There is a problem with the internal block definition!')
end
if ( b2~=b(end) || b1~=b(1) || any(b-transpose(b1:1:b2)) )
diff --git a/matlab/utilities/general/clean_current_folder.m b/matlab/utilities/general/clean_current_folder.m
index e3408bec1577cf074d23ecb46e4eab4faf58f90a..8375c5607aeb5e5d7175036da12278402c2f33dd 100644
--- a/matlab/utilities/general/clean_current_folder.m
+++ b/matlab/utilities/general/clean_current_folder.m
@@ -1,4 +1,4 @@
-function clean()
+function clean_current_folder()
% Copyright (C) 2014 Dynare Team
%
diff --git a/matlab/utilities/general/compare_vectors.m b/matlab/utilities/general/compare_vectors.m
index 97e916c7573cee000345a042880d70d8d4acc71e..e0ec681a2cbfdfdd82cafaf3a26f975ffc10f2b1 100644
--- a/matlab/utilities/general/compare_vectors.m
+++ b/matlab/utilities/general/compare_vectors.m
@@ -1,5 +1,5 @@
function C = compare_vectors(f, A, B) % --*-- Unitary tests --*--
-
+
% Performs lexicographical comparison of vectors.
%
% INPUTS
@@ -12,7 +12,7 @@ function C = compare_vectors(f, A, B) % --*-- Unitary tests --*--
%
% REMARKS
% o It is assumed that vectors A and B have the same number of elements.
-
+
% Copyright (C) 2013 Dynare Team
%
% This file is part of Dynare.
diff --git a/matlab/utilities/general/demean.m b/matlab/utilities/general/demean.m
index 4bf53aaa62d6ed66275093e09b442be7a597a202..aed9eb4b112b1161bbe2bd3cdd9c5f34cf75ebdb 100644
--- a/matlab/utilities/general/demean.m
+++ b/matlab/utilities/general/demean.m
@@ -1,6 +1,6 @@
function c = demean(x)
% Removes the mean of each column of a matrix.
-
+
%@info:
%! @deftypefn {Function File} {@var{c} =} demean (@var{x})
%! @anchor{demean}
@@ -45,7 +45,7 @@ function c = demean(x)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
+
if ndim(x)==1
c = x-mean(x);
elseif ndim(x)==2
diff --git a/matlab/utilities/general/dyn_mex.m b/matlab/utilities/general/dyn_mex.m
index 7892ee9bed66ce2563e0f36443f820641ff895e2..d4fd7336f952ea3ffb77d7c2fadd9e0f9b7df92e 100644
--- a/matlab/utilities/general/dyn_mex.m
+++ b/matlab/utilities/general/dyn_mex.m
@@ -14,7 +14,7 @@ function dyn_mex(win_compiler,basename,force)
% none
%
-
+
% Copyright (C) 2015-2016 Dynare Team
%
% This file is part of Dynare.
@@ -47,26 +47,26 @@ end
if ~exist('OCTAVE_VERSION')
% Some mex commands are enclosed in an eval(), because otherwise it will make Octave fail
if ispc
- if strcmp(win_compiler,'msvc')
- % MATLAB/Windows + Microsoft Visual C++
- % Add /TP flag as fix for #1227
- eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" COMPFLAGS="/TP" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
- eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Static" COMPFLAGS="/TP" ' basename '_static.c ' basename '_static_mex.c'])
- elseif strcmp(win_compiler,'mingw')
- eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
- eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Static" ' basename '_static.c ' basename '_static_mex.c'])
- elseif strcmp(win_compiler,'cygwin') %legacy support for Cygwin with mexopts.bat
- % MATLAB/Windows + Cygwin g++
- eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
- 'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
- basename '_dynamic.c ' basename '_dynamic_mex.c'])
- eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
- 'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
- basename '_static.c ' basename '_static_mex.c'])
- else
- error(['When using the USE_DLL option, you must give either ' ...
- '''cygwin'', ''mingw'' or ''msvc'' option to the ''dynare'' command'])
- end
+ if strcmp(win_compiler,'msvc')
+ % MATLAB/Windows + Microsoft Visual C++
+ % Add /TP flag as fix for #1227
+ eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" COMPFLAGS="/TP" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
+ eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Static" COMPFLAGS="/TP" ' basename '_static.c ' basename '_static_mex.c'])
+ elseif strcmp(win_compiler,'mingw')
+ eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Dynamic" ' basename '_dynamic.c ' basename '_dynamic_mex.c'])
+ eval(['mex -O LINKFLAGS="$LINKFLAGS /export:Static" ' basename '_static.c ' basename '_static_mex.c'])
+ elseif strcmp(win_compiler,'cygwin') %legacy support for Cygwin with mexopts.bat
+ % MATLAB/Windows + Cygwin g++
+ eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
+ 'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
+ basename '_dynamic.c ' basename '_dynamic_mex.c'])
+ eval(['mex -O PRELINK_CMDS1="echo EXPORTS > mex.def & echo ' ...
+ 'mexFunction >> mex.def & echo Dynamic >> mex.def" ' ...
+ basename '_static.c ' basename '_static_mex.c'])
+ else
+ error(['When using the USE_DLL option, you must give either ' ...
+ '''cygwin'', ''mingw'' or ''msvc'' option to the ''dynare'' command'])
+ end
elseif isunix && ~ismac
% MATLAB/Linux
if matlab_ver_less_than('8.3')
diff --git a/matlab/utilities/general/isinestimationobjective.m b/matlab/utilities/general/isinestimationobjective.m
index b6bbb5faf4e5b35390c658b692b4392d92e285fd..023e4cb14e77c29bffbeff747d3003ae15425651 100644
--- a/matlab/utilities/general/isinestimationobjective.m
+++ b/matlab/utilities/general/isinestimationobjective.m
@@ -25,8 +25,8 @@ tmp = struct2cell(dbstack);
list_of_previously_called_routines = transpose(tmp(2,:));
if ~isempty(strmatch('dsge_likelihood', list_of_previously_called_routines, 'exact')) || ...
- ~isempty(strmatch('dsge_var_likelihood', list_of_previously_called_routines, 'exact')) || ...
- ~isempty(strmatch('non_linear_dsge_likelihood', list_of_previously_called_routines, 'exact')) || ...
- ~isempty(strmatch('simulated_moments_estimation', list_of_previously_called_routines, 'exact'))
+ ~isempty(strmatch('dsge_var_likelihood', list_of_previously_called_routines, 'exact')) || ...
+ ~isempty(strmatch('non_linear_dsge_likelihood', list_of_previously_called_routines, 'exact')) || ...
+ ~isempty(strmatch('simulated_moments_estimation', list_of_previously_called_routines, 'exact'))
b = true;
end
diff --git a/matlab/utilities/general/ispd.m b/matlab/utilities/general/ispd.m
index e319a47a1f1c3a21ffb16829d8379bac880cab51..7fed2d44c1c838a48f89920fd03e997af4d54647 100644
--- a/matlab/utilities/general/ispd.m
+++ b/matlab/utilities/general/ispd.m
@@ -44,7 +44,7 @@ function [test, penalty] = ispd(A)
if ~isquare(A)
error(['ispd:: Input argument ' inputname(1) ' has to be a square matrix!'])
end
-
+
[cholA, info] = chol(A);
test = ~info;
diff --git a/matlab/utilities/graphics/colorspace.m b/matlab/utilities/graphics/colorspace.m
index dc46d5e850488346126c6ca9a3d7902d7a5e2038..3cd4f1c23224279dccb06d1d7e3d9e15ebed4a0f 100644
--- a/matlab/utilities/graphics/colorspace.m
+++ b/matlab/utilities/graphics/colorspace.m
@@ -88,11 +88,11 @@ function varargout = colorspace(Conversion,varargin)
% modification, are permitted provided that the following conditions are
% met:
%
- % * Redistributions of source code must retain the above copyright
- % notice, this list of conditions and the following disclaimer.
- % * Redistributions in binary form must reproduce the above copyright
- % notice, this list of conditions and the following disclaimer in
- % the documentation and/or other materials provided with the distribution
+% * Redistributions of source code must retain the above copyright
+% notice, this list of conditions and the following disclaimer.
+% * Redistributions in binary form must reproduce the above copyright
+% notice, this list of conditions and the following disclaimer in
+% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
@@ -111,11 +111,11 @@ if nargin < 2, error('Not enough input arguments.'); end
[SrcSpace,DestSpace] = parse(Conversion);
if nargin == 2
- Image = varargin{1};
+ Image = varargin{1};
elseif nargin >= 3
- Image = cat(3,varargin{:});
+ Image = cat(3,varargin{:});
else
- error('Invalid number of input arguments.');
+ error('Invalid number of input arguments.');
end
FlipDims = (size(Image,3) == 1);
@@ -128,31 +128,31 @@ SrcT = gettransform(SrcSpace);
DestT = gettransform(DestSpace);
if ~ischar(SrcT) && ~ischar(DestT)
- % Both source and destination transforms are affine, so they
- % can be composed into one affine operation
- T = [DestT(:,1:3)*SrcT(:,1:3),DestT(:,1:3)*SrcT(:,4)+DestT(:,4)];
- Temp = zeros(size(Image));
- Temp(:,:,1) = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
- Temp(:,:,2) = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
- Temp(:,:,3) = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
- Image = Temp;
+ % Both source and destination transforms are affine, so they
+ % can be composed into one affine operation
+ T = [DestT(:,1:3)*SrcT(:,1:3),DestT(:,1:3)*SrcT(:,4)+DestT(:,4)];
+ Temp = zeros(size(Image));
+ Temp(:,:,1) = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
+ Temp(:,:,2) = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
+ Temp(:,:,3) = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
+ Image = Temp;
elseif ~ischar(DestT)
- Image = rgb(Image,SrcSpace);
- Temp = zeros(size(Image));
- Temp(:,:,1) = DestT(1)*Image(:,:,1) + DestT(4)*Image(:,:,2) + DestT(7)*Image(:,:,3) + DestT(10);
- Temp(:,:,2) = DestT(2)*Image(:,:,1) + DestT(5)*Image(:,:,2) + DestT(8)*Image(:,:,3) + DestT(11);
- Temp(:,:,3) = DestT(3)*Image(:,:,1) + DestT(6)*Image(:,:,2) + DestT(9)*Image(:,:,3) + DestT(12);
- Image = Temp;
+ Image = rgb(Image,SrcSpace);
+ Temp = zeros(size(Image));
+ Temp(:,:,1) = DestT(1)*Image(:,:,1) + DestT(4)*Image(:,:,2) + DestT(7)*Image(:,:,3) + DestT(10);
+ Temp(:,:,2) = DestT(2)*Image(:,:,1) + DestT(5)*Image(:,:,2) + DestT(8)*Image(:,:,3) + DestT(11);
+ Temp(:,:,3) = DestT(3)*Image(:,:,1) + DestT(6)*Image(:,:,2) + DestT(9)*Image(:,:,3) + DestT(12);
+ Image = Temp;
else
- Image = feval(DestT,Image,SrcSpace);
+ Image = feval(DestT,Image,SrcSpace);
end
%%% Output format %%%
if nargout > 1
- varargout = {Image(:,:,1),Image(:,:,2),Image(:,:,3)};
+ varargout = {Image(:,:,1),Image(:,:,2),Image(:,:,3)};
else
- if FlipDims, Image = permute(Image,[1,3,2]); end
- varargout = {Image};
+ if FlipDims, Image = permute(Image,[1,3,2]); end
+ varargout = {Image};
end
return;
@@ -162,29 +162,29 @@ function [SrcSpace,DestSpace] = parse(Str)
% Parse conversion argument
if ischar(Str)
- Str = lower(strrep(strrep(Str,'-',''),'=',''));
- k = find(Str == '>');
-
- if length(k) == 1 % Interpret the form 'src->dest'
- SrcSpace = Str(1:k-1);
- DestSpace = Str(k+1:end);
- else
- k = find(Str == '<');
-
- if length(k) == 1 % Interpret the form 'dest<-src'
- DestSpace = Str(1:k-1);
- SrcSpace = Str(k+1:end);
- else
- error(['Invalid conversion, ''',Str,'''.']);
- end
- end
-
- SrcSpace = alias(SrcSpace);
- DestSpace = alias(DestSpace);
+ Str = lower(strrep(strrep(Str,'-',''),'=',''));
+ k = find(Str == '>');
+
+ if length(k) == 1 % Interpret the form 'src->dest'
+ SrcSpace = Str(1:k-1);
+ DestSpace = Str(k+1:end);
+ else
+ k = find(Str == '<');
+
+ if length(k) == 1 % Interpret the form 'dest<-src'
+ DestSpace = Str(1:k-1);
+ SrcSpace = Str(k+1:end);
+ else
+ error(['Invalid conversion, ''',Str,'''.']);
+ end
+ end
+
+ SrcSpace = alias(SrcSpace);
+ DestSpace = alias(DestSpace);
else
- SrcSpace = 1; % No source pre-transform
- DestSpace = Conversion;
- if any(size(Conversion) ~= 3), error('Transformation matrix must be 3x3.'); end
+ SrcSpace = 1; % No source pre-transform
+ DestSpace = Conversion;
+ if any(size(Conversion) ~= 3), error('Transformation matrix must be 3x3.'); end
end
return;
@@ -193,18 +193,18 @@ function Space = alias(Space)
Space = strrep(strrep(Space,'cie',''),' ','');
if isempty(Space)
- Space = 'rgb';
+ Space = 'rgb';
end
switch Space
-case {'ycbcr','ycc'}
- Space = 'ycbcr';
-case {'hsv','hsb'}
- Space = 'hsv';
-case {'hsl','hsi','hls'}
- Space = 'hsl';
-case {'rgb','yuv','yiq','ydbdr','ycbcr','jpegycbcr','xyz','lab','luv','lch'}
- return;
+ case {'ycbcr','ycc'}
+ Space = 'ycbcr';
+ case {'hsv','hsb'}
+ Space = 'hsv';
+ case {'hsl','hsi','hls'}
+ Space = 'hsl';
+ case {'rgb','yuv','yiq','ydbdr','ycbcr','jpegycbcr','xyz','lab','luv','lch'}
+ return;
end
return;
@@ -213,32 +213,32 @@ function T = gettransform(Space)
% Get a colorspace transform: either a matrix describing an affine transform,
% or a string referring to a conversion subroutine
switch Space
-case 'ypbpr'
- T = [0.299,0.587,0.114,0;-0.1687367,-0.331264,0.5,0;0.5,-0.418688,-0.081312,0];
-case 'yuv'
- % sRGB to NTSC/PAL YUV
- % Wikipedia: http://en.wikipedia.org/wiki/YUV
- T = [0.299,0.587,0.114,0;-0.147,-0.289,0.436,0;0.615,-0.515,-0.100,0];
-case 'ydbdr'
- % sRGB to SECAM YDbDr
- % Wikipedia: http://en.wikipedia.org/wiki/YDbDr
- T = [0.299,0.587,0.114,0;-0.450,-0.883,1.333,0;-1.333,1.116,0.217,0];
-case 'yiq'
- % sRGB in [0,1] to NTSC YIQ in [0,1];[-0.595716,0.595716];[-0.522591,0.522591];
- % Wikipedia: http://en.wikipedia.org/wiki/YIQ
- T = [0.299,0.587,0.114,0;0.595716,-0.274453,-0.321263,0;0.211456,-0.522591,0.311135,0];
-case 'ycbcr'
- % sRGB (range [0,1]) to ITU-R BRT.601 (CCIR 601) Y'CbCr
- % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
- % Poynton, Equation 3, scaling of R'G'B to Y'PbPr conversion
- T = [65.481,128.553,24.966,16;-37.797,-74.203,112.0,128;112.0,-93.786,-18.214,128];
-case 'jpegycbcr'
- % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
- T = [0.299,0.587,0.114,0;-0.168736,-0.331264,0.5,0.5;0.5,-0.418688,-0.081312,0.5]*255;
-case {'rgb','xyz','hsv','hsl','lab','luv','lch','cat02lms'}
- T = Space;
-otherwise
- error(['Unknown color space, ''',Space,'''.']);
+ case 'ypbpr'
+ T = [0.299,0.587,0.114,0;-0.1687367,-0.331264,0.5,0;0.5,-0.418688,-0.081312,0];
+ case 'yuv'
+ % sRGB to NTSC/PAL YUV
+ % Wikipedia: http://en.wikipedia.org/wiki/YUV
+ T = [0.299,0.587,0.114,0;-0.147,-0.289,0.436,0;0.615,-0.515,-0.100,0];
+ case 'ydbdr'
+ % sRGB to SECAM YDbDr
+ % Wikipedia: http://en.wikipedia.org/wiki/YDbDr
+ T = [0.299,0.587,0.114,0;-0.450,-0.883,1.333,0;-1.333,1.116,0.217,0];
+ case 'yiq'
+ % sRGB in [0,1] to NTSC YIQ in [0,1];[-0.595716,0.595716];[-0.522591,0.522591];
+ % Wikipedia: http://en.wikipedia.org/wiki/YIQ
+ T = [0.299,0.587,0.114,0;0.595716,-0.274453,-0.321263,0;0.211456,-0.522591,0.311135,0];
+ case 'ycbcr'
+ % sRGB (range [0,1]) to ITU-R BRT.601 (CCIR 601) Y'CbCr
+ % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
+ % Poynton, Equation 3, scaling of R'G'B to Y'PbPr conversion
+ T = [65.481,128.553,24.966,16;-37.797,-74.203,112.0,128;112.0,-93.786,-18.214,128];
+ case 'jpegycbcr'
+ % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
+ T = [0.299,0.587,0.114,0;-0.168736,-0.331264,0.5,0.5;0.5,-0.418688,-0.081312,0.5]*255;
+ case {'rgb','xyz','hsv','hsl','lab','luv','lch','cat02lms'}
+ T = Space;
+ otherwise
+ error(['Unknown color space, ''',Space,'''.']);
end
return;
@@ -246,43 +246,43 @@ return;
function Image = rgb(Image,SrcSpace)
% Convert to sRGB from 'SrcSpace'
switch SrcSpace
-case 'rgb'
- return;
-case 'hsv'
- % Convert HSV to sRGB
- Image = huetorgb((1 - Image(:,:,2)).*Image(:,:,3),Image(:,:,3),Image(:,:,1));
-case 'hsl'
- % Convert HSL to sRGB
- L = Image(:,:,3);
- Delta = Image(:,:,2).*min(L,1-L);
- Image = huetorgb(L-Delta,L+Delta,Image(:,:,1));
-case {'xyz','lab','luv','lch','cat02lms'}
- % Convert to CIE XYZ
- Image = xyz(Image,SrcSpace);
- % Convert XYZ to RGB
- T = [3.2406, -1.5372, -0.4986; -0.9689, 1.8758, 0.0415; 0.0557, -0.2040, 1.057];
- R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3); % R
- G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3); % G
- B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3); % B
- % Desaturate and rescale to constrain resulting RGB values to [0,1]
- AddWhite = -min(min(min(R,G),B),0);
- R = R + AddWhite;
- G = G + AddWhite;
- B = B + AddWhite;
- % Apply gamma correction to convert linear RGB to sRGB
- Image(:,:,1) = gammacorrection(R); % R'
- Image(:,:,2) = gammacorrection(G); % G'
- Image(:,:,3) = gammacorrection(B); % B'
-otherwise % Conversion is through an affine transform
- T = gettransform(SrcSpace);
- temp = inv(T(:,1:3));
- T = [temp,-temp*T(:,4)];
- R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
- G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
- B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
- Image(:,:,1) = R;
- Image(:,:,2) = G;
- Image(:,:,3) = B;
+ case 'rgb'
+ return;
+ case 'hsv'
+ % Convert HSV to sRGB
+ Image = huetorgb((1 - Image(:,:,2)).*Image(:,:,3),Image(:,:,3),Image(:,:,1));
+ case 'hsl'
+ % Convert HSL to sRGB
+ L = Image(:,:,3);
+ Delta = Image(:,:,2).*min(L,1-L);
+ Image = huetorgb(L-Delta,L+Delta,Image(:,:,1));
+ case {'xyz','lab','luv','lch','cat02lms'}
+ % Convert to CIE XYZ
+ Image = xyz(Image,SrcSpace);
+ % Convert XYZ to RGB
+ T = [3.2406, -1.5372, -0.4986; -0.9689, 1.8758, 0.0415; 0.0557, -0.2040, 1.057];
+ R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3); % R
+ G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3); % G
+ B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3); % B
+ % Desaturate and rescale to constrain resulting RGB values to [0,1]
+ AddWhite = -min(min(min(R,G),B),0);
+ R = R + AddWhite;
+ G = G + AddWhite;
+ B = B + AddWhite;
+ % Apply gamma correction to convert linear RGB to sRGB
+ Image(:,:,1) = gammacorrection(R); % R'
+ Image(:,:,2) = gammacorrection(G); % G'
+ Image(:,:,3) = gammacorrection(B); % B'
+ otherwise % Conversion is through an affine transform
+ T = gettransform(SrcSpace);
+ temp = inv(T(:,1:3));
+ T = [temp,-temp*T(:,4)];
+ R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
+ G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
+ B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
+ Image(:,:,1) = R;
+ Image(:,:,2) = G;
+ Image(:,:,3) = B;
end
% Clip to [0,1]
@@ -295,50 +295,50 @@ function Image = xyz(Image,SrcSpace)
WhitePoint = [0.950456,1,1.088754];
switch SrcSpace
-case 'xyz'
- return;
-case 'luv'
- % Convert CIE L*uv to XYZ
- WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
- WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
- L = Image(:,:,1);
- Y = (L + 16)/116;
- Y = invf(Y)*WhitePoint(2);
- U = Image(:,:,2)./(13*L + 1e-6*(L==0)) + WhitePointU;
- V = Image(:,:,3)./(13*L + 1e-6*(L==0)) + WhitePointV;
- Image(:,:,1) = -(9*Y.*U)./((U-4).*V - U.*V); % X
- Image(:,:,2) = Y; % Y
- Image(:,:,3) = (9*Y - (15*V.*Y) - (V.*Image(:,:,1)))./(3*V); % Z
-case {'lab','lch'}
- Image = lab(Image,SrcSpace);
- % Convert CIE L*ab to XYZ
- fY = (Image(:,:,1) + 16)/116;
- fX = fY + Image(:,:,2)/500;
- fZ = fY - Image(:,:,3)/200;
- Image(:,:,1) = WhitePoint(1)*invf(fX); % X
- Image(:,:,2) = WhitePoint(2)*invf(fY); % Y
- Image(:,:,3) = WhitePoint(3)*invf(fZ); % Z
-case 'cat02lms'
+ case 'xyz'
+ return;
+ case 'luv'
+ % Convert CIE L*uv to XYZ
+ WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
+ WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
+ L = Image(:,:,1);
+ Y = (L + 16)/116;
+ Y = invf(Y)*WhitePoint(2);
+ U = Image(:,:,2)./(13*L + 1e-6*(L==0)) + WhitePointU;
+ V = Image(:,:,3)./(13*L + 1e-6*(L==0)) + WhitePointV;
+ Image(:,:,1) = -(9*Y.*U)./((U-4).*V - U.*V); % X
+ Image(:,:,2) = Y; % Y
+ Image(:,:,3) = (9*Y - (15*V.*Y) - (V.*Image(:,:,1)))./(3*V); % Z
+ case {'lab','lch'}
+ Image = lab(Image,SrcSpace);
+ % Convert CIE L*ab to XYZ
+ fY = (Image(:,:,1) + 16)/116;
+ fX = fY + Image(:,:,2)/500;
+ fZ = fY - Image(:,:,3)/200;
+ Image(:,:,1) = WhitePoint(1)*invf(fX); % X
+ Image(:,:,2) = WhitePoint(2)*invf(fY); % Y
+ Image(:,:,3) = WhitePoint(3)*invf(fZ); % Z
+ case 'cat02lms'
% Convert CAT02 LMS to XYZ
- T = inv([0.7328, 0.4296, -0.1624;-0.7036, 1.6975, 0.0061; 0.0030, 0.0136, 0.9834]);
- L = Image(:,:,1);
- M = Image(:,:,2);
- S = Image(:,:,3);
- Image(:,:,1) = T(1)*L + T(4)*M + T(7)*S; % X
- Image(:,:,2) = T(2)*L + T(5)*M + T(8)*S; % Y
- Image(:,:,3) = T(3)*L + T(6)*M + T(9)*S; % Z
-otherwise % Convert from some gamma-corrected space
- % Convert to sRGB
- Image = rgb(Image,SrcSpace);
- % Undo gamma correction
- R = invgammacorrection(Image(:,:,1));
- G = invgammacorrection(Image(:,:,2));
- B = invgammacorrection(Image(:,:,3));
- % Convert RGB to XYZ
- T = inv([3.2406, -1.5372, -0.4986; -0.9689, 1.8758, 0.0415; 0.0557, -0.2040, 1.057]);
- Image(:,:,1) = T(1)*R + T(4)*G + T(7)*B; % X
- Image(:,:,2) = T(2)*R + T(5)*G + T(8)*B; % Y
- Image(:,:,3) = T(3)*R + T(6)*G + T(9)*B; % Z
+ T = inv([0.7328, 0.4296, -0.1624;-0.7036, 1.6975, 0.0061; 0.0030, 0.0136, 0.9834]);
+ L = Image(:,:,1);
+ M = Image(:,:,2);
+ S = Image(:,:,3);
+ Image(:,:,1) = T(1)*L + T(4)*M + T(7)*S; % X
+ Image(:,:,2) = T(2)*L + T(5)*M + T(8)*S; % Y
+ Image(:,:,3) = T(3)*L + T(6)*M + T(9)*S; % Z
+ otherwise % Convert from some gamma-corrected space
+ % Convert to sRGB
+ Image = rgb(Image,SrcSpace);
+ % Undo gamma correction
+ R = invgammacorrection(Image(:,:,1));
+ G = invgammacorrection(Image(:,:,2));
+ B = invgammacorrection(Image(:,:,3));
+ % Convert RGB to XYZ
+ T = inv([3.2406, -1.5372, -0.4986; -0.9689, 1.8758, 0.0415; 0.0557, -0.2040, 1.057]);
+ Image(:,:,1) = T(1)*R + T(4)*G + T(7)*B; % X
+ Image(:,:,2) = T(2)*R + T(5)*G + T(8)*B; % Y
+ Image(:,:,3) = T(3)*R + T(6)*G + T(9)*B; % Z
end
return;
@@ -357,25 +357,25 @@ return;
function Image = hsl(Image,SrcSpace)
% Convert to HSL
switch SrcSpace
-case 'hsv'
- % Convert HSV to HSL
- MaxVal = Image(:,:,3);
- MinVal = (1 - Image(:,:,2)).*MaxVal;
- L = 0.5*(MaxVal + MinVal);
- temp = min(L,1-L);
- Image(:,:,2) = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
- Image(:,:,3) = L;
-otherwise
- Image = rgb(Image,SrcSpace); % Convert to sRGB
- % Convert sRGB to HSL
- MinVal = min(Image,[],3);
- MaxVal = max(Image,[],3);
- L = 0.5*(MaxVal + MinVal);
- temp = min(L,1-L);
- S = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
- Image(:,:,1) = rgbtohue(Image);
- Image(:,:,2) = S;
- Image(:,:,3) = L;
+ case 'hsv'
+ % Convert HSV to HSL
+ MaxVal = Image(:,:,3);
+ MinVal = (1 - Image(:,:,2)).*MaxVal;
+ L = 0.5*(MaxVal + MinVal);
+ temp = min(L,1-L);
+ Image(:,:,2) = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
+ Image(:,:,3) = L;
+ otherwise
+ Image = rgb(Image,SrcSpace); % Convert to sRGB
+ % Convert sRGB to HSL
+ MinVal = min(Image,[],3);
+ MaxVal = max(Image,[],3);
+ L = 0.5*(MaxVal + MinVal);
+ temp = min(L,1-L);
+ S = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
+ Image(:,:,1) = rgbtohue(Image);
+ Image(:,:,2) = S;
+ Image(:,:,3) = L;
end
return;
@@ -385,25 +385,25 @@ function Image = lab(Image,SrcSpace)
WhitePoint = [0.950456,1,1.088754];
switch SrcSpace
-case 'lab'
- return;
-case 'lch'
- % Convert CIE L*CH to CIE L*ab
- C = Image(:,:,2);
- Image(:,:,2) = cos(Image(:,:,3)*pi/180).*C; % a*
- Image(:,:,3) = sin(Image(:,:,3)*pi/180).*C; % b*
-otherwise
- Image = xyz(Image,SrcSpace); % Convert to XYZ
- % Convert XYZ to CIE L*a*b*
- X = Image(:,:,1)/WhitePoint(1);
- Y = Image(:,:,2)/WhitePoint(2);
- Z = Image(:,:,3)/WhitePoint(3);
- fX = f(X);
- fY = f(Y);
- fZ = f(Z);
- Image(:,:,1) = 116*fY - 16; % L*
- Image(:,:,2) = 500*(fX - fY); % a*
- Image(:,:,3) = 200*(fY - fZ); % b*
+ case 'lab'
+ return;
+ case 'lch'
+ % Convert CIE L*CH to CIE L*ab
+ C = Image(:,:,2);
+ Image(:,:,2) = cos(Image(:,:,3)*pi/180).*C; % a*
+ Image(:,:,3) = sin(Image(:,:,3)*pi/180).*C; % b*
+ otherwise
+ Image = xyz(Image,SrcSpace); % Convert to XYZ
+ % Convert XYZ to CIE L*a*b*
+ X = Image(:,:,1)/WhitePoint(1);
+ Y = Image(:,:,2)/WhitePoint(2);
+ Z = Image(:,:,3)/WhitePoint(3);
+ fX = f(X);
+ fY = f(Y);
+ fZ = f(Z);
+ Image(:,:,1) = 116*fY - 16; % L*
+ Image(:,:,2) = 500*(fX - fY); % a*
+ Image(:,:,3) = 200*(fY - fZ); % b*
end
return;
diff --git a/matlab/utilities/graphics/distinguishable_colors.m b/matlab/utilities/graphics/distinguishable_colors.m
index 0b9fb7a33fddfe0cd45b2082e0e176e7353291b8..f2c66231d55ede2665b5197e8bf3a96265387e07 100644
--- a/matlab/utilities/graphics/distinguishable_colors.m
+++ b/matlab/utilities/graphics/distinguishable_colors.m
@@ -61,11 +61,11 @@ function colors = distinguishable_colors(n_colors,bg,func)
% modification, are permitted provided that the following conditions are
% met:
%
- % * Redistributions of source code must retain the above copyright
- % notice, this list of conditions and the following disclaimer.
- % * Redistributions in binary form must reproduce the above copyright
- % notice, this list of conditions and the following disclaimer in
- % the documentation and/or other materials provided with the distribution
+% * Redistributions of source code must retain the above copyright
+% notice, this list of conditions and the following disclaimer.
+% * Redistributions in binary form must reproduce the above copyright
+% notice, this list of conditions and the following disclaimer in
+% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
diff --git a/matlab/vnorm.m b/matlab/vnorm.m
index b6bb123d944da1b125d58402e5e90e40e767fe14..fc9f5850a1f67626ae06722fb527033e8a2c1031 100644
--- a/matlab/vnorm.m
+++ b/matlab/vnorm.m
@@ -71,7 +71,7 @@ if isempty(dim)
idx = find(size(A)~=1);
dim = idx(1);
end
-
+
if isempty(ntype)
y = sqrt(sum( abs(A).^2 , dim) );
elseif ntype==1
diff --git a/matlab/writeVarExpectationFunction.m b/matlab/writeVarExpectationFunction.m
index 50fcd10baa8d941e6c15f3b2bd5a205e35ba63e4..513a5cb8abd312e56f7e857d981188c0d45b8506 100644
--- a/matlab/writeVarExpectationFunction.m
+++ b/matlab/writeVarExpectationFunction.m
@@ -176,8 +176,8 @@ else
spaces = ' ';
end
fprintf(fid, '%sy = ...\n%s[ ... %% intercept\n%s', fs, spaces, ns);
-fprintf(fid, [repmat('% f ', 1, size(mu, 2)) '; ...\n' ns], mu');
+ fprintf(fid, [repmat('% f ', 1, size(mu, 2)) '; ...\n' ns], mu');
fprintf(fid, ' ] + ...\n%s[ ... %% autoregressive matrices\n%s', spaces, ns);
-fprintf(fid, [repmat('% f ', 1, size(A, 2)) '; ...\n' ns], A');
+ fprintf(fid, [repmat('% f ', 1, size(A, 2)) '; ...\n' ns], A');
fprintf(fid, ' ] * y;\n');
end
\ No newline at end of file
diff --git a/matlab/write_latex_definitions.m b/matlab/write_latex_definitions.m
index 2bf3acf366eb9bdcfd9cc5cb37ba6fb440d76bd0..144f6242be85ccd1147fa58f48cbdcdc1af09b64 100644
--- a/matlab/write_latex_definitions.m
+++ b/matlab/write_latex_definitions.m
@@ -65,9 +65,9 @@ for i=1:length(tables)
long = eval([M_var_root{i} '_names_long']);
for j=1:size(names,1)
fprintf(fid, '\\texttt{%s} & $%s$ & %s\\\\\n', ...
- regexprep(strtrim(names(j,:)), '_', '\\_'), ...
- strtrim(tex(j,:)), ...
- regexprep(strtrim(long(j,:)), '_', '\\_'));
+ regexprep(strtrim(names(j,:)), '_', '\\_'), ...
+ strtrim(tex(j,:)), ...
+ regexprep(strtrim(long(j,:)), '_', '\\_'));
end
fprintf(fid, '\\hline%%\n');
fprintf(fid, '\\end{longtable}\n');
diff --git a/matlab/write_latex_parameter_table.m b/matlab/write_latex_parameter_table.m
index c2d7398019ed49f09dd1def5e5ac2fbb8c4a5827..5a753c31cf6e9824896079b44b11477b5e0fd7f1 100644
--- a/matlab/write_latex_parameter_table.m
+++ b/matlab/write_latex_parameter_table.m
@@ -75,20 +75,20 @@ fprintf(fid, '\\endhead\n');
tex = M_.param_names_tex;
long = M_.param_names_long;
for j=1:size(tex,1)
-if Long_names_present==1;
- % replace underscores
- long_names_temp=regexprep(strtrim(long(j,:)), '_', '\\_');
- % replace percent
- long_names_temp=regexprep(long_names_temp, '%', '\\%');
- fprintf(fid, '$%s$ \t & \t %4.3f \t & \t %s\\\\\n', ...
- strtrim(tex(j,:)), ...
- M_.params(j,:),...
- long_names_temp);
-else
- fprintf(fid, '$%s$ \t & \t %4.3f \\\\\n', ...
- strtrim(tex(j,:)), ...
- M_.params(j,:));
-end
+ if Long_names_present==1;
+ % replace underscores
+ long_names_temp=regexprep(strtrim(long(j,:)), '_', '\\_');
+ % replace percent
+ long_names_temp=regexprep(long_names_temp, '%', '\\%');
+ fprintf(fid, '$%s$ \t & \t %4.3f \t & \t %s\\\\\n', ...
+ strtrim(tex(j,:)), ...
+ M_.params(j,:),...
+ long_names_temp);
+ else
+ fprintf(fid, '$%s$ \t & \t %4.3f \\\\\n', ...
+ strtrim(tex(j,:)), ...
+ M_.params(j,:));
+ end
end
fprintf(fid, '\\bottomrule%%\n');
fprintf(fid, '\\end{longtable}\n');
diff --git a/matlab/write_latex_prior_table.m b/matlab/write_latex_prior_table.m
index 46a5e3bf5258d6f955c93fbfffb6794369869493..db9c8bfbcea75dbca24005dd2a5ec77c74000268 100644
--- a/matlab/write_latex_prior_table.m
+++ b/matlab/write_latex_prior_table.m
@@ -37,11 +37,11 @@ end
if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
% Prior over measurement errors are defined...
- if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
- % ... But the list of observed variabled is not yet defined.
- fprintf(['\nwrite_latex_prior_table:: varobs should be declared before. Skipping table creation.\n'])
- return
- end
+ if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
+ % ... But the list of observed variabled is not yet defined.
+ fprintf(['\nwrite_latex_prior_table:: varobs should be declared before. Skipping table creation.\n'])
+ return
+ end
end
% Fill or update bayestopt_ structure
@@ -115,56 +115,56 @@ for i=1:size(BayesOptions.name,1)
PriorMode = BayesOptions.p5(i);
PriorStandardDeviation = BayesOptions.p2(i);
switch BayesOptions.pshape(i)
- case { 1 , 5 }
+ case { 1 , 5 }
+ LowerBound = BayesOptions.p3(i);
+ UpperBound = BayesOptions.p4(i);
+ if ~isinf(lb(i))
+ LowerBound=max(LowerBound,lb(i));
+ end
+ if ~isinf(ub(i))
+ UpperBound=min(UpperBound,ub(i));
+ end
+ case { 2 , 4 , 6, 8 }
+ LowerBound = BayesOptions.p3(i);
+ if ~isinf(lb(i))
+ LowerBound=max(LowerBound,lb(i));
+ end
+ if ~isinf(ub(i))
+ UpperBound=ub(i);
+ else
+ UpperBound = '$\infty$';
+ end
+ case 3
+ if isinf(BayesOptions.p3(i)) && isinf(lb(i))
+ LowerBound = '$-\infty$';
+ else
LowerBound = BayesOptions.p3(i);
- UpperBound = BayesOptions.p4(i);
if ~isinf(lb(i))
LowerBound=max(LowerBound,lb(i));
end
+ end
+ if isinf(BayesOptions.p4(i)) && isinf(ub(i))
+ UpperBound = '$\infty$';
+ else
+ UpperBound = BayesOptions.p4(i);
if ~isinf(ub(i))
UpperBound=min(UpperBound,ub(i));
end
- case { 2 , 4 , 6, 8 }
- LowerBound = BayesOptions.p3(i);
- if ~isinf(lb(i))
- LowerBound=max(LowerBound,lb(i));
- end
- if ~isinf(ub(i))
- UpperBound=ub(i);
- else
- UpperBound = '$\infty$';
- end
- case 3
- if isinf(BayesOptions.p3(i)) && isinf(lb(i))
- LowerBound = '$-\infty$';
- else
- LowerBound = BayesOptions.p3(i);
- if ~isinf(lb(i))
- LowerBound=max(LowerBound,lb(i));
- end
- end
- if isinf(BayesOptions.p4(i)) && isinf(ub(i))
- UpperBound = '$\infty$';
- else
- UpperBound = BayesOptions.p4(i);
- if ~isinf(ub(i))
- UpperBound=min(UpperBound,ub(i));
- end
- end
- otherwise
- error('write_latex_prior_table:: Dynare bug!')
+ end
+ otherwise
+ error('write_latex_prior_table:: Dynare bug!')
end
format_string = build_format_string(PriorMode, PriorStandardDeviation,LowerBound,UpperBound);
fprintf(fidTeX,format_string, ...
- TexName, ...
- PriorShape, ...
- PriorMean, ...
- PriorMode, ...
- PriorStandardDeviation, ...
- LowerBound, ...
- UpperBound, ...
- PriorIntervals.lb(i), ...
- PriorIntervals.ub(i) );
+ TexName, ...
+ PriorShape, ...
+ PriorMean, ...
+ PriorMode, ...
+ PriorStandardDeviation, ...
+ LowerBound, ...
+ UpperBound, ...
+ PriorIntervals.lb(i), ...
+ PriorIntervals.ub(i) );
end
fprintf(fidTeX,'\\end{longtable}\n ');
fprintf(fidTeX,'\\end{center}\n');
diff --git a/matlab/writecellofchar.m b/matlab/writecellofchar.m
index be636e6f171e0ed31daca256c1fac1031dba40af..9ad378cb21bdd7ba083dcb18a3c4c7ef015ab5fa 100644
--- a/matlab/writecellofchar.m
+++ b/matlab/writecellofchar.m
@@ -37,27 +37,27 @@ function str = writecellofchar(c)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-
+
str = '{';
for i=1:size(c, 1)
- for j=1:size(c, 2)
- if iscell(c{i,j})
- str = sprintf('%s%s', str, writecellofchar(c{i, j}));
- elseif ischar(c{i, j})
- if size(c{i, j}, 1)>1
- str = sprintf('%s''%s''', str, writematrixofchar(c{i, j}));
- else
- str = sprintf('%s''%s''', str, c{i, j});
- end
- else
- error('Type not implemenented!')
- end
- if j<size(c, 2)
- str = sprintf('%s, ', str);
- end
- end
- if i<size(c, 1)
- str = sprintf('%s; ', str);
- end
+for j=1:size(c, 2)
+if iscell(c{i,j})
+str = sprintf('%s%s', str, writecellofchar(c{i, j}));
+elseif ischar(c{i, j})
+if size(c{i, j}, 1)>1
+str = sprintf('%s''%s''', str, writematrixofchar(c{i, j}));
+else
+str = sprintf('%s''%s''', str, c{i, j});
+end
+else
+error('Type not implemenented!')
+end
+if j<size(c, 2)
+str = sprintf('%s, ', str);
+end
+end
+if i<size(c, 1)
+str = sprintf('%s; ', str);
+end
end
str = sprintf('%s}', str);
\ No newline at end of file
diff --git a/matlab/writematrixofchar.m b/matlab/writematrixofchar.m
index 6f230e554f7a7d9cf4b363da9bfc7f5c4345a630..aca78395580077ba7dddbe711fea6aa8cc3d895b 100644
--- a/matlab/writematrixofchar.m
+++ b/matlab/writematrixofchar.m
@@ -16,7 +16,7 @@ function str = writematrixofchar(m)
% ['a'; 'b']
%
% where the returned argument is a string which can be evaluated or printed.
-
+
% Copyright (C) 2015 Dynare Team
%
% This file is part of Dynare.
diff --git a/mex/sources/bytecode/testing/bytecode_debug.m b/mex/sources/bytecode/testing/bytecode_debug.m
index 7f2e8f9e597123365e5ba32d8e4b8dedc5890b05..e13535841b866ef58b504ef9325c92eaafa05799 100644
--- a/mex/sources/bytecode/testing/bytecode_debug.m
+++ b/mex/sources/bytecode/testing/bytecode_debug.m
@@ -4,18 +4,18 @@ fid = fopen([M_.fname '_options.txt'],'wt');
nfields = fieldnames(options_);
fprintf(fid, '%d %d %d\n',size(nfields,1), size(options_,1), size(options_,2));
for i=1:size(nfields, 1)
- disp(nfields(i));
- if iscell(nfields(i))
- AA = cell2mat(nfields(i));
- else
- AA = nfields(i);
- end;
- if iscell(AA)
- AA = cell2mat(AA);
- end;
- fprintf(fid, '%s\n', AA);
- Z = getfield(options_, AA);
- print_object(fid, Z);
+ disp(nfields(i));
+ if iscell(nfields(i))
+ AA = cell2mat(nfields(i));
+ else
+ AA = nfields(i);
+ end;
+ if iscell(AA)
+ AA = cell2mat(AA);
+ end;
+ fprintf(fid, '%s\n', AA);
+ Z = getfield(options_, AA);
+ print_object(fid, Z);
end;
fclose(fid);
@@ -23,14 +23,14 @@ fid = fopen([M_.fname '_M.txt'],'wt');
nfields = fields(M_);
fprintf(fid, '%d %d %d\n',size(nfields,1), size(M_,1), size(M_,2));
for i=1:size(nfields, 1)
- disp(nfields(i));
- if iscell(nfields(i))
- AA = cell2mat(nfields(i));
- else
- AA = nfields(i);
- end;
- fprintf(fid, '%s\n', AA);
- print_object(fid, getfield(M_, AA));
+ disp(nfields(i));
+ if iscell(nfields(i))
+ AA = cell2mat(nfields(i));
+ else
+ AA = nfields(i);
+ end;
+ fprintf(fid, '%s\n', AA);
+ print_object(fid, getfield(M_, AA));
end;
fclose(fid);
@@ -39,65 +39,65 @@ fid = fopen([M_.fname '_oo.txt'],'wt');
nfields = fields(oo_);
fprintf(fid, '%d %d %d\n',size(nfields,1), size(oo_,1), size(oo_,2));
for i=1:size(nfields, 1)
- disp(nfields(i));
- if iscell(nfields(i))
- AA = cell2mat(nfields(i));
- else
- AA = nfields(i);
- end;
- if iscell(AA)
- AA = cell2mat(AA);
- end;
- fprintf(fid, '%s\n', AA);
- print_object(fid, getfield(oo_, AA));
+ disp(nfields(i));
+ if iscell(nfields(i))
+ AA = cell2mat(nfields(i));
+ else
+ AA = nfields(i);
+ end;
+ if iscell(AA)
+ AA = cell2mat(AA);
+ end;
+ fprintf(fid, '%s\n', AA);
+ print_object(fid, getfield(oo_, AA));
end;
fclose(fid);
function print_object(fid, object_arg)
- if iscell(object_arg)
- object = cell2mat(object_arg);
- else
- object = object_arg;
- end;
- if isa(object,'float') == 1
- fprintf(fid, '%d ', 0);
- fprintf(fid, '%d %d\n',size(object,1), size(object,2));
- fprintf(fid, '%f\n', object);
- %for i=1:size(object, 2)
- %for j=1:size(object, 1)
- %fprintf(fid, '%f\n', object(i,j));
- %end;
- %end;
- elseif isa(object,'char') == 1
- fprintf(fid, '%d ', 3);
- fprintf(fid, '%d %d\n',size(object,1), size(object,2));
- %object
- for i=1:size(object, 1)
- %for i=1:size(object, 2)
- fprintf(fid, '%s ', object(i,:));
- %end;
- %fprintf(fid, '\n');
- end;
- fprintf(fid, '\n');
- elseif isa(object,'struct') == 1
- fprintf(fid, '%d ', 5);
- nfields = fields(object);
- fprintf(fid, '%d %d %d\n',size(nfields,1), size(object,1), size(object,2));
- for j=1:size(object, 1) * size(object, 2)
- nfields = fields(object(j));
- for i=1:size(nfields, 1)
- if iscell(nfields(i))
- AA = cell2mat(nfields(i));
- else
- AA = nfields(i);
- end;
- fprintf(fid, '%s\n', AA);
- print_object(fid, getfield(object, AA));
- end;
- end;
- else
- disp(['type ' object 'note handle']);
- end;
+if iscell(object_arg)
+ object = cell2mat(object_arg);
+else
+ object = object_arg;
+end;
+if isa(object,'float') == 1
+ fprintf(fid, '%d ', 0);
+ fprintf(fid, '%d %d\n',size(object,1), size(object,2));
+ fprintf(fid, '%f\n', object);
+ %for i=1:size(object, 2)
+ %for j=1:size(object, 1)
+ %fprintf(fid, '%f\n', object(i,j));
+ %end;
+ %end;
+elseif isa(object,'char') == 1
+ fprintf(fid, '%d ', 3);
+ fprintf(fid, '%d %d\n',size(object,1), size(object,2));
+ %object
+ for i=1:size(object, 1)
+ %for i=1:size(object, 2)
+ fprintf(fid, '%s ', object(i,:));
+ %end;
+ %fprintf(fid, '\n');
+ end;
+ fprintf(fid, '\n');
+elseif isa(object,'struct') == 1
+ fprintf(fid, '%d ', 5);
+ nfields = fields(object);
+ fprintf(fid, '%d %d %d\n',size(nfields,1), size(object,1), size(object,2));
+ for j=1:size(object, 1) * size(object, 2)
+ nfields = fields(object(j));
+ for i=1:size(nfields, 1)
+ if iscell(nfields(i))
+ AA = cell2mat(nfields(i));
+ else
+ AA = nfields(i);
+ end;
+ fprintf(fid, '%s\n', AA);
+ print_object(fid, getfield(object, AA));
+ end;
+ end;
+else
+ disp(['type ' object 'note handle']);
+end;
diff --git a/mex/sources/bytecode/testing/simulate_debug.m b/mex/sources/bytecode/testing/simulate_debug.m
index 9384eb7fe9daf8ea26986203162fec8cf1212fb0..4c3e818f143216cde0d9c797b6e1ef9fb8f48c49 100644
--- a/mex/sources/bytecode/testing/simulate_debug.m
+++ b/mex/sources/bytecode/testing/simulate_debug.m
@@ -2,7 +2,7 @@ function simulate_debug(steady_state)
global M_ oo_ options_;
fid = fopen([M_.fname '_options.txt'],'wt');
if steady_state~=1
- fprintf(fid,'%d\n',options_.periods);
+ fprintf(fid,'%d\n',options_.periods);
end;
fprintf(fid,'%d\n',options_.simul.maxit);
fprintf(fid,'%6.20f\n',options_.slowc);
@@ -17,11 +17,11 @@ fprintf(fid,'%d\n',M_.maximum_lead);
fprintf(fid,'%d\n',M_.maximum_endo_lag);
fprintf(fid,'%d\n',M_.param_nbr);
if steady_state==1
- fprintf(fid,'%d\n',size(oo_.exo_steady_state, 1));
- fprintf(fid,'%d\n',size(oo_.exo_steady_state, 2));
+ fprintf(fid,'%d\n',size(oo_.exo_steady_state, 1));
+ fprintf(fid,'%d\n',size(oo_.exo_steady_state, 2));
else
- fprintf(fid,'%d\n',size(oo_.exo_simul, 1));
- fprintf(fid,'%d\n',size(oo_.exo_simul, 2));
+ fprintf(fid,'%d\n',size(oo_.exo_simul, 1));
+ fprintf(fid,'%d\n',size(oo_.exo_simul, 2));
end;
fprintf(fid,'%d\n',M_.endo_nbr);
if steady_state==1
@@ -41,11 +41,11 @@ fprintf(fid,'%6.20f\n',M_.params);
fclose(fid);
fid = fopen([M_.fname '_oo.txt'],'wt');
if steady_state==1
- fprintf(fid,'%6.20f\n',oo_.steady_state);
- fprintf(fid,'%6.20f\n',oo_.exo_steady_state);
+ fprintf(fid,'%6.20f\n',oo_.steady_state);
+ fprintf(fid,'%6.20f\n',oo_.exo_steady_state);
else
- fprintf(fid,'%6.20f\n',oo_.endo_simul);
- fprintf(fid,'%6.20f\n',oo_.exo_simul);
+ fprintf(fid,'%6.20f\n',oo_.endo_simul);
+ fprintf(fid,'%6.20f\n',oo_.exo_simul);
end;
fprintf(fid,'%6.20f\n',oo_.steady_state);
fprintf(fid,'%6.20f\n',oo_.exo_steady_state);
diff --git a/mex/sources/estimation/tests/logposterior_dll_test/random_walk_metropolis_hastings_core.m b/mex/sources/estimation/tests/logposterior_dll_test/random_walk_metropolis_hastings_core.m
index 9e741fa7f7cdf6763754338f700c61d760a08d87..9dd8fc77228305436b1560747473a96f29d44252 100644
--- a/mex/sources/estimation/tests/logposterior_dll_test/random_walk_metropolis_hastings_core.m
+++ b/mex/sources/estimation/tests/logposterior_dll_test/random_walk_metropolis_hastings_core.m
@@ -94,10 +94,10 @@ varargin=myinputs.varargin;
% Necessary only for remote computing!
if whoiam
- Parallel=myinputs.Parallel;
- % initialize persistent variables in priordens()
- priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, ...
- bayestopt_.p3,bayestopt_.p4,1);
+ Parallel=myinputs.Parallel;
+ % initialize persistent variables in priordens()
+ priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, ...
+ bayestopt_.p3,bayestopt_.p4,1);
end
% (re)Set the penalty
diff --git a/mex/sources/estimation/tests/logposterior_dll_test/rawdata_euromodel_1.m b/mex/sources/estimation/tests/logposterior_dll_test/rawdata_euromodel_1.m
index 25bbc92414efddc7adf66d5ab4cc8545af1e71f7..6b9fee7203e6fccb28de487aa0ac3e8324bf76d6 100644
--- a/mex/sources/estimation/tests/logposterior_dll_test/rawdata_euromodel_1.m
+++ b/mex/sources/estimation/tests/logposterior_dll_test/rawdata_euromodel_1.m
@@ -1,25 +1,25 @@
C =[
- -7.4073
- -6.1860
- -6.5983
- -5.6088
- -5.0547
- -4.4774
- -3.8081
- -3.8425
- -2.4178
- -1.9835
- -1.0395
- -0.1583
- -0.0397
+ -7.4073
+ -6.1860
+ -6.5983
+ -5.6088
+ -5.0547
+ -4.4774
+ -3.8081
+ -3.8425
+ -2.4178
+ -1.9835
+ -1.0395
+ -0.1583
+ -0.0397
0.3505
- -0.1879
- -0.0067
+ -0.1879
+ -0.0067
0.0478
- -1.2247
- -1.4349
- -0.7973
- -0.0461
+ -1.2247
+ -1.4349
+ -0.7973
+ -0.0461
0.5844
1.1372
1.3801
@@ -47,26 +47,26 @@ C =[
1.5563
1.2308
0.3264
- -0.2208
- -0.2483
- -0.4082
- -1.0315
- -1.6030
- -1.5499
- -1.3777
- -2.1675
- -2.5138
- -2.8820
- -2.6958
- -2.4719
- -1.9854
- -1.7954
- -2.2362
- -1.0595
- -0.8808
- -0.8548
- -1.2839
- -0.1363
+ -0.2208
+ -0.2483
+ -0.4082
+ -1.0315
+ -1.6030
+ -1.5499
+ -1.3777
+ -2.1675
+ -2.5138
+ -2.8820
+ -2.6958
+ -2.4719
+ -1.9854
+ -1.7954
+ -2.2362
+ -1.0595
+ -0.8808
+ -0.8548
+ -1.2839
+ -0.1363
0.2104
0.8810
0.3555
@@ -93,31 +93,31 @@ C =[
0.0847
0.0104
0.2115
- -0.6649
- -0.9625
- -0.7330
- -0.8664
- -1.4441
- -1.0179
- -1.2729
- -1.9539
- -1.4427
- -2.0371
- -1.9764
- -2.5654
- -2.8570
- -2.5842
- -3.0427
- -2.8312
- -2.3320
- -2.2768
- -2.1816
- -2.1043
- -1.8969
- -2.2388
- -2.1679
- -2.1172
-];
+ -0.6649
+ -0.9625
+ -0.7330
+ -0.8664
+ -1.4441
+ -1.0179
+ -1.2729
+ -1.9539
+ -1.4427
+ -2.0371
+ -1.9764
+ -2.5654
+ -2.8570
+ -2.5842
+ -3.0427
+ -2.8312
+ -2.3320
+ -2.2768
+ -2.1816
+ -2.1043
+ -1.8969
+ -2.2388
+ -2.1679
+ -2.1172
+ ];
E =[
0.6263
@@ -139,23 +139,23 @@ E =[
1.2422
0.8084
0.4710
- -0.3704
- -0.6427
- -0.5323
- -0.5562
- -0.3651
- -0.4356
- -0.7164
- -0.5816
- -0.4635
- -0.8456
- -0.9708
- -0.7138
- -0.7499
- -0.6941
- -0.6656
- -0.2912
- -0.1650
+ -0.3704
+ -0.6427
+ -0.5323
+ -0.5562
+ -0.3651
+ -0.4356
+ -0.7164
+ -0.5816
+ -0.4635
+ -0.8456
+ -0.9708
+ -0.7138
+ -0.7499
+ -0.6941
+ -0.6656
+ -0.2912
+ -0.1650
0.0774
0.2307
0.4484
@@ -163,34 +163,34 @@ E =[
0.4653
0.2196
0.1736
- -0.1595
- -0.3918
- -0.4611
- -0.8493
- -0.7384
- -1.0604
- -1.2166
- -1.7187
- -1.6932
- -1.7830
- -1.7035
- -2.2079
- -2.3769
- -2.2511
- -2.1093
- -2.4638
- -2.4027
- -2.1313
- -1.9199
- -1.7941
- -1.4661
- -1.2269
- -1.0392
- -1.0725
- -0.7156
- -0.4778
- -0.4233
- -0.0409
+ -0.1595
+ -0.3918
+ -0.4611
+ -0.8493
+ -0.7384
+ -1.0604
+ -1.2166
+ -1.7187
+ -1.6932
+ -1.7830
+ -1.7035
+ -2.2079
+ -2.3769
+ -2.2511
+ -2.1093
+ -2.4638
+ -2.4027
+ -2.1313
+ -1.9199
+ -1.7941
+ -1.4661
+ -1.2269
+ -1.0392
+ -1.0725
+ -0.7156
+ -0.4778
+ -0.4233
+ -0.0409
0.1620
0.4280
0.5873
@@ -211,34 +211,34 @@ E =[
1.9223
1.1777
0.4483
- -0.0661
- -0.4424
- -0.9000
- -1.1478
- -1.2047
- -1.1412
- -1.2383
- -1.1048
- -0.9716
- -0.9287
- -1.0057
- -1.0827
- -1.0200
- -1.0072
- -1.1740
- -1.2809
- -1.1086
- -0.9866
- -0.8947
- -0.5875
- -0.2329
+ -0.0661
+ -0.4424
+ -0.9000
+ -1.1478
+ -1.2047
+ -1.1412
+ -1.2383
+ -1.1048
+ -0.9716
+ -0.9287
+ -1.0057
+ -1.0827
+ -1.0200
+ -1.0072
+ -1.1740
+ -1.2809
+ -1.1086
+ -0.9866
+ -0.8947
+ -0.5875
+ -0.2329
0.1493
0.4906
0.8400
1.0720
1.2648
1.5431
-];
+ ];
I =[
2.6617
@@ -253,29 +253,29 @@ I =[
7.8923
9.4297
9.5010
- 10.0150
- 10.0413
+ 10.0150
+ 10.0413
9.6046
6.4766
5.9647
3.0114
0.5683
- -2.1226
- -2.1855
- -0.8329
- -1.5207
- -1.3419
- -1.7897
- -0.1476
+ -2.1226
+ -2.1855
+ -0.8329
+ -1.5207
+ -1.3419
+ -1.7897
+ -0.1476
0.4675
- -1.6516
- -1.5419
- -1.3050
- -1.2451
- -0.7815
- -0.7796
- -0.3612
- -2.4072
+ -1.6516
+ -1.5419
+ -1.3050
+ -1.2451
+ -0.7815
+ -0.7796
+ -0.3612
+ -2.4072
1.1162
1.1383
3.4132
@@ -283,36 +283,36 @@ I =[
2.8016
2.1734
0.9366
- -0.7050
- -1.5021
- -2.9868
- -6.0237
- -6.2589
- -6.9138
- -8.2340
- -9.2589
- -9.2465
- -9.6988
- -9.7782
- -10.5645
- -10.7544
- -13.1583
- -12.2718
- -12.0131
- -13.5983
- -12.3579
- -10.9146
- -11.1572
- -12.4935
- -9.4393
- -8.5535
- -7.3723
- -10.0169
- -6.6088
- -5.2045
- -4.1024
- -2.8472
- -1.3139
+ -0.7050
+ -1.5021
+ -2.9868
+ -6.0237
+ -6.2589
+ -6.9138
+ -8.2340
+ -9.2589
+ -9.2465
+ -9.6988
+ -9.7782
+ -10.5645
+ -10.7544
+ -13.1583
+ -12.2718
+ -12.0131
+ -13.5983
+ -12.3579
+ -10.9146
+ -11.1572
+ -12.4935
+ -9.4393
+ -8.5535
+ -7.3723
+ -10.0169
+ -6.6088
+ -5.2045
+ -4.1024
+ -2.8472
+ -1.3139
0.0477
1.5629
3.6947
@@ -324,30 +324,30 @@ I =[
7.6576
8.8022
8.9611
- 10.0871
+ 10.0871
9.4797
9.3964
- 10.0363
+ 10.0363
8.6340
6.6522
4.4471
0.2854
- -2.1879
- -2.9879
- -4.1021
- -2.7713
- -2.2281
- -1.2908
- -0.3250
+ -2.1879
+ -2.9879
+ -4.1021
+ -2.7713
+ -2.2281
+ -1.2908
+ -0.3250
0.6534
0.3942
0.3534
- -0.1532
- -1.7936
+ -0.1532
+ -1.7936
0.4909
0.3634
0.4290
- -0.9709
+ -0.9709
0.1942
0.6103
1.4426
@@ -359,7 +359,7 @@ I =[
5.3312
6.4402
6.6529
-];
+ ];
L =[
0.6263
@@ -381,23 +381,23 @@ L =[
1.2422
0.8084
0.4710
- -0.3704
- -0.6427
- -0.5323
- -0.5562
- -0.3651
- -0.4356
- -0.7164
- -0.5816
- -0.4635
- -0.8456
- -0.9708
- -0.7138
- -0.7499
- -0.6941
- -0.6656
- -0.2912
- -0.1650
+ -0.3704
+ -0.6427
+ -0.5323
+ -0.5562
+ -0.3651
+ -0.4356
+ -0.7164
+ -0.5816
+ -0.4635
+ -0.8456
+ -0.9708
+ -0.7138
+ -0.7499
+ -0.6941
+ -0.6656
+ -0.2912
+ -0.1650
0.0774
0.2307
0.4484
@@ -405,34 +405,34 @@ L =[
0.4653
0.2196
0.1736
- -0.1595
- -0.3918
- -0.4611
- -0.8493
- -0.7384
- -1.0604
- -1.2166
- -1.7187
- -1.6932
- -1.7830
- -1.7035
- -2.2079
- -2.3769
- -2.2511
- -2.1093
- -2.4638
- -2.4027
- -2.1313
- -1.9199
- -1.7941
- -1.4661
- -1.2269
- -1.0392
- -1.0725
- -0.7156
- -0.4778
- -0.4233
- -0.0409
+ -0.1595
+ -0.3918
+ -0.4611
+ -0.8493
+ -0.7384
+ -1.0604
+ -1.2166
+ -1.7187
+ -1.6932
+ -1.7830
+ -1.7035
+ -2.2079
+ -2.3769
+ -2.2511
+ -2.1093
+ -2.4638
+ -2.4027
+ -2.1313
+ -1.9199
+ -1.7941
+ -1.4661
+ -1.2269
+ -1.0392
+ -1.0725
+ -0.7156
+ -0.4778
+ -0.4233
+ -0.0409
0.1620
0.4280
0.5873
@@ -453,48 +453,48 @@ L =[
1.9223
1.1777
0.4483
- -0.0661
- -0.4424
- -0.9000
- -1.1478
- -1.2047
- -1.1412
- -1.2383
- -1.1048
- -0.9716
- -0.9287
- -1.0057
- -1.0827
- -1.0200
- -1.0072
- -1.1740
- -1.2809
- -1.1086
- -0.9866
- -0.8947
- -0.5875
- -0.2329
+ -0.0661
+ -0.4424
+ -0.9000
+ -1.1478
+ -1.2047
+ -1.1412
+ -1.2383
+ -1.1048
+ -0.9716
+ -0.9287
+ -1.0057
+ -1.0827
+ -1.0200
+ -1.0072
+ -1.1740
+ -1.2809
+ -1.1086
+ -0.9866
+ -0.8947
+ -0.5875
+ -0.2329
0.1493
0.4906
0.8400
1.0720
1.2648
1.5431
-];
+ ];
PIE =[
- -1.0113
- -0.8305
+ -1.0113
+ -0.8305
0.2332
- -0.8746
- -0.7978
- -0.9220
- -0.2487
- -0.7749
- -0.5460
- -0.5347
+ -0.8746
+ -0.7978
+ -0.9220
+ -0.2487
+ -0.7749
+ -0.5460
+ -0.5347
0.5050
- -0.0334
+ -0.0334
0.6756
0.8791
0.7267
@@ -516,7 +516,7 @@ PIE =[
0.4025
0.4799
0.5981
- -0.1523
+ -0.1523
0.4458
0.2182
0.9793
@@ -538,108 +538,108 @@ PIE =[
0.3910
0.3301
0.2547
- -0.2702
- -0.2998
- -0.1953
- -0.2293
- -0.3284
+ -0.2702
+ -0.2998
+ -0.1953
+ -0.2293
+ -0.3284
0.0480
- -0.0374
+ -0.0374
0.3253
- -0.3434
- -0.3892
- -0.7178
- -0.4758
- -0.6794
- -0.8505
- -0.3512
- -0.4436
- -0.5101
- -0.4574
- -0.2696
- -0.1047
- -0.5745
- -0.2989
- -0.0063
+ -0.3434
+ -0.3892
+ -0.7178
+ -0.4758
+ -0.6794
+ -0.8505
+ -0.3512
+ -0.4436
+ -0.5101
+ -0.4574
+ -0.2696
+ -0.1047
+ -0.5745
+ -0.2989
+ -0.0063
0.0088
- -0.1184
- -0.1506
- -0.4073
+ -0.1184
+ -0.1506
+ -0.4073
0.2674
0.2896
0.0669
0.1166
- -0.1699
- -0.2518
- -0.0562
- -0.3269
- -0.0703
- -0.1046
- -0.4888
- -0.3524
- -0.2485
- -0.5870
- -0.4546
- -0.3970
- -0.2353
- -0.0352
- -0.2171
- -0.3754
- -0.4322
- -0.4572
- -0.4903
- -0.4518
- -0.6435
- -0.6304
- -0.4148
- -0.2892
- -0.4318
- -0.6010
- -0.4148
- -0.4315
- -0.3531
- -0.8053
- -0.4680
- -0.4263
-];
+ -0.1699
+ -0.2518
+ -0.0562
+ -0.3269
+ -0.0703
+ -0.1046
+ -0.4888
+ -0.3524
+ -0.2485
+ -0.5870
+ -0.4546
+ -0.3970
+ -0.2353
+ -0.0352
+ -0.2171
+ -0.3754
+ -0.4322
+ -0.4572
+ -0.4903
+ -0.4518
+ -0.6435
+ -0.6304
+ -0.4148
+ -0.2892
+ -0.4318
+ -0.6010
+ -0.4148
+ -0.4315
+ -0.3531
+ -0.8053
+ -0.4680
+ -0.4263
+ ];
R =[
- -1.0750
- -1.1540
- -1.3682
- -1.4569
- -1.3490
- -1.4011
- -1.6486
- -1.6968
- -1.6976
- -1.2567
- -1.1392
- -0.7783
- -0.3021
- -0.0435
+ -1.0750
+ -1.1540
+ -1.3682
+ -1.4569
+ -1.3490
+ -1.4011
+ -1.6486
+ -1.6968
+ -1.6976
+ -1.2567
+ -1.1392
+ -0.7783
+ -0.3021
+ -0.0435
0.0066
- -0.0043
+ -0.0043
0.1029
- -0.0628
- -0.5358
- -0.9627
- -1.1079
- -1.0918
- -0.9966
- -0.6223
- -0.3616
- -0.2711
- -0.0997
- -0.2810
- -0.3710
- -0.3167
- -0.5301
- -0.5826
- -0.3194
- -0.2713
- -0.5287
- -0.2432
+ -0.0628
+ -0.5358
+ -0.9627
+ -1.1079
+ -1.0918
+ -0.9966
+ -0.6223
+ -0.3616
+ -0.2711
+ -0.0997
+ -0.2810
+ -0.3710
+ -0.3167
+ -0.5301
+ -0.5826
+ -0.3194
+ -0.2713
+ -0.5287
+ -0.2432
0.1098
0.5349
0.7094
@@ -667,15 +667,15 @@ R =[
0.1741
0.0348
0.0771
- -0.1005
- -0.1518
- -0.1104
- -0.0681
- -0.0059
+ -0.1005
+ -0.1518
+ -0.1104
+ -0.0681
+ -0.0059
0.0256
0.0404
- -0.1721
- -0.2002
+ -0.1721
+ -0.2002
0.0015
0.1249
0.3738
@@ -699,51 +699,51 @@ R =[
0.3178
0.1472
0.0227
- -0.0799
- -0.0611
- -0.0140
+ -0.0799
+ -0.0611
+ -0.0140
0.1132
0.1774
0.0782
0.0436
- -0.1596
- -0.2691
- -0.2895
- -0.3791
- -0.4020
- -0.4166
- -0.4037
- -0.3636
- -0.4075
- -0.4311
- -0.4470
- -0.5111
- -0.6274
- -0.7261
- -0.6974
- -0.5012
-];
+ -0.1596
+ -0.2691
+ -0.2895
+ -0.3791
+ -0.4020
+ -0.4166
+ -0.4037
+ -0.3636
+ -0.4075
+ -0.4311
+ -0.4470
+ -0.5111
+ -0.6274
+ -0.7261
+ -0.6974
+ -0.5012
+ ];
W =[
- -14.8791
- -13.2300
- -13.5037
- -13.0249
- -11.2546
- -10.0148
- -8.8586
- -8.5739
- -7.7851
- -6.7136
- -5.5878
- -4.6881
- -3.8039
- -3.0366
- -2.7342
- -1.3135
- -0.7387
- -0.1131
- -0.2769
+ -14.8791
+ -13.2300
+ -13.5037
+ -13.0249
+ -11.2546
+ -10.0148
+ -8.8586
+ -8.5739
+ -7.7851
+ -6.7136
+ -5.5878
+ -4.6881
+ -3.8039
+ -3.0366
+ -2.7342
+ -1.3135
+ -0.7387
+ -0.1131
+ -0.2769
0.8696
1.8855
2.3667
@@ -788,26 +788,26 @@ W =[
1.6092
1.2741
0.2031
- -0.0236
- -0.1004
- -0.3034
- -1.0273
- -0.2205
+ -0.0236
+ -0.1004
+ -0.3034
+ -1.0273
+ -0.2205
0.0458
0.2386
- -0.0977
- -0.3145
- -0.1416
- -0.7009
- -0.9082
- -0.8802
- -0.5644
- -0.5852
- -0.5346
+ -0.0977
+ -0.3145
+ -0.1416
+ -0.7009
+ -0.9082
+ -0.8802
+ -0.5644
+ -0.5852
+ -0.5346
0.0652
0.1301
0.3444
- -0.3592
+ -0.3592
0.8096
0.9644
1.0289
@@ -819,43 +819,43 @@ W =[
0.6506
0.5531
0.2456
- -0.5351
- -0.8183
- -0.8967
- -0.7268
- -1.0738
- -1.2844
- -1.4338
- -1.6995
- -1.7085
- -2.2889
- -2.1018
- -2.4273
- -2.4609
- -2.1407
- -2.3847
- -3.1689
- -4.5581
- -4.1027
- -4.2436
- -4.8836
- -5.9660
- -4.9971
- -5.2386
- -5.6618
-];
+ -0.5351
+ -0.8183
+ -0.8967
+ -0.7268
+ -1.0738
+ -1.2844
+ -1.4338
+ -1.6995
+ -1.7085
+ -2.2889
+ -2.1018
+ -2.4273
+ -2.4609
+ -2.1407
+ -2.3847
+ -3.1689
+ -4.5581
+ -4.1027
+ -4.2436
+ -4.8836
+ -5.9660
+ -4.9971
+ -5.2386
+ -5.6618
+ ];
Y =[
- -4.9347
- -4.6205
- -5.2198
- -4.5937
- -3.8015
- -3.6643
- -2.7239
- -2.7524
- -2.0634
- -1.0112
+ -4.9347
+ -4.6205
+ -5.2198
+ -4.5937
+ -3.8015
+ -3.6643
+ -2.7239
+ -2.7524
+ -2.0634
+ -1.0112
0.0530
0.7623
1.7927
@@ -863,12 +863,12 @@ Y =[
2.4866
2.1456
2.1671
- -0.0254
- -1.6716
- -1.9673
- -1.6109
- -1.0292
- -0.1222
+ -0.0254
+ -1.6716
+ -1.9673
+ -1.6109
+ -1.0292
+ -0.1222
0.7329
1.1234
2.0603
@@ -894,28 +894,28 @@ Y =[
0.7594
0.3695
0.0832
- -0.8118
- -1.4557
- -1.4850
- -1.2346
- -1.5696
- -1.3785
- -0.7682
- -2.0308
- -1.7778
- -1.7801
- -2.1711
- -1.7469
- -1.3413
- -1.3352
- -2.4390
- -1.2125
- -1.1695
- -1.0891
- -2.4753
- -1.3503
- -0.9412
- -0.1470
+ -0.8118
+ -1.4557
+ -1.4850
+ -1.2346
+ -1.5696
+ -1.3785
+ -0.7682
+ -2.0308
+ -1.7778
+ -1.7801
+ -2.1711
+ -1.7469
+ -1.3413
+ -1.3352
+ -2.4390
+ -1.2125
+ -1.1695
+ -1.0891
+ -2.4753
+ -1.3503
+ -0.9412
+ -0.1470
0.0026
0.1108
0.6890
@@ -936,32 +936,32 @@ Y =[
2.4609
1.9534
0.9962
- -0.7904
- -1.1672
- -1.2586
- -1.3593
- -1.3443
- -0.9413
- -0.6023
- -0.4516
- -0.5129
- -0.8741
- -1.0784
- -1.4091
- -1.3627
- -1.5731
- -1.6037
- -1.8814
- -2.1482
- -1.3597
- -1.1855
- -1.1122
- -0.8424
- -0.9747
- -1.1385
- -1.4548
- -1.4284
- -1.4633
- -1.0621
- -0.7871
-];
+ -0.7904
+ -1.1672
+ -1.2586
+ -1.3593
+ -1.3443
+ -0.9413
+ -0.6023
+ -0.4516
+ -0.5129
+ -0.8741
+ -1.0784
+ -1.4091
+ -1.3627
+ -1.5731
+ -1.6037
+ -1.8814
+ -2.1482
+ -1.3597
+ -1.1855
+ -1.1122
+ -0.8424
+ -0.9747
+ -1.1385
+ -1.4548
+ -1.4284
+ -1.4633
+ -1.0621
+ -0.7871
+ ];
diff --git a/mex/sources/estimation/tests/random_walk_metropolis_hastings_core.m b/mex/sources/estimation/tests/random_walk_metropolis_hastings_core.m
index 14646c1578d231f9ed7d05356e3a11ab1988fcf6..5a615861237a97dc81a44ca6c2665b0f93fb78a5 100644
--- a/mex/sources/estimation/tests/random_walk_metropolis_hastings_core.m
+++ b/mex/sources/estimation/tests/random_walk_metropolis_hastings_core.m
@@ -94,12 +94,12 @@ varargin=myinputs.varargin;
% Necessary only for remote computing!
if whoiam
- Parallel=myinputs.Parallel;
- MasterName=myinputs.MasterName;
- DyMo=myinputs.DyMo;
- % initialize persistent variables in priordens()
- priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, ...
- bayestopt_.p3,bayestopt_.p4,1);
+ Parallel=myinputs.Parallel;
+ MasterName=myinputs.MasterName;
+ DyMo=myinputs.DyMo;
+ % initialize persistent variables in priordens()
+ priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, ...
+ bayestopt_.p3,bayestopt_.p4,1);
end
% (re)Set the penalty
@@ -122,185 +122,185 @@ jscale = diag(bayestopt_.jscale);
jloop=0;
if options_.use_dll==1
-
-%%%TEST%%%
-oldoptions_console_mode=options_.console_mode;
-%options_.console_mode=1;
+
+ %%%TEST%%%
+ oldoptions_console_mode=options_.console_mode;
+ %options_.console_mode=1;
- if exist('OCTAVE_VERSION')
- oldoptions_console_mode=options_.console_mode;
- options_.console_mode=1;
- end
- for b = fblck:nblck,
- record.Seeds(b).Normal = randn('state');
- record.Seeds(b).Unifor = rand('state');
- end
- % calculate draws and get last line run in the last MH block sub-array
- [err irun] = logMHMCMCposterior( xparam1, varargin{2}, mexext, fblck, nblck, nruns, vv)
- mexErrCheck('logMHMCMCposterior',err);
- if irun<0
- error('Error in logMHMCMCposterior');
- end
- for b = fblck:nblck,
- record.Seeds(b).Normal = randn('state');
- record.Seeds(b).Unifor = rand('state');
- OutputFileName(b,:) = {[MhDirectoryName,filesep], [ModelName '_mh*_blck' int2str(b) '.mat']};
- end
+ if exist('OCTAVE_VERSION')
+ oldoptions_console_mode=options_.console_mode;
+ options_.console_mode=1;
+ end
+ for b = fblck:nblck,
+ record.Seeds(b).Normal = randn('state');
+ record.Seeds(b).Unifor = rand('state');
+ end
+ % calculate draws and get last line run in the last MH block sub-array
+ [err irun] = logMHMCMCposterior( xparam1, varargin{2}, mexext, fblck, nblck, nruns, vv)
+ mexErrCheck('logMHMCMCposterior',err);
+ if irun<0
+ error('Error in logMHMCMCposterior');
+ end
+ for b = fblck:nblck,
+ record.Seeds(b).Normal = randn('state');
+ record.Seeds(b).Unifor = rand('state');
+ OutputFileName(b,:) = {[MhDirectoryName,filesep], [ModelName '_mh*_blck' int2str(b) '.mat']};
+ end
- if exist('OCTAVE_VERSION')
+ if exist('OCTAVE_VERSION')
+ options_.console_mode=oldoptions_console_mode;
+ options_.console_mode=1;
+ end
+ record.AcceptationRates=record_AcceptationRates;
+ record.LastLogLiK=record_LastLogLiK;
+ record.LastParameters=record_LastParameters;
options_.console_mode=oldoptions_console_mode;
- options_.console_mode=1;
- end
- record.AcceptationRates=record_AcceptationRates;
- record.LastLogLiK=record_LastLogLiK;
- record.LastParameters=record_LastParameters;
- options_.console_mode=oldoptions_console_mode;
else
- for b = fblck:nblck,
- jloop=jloop+1;
- randn('state',record.Seeds(b).Normal);
- rand('state',record.Seeds(b).Unifor);
- if (options_.load_mh_file~=0) & (fline(b)>1) & OpenOldFile(b)
- load(['./' MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) ...
- '_blck' int2str(b) '.mat'])
- x2 = [x2;zeros(InitSizeArray(b)-fline(b)+1,npar)];
- logpo2 = [logpo2;zeros(InitSizeArray(b)-fline(b)+1,1)];
- OpenOldFile(b) = 0;
- else
- x2 = zeros(InitSizeArray(b),npar);
- logpo2 = zeros(InitSizeArray(b),1);
- end
- if exist('OCTAVE_VERSION') || options_.console_mode
- diary off
- disp(' ')
- elseif whoiam
- % keyboard;
- waitbarString = ['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...'];
- % waitbarTitle=['Metropolis-Hastings ',options_.parallel(ThisMatlab).PcName];
- if options_.parallel(ThisMatlab).Local,
- waitbarTitle=['Local '];
+ for b = fblck:nblck,
+ jloop=jloop+1;
+ randn('state',record.Seeds(b).Normal);
+ rand('state',record.Seeds(b).Unifor);
+ if (options_.load_mh_file~=0) & (fline(b)>1) & OpenOldFile(b)
+ load(['./' MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) ...
+ '_blck' int2str(b) '.mat'])
+ x2 = [x2;zeros(InitSizeArray(b)-fline(b)+1,npar)];
+ logpo2 = [logpo2;zeros(InitSizeArray(b)-fline(b)+1,1)];
+ OpenOldFile(b) = 0;
else
- waitbarTitle=[options_.parallel(ThisMatlab).PcName];
- end
- fMessageStatus(0,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab), MasterName, DyMo);
- else,
- hh = waitbar(0,['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...']);
- set(hh,'Name','Metropolis-Hastings');
-
- end
- isux = 0;
- jsux = 0;
- irun = fline(b);
- j = 1;
- load urand_1_1.csv
- load paramdraws_1_1.csv
- while j <= nruns(b)
- par = feval(ProposalFun, ix2(b,:), d * jscale, n);
- par=paramdraws_1_1(j,:);
- if all( par(:) > mh_bounds(:,1) ) & all( par(:) < mh_bounds(:,2) )
- try
- logpost = - feval(TargetFun, par(:),varargin{:});
- catch
- logpost = -inf;
- end
- else
- logpost = -inf;
+ x2 = zeros(InitSizeArray(b),npar);
+ logpo2 = zeros(InitSizeArray(b),1);
end
- lurand=log(urand_1_1(j));
-% if (logpost > -inf) && (log(rand) < logpost-ilogpo2(b))
- if (logpost > -inf) && (lurand < logpost-ilogpo2(b))
- x2(irun,:) = par;
- ix2(b,:) = par;
- logpo2(irun) = logpost;
- ilogpo2(b) = logpost;
- isux = isux + 1;
- jsux = jsux + 1;
- else
- x2(irun,:) = ix2(b,:);
- logpo2(irun) = ilogpo2(b);
- end
- prtfrc = j/nruns(b);
if exist('OCTAVE_VERSION') || options_.console_mode
- if mod(j, 10) == 0
- if exist('OCTAVE_VERSION')
- printf('MH: Computing Metropolis-Hastings (chain %d/%d): %3.f%% done, acception rate: %3.f%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
- else
- fprintf(' MH: Computing Metropolis-Hastings (chain %d/%d): %3.f \b%% done, acceptance rate: %3.f \b%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
- end
- end
- if mod(j,50)==0 & whoiam
- % keyboard;
- waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) '), ' sprintf('accept. %3.f%%%%', 100 * isux/j)];
- fMessageStatus(prtfrc,whoiam,waitbarString, '', options_.parallel(ThisMatlab), MasterName, DyMo)
- end
- else
- if mod(j, 3)==0 & ~whoiam
- waitbar(prtfrc,hh,[ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)]);
- elseif mod(j,50)==0 & whoiam,
- % keyboard;
- waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)];
- fMessageStatus(prtfrc,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab), MasterName, DyMo)
- end
+ diary off
+ disp(' ')
+ elseif whoiam
+ % keyboard;
+ waitbarString = ['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...'];
+ % waitbarTitle=['Metropolis-Hastings ',options_.parallel(ThisMatlab).PcName];
+ if options_.parallel(ThisMatlab).Local,
+ waitbarTitle=['Local '];
+ else
+ waitbarTitle=[options_.parallel(ThisMatlab).PcName];
+ end
+ fMessageStatus(0,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab), MasterName, DyMo);
+ else,
+ hh = waitbar(0,['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...']);
+ set(hh,'Name','Metropolis-Hastings');
+
end
-
- if (irun == InitSizeArray(b)) | (j == nruns(b)) % Now I save the simulations
- save([MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'],'x2','logpo2');
- fidlog = fopen([MhDirectoryName '/metropolis.log'],'a');
- fprintf(fidlog,['\n']);
- fprintf(fidlog,['%% Mh' int2str(NewFile(b)) 'Blck' int2str(b) ' (' datestr(now,0) ')\n']);
- fprintf(fidlog,' \n');
- fprintf(fidlog,[' Number of simulations.: ' int2str(length(logpo2)) '\n']);
- fprintf(fidlog,[' Acceptation rate......: ' num2str(jsux/length(logpo2)) '\n']);
- fprintf(fidlog,[' Posterior mean........:\n']);
- for i=1:length(x2(1,:))
- fprintf(fidlog,[' params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
+ isux = 0;
+ jsux = 0;
+ irun = fline(b);
+ j = 1;
+ load urand_1_1.csv
+ load paramdraws_1_1.csv
+ while j <= nruns(b)
+ par = feval(ProposalFun, ix2(b,:), d * jscale, n);
+ par=paramdraws_1_1(j,:);
+ if all( par(:) > mh_bounds(:,1) ) & all( par(:) < mh_bounds(:,2) )
+ try
+ logpost = - feval(TargetFun, par(:),varargin{:});
+ catch
+ logpost = -inf;
+ end
+ else
+ logpost = -inf;
end
- fprintf(fidlog,[' log2po:' num2str(mean(logpo2)) '\n']);
- fprintf(fidlog,[' Minimum value.........:\n']);;
- for i=1:length(x2(1,:))
- fprintf(fidlog,[' params:' int2str(i) ': ' num2str(min(x2(:,i))) '\n']);
+ lurand=log(urand_1_1(j));
+ % if (logpost > -inf) && (log(rand) < logpost-ilogpo2(b))
+ if (logpost > -inf) && (lurand < logpost-ilogpo2(b))
+ x2(irun,:) = par;
+ ix2(b,:) = par;
+ logpo2(irun) = logpost;
+ ilogpo2(b) = logpost;
+ isux = isux + 1;
+ jsux = jsux + 1;
+ else
+ x2(irun,:) = ix2(b,:);
+ logpo2(irun) = ilogpo2(b);
end
- fprintf(fidlog,[' log2po:' num2str(min(logpo2)) '\n']);
- fprintf(fidlog,[' Maximum value.........:\n']);
- for i=1:length(x2(1,:))
- fprintf(fidlog,[' params:' int2str(i) ': ' num2str(max(x2(:,i))) '\n']);
+ prtfrc = j/nruns(b);
+ if exist('OCTAVE_VERSION') || options_.console_mode
+ if mod(j, 10) == 0
+ if exist('OCTAVE_VERSION')
+ printf('MH: Computing Metropolis-Hastings (chain %d/%d): %3.f%% done, acception rate: %3.f%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
+ else
+ fprintf(' MH: Computing Metropolis-Hastings (chain %d/%d): %3.f \b%% done, acceptance rate: %3.f \b%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
+ end
+ end
+ if mod(j,50)==0 & whoiam
+ % keyboard;
+ waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) '), ' sprintf('accept. %3.f%%%%', 100 * isux/j)];
+ fMessageStatus(prtfrc,whoiam,waitbarString, '', options_.parallel(ThisMatlab), MasterName, DyMo)
+ end
+ else
+ if mod(j, 3)==0 & ~whoiam
+ waitbar(prtfrc,hh,[ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)]);
+ elseif mod(j,50)==0 & whoiam,
+ % keyboard;
+ waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)];
+ fMessageStatus(prtfrc,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab), MasterName, DyMo)
+ end
end
- fprintf(fidlog,[' log2po:' num2str(max(logpo2)) '\n']);
- fprintf(fidlog,' \n');
- fclose(fidlog);
- jsux = 0;
- if j == nruns(b) % I record the last draw...
- record.LastParameters(b,:) = x2(end,:);
- record.LastLogLiK(b) = logpo2(end);
+
+ if (irun == InitSizeArray(b)) | (j == nruns(b)) % Now I save the simulations
+ save([MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'],'x2','logpo2');
+ fidlog = fopen([MhDirectoryName '/metropolis.log'],'a');
+ fprintf(fidlog,['\n']);
+ fprintf(fidlog,['%% Mh' int2str(NewFile(b)) 'Blck' int2str(b) ' (' datestr(now,0) ')\n']);
+ fprintf(fidlog,' \n');
+ fprintf(fidlog,[' Number of simulations.: ' int2str(length(logpo2)) '\n']);
+ fprintf(fidlog,[' Acceptation rate......: ' num2str(jsux/length(logpo2)) '\n']);
+ fprintf(fidlog,[' Posterior mean........:\n']);
+ for i=1:length(x2(1,:))
+ fprintf(fidlog,[' params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
+ end
+ fprintf(fidlog,[' log2po:' num2str(mean(logpo2)) '\n']);
+ fprintf(fidlog,[' Minimum value.........:\n']);;
+ for i=1:length(x2(1,:))
+ fprintf(fidlog,[' params:' int2str(i) ': ' num2str(min(x2(:,i))) '\n']);
+ end
+ fprintf(fidlog,[' log2po:' num2str(min(logpo2)) '\n']);
+ fprintf(fidlog,[' Maximum value.........:\n']);
+ for i=1:length(x2(1,:))
+ fprintf(fidlog,[' params:' int2str(i) ': ' num2str(max(x2(:,i))) '\n']);
+ end
+ fprintf(fidlog,[' log2po:' num2str(max(logpo2)) '\n']);
+ fprintf(fidlog,' \n');
+ fclose(fidlog);
+ jsux = 0;
+ if j == nruns(b) % I record the last draw...
+ record.LastParameters(b,:) = x2(end,:);
+ record.LastLogLiK(b) = logpo2(end);
+ end
+ % size of next file in chain b
+ InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);
+ % initialization of next file if necessary
+ if InitSizeArray(b)
+ x2 = zeros(InitSizeArray(b),npar);
+ logpo2 = zeros(InitSizeArray(b),1);
+ NewFile(b) = NewFile(b) + 1;
+ irun = 0;
+ end
end
- % size of next file in chain b
- InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);
- % initialization of next file if necessary
- if InitSizeArray(b)
- x2 = zeros(InitSizeArray(b),npar);
- logpo2 = zeros(InitSizeArray(b),1);
- NewFile(b) = NewFile(b) + 1;
- irun = 0;
+ j=j+1;
+ irun = irun + 1;
+ end% End of the simulations for one mh-block.
+ record.AcceptationRates(b) = isux/j;
+ if exist('OCTAVE_VERSION') || options_.console_mode
+ if exist('OCTAVE_VERSION')
+ printf('\n');
+ else
+ fprintf('\n');
end
+ diary on;
+ elseif ~whoiam
+ close(hh);
end
- j=j+1;
- irun = irun + 1;
- end% End of the simulations for one mh-block.
- record.AcceptationRates(b) = isux/j;
- if exist('OCTAVE_VERSION') || options_.console_mode
- if exist('OCTAVE_VERSION')
- printf('\n');
- else
- fprintf('\n');
- end
- diary on;
- elseif ~whoiam
- close(hh);
- end
- record.Seeds(b).Normal = randn('state');
- record.Seeds(b).Unifor = rand('state');
- OutputFileName(jloop,:) = {[MhDirectoryName,filesep], [ModelName '_mh*_blck' int2str(b) '.mat']};
- end% End of the loop over the mh-blocks.
+ record.Seeds(b).Normal = randn('state');
+ record.Seeds(b).Unifor = rand('state');
+ OutputFileName(jloop,:) = {[MhDirectoryName,filesep], [ModelName '_mh*_blck' int2str(b) '.mat']};
+ end% End of the loop over the mh-blocks.
end % if use_dll
myoutput.record = record;
diff --git a/mex/sources/estimation/tests/rawdata_euromodel_1.m b/mex/sources/estimation/tests/rawdata_euromodel_1.m
index 25bbc92414efddc7adf66d5ab4cc8545af1e71f7..6b9fee7203e6fccb28de487aa0ac3e8324bf76d6 100644
--- a/mex/sources/estimation/tests/rawdata_euromodel_1.m
+++ b/mex/sources/estimation/tests/rawdata_euromodel_1.m
@@ -1,25 +1,25 @@
C =[
- -7.4073
- -6.1860
- -6.5983
- -5.6088
- -5.0547
- -4.4774
- -3.8081
- -3.8425
- -2.4178
- -1.9835
- -1.0395
- -0.1583
- -0.0397
+ -7.4073
+ -6.1860
+ -6.5983
+ -5.6088
+ -5.0547
+ -4.4774
+ -3.8081
+ -3.8425
+ -2.4178
+ -1.9835
+ -1.0395
+ -0.1583
+ -0.0397
0.3505
- -0.1879
- -0.0067
+ -0.1879
+ -0.0067
0.0478
- -1.2247
- -1.4349
- -0.7973
- -0.0461
+ -1.2247
+ -1.4349
+ -0.7973
+ -0.0461
0.5844
1.1372
1.3801
@@ -47,26 +47,26 @@ C =[
1.5563
1.2308
0.3264
- -0.2208
- -0.2483
- -0.4082
- -1.0315
- -1.6030
- -1.5499
- -1.3777
- -2.1675
- -2.5138
- -2.8820
- -2.6958
- -2.4719
- -1.9854
- -1.7954
- -2.2362
- -1.0595
- -0.8808
- -0.8548
- -1.2839
- -0.1363
+ -0.2208
+ -0.2483
+ -0.4082
+ -1.0315
+ -1.6030
+ -1.5499
+ -1.3777
+ -2.1675
+ -2.5138
+ -2.8820
+ -2.6958
+ -2.4719
+ -1.9854
+ -1.7954
+ -2.2362
+ -1.0595
+ -0.8808
+ -0.8548
+ -1.2839
+ -0.1363
0.2104
0.8810
0.3555
@@ -93,31 +93,31 @@ C =[
0.0847
0.0104
0.2115
- -0.6649
- -0.9625
- -0.7330
- -0.8664
- -1.4441
- -1.0179
- -1.2729
- -1.9539
- -1.4427
- -2.0371
- -1.9764
- -2.5654
- -2.8570
- -2.5842
- -3.0427
- -2.8312
- -2.3320
- -2.2768
- -2.1816
- -2.1043
- -1.8969
- -2.2388
- -2.1679
- -2.1172
-];
+ -0.6649
+ -0.9625
+ -0.7330
+ -0.8664
+ -1.4441
+ -1.0179
+ -1.2729
+ -1.9539
+ -1.4427
+ -2.0371
+ -1.9764
+ -2.5654
+ -2.8570
+ -2.5842
+ -3.0427
+ -2.8312
+ -2.3320
+ -2.2768
+ -2.1816
+ -2.1043
+ -1.8969
+ -2.2388
+ -2.1679
+ -2.1172
+ ];
E =[
0.6263
@@ -139,23 +139,23 @@ E =[
1.2422
0.8084
0.4710
- -0.3704
- -0.6427
- -0.5323
- -0.5562
- -0.3651
- -0.4356
- -0.7164
- -0.5816
- -0.4635
- -0.8456
- -0.9708
- -0.7138
- -0.7499
- -0.6941
- -0.6656
- -0.2912
- -0.1650
+ -0.3704
+ -0.6427
+ -0.5323
+ -0.5562
+ -0.3651
+ -0.4356
+ -0.7164
+ -0.5816
+ -0.4635
+ -0.8456
+ -0.9708
+ -0.7138
+ -0.7499
+ -0.6941
+ -0.6656
+ -0.2912
+ -0.1650
0.0774
0.2307
0.4484
@@ -163,34 +163,34 @@ E =[
0.4653
0.2196
0.1736
- -0.1595
- -0.3918
- -0.4611
- -0.8493
- -0.7384
- -1.0604
- -1.2166
- -1.7187
- -1.6932
- -1.7830
- -1.7035
- -2.2079
- -2.3769
- -2.2511
- -2.1093
- -2.4638
- -2.4027
- -2.1313
- -1.9199
- -1.7941
- -1.4661
- -1.2269
- -1.0392
- -1.0725
- -0.7156
- -0.4778
- -0.4233
- -0.0409
+ -0.1595
+ -0.3918
+ -0.4611
+ -0.8493
+ -0.7384
+ -1.0604
+ -1.2166
+ -1.7187
+ -1.6932
+ -1.7830
+ -1.7035
+ -2.2079
+ -2.3769
+ -2.2511
+ -2.1093
+ -2.4638
+ -2.4027
+ -2.1313
+ -1.9199
+ -1.7941
+ -1.4661
+ -1.2269
+ -1.0392
+ -1.0725
+ -0.7156
+ -0.4778
+ -0.4233
+ -0.0409
0.1620
0.4280
0.5873
@@ -211,34 +211,34 @@ E =[
1.9223
1.1777
0.4483
- -0.0661
- -0.4424
- -0.9000
- -1.1478
- -1.2047
- -1.1412
- -1.2383
- -1.1048
- -0.9716
- -0.9287
- -1.0057
- -1.0827
- -1.0200
- -1.0072
- -1.1740
- -1.2809
- -1.1086
- -0.9866
- -0.8947
- -0.5875
- -0.2329
+ -0.0661
+ -0.4424
+ -0.9000
+ -1.1478
+ -1.2047
+ -1.1412
+ -1.2383
+ -1.1048
+ -0.9716
+ -0.9287
+ -1.0057
+ -1.0827
+ -1.0200
+ -1.0072
+ -1.1740
+ -1.2809
+ -1.1086
+ -0.9866
+ -0.8947
+ -0.5875
+ -0.2329
0.1493
0.4906
0.8400
1.0720
1.2648
1.5431
-];
+ ];
I =[
2.6617
@@ -253,29 +253,29 @@ I =[
7.8923
9.4297
9.5010
- 10.0150
- 10.0413
+ 10.0150
+ 10.0413
9.6046
6.4766
5.9647
3.0114
0.5683
- -2.1226
- -2.1855
- -0.8329
- -1.5207
- -1.3419
- -1.7897
- -0.1476
+ -2.1226
+ -2.1855
+ -0.8329
+ -1.5207
+ -1.3419
+ -1.7897
+ -0.1476
0.4675
- -1.6516
- -1.5419
- -1.3050
- -1.2451
- -0.7815
- -0.7796
- -0.3612
- -2.4072
+ -1.6516
+ -1.5419
+ -1.3050
+ -1.2451
+ -0.7815
+ -0.7796
+ -0.3612
+ -2.4072
1.1162
1.1383
3.4132
@@ -283,36 +283,36 @@ I =[
2.8016
2.1734
0.9366
- -0.7050
- -1.5021
- -2.9868
- -6.0237
- -6.2589
- -6.9138
- -8.2340
- -9.2589
- -9.2465
- -9.6988
- -9.7782
- -10.5645
- -10.7544
- -13.1583
- -12.2718
- -12.0131
- -13.5983
- -12.3579
- -10.9146
- -11.1572
- -12.4935
- -9.4393
- -8.5535
- -7.3723
- -10.0169
- -6.6088
- -5.2045
- -4.1024
- -2.8472
- -1.3139
+ -0.7050
+ -1.5021
+ -2.9868
+ -6.0237
+ -6.2589
+ -6.9138
+ -8.2340
+ -9.2589
+ -9.2465
+ -9.6988
+ -9.7782
+ -10.5645
+ -10.7544
+ -13.1583
+ -12.2718
+ -12.0131
+ -13.5983
+ -12.3579
+ -10.9146
+ -11.1572
+ -12.4935
+ -9.4393
+ -8.5535
+ -7.3723
+ -10.0169
+ -6.6088
+ -5.2045
+ -4.1024
+ -2.8472
+ -1.3139
0.0477
1.5629
3.6947
@@ -324,30 +324,30 @@ I =[
7.6576
8.8022
8.9611
- 10.0871
+ 10.0871
9.4797
9.3964
- 10.0363
+ 10.0363
8.6340
6.6522
4.4471
0.2854
- -2.1879
- -2.9879
- -4.1021
- -2.7713
- -2.2281
- -1.2908
- -0.3250
+ -2.1879
+ -2.9879
+ -4.1021
+ -2.7713
+ -2.2281
+ -1.2908
+ -0.3250
0.6534
0.3942
0.3534
- -0.1532
- -1.7936
+ -0.1532
+ -1.7936
0.4909
0.3634
0.4290
- -0.9709
+ -0.9709
0.1942
0.6103
1.4426
@@ -359,7 +359,7 @@ I =[
5.3312
6.4402
6.6529
-];
+ ];
L =[
0.6263
@@ -381,23 +381,23 @@ L =[
1.2422
0.8084
0.4710
- -0.3704
- -0.6427
- -0.5323
- -0.5562
- -0.3651
- -0.4356
- -0.7164
- -0.5816
- -0.4635
- -0.8456
- -0.9708
- -0.7138
- -0.7499
- -0.6941
- -0.6656
- -0.2912
- -0.1650
+ -0.3704
+ -0.6427
+ -0.5323
+ -0.5562
+ -0.3651
+ -0.4356
+ -0.7164
+ -0.5816
+ -0.4635
+ -0.8456
+ -0.9708
+ -0.7138
+ -0.7499
+ -0.6941
+ -0.6656
+ -0.2912
+ -0.1650
0.0774
0.2307
0.4484
@@ -405,34 +405,34 @@ L =[
0.4653
0.2196
0.1736
- -0.1595
- -0.3918
- -0.4611
- -0.8493
- -0.7384
- -1.0604
- -1.2166
- -1.7187
- -1.6932
- -1.7830
- -1.7035
- -2.2079
- -2.3769
- -2.2511
- -2.1093
- -2.4638
- -2.4027
- -2.1313
- -1.9199
- -1.7941
- -1.4661
- -1.2269
- -1.0392
- -1.0725
- -0.7156
- -0.4778
- -0.4233
- -0.0409
+ -0.1595
+ -0.3918
+ -0.4611
+ -0.8493
+ -0.7384
+ -1.0604
+ -1.2166
+ -1.7187
+ -1.6932
+ -1.7830
+ -1.7035
+ -2.2079
+ -2.3769
+ -2.2511
+ -2.1093
+ -2.4638
+ -2.4027
+ -2.1313
+ -1.9199
+ -1.7941
+ -1.4661
+ -1.2269
+ -1.0392
+ -1.0725
+ -0.7156
+ -0.4778
+ -0.4233
+ -0.0409
0.1620
0.4280
0.5873
@@ -453,48 +453,48 @@ L =[
1.9223
1.1777
0.4483
- -0.0661
- -0.4424
- -0.9000
- -1.1478
- -1.2047
- -1.1412
- -1.2383
- -1.1048
- -0.9716
- -0.9287
- -1.0057
- -1.0827
- -1.0200
- -1.0072
- -1.1740
- -1.2809
- -1.1086
- -0.9866
- -0.8947
- -0.5875
- -0.2329
+ -0.0661
+ -0.4424
+ -0.9000
+ -1.1478
+ -1.2047
+ -1.1412
+ -1.2383
+ -1.1048
+ -0.9716
+ -0.9287
+ -1.0057
+ -1.0827
+ -1.0200
+ -1.0072
+ -1.1740
+ -1.2809
+ -1.1086
+ -0.9866
+ -0.8947
+ -0.5875
+ -0.2329
0.1493
0.4906
0.8400
1.0720
1.2648
1.5431
-];
+ ];
PIE =[
- -1.0113
- -0.8305
+ -1.0113
+ -0.8305
0.2332
- -0.8746
- -0.7978
- -0.9220
- -0.2487
- -0.7749
- -0.5460
- -0.5347
+ -0.8746
+ -0.7978
+ -0.9220
+ -0.2487
+ -0.7749
+ -0.5460
+ -0.5347
0.5050
- -0.0334
+ -0.0334
0.6756
0.8791
0.7267
@@ -516,7 +516,7 @@ PIE =[
0.4025
0.4799
0.5981
- -0.1523
+ -0.1523
0.4458
0.2182
0.9793
@@ -538,108 +538,108 @@ PIE =[
0.3910
0.3301
0.2547
- -0.2702
- -0.2998
- -0.1953
- -0.2293
- -0.3284
+ -0.2702
+ -0.2998
+ -0.1953
+ -0.2293
+ -0.3284
0.0480
- -0.0374
+ -0.0374
0.3253
- -0.3434
- -0.3892
- -0.7178
- -0.4758
- -0.6794
- -0.8505
- -0.3512
- -0.4436
- -0.5101
- -0.4574
- -0.2696
- -0.1047
- -0.5745
- -0.2989
- -0.0063
+ -0.3434
+ -0.3892
+ -0.7178
+ -0.4758
+ -0.6794
+ -0.8505
+ -0.3512
+ -0.4436
+ -0.5101
+ -0.4574
+ -0.2696
+ -0.1047
+ -0.5745
+ -0.2989
+ -0.0063
0.0088
- -0.1184
- -0.1506
- -0.4073
+ -0.1184
+ -0.1506
+ -0.4073
0.2674
0.2896
0.0669
0.1166
- -0.1699
- -0.2518
- -0.0562
- -0.3269
- -0.0703
- -0.1046
- -0.4888
- -0.3524
- -0.2485
- -0.5870
- -0.4546
- -0.3970
- -0.2353
- -0.0352
- -0.2171
- -0.3754
- -0.4322
- -0.4572
- -0.4903
- -0.4518
- -0.6435
- -0.6304
- -0.4148
- -0.2892
- -0.4318
- -0.6010
- -0.4148
- -0.4315
- -0.3531
- -0.8053
- -0.4680
- -0.4263
-];
+ -0.1699
+ -0.2518
+ -0.0562
+ -0.3269
+ -0.0703
+ -0.1046
+ -0.4888
+ -0.3524
+ -0.2485
+ -0.5870
+ -0.4546
+ -0.3970
+ -0.2353
+ -0.0352
+ -0.2171
+ -0.3754
+ -0.4322
+ -0.4572
+ -0.4903
+ -0.4518
+ -0.6435
+ -0.6304
+ -0.4148
+ -0.2892
+ -0.4318
+ -0.6010
+ -0.4148
+ -0.4315
+ -0.3531
+ -0.8053
+ -0.4680
+ -0.4263
+ ];
R =[
- -1.0750
- -1.1540
- -1.3682
- -1.4569
- -1.3490
- -1.4011
- -1.6486
- -1.6968
- -1.6976
- -1.2567
- -1.1392
- -0.7783
- -0.3021
- -0.0435
+ -1.0750
+ -1.1540
+ -1.3682
+ -1.4569
+ -1.3490
+ -1.4011
+ -1.6486
+ -1.6968
+ -1.6976
+ -1.2567
+ -1.1392
+ -0.7783
+ -0.3021
+ -0.0435
0.0066
- -0.0043
+ -0.0043
0.1029
- -0.0628
- -0.5358
- -0.9627
- -1.1079
- -1.0918
- -0.9966
- -0.6223
- -0.3616
- -0.2711
- -0.0997
- -0.2810
- -0.3710
- -0.3167
- -0.5301
- -0.5826
- -0.3194
- -0.2713
- -0.5287
- -0.2432
+ -0.0628
+ -0.5358
+ -0.9627
+ -1.1079
+ -1.0918
+ -0.9966
+ -0.6223
+ -0.3616
+ -0.2711
+ -0.0997
+ -0.2810
+ -0.3710
+ -0.3167
+ -0.5301
+ -0.5826
+ -0.3194
+ -0.2713
+ -0.5287
+ -0.2432
0.1098
0.5349
0.7094
@@ -667,15 +667,15 @@ R =[
0.1741
0.0348
0.0771
- -0.1005
- -0.1518
- -0.1104
- -0.0681
- -0.0059
+ -0.1005
+ -0.1518
+ -0.1104
+ -0.0681
+ -0.0059
0.0256
0.0404
- -0.1721
- -0.2002
+ -0.1721
+ -0.2002
0.0015
0.1249
0.3738
@@ -699,51 +699,51 @@ R =[
0.3178
0.1472
0.0227
- -0.0799
- -0.0611
- -0.0140
+ -0.0799
+ -0.0611
+ -0.0140
0.1132
0.1774
0.0782
0.0436
- -0.1596
- -0.2691
- -0.2895
- -0.3791
- -0.4020
- -0.4166
- -0.4037
- -0.3636
- -0.4075
- -0.4311
- -0.4470
- -0.5111
- -0.6274
- -0.7261
- -0.6974
- -0.5012
-];
+ -0.1596
+ -0.2691
+ -0.2895
+ -0.3791
+ -0.4020
+ -0.4166
+ -0.4037
+ -0.3636
+ -0.4075
+ -0.4311
+ -0.4470
+ -0.5111
+ -0.6274
+ -0.7261
+ -0.6974
+ -0.5012
+ ];
W =[
- -14.8791
- -13.2300
- -13.5037
- -13.0249
- -11.2546
- -10.0148
- -8.8586
- -8.5739
- -7.7851
- -6.7136
- -5.5878
- -4.6881
- -3.8039
- -3.0366
- -2.7342
- -1.3135
- -0.7387
- -0.1131
- -0.2769
+ -14.8791
+ -13.2300
+ -13.5037
+ -13.0249
+ -11.2546
+ -10.0148
+ -8.8586
+ -8.5739
+ -7.7851
+ -6.7136
+ -5.5878
+ -4.6881
+ -3.8039
+ -3.0366
+ -2.7342
+ -1.3135
+ -0.7387
+ -0.1131
+ -0.2769
0.8696
1.8855
2.3667
@@ -788,26 +788,26 @@ W =[
1.6092
1.2741
0.2031
- -0.0236
- -0.1004
- -0.3034
- -1.0273
- -0.2205
+ -0.0236
+ -0.1004
+ -0.3034
+ -1.0273
+ -0.2205
0.0458
0.2386
- -0.0977
- -0.3145
- -0.1416
- -0.7009
- -0.9082
- -0.8802
- -0.5644
- -0.5852
- -0.5346
+ -0.0977
+ -0.3145
+ -0.1416
+ -0.7009
+ -0.9082
+ -0.8802
+ -0.5644
+ -0.5852
+ -0.5346
0.0652
0.1301
0.3444
- -0.3592
+ -0.3592
0.8096
0.9644
1.0289
@@ -819,43 +819,43 @@ W =[
0.6506
0.5531
0.2456
- -0.5351
- -0.8183
- -0.8967
- -0.7268
- -1.0738
- -1.2844
- -1.4338
- -1.6995
- -1.7085
- -2.2889
- -2.1018
- -2.4273
- -2.4609
- -2.1407
- -2.3847
- -3.1689
- -4.5581
- -4.1027
- -4.2436
- -4.8836
- -5.9660
- -4.9971
- -5.2386
- -5.6618
-];
+ -0.5351
+ -0.8183
+ -0.8967
+ -0.7268
+ -1.0738
+ -1.2844
+ -1.4338
+ -1.6995
+ -1.7085
+ -2.2889
+ -2.1018
+ -2.4273
+ -2.4609
+ -2.1407
+ -2.3847
+ -3.1689
+ -4.5581
+ -4.1027
+ -4.2436
+ -4.8836
+ -5.9660
+ -4.9971
+ -5.2386
+ -5.6618
+ ];
Y =[
- -4.9347
- -4.6205
- -5.2198
- -4.5937
- -3.8015
- -3.6643
- -2.7239
- -2.7524
- -2.0634
- -1.0112
+ -4.9347
+ -4.6205
+ -5.2198
+ -4.5937
+ -3.8015
+ -3.6643
+ -2.7239
+ -2.7524
+ -2.0634
+ -1.0112
0.0530
0.7623
1.7927
@@ -863,12 +863,12 @@ Y =[
2.4866
2.1456
2.1671
- -0.0254
- -1.6716
- -1.9673
- -1.6109
- -1.0292
- -0.1222
+ -0.0254
+ -1.6716
+ -1.9673
+ -1.6109
+ -1.0292
+ -0.1222
0.7329
1.1234
2.0603
@@ -894,28 +894,28 @@ Y =[
0.7594
0.3695
0.0832
- -0.8118
- -1.4557
- -1.4850
- -1.2346
- -1.5696
- -1.3785
- -0.7682
- -2.0308
- -1.7778
- -1.7801
- -2.1711
- -1.7469
- -1.3413
- -1.3352
- -2.4390
- -1.2125
- -1.1695
- -1.0891
- -2.4753
- -1.3503
- -0.9412
- -0.1470
+ -0.8118
+ -1.4557
+ -1.4850
+ -1.2346
+ -1.5696
+ -1.3785
+ -0.7682
+ -2.0308
+ -1.7778
+ -1.7801
+ -2.1711
+ -1.7469
+ -1.3413
+ -1.3352
+ -2.4390
+ -1.2125
+ -1.1695
+ -1.0891
+ -2.4753
+ -1.3503
+ -0.9412
+ -0.1470
0.0026
0.1108
0.6890
@@ -936,32 +936,32 @@ Y =[
2.4609
1.9534
0.9962
- -0.7904
- -1.1672
- -1.2586
- -1.3593
- -1.3443
- -0.9413
- -0.6023
- -0.4516
- -0.5129
- -0.8741
- -1.0784
- -1.4091
- -1.3627
- -1.5731
- -1.6037
- -1.8814
- -2.1482
- -1.3597
- -1.1855
- -1.1122
- -0.8424
- -0.9747
- -1.1385
- -1.4548
- -1.4284
- -1.4633
- -1.0621
- -0.7871
-];
+ -0.7904
+ -1.1672
+ -1.2586
+ -1.3593
+ -1.3443
+ -0.9413
+ -0.6023
+ -0.4516
+ -0.5129
+ -0.8741
+ -1.0784
+ -1.4091
+ -1.3627
+ -1.5731
+ -1.6037
+ -1.8814
+ -2.1482
+ -1.3597
+ -1.1855
+ -1.1122
+ -0.8424
+ -0.9747
+ -1.1385
+ -1.4548
+ -1.4284
+ -1.4633
+ -1.0621
+ -0.7871
+ ];
diff --git a/mex/sources/k_order_perturbation/tests/first_order.m b/mex/sources/k_order_perturbation/tests/first_order.m
index f46631c220260266dc87fcb98ad22cf34c9c872c..a282ae3c3b50d9516d41e34ca44f8ba0bb61c603 100644
--- a/mex/sources/k_order_perturbation/tests/first_order.m
+++ b/mex/sources/k_order_perturbation/tests/first_order.m
@@ -51,20 +51,20 @@ off=off+ nu;
n= ypart.ny+ypart.nboth;
%TwoDMatrix
matD=zeros(n,n);
-% matD.place(fypzero,0,0);
+% matD.place(fypzero,0,0);
matD(1:n-ypart.nboth,1:ypart.npred)= fypzero;
-% matD.place(fybzero,0,ypart.npred);
+% matD.place(fybzero,0,ypart.npred);
matD(1:n-ypart.nboth,ypart.npred+1:ypart.npred+ypart.nboth)=fybzero;
-% matD.place(fyplus,0,ypart.nys()+ypart.nstat);
+% matD.place(fyplus,0,ypart.nys()+ypart.nstat);
matD(1:n-ypart.nboth,ypart.nys+ypart.nstat+1:ypart.nys+ypart.nstat+ypart.nyss)=fyplus;
for i=1:ypart.nboth
matD(ypart.ny()+i,ypart.npred+i)= 1.0;
end
matE=[fymins, fyszero, zeros(n-ypart.nboth,ypart.nboth), fyfzero; zeros(ypart.nboth,n)];
-% matE.place(fymins;
-% matE.place(fyszero,0,ypart.nys());
-% matE.place(fyfzero,0,ypart.nys()+ypart.nstat+ypart.nboth);
+% matE.place(fymins;
+% matE.place(fyszero,0,ypart.nys());
+% matE.place(fyfzero,0,ypart.nys()+ypart.nstat+ypart.nboth);
for i= 1:ypart.nboth
matE(ypart.ny()+i,ypart.nys()+ypart.nstat+i)= -1.0;
@@ -72,39 +72,39 @@ end
matE=-matE; %matE.mult(-1.0);
% vsl=zeros(n,n);
-% vsr=zeros(n,n);
-% lwork= 100*n+16;
-% work=zeros(1,lwork);
-% bwork=zeros(1,n);
+% vsr=zeros(n,n);
+% lwork= 100*n+16;
+% work=zeros(1,lwork);
+% bwork=zeros(1,n);
%int info;
-% LAPACK_dgges("N","V","S",order_eigs,&n,matE.getData().base(),&n,
-% matD.getData().base(),&n,&sdim,alphar.base(),alphai.base(),
-% beta.base(),vsl.getData().base(),&n,vsr.getData().base(),&n,
-% work.base(),&lwork,&(bwork[0]),&info);
+% LAPACK_dgges("N","V","S",order_eigs,&n,matE.getData().base(),&n,
+% matD.getData().base(),&n,&sdim,alphar.base(),alphai.base(),
+% beta.base(),vsl.getData().base(),&n,vsr.getData().base(),&n,
+% work.base(),&lwork,&(bwork[0]),&info);
[matE1,matD1,vsr,sdim,dr.eigval,info] = mjdgges(matE,matD,1);
bk_cond= (sdim==ypart.nys);
-% ConstGeneralMatrix z11(vsr,0,0,ypart.nys(),ypart.nys());
+% ConstGeneralMatrix z11(vsr,0,0,ypart.nys(),ypart.nys());
z11=vsr(1:ypart.nys,1:ypart.nys);
-% ConstGeneralMatrix z12(vsr,0,ypart.nys(),ypart.nys(),n-ypart.nys());
+% ConstGeneralMatrix z12(vsr,0,ypart.nys(),ypart.nys(),n-ypart.nys());
z12=vsr(1:ypart.nys(),ypart.nys+1:end);%, n-ypart.nys);
- % ConstGeneralMatrix z21(vsr,ypart.nys(),0,n-ypart.nys(),ypart.nys());
+ % ConstGeneralMatrix z21(vsr,ypart.nys(),0,n-ypart.nys(),ypart.nys());
z21=vsr(ypart.nys+1:end,1:ypart.nys);
-% ConstGeneralMatrix z22(vsr,ypart.nys(),ypart.nys(),n-ypart.nys(),n-ypart.nys());
+% ConstGeneralMatrix z22(vsr,ypart.nys(),ypart.nys(),n-ypart.nys(),n-ypart.nys());
z22=vsr(ypart.nys+1:end,ypart.nys+1:end);
-% GeneralMatrix sfder(z12,"transpose");
+% GeneralMatrix sfder(z12,"transpose");
sfder=z12';%,"transpose");
- % z22.multInvLeftTrans(sfder);
+ % z22.multInvLeftTrans(sfder);
sfder=z22'\sfder;
sfder=-sfder;% .mult(-1);
%s11(matE,0,0,ypart.nys(),ypart.nys());
s11=matE1(1:ypart.nys,1:ypart.nys);
-% t11=(matD1,0,0,ypart.nys(),ypart.nys());
+% t11=(matD1,0,0,ypart.nys(),ypart.nys());
t11=matD1(1:ypart.nys,1:ypart.nys);
dumm=(s11');%,"transpose");
%z11.multInvLeftTrans(dumm);
@@ -115,15 +115,15 @@ preder=t11\preder;
%preder.multLeft(z11);
preder= z11*preder;
-% gy.place(preder,ypart.nstat,0);
-% gy=(zeros(ypart.nstat,size(preder,2)) ;preder);
-% sder(sfder,0,0,ypart.nstat,ypart.nys());
+% gy.place(preder,ypart.nstat,0);
+% gy=(zeros(ypart.nstat,size(preder,2)) ;preder);
+% sder(sfder,0,0,ypart.nstat,ypart.nys());
sder=sfder(1:ypart.nstat,1:ypart.nys);
-% gy.place(sder,0,0);
-% gy(1:ypart.nstat, 1:ypart.nys)=sder;
+% gy.place(sder,0,0);
+% gy(1:ypart.nstat, 1:ypart.nys)=sder;
% gy=[sder;preder];
-% fder(sfder,ypart.nstat+ypart.nboth,0,ypart.nforw,ypart.nys());
+% fder(sfder,ypart.nstat+ypart.nboth,0,ypart.nforw,ypart.nys());
fder=sfder(ypart.nstat+ypart.nboth+1:ypart.nstat+ypart.nboth+ypart.nforw,1:ypart.nys);
-% gy.place(fder,ypart.nstat+ypart.nys(),0);
-% gy(ypart.nstat+ypart.nys,1)=fder;
+% gy.place(fder,ypart.nstat+ypart.nys(),0);
+% gy(ypart.nstat+ypart.nys,1)=fder;
gy=[sder;preder;fder];
diff --git a/tests/AIM/data_ca1.m b/tests/AIM/data_ca1.m
index c28fae1a2800e83eda0e6343196e8aeafad2935f..ca003056bded971e9bf00232802e453a096e9cdf 100644
--- a/tests/AIM/data_ca1.m
+++ b/tests/AIM/data_ca1.m
@@ -1,98 +1,98 @@
data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
--0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
--0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
--0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
--0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
--0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
--0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
-1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
-2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
-1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
-1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
-1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
-1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
-0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
-1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
-1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
-0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
-1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
-1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
--0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
-0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
-0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
--0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
-2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
-1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
-1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
-1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
-1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
-1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
-0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
-0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
-1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
-0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
-0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
-0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
-0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
--0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
--0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
--0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
--1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
-0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
-0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
-0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
--0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
-0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
-0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
-0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
-0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
-0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
-0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
-0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
-1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
-1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
-1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
-0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
-0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
--0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
-0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
-0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
-0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
-0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
-1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
-0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
-0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
-1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
-1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
-0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
-1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
-0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
-1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
-1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
-1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
-1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
-1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
-1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
-1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
-0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
-1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
-0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
-0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
-0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
--0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
-0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
-1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
-1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
-0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
-];
-
+ -0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
+ -0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
+ -0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
+ -0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
+ -0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
+ -0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
+ 1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
+ 2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
+ 1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
+ 1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
+ 1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
+ 1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
+ 0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
+ 1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
+ 1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
+ 0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
+ 1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
+ 1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
+ -0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
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+ 1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
+ 1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
+ 0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
+ 0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
+ 1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
+ 0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
+ 0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
+ 0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
+ 0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
+ -0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
+ -0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
+ -0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
+ -1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
+ 0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
+ 0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
+ 0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
+ -0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
+ 0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
+ 0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
+ 0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
+ 0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
+ 0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
+ 0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
+ 0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
+ 1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
+ 1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
+ 1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
+ 0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
+ 0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
+ -0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
+ 0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
+ 0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
+ 0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
+ 0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
+ 1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
+ 0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
+ 0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
+ 1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
+ 1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
+ 0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
+ 1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
+ 0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
+ 1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
+ 1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
+ 1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
+ 1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
+ 1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
+ 1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
+ 1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
+ 0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
+ 1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
+ 0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
+ 0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
+ 0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
+ -0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
+ 0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
+ 1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
+ 1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
+ 0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
+ ];
+
data = reshape(data,5,86)';
y_obs = data(:,1);
pie_obs = data(:,2);
R_obs = data(:,3);
de = data(:,4);
dq = data(:,5);
-
+
%Country: Canada
%Sample Range: 1981:2 to 2002:3
%Observations: 86
diff --git a/tests/AIM/fsdat.m b/tests/AIM/fsdat.m
index aba209b908a794044e570302331b8efe9eaac673..ef1279c0b39764c26ded95083e89500f17aeb45d 100644
--- a/tests/AIM/fsdat.m
+++ b/tests/AIM/fsdat.m
@@ -1,198 +1,198 @@
data_q = [
-18.02 1474.5 150.2
-17.94 1538.2 150.9
-18.01 1584.5 151.4
-18.42 1644.1 152
-18.73 1678.6 152.7
-19.46 1693.1 153.3
-19.55 1724 153.9
-19.56 1758.2 154.7
-19.79 1760.6 155.4
-19.77 1779.2 156
-19.82 1778.8 156.6
-20.03 1790.9 157.3
-20.12 1846 158
-20.1 1882.6 158.6
-20.14 1897.3 159.2
-20.22 1887.4 160
-20.27 1858.2 160.7
-20.34 1849.9 161.4
-20.39 1848.5 162
-20.42 1868.9 162.8
-20.47 1905.6 163.6
-20.56 1959.6 164.3
-20.62 1994.4 164.9
-20.78 2020.1 165.7
-21 2030.5 166.5
-21.2 2023.6 167.2
-21.33 2037.7 167.9
-21.62 2033.4 168.7
-21.71 2066.2 169.5
-22.01 2077.5 170.2
-22.15 2071.9 170.9
-22.27 2094 171.7
-22.29 2070.8 172.5
-22.56 2012.6 173.1
-22.64 2024.7 173.8
-22.77 2072.3 174.5
-22.88 2120.6 175.3
-22.92 2165 176.045
-22.91 2223.3 176.727
-22.94 2221.4 177.481
-23.03 2230.95 178.268
-23.13 2279.22 179.694
-23.22 2265.48 180.335
-23.32 2268.29 181.094
-23.4 2238.57 181.915
-23.45 2251.68 182.634
-23.51 2292.02 183.337
-23.56 2332.61 184.103
-23.63 2381.01 184.894
-23.75 2422.59 185.553
-23.81 2448.01 186.203
-23.87 2471.86 186.926
-23.94 2476.67 187.68
-24 2508.7 188.299
-24.07 2538.05 188.906
-24.12 2586.26 189.631
-24.29 2604.62 190.362
-24.35 2666.69 190.954
-24.41 2697.54 191.56
-24.52 2729.63 192.256
-24.64 2739.75 192.938
-24.77 2808.88 193.467
-24.88 2846.34 193.994
-25.01 2898.79 194.647
-25.17 2970.48 195.279
-25.32 3042.35 195.763
-25.53 3055.53 196.277
-25.79 3076.51 196.877
-26.02 3102.36 197.481
-26.14 3127.15 197.967
-26.31 3129.53 198.455
-26.6 3154.19 199.012
-26.9 3177.98 199.572
-27.21 3236.18 199.995
-27.49 3292.07 200.452
-27.75 3316.11 200.997
-28.12 3331.22 201.538
-28.39 3381.86 201.955
-28.73 3390.23 202.419
-29.14 3409.65 202.986
-29.51 3392.6 203.584
-29.94 3386.49 204.086
-30.36 3391.61 204.721
-30.61 3422.95 205.419
-31.02 3389.36 206.13
-31.5 3481.4 206.763
-31.93 3500.95 207.362
-32.27 3523.8 208
-32.54 3533.79 208.642
-33.02 3604.73 209.142
-33.2 3687.9 209.637
-33.49 3726.18 210.181
-33.95 3790.44 210.737
-34.36 3892.22 211.192
-34.94 3919.01 211.663
-35.61 3907.08 212.191
-36.29 3947.11 212.708
-37.01 3908.15 213.144
-37.79 3922.57 213.602
-38.96 3879.98 214.147
-40.13 3854.13 214.7
-41.05 3800.93 215.135
-41.66 3835.21 215.652
-42.41 3907.02 216.289
-43.19 3952.48 216.848
-43.69 4044.59 217.314
-44.15 4072.19 217.776
-44.77 4088.49 218.338
-45.57 4126.39 218.917
-46.32 4176.28 219.427
-47.07 4260.08 219.956
-47.66 4329.46 220.573
-48.63 4328.33 221.201
-49.42 4345.51 221.719
-50.41 4510.73 222.281
-51.27 4552.14 222.933
-52.35 4603.65 223.583
-53.51 4605.65 224.152
-54.65 4615.64 224.737
-55.82 4644.93 225.418
-56.92 4656.23 226.117
-58.18 4678.96 226.754
-59.55 4566.62 227.389
-61.01 4562.25 228.07
-62.59 4651.86 228.689
-64.15 4739.16 229.155
-65.37 4696.82 229.674
-66.65 4753.02 230.301
-67.87 4693.76 230.903
-68.86 4615.89 231.395
-69.72 4634.88 231.906
-70.66 4612.08 232.498
-71.44 4618.26 233.074
-72.08 4662.97 233.546
-72.83 4763.57 234.028
-73.48 4849 234.603
-74.19 4939.23 235.153
-75.02 5053.56 235.605
-75.58 5132.87 236.082
-76.25 5170.34 236.657
-76.81 5203.68 237.232
-77.63 5257.26 237.673
-78.25 5283.73 238.176
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-];
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+ 111.76 7311.24 268.171
+ 112.08 7364.63 268.815
+ ];
%GDPD GDPQ GPOP
series = zeros(193,2);
diff --git a/tests/analytic_derivatives/fsdat_simul.m b/tests/analytic_derivatives/fsdat_simul.m
index d4f4a8066f17ba49faad004256693ebc1b9b01e9..159612e577c3b91d585970404c9cf576c0e8a8d6 100644
--- a/tests/analytic_derivatives/fsdat_simul.m
+++ b/tests/analytic_derivatives/fsdat_simul.m
@@ -1,828 +1,828 @@
gy_obs =[
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-];
+ ];
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+ 1.4095764
+ 1.4080055
+ 1.4095882
+ 1.4108374
+ 1.4164143
+ 1.4283402
+ 1.4343939
+ 1.4392909
+ 1.4406097
+ 1.4468355
+ 1.4412132
+ 1.4305562
+ 1.4252445
+ 1.4103094
+ 1.4059847
+ 1.4141106
+ 1.4429769
+ 1.4489679
+ 1.4559263
+ 1.4593079
+ 1.4627911
+ 1.453154
+ 1.4416665
+ 1.4101485
+ 1.4175823
+ 1.4266407
-];
+ ];
diff --git a/tests/block_bytecode/run_ls2003.m b/tests/block_bytecode/run_ls2003.m
index 0b1d898e343d413c32514f48bf10b5f05dc7af02..f0c11d31f7f2d156d11d458d4486187ef9ee2d79 100644
--- a/tests/block_bytecode/run_ls2003.m
+++ b/tests/block_bytecode/run_ls2003.m
@@ -17,20 +17,20 @@ function run_ls2003(block, bytecode, solve_algo, stack_solve_algo)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
- % Workaround for a strange race condition under Octave
- if exist('ls2003_tmp.m') == 2
- delete('ls2003_tmp.m')
- end
+% Workaround for a strange race condition under Octave
+if exist('ls2003_tmp.m') == 2
+ delete('ls2003_tmp.m')
+end
- disp(['TEST: ls2003 (block=' num2str(block) ', bytecode=' ...
+disp(['TEST: ls2003 (block=' num2str(block) ', bytecode=' ...
num2str(bytecode) ', solve_algo=' num2str(solve_algo) ...
', stack_solve_algo=' num2str(stack_solve_algo) ')...']);
- fid = fopen('ls2003_tmp.mod', 'w');
- assert(fid > 0);
- fprintf(fid, ['@#define block = %d\n@#define bytecode = %d\n' ...
- '@#define solve_algo = %d\n@#define stack_solve_algo = %d\n' ...
- '@#include \"ls2003.mod\"\n'], block, bytecode, ...
- solve_algo, stack_solve_algo);
- fclose(fid);
- dynare('ls2003_tmp.mod','console')
+fid = fopen('ls2003_tmp.mod', 'w');
+assert(fid > 0);
+fprintf(fid, ['@#define block = %d\n@#define bytecode = %d\n' ...
+ '@#define solve_algo = %d\n@#define stack_solve_algo = %d\n' ...
+ '@#include \"ls2003.mod\"\n'], block, bytecode, ...
+ solve_algo, stack_solve_algo);
+fclose(fid);
+dynare('ls2003_tmp.mod','console')
end
diff --git a/tests/bvar_a_la_sims/bvar_sample.m b/tests/bvar_a_la_sims/bvar_sample.m
index 8093afe18fb67fa91306cf6e7b07262ae3911133..3dd002e3b84d7150e1da8de9d1d9574ca090da40 100644
--- a/tests/bvar_a_la_sims/bvar_sample.m
+++ b/tests/bvar_a_la_sims/bvar_sample.m
@@ -1,1006 +1,1006 @@
bvar_data = [
-0.00000000000, 0.00000000000;
--0.00485199480, -0.00034195121;
--0.00369702440, -0.01212953600;
-0.01577573000, -0.00131845390;
--0.01132415000, -0.00364839770;
-0.01158109800, -0.00262917340;
--0.00385969150, -0.00374515890;
--0.00605244640, 0.00760215990;
--0.00027881367, -0.01014485600;
-0.00328156560, -0.00358791610;
--0.01152432500, -0.00064990774;
--0.00417541420, -0.02726543200;
-0.01165937200, -0.01565713900;
-0.00327415420, -0.01403374100;
--0.00262015080, -0.01667459700;
--0.01146453600, -0.01088215300;
-0.00521521470, -0.02177149300;
-0.00767451980, -0.01671285200;
-0.01259883800, 0.00306304710;
--0.01790411600, -0.01240549400;
-0.00451096210, 0.00713706070;
-0.02171339100, -0.00405944740;
--0.00984485920, -0.00280935440;
-0.00303833090, -0.00658279110;
-0.00496516950, -0.00329239970;
-0.01425115100, -0.01417161800;
--0.00848068480, -0.01036798700;
-0.01384593500, 0.00307931740;
--0.00911261030, -0.00409005360;
-0.00104231040, -0.00503950650;
--0.01669998300, -0.00919063320;
-0.02192734300, -0.00900963420;
-0.00895830410, 0.00889444930;
--0.00078210473, 0.00303194750;
--0.00369206110, 0.00307427120;
-0.01569838000, -0.00494547550;
--0.00518999260, -0.02284842300;
-0.01668021700, -0.02845341900;
--0.02568046200, -0.03263100900;
-0.00662056690, -0.00759856640;
--0.00051553622, -0.00266545800;
-0.00588163920, 0.00553735730;
-0.00496782960, -0.00439611810;
--0.00917153500, -0.02059437900;
-0.01824072100, -0.01633834000;
-0.01007005900, -0.02248933700;
--0.00560011310, -0.02417716500;
--0.00769812730, -0.00017818698;
-0.00133784330, -0.00149211110;
--0.01435760300, 0.00692602660;
-0.00404759460, -0.01017014800;
--0.00663831950, -0.00315748290;
--0.00085548858, -0.02827386400;
-0.00421366420, -0.02999430500;
-0.00228340080, 0.00351766350;
-0.00067972662, 0.00394969830;
-0.00010249414, -0.00227112120;
-0.01352092200, 0.00054581385;
--0.01119695100, 0.01346546700;
-0.03037738600, 0.01926781500;
--0.00648092580, 0.03341492100;
-0.00742909040, 0.03054532700;
-0.00348382550, 0.01877144600;
-0.00246629280, 0.00970566090;
-0.01095858000, 0.01093582600;
--0.01513041900, 0.02512391700;
-0.00903471840, 0.03498919400;
--0.00618408790, 0.02227141800;
-0.01096205900, -0.00920153520;
-0.00262835580, -0.00765528570;
-0.01114218200, 0.00619863290;
--0.00919430790, 0.01755913200;
-0.01006375400, 0.01441648800;
--0.01990465700, 0.00916741770;
-0.00855497720, -0.00323036000;
--0.01369815600, 0.00361482240;
--0.00712474120, 0.01056456100;
--0.01264778000, 0.00232601930;
--0.01423405400, -0.00320915910;
--0.00016743283, 0.00840220560;
-0.00285416310, 0.01212758900;
--0.00345522980, -0.01240091400;
--0.01061054000, -0.00392867620;
--0.01603832900, 0.00257474180;
-0.00324895860, 0.01867151600;
--0.00291754020, 0.01065674500;
-0.02361004800, 0.01609781500;
--0.00318450010, 0.00712827010;
-0.02139297300, 0.01445064800;
-0.01460282300, -0.00449260470;
-0.00647488450, 0.02045964500;
-0.00854365700, -0.00148195210;
--0.02251529400, -0.00256425170;
-0.00222816500, -0.01526768300;
-0.02185776000, -0.00714712190;
--0.00455624340, -0.00160773030;
--0.01246960000, 0.00150635870;
-0.00924045130, -0.00910181770;
-0.00343446870, 0.01052361000;
-0.01349456500, -0.01018272900;
--0.00740446960, -0.00215583630;
-0.00813427010, 0.00314027880;
--0.00739251150, -0.01104516400;
--0.00135688900, -0.00758587260;
-0.01004834800, 0.00714729720;
-0.00071365274, 0.00981284720;
-0.00354068790, -0.01254811000;
--0.02248783700, 0.00397571800;
--0.00865090470, 0.00869799720;
-0.00755320970, -0.00274643140;
-0.00718197010, -0.01057977500;
--0.00890055570, -0.01187215200;
--0.00221861280, -0.00737277380;
--0.00176862730, -0.00544962520;
--0.01488865900, -0.00755715830;
--0.01003201200, -0.01536865400;
--0.00885387310, 0.00157814460;
--0.01057416100, -0.00872661580;
-0.00529372600, -0.02309533000;
--0.00129770280, 0.00439926800;
--0.00471011830, 0.00686916260;
-0.00221095220, -0.00075376512;
-0.00396970840, -0.00307360450;
--0.00949946950, -0.00932127890;
--0.01425166300, -0.00887158980;
--0.00839041170, -0.00739858640;
-0.01094292700, -0.00463808010;
--0.01503297800, -0.00486242570;
--0.00780511670, -0.00013977193;
-0.00295906390, -0.00244925080;
-0.00888417030, -0.00027793976;
--0.00264289810, -0.00356239480;
--0.00233235380, 0.00853562660;
-0.00999011710, -0.01266408700;
-0.00615571440, -0.01048649200;
-0.00129589980, -0.02057649900;
--0.00167519580, -0.00756073410;
-0.00573725950, -0.00893045730;
--0.01489402600, 0.00410294180;
-0.02847956800, -0.00493285520;
--0.01474864500, -0.00667757730;
-0.01322265000, -0.01081593400;
--0.01326839900, -0.01371889900;
-0.01640160600, -0.01247788100;
--0.00374336080, 0.00492074290;
-0.00615137690, 0.01806502100;
-0.01356203500, 0.01259282000;
--0.01542659000, 0.00767045720;
-0.00642653950, -0.00537146090;
-0.00310662030, 0.00506285650;
--0.00504839670, 0.00859490920;
-0.00355458360, -0.00183994500;
--0.02023486200, 0.00574867890;
-0.00454694680, 0.01137622400;
--0.00387160520, 0.01413229400;
--0.01348735800, 0.00439081620;
-0.01672376100, 0.00070763533;
--0.00455330340, -0.00686520060;
-0.00822146830, -0.01299495800;
--0.00426795680, 0.00457871690;
-0.00550981790, 0.00756567730;
--0.01690307400, -0.00726203990;
-0.01440696000, -0.01560053400;
-0.00957262890, -0.01337257400;
--0.00220497700, -0.00441573200;
--0.00056364617, -0.00045505510;
--0.00194646630, 0.00321663400;
-0.01391187800, -0.01801557100;
-0.00082409925, -0.01950009600;
--0.01465276400, -0.00260673980;
--0.00650069260, -0.01902260000;
-0.00029876759, -0.01215615700;
-0.01261499800, -0.01651988700;
-0.00063004297, -0.02457780800;
-0.00169295250, 0.00033544910;
--0.00976376420, 0.00505643970;
--0.00711589770, -0.00639119460;
-0.01025748200, -0.00422405210;
--0.00945158550, 0.00792116440;
--0.01345565600, -0.00015260044;
-0.00347003790, -0.00603041040;
--0.01301538300, -0.01133294300;
--0.00750695770, -0.00933159140;
--0.00440517780, -0.02171622400;
--0.00147922330, -0.02082012000;
--0.00906688280, -0.02642304400;
-0.00194159560, -0.02100981100;
--0.00420262710, -0.00838592350;
-0.01891564200, 0.01082611100;
-0.00182342580, -0.00240049780;
--0.01810654000, 0.01711595900;
--0.00212834600, 0.00352360610;
-0.00178391370, -0.00108616550;
--0.00043459404, 0.00088082942;
--0.00412604630, 0.01755816600;
--0.00064959885, 0.01457685600;
-0.01347751700, 0.00931501890;
-0.00352526210, 0.00083007064;
--0.01322128800, -0.00201091000;
-0.00009614403, -0.00995798470;
-0.00444349320, -0.01249145900;
-0.00425595950, -0.00116484430;
--0.01175602700, 0.00285725700;
-0.00657894370, 0.00180430300;
--0.00704443210, -0.00289686610;
--0.00639969420, 0.00144511130;
--0.01682459800, -0.01084454800;
-0.00046732062, -0.01183596800;
--0.00265618720, -0.00617053630;
--0.00627824550, -0.01772563100;
--0.01435354300, -0.00991358750;
-0.00428677970, 0.01192595200;
-0.00863044650, 0.00914107900;
--0.00273840730, -0.00467048220;
-0.01769520800, -0.01364493500;
--0.00860223420, -0.00593776970;
-0.00834198560, -0.00768299240;
--0.01394435200, -0.01339647000;
--0.00251396410, -0.02358333200;
-0.00252897310, 0.00127176740;
-0.00229634080, 0.01985044500;
--0.01441004500, 0.00210260990;
-0.00994943720, 0.01327428000;
--0.02038994400, 0.01331653800;
-0.01941131500, 0.00250981050;
--0.02126588500, 0.00117877630;
-0.01703305400, -0.00767290020;
--0.01349053100, -0.00602226180;
--0.01591448200, -0.00589764240;
-0.00843590470, 0.01499918200;
--0.00014857487, 0.00860687000;
--0.02363695800, -0.00112907670;
--0.00706394760, -0.00303277220;
--0.01303618000, 0.01329845500;
--0.00483198940, -0.02019482500;
-0.00339302700, 0.00572997660;
--0.00551248650, 0.00769795860;
-0.00419796130, 0.00406189010;
--0.00370703540, 0.00782197680;
-0.00867027450, 0.00822771140;
-0.00596786670, 0.02899416200;
-0.00681206890, 0.02001652400;
-0.00165106500, 0.00849942220;
-0.01299251700, 0.00086235046;
--0.00748960250, 0.00782463780;
-0.00209973650, 0.00815596800;
--0.00850592120, 0.02334669100;
-0.01134628100, 0.01134613400;
--0.01352224100, -0.00836556270;
-0.00671531310, -0.01956063600;
--0.00242272570, -0.00033519061;
-0.01284221800, -0.00689856370;
--0.01145770400, -0.01209985200;
-0.00424026140, -0.00680320230;
-0.00499683310, -0.00186745700;
--0.00379175090, -0.00973519550;
--0.00284374760, -0.00534973380;
-0.00751068230, -0.00172811330;
--0.00973646530, -0.01550932400;
-0.01166327400, -0.01425923100;
-0.00160914390, -0.00604851180;
--0.00663530550, -0.00054579530;
-0.00060567236, 0.02071117500;
--0.00036915903, 0.01075306500;
--0.02211141600, 0.01878292800;
-0.00686511410, 0.00102341950;
--0.00340655980, 0.00238579960;
--0.00067203259, 0.00114114460;
--0.00102357060, 0.00753831560;
--0.00768802670, -0.00839238920;
-0.01638687000, 0.00724520850;
--0.00347730340, 0.00605236490;
-0.00269696720, 0.02358320200;
--0.01185299400, -0.00448258220;
--0.00641221420, 0.00183018270;
-0.02135322200, 0.00817785940;
-0.00869419570, -0.00233532870;
--0.00769650030, -0.00544744100;
-0.00878221650, -0.00288025810;
--0.00427262690, -0.00458403970;
-0.01034842700, -0.00944620050;
-0.00019401874, -0.02223294900;
--0.00148483690, -0.00943247910;
--0.00436740400, -0.01781865300;
-0.00173273960, -0.00608950980;
--0.01099621500, -0.00400127680;
-0.02741524200, -0.01422095500;
--0.00882605320, -0.00466699960;
-0.01370161600, -0.01526308600;
-0.00054116472, -0.00448123640;
-0.00621596090, 0.00724029820;
--0.01069932100, -0.00560117420;
--0.01421629600, 0.00928500080;
-0.01192448400, 0.02028709800;
--0.01069542700, 0.01139402200;
-0.01009124900, 0.00256825410;
-0.00127767120, 0.00434814080;
-0.00499351370, -0.00928891760;
--0.00473548910, -0.00480540750;
-0.00740867780, 0.00620164840;
--0.00569582270, -0.00336372610;
-0.02118048300, 0.00751171350;
--0.02275689800, 0.00448348720;
--0.00180431540, 0.00477640310;
--0.00343045450, 0.02132982500;
--0.00110349160, 0.01799333500;
-0.00566623340, 0.00824425200;
-0.01008643600, -0.01564701600;
-0.01109274800, -0.01687864900;
--0.01099148700, -0.01485227600;
-0.02434629100, -0.02618405000;
-0.00145410160, -0.01696036900;
--0.02344772100, 0.00088506469;
-0.01081117400, 0.01607764500;
--0.01274696100, 0.01073171700;
--0.02814399500, -0.01204674900;
-0.01467717300, 0.01001183700;
--0.01282640100, 0.00785533930;
--0.00039151466, -0.00158353920;
-0.00506089730, 0.01103509500;
-0.00520334890, 0.00764525420;
--0.00791932050, 0.01578666600;
--0.02282695200, 0.02060965800;
--0.00477700370, 0.00444865870;
--0.00001976087, 0.00925757550;
-0.00031481039, -0.01739030000;
-0.01023393500, -0.00412322340;
--0.00657133560, 0.01098436400;
-0.01332517600, 0.01175247000;
-0.00853561840, 0.01257092400;
--0.00990227900, 0.00971206290;
-0.00601343650, 0.01676401700;
-0.02034657200, 0.00380624610;
--0.01610970400, -0.00382222490;
--0.00784886630, -0.01541948200;
--0.01486246300, 0.00046089183;
-0.00059140790, 0.00977498470;
-0.00131100280, 0.01144934500;
--0.00249520130, 0.00763555490;
--0.00995953580, 0.00947764900;
-0.00515067260, -0.00447725650;
--0.00628485890, -0.00625777520;
--0.00055236814, -0.00045996980;
-0.00907149900, 0.00550255670;
--0.02189032300, 0.00647248100;
-0.02009786100, 0.00745576110;
--0.00891947350, -0.01155851100;
-0.00125060520, -0.01440449400;
--0.00331509880, -0.01211877600;
-0.00176437740, 0.00308533290;
--0.00423796010, 0.00764018890;
-0.00502629330, -0.00524800480;
--0.01859839300, -0.01602257900;
-0.00760957450, -0.00063382743;
-0.01352106500, -0.02070491800;
--0.01484082600, -0.01837788900;
--0.00180805220, 0.00180058220;
-0.00646895960, -0.01067731900;
--0.00319460360, 0.00204543950;
-0.01283819000, 0.00788961100;
-0.00897728170, 0.01235817300;
--0.00911367310, 0.00216576220;
-0.00247233850, 0.00197748290;
-0.01385073000, -0.00628759110;
--0.00244131680, -0.00549276880;
-0.00247377740, 0.00010081507;
-0.00223374800, 0.00167638370;
-0.00622179480, 0.00636981950;
--0.01451075100, 0.00349200990;
-0.01701029100, 0.01655859500;
-0.00540505380, 0.00805163880;
--0.01032426300, 0.00545309340;
-0.01498743600, 0.00845128410;
-0.02407752900, 0.00198533570;
--0.00960939560, 0.00853352640;
--0.01023723300, 0.01335469200;
--0.00270911950, 0.00849549120;
--0.00369286780, -0.00121018950;
-0.00826982030, 0.00814966480;
--0.00479820070, -0.00430578830;
-0.01720646400, 0.01156837700;
-0.01664923600, -0.00173370660;
--0.00369501470, 0.02465627800;
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+ -0.00149920540, -0.00471902850;
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+ -0.00095826091, 0.00110351110;
+ 0.01922539900, 0.00976795960;
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+ 0.00184468230, -0.01900434900;
+ 0.00206808720, -0.01044463400;
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+ -0.00390662100, -0.00260237180;
+ 0.00692129190, -0.00378992080;
+ 0.00029091915, -0.00259509570;
+ -0.00360680420, -0.00713074060;
+ -0.00860419840, -0.02057721600;
+ 0.00044751379, -0.01449312400;
+ -0.00145591500, -0.01523862700;
+ 0.01682939900, -0.01270554100;
+ 0.00567881680, -0.01943714400;
+ 0.00848238640, -0.00223198500;
+ 0.00325092290, -0.00082652871;
+ 0.01223402300, 0.00644947070;
+ 0.00051464095, 0.00898356600;
+ 0.01260583200, 0.00834137450;
+ 0.01064180800, -0.00190874350;
+ 0.01005380200, -0.00116214780;
+ -0.00116234090, -0.00675879300;
+ -0.02332964500, -0.01838281800;
+ -0.00696804150, -0.00944441820;
+ -0.01819834100, -0.01808908500;
+ 0.02201795200, -0.00716774540;
+ -0.01672468800, -0.00826797040;
+ 0.01170834800, -0.00949888840;
+ -0.00615648660, -0.00355936780;
+ 0.00535839890, 0.00559401360;
+ -0.00703042400, -0.01059598600;
+ 0.00318876840, 0.00011921905;
+ -0.01874264500, 0.00790409920;
+ 0.00552417410, 0.00766524880;
+ -0.01497631100, -0.00238697410;
+ -0.00439945850, 0.00914147460;
+ 0.01828331600, -0.00206804820;
+ 0.00068550745, -0.00122205150;
+ -0.00759101970, -0.01153777900;
+ 0.00648242600, -0.01640673600;
+ -0.00543452600, -0.02063951400;
+ 0.00155147620, -0.02622296300;
+ -0.00564122550, -0.01103051900;
+ 0.00341228520, 0.00583169540;
+ 0.00646375290, 0.00936322370;
+ -0.00478098810, 0.01100696200;
+ 0.00573895680, -0.00239250900;
+ -0.00970467940, 0.00871843140;
+ 0.02038830400, 0.01480759600;
+ 0.00026758833, 0.02045451100;
+ 0.00791676330, 0.01696119900;
+ 0.00151902060, 0.00369441970;
+ 0.00695727800, -0.00513502650;
+ -0.01493114700, 0.00259436800;
+ 0.01356549700, -0.00125019700;
+ 0.01129927200, -0.01805236600;
+ -0.01605158000, -0.00110185760;
+ 0.01796653700, -0.01000584500;
+ -0.01611534000, -0.00357616450;
+ 0.00480241820, -0.01235228300;
+ 0.00765884520, 0.00199081530;
+ -0.00445450970, 0.00373594290;
+ 0.00836337530, 0.00844062310;
+ 0.00000608640, 0.00814252730;
+ -0.00242506490, 0.00107690310;
+ -0.00655880720, -0.01044158000;
+ 0.00959842540, -0.01282298400;
+ 0.00710086670, -0.00628089330;
+ -0.01539525500, 0.00145629030;
+ -0.01019127500, 0.00506089250;
+ -0.00415539980, 0.00121860340;
+ -0.00222234300, -0.00235775530;
+ 0.00145298240, 0.00074290054;
+ 0.00383045620, 0.00499606180;
+ -0.01581364300, 0.00991048820;
+ -0.00505534040, 0.00362636340;
+ 0.01275955400, -0.00852445050;
+ -0.00646790680, -0.00849281040;
+ 0.00822635380, 0.00506450010;
+ -0.00999777920, 0.01192172300;
+ 0.01259965900, -0.01267426000;
+ -0.00334331530, -0.01193505000;
+ 0.00531845400, 0.00036486233;
+ 0.00324434670, 0.01617751400;
+ -0.00182198070, 0.01450107800;
+ -0.01316162900, 0.00575151330;
+ -0.00245819090, -0.00185123220;
+ 0.00716449280, 0.00787328720;
+ -0.00111474240, 0.00293388970;
+ -0.01296516800, -0.00225148550;
+ 0.00284074300, -0.00331728310;
+ 0.01133071500, 0.02057075900;
+ -0.00100379480, 0.01280891700;
+ 0.00049387445, 0.00507951680;
+ 0.00229614130, -0.00764568280;
+ -0.01313835900, -0.00525744170;
+ 0.00966316020, -0.00556556230;
+ 0.01118421300, -0.00490035560;
+ 0.01672044300, -0.00988298390;
+ -0.01088174000, -0.00216302190;
+ -0.00920015300, 0.00190343440;
+ 0.00027256857, -0.00606717780;
+ 0.00745269630, 0.00488672340;
+ -0.00485878040, 0.00337006910;
+ -0.01014946800, 0.00628172350;
+ 0.00133496050, -0.00095504333;
+ 0.01278446800, 0.00344898280;
+ -0.00703416400, 0.00739247270;
+ 0.01328898700, 0.00304838790;
+ -0.01491870500, 0.00633638140;
+ 0.00873045630, 0.00319640990;
+ 0.00254909610, 0.01211332200;
+ 0.00232342310, 0.00470528570;
+ -0.00764997870, -0.00240813430;
+ -0.01090000900, -0.01170578800;
+ 0.00843239100, -0.01170118700;
+ -0.02507916900, -0.01158650100;
+ 0.04003462300, -0.01629908500;
+ -0.01222446500, -0.01477344200;
+ 0.00521617900, -0.00968336430;
+ 0.01952769300, -0.02338928900;
+ -0.00455805500, -0.01208632300;
+ -0.00421777900, -0.01515016500;
+ -0.02292950600, 0.00681974800;
+ 0.02065804000, -0.01601951200;
+ -0.01175698600, -0.01763198100;
+ 0.00847885710, -0.01709137000;
+ -0.00522204640, 0.00520781450;
+ 0.01653303200, 0.01230743800;
+ 0.01491689300, 0.02278097800;
+ -0.00390021000, -0.00599679960;
+ 0.00987391320, -0.01527627300;
+ -0.01292332700, -0.01725711000;
+ -0.00611290270, -0.00008627481;
+ 0.01173359600, 0.00525802720;
+ 0.00023658130, 0.00219369170;
+ 0.00527818840, -0.01008107600;
+ -0.00805139170, -0.01193710000;
+ -0.01872491200, 0.00860016370;
+ -0.01259698500, -0.01756964500;
+ -0.00088728350, -0.01512096000;
+ -0.01535824800, -0.01295353000;
+ 0.02003583600, -0.00614556620;
+ -0.00662873120, 0.00258939970;
+ 0.02039917600, -0.00012182475;
+ -0.00137236040, -0.00075700965;
+ -0.01450454300, -0.00207827570;
+ 0.01535757000, 0.00707412260;
+ -0.01704817500, -0.00431544930;
+ 0.00832049550, -0.01881308400;
+ 0.00788077160, -0.01301967100;
+ -0.00535764100, -0.02307963700;
+ 0.01684468900, -0.01761785700;
+ -0.00494331070, -0.02023850500;
+ -0.01844518300, -0.01120281300;
+ 0.01030114100, -0.00246253960;
+ 0.00737732820, 0.01367079900;
+ 0.00930385080, -0.01638037700;
+ 0.01184141400, -0.00128950800;
+ -0.02316092800, 0.00518400230;
+ 0.01259350700, 0.01851841100;
+ 0.00032334407, 0.00708510310;
+ -0.00380004070, 0.01466849000;
+ 0.00572216670, 0.02763848700;
+ 0.01450073300, 0.02130598400;
+ -0.00061716695, 0.01299269100;
+ 0.00032716641, 0.02931071500;
+ 0.00102466110, 0.01050204800;
+ -0.00339657480, -0.00413612190;
+ 0.01311750600, -0.00235546960;
+ 0.00609382540, 0.00646251750;
+ -0.00126880700, 0.01452674700;
+ -0.00768738760, 0.01051401500;
+ 0.01058750900, 0.00661389120;
+ -0.00857671250, 0.00564091490;
+ 0.00603614820, 0.00790147090;
+ -0.00383731350, 0.01219309200;
+ -0.00750234770, -0.00565016960;
+ -0.01197183700, -0.01579100100;
+ 0.00940025550, 0.01919136600;
+ -0.00001261024, 0.02626728500;
+ -0.01119718500, 0.00144775010;
+ 0.02249039100, 0.02039881000;
+ -0.00379732890, -0.01609038000;
+ 0.01565908800, -0.00588713290;
+ 0.00875294490, 0.00373467350;
+ 0.00256396420, 0.01632317700;
+ -0.00451786920, -0.00480455360;
+ -0.00790291250, 0.00345569200;
+ -0.01455265100, -0.01382336400;
+ 0.01350253300, 0.00019931557;
+ -0.00674284800, 0.00449999260;
+ 0.02374096100, -0.00641924120;
+ -0.02211973900, -0.00053401624;
+ 0.01207118500, -0.00983951590;
+ -0.00702998800, -0.02173194800;
+ 0.00650176870, -0.03939793300;
+ 0.01983586800, -0.01688164300;
+ -0.00523048000, -0.01586871000;
+ -0.00024937096, -0.00762047480;
+ 0.01848711000, -0.02202737800;
+ 0.00320481510, 0.00115403630;
+ -0.00339570830, -0.00416020450;
+ 0.00292867830, -0.00800372950;
+ -0.00085958037, -0.00702345910;
+ -0.00661261130, -0.00240180080;
+ 0.00915375200, -0.00072184277;
+ -0.02097610500, 0.00164769990;
+ 0.00794447800, 0.00132242720;
+ 0.00417510660, -0.00030181197;
+ -0.00634899330, -0.01677800600;
+ 0.00999671400, -0.00762022470;
+ -0.01004270400, 0.00644466200;
+ 0.00245296560, -0.00189053390;
+ -0.00558639080, -0.00629843410;
+ 0.00063566426, 0.00748679840;
+ -0.00197196620, -0.01231472900;
+ -0.00858143530, -0.00982022010;
+ 0.01118201300, -0.00005704770;
+ -0.01452712100, 0.00525489720;
+ 0.00556580190, -0.00614996780;
+ 0.00722027820, 0.00126887710;
+ -0.00106240920, 0.00248088000;
+ 0.00266047030, 0.00506315460;
+ -0.01615042800, -0.00024735048;
+ 0.00690899540, -0.02230154100;
+ 0.00016806370, -0.00703005220;
+ 0.01157961100, -0.00046847502;
+ -0.00569027140, -0.00074088147;
+ 0.00279183700, -0.01112727400;
+ 0.01159847500, -0.01397495400;
+ -0.01618859800, -0.03057030300;
+ 0.00051325767, -0.00865006790;
+ 0.00549707250, 0.00357878980;
+ -0.00970590920, -0.00253900500;
+ 0.01545455300, 0.00679668570;
+ -0.01066617100, -0.00621207310;
+ 0.00575491460, -0.00657640880;
+ -0.00605682630, 0.01005724800;
+ -0.00028086638, 0.01720437100;
+ 0.00314218690, -0.00112510120;
+ -0.01232869600, 0.00529184200;
+ 0.00156071380, 0.00332171610;
+ 0.01586504900, -0.00687919610;
+ -0.00700561370, 0.00627937710;
+ 0.00416313760, 0.01338622200;
+ -0.00469103860, -0.00207565840;
+ -0.00515967500, -0.02677465800;
+ -0.00329796470, -0.02347414900;
+ 0.00458593860, -0.01994345300;
+ 0.00449779160, -0.01500910700;
+ -0.01450006100, -0.02433756900;
+ -0.00982530810, 0.00515777190;
+ 0.02169452900, 0.00395559630;
+ -0.02732960700, -0.01296309000;
+ -0.00943391470, 0.00970794530;
+ 0.00883871190, -0.00847782470;
+ -0.00641242250, -0.00159739350;
+ -0.00622211610, -0.00023249961;
+ 0.01103231400, 0.01345929400;
+ -0.01604871600, 0.01444581500;
+ 0.00408581520, 0.00391976300;
+ -0.00585816760, -0.00136833520;
+ 0.01285105500, -0.01355861400;
+ -0.01282979900, -0.02075753400;
+ 0.00240514790, -0.00583408830;
+ -0.00821620830, -0.00917124260;
+ 0.01161346900, 0.00589059210;
+ -0.00416354780, 0.01478035400;
+ 0.00910147760, 0.00162353340;
+ 0.00645020290, -0.00029596225;
+ 0.00664065850, 0.00334591190;
+ -0.01628421500, -0.01667108200;
+ 0.01017428900, -0.01182986900;
+ -0.01740919900, 0.00421845090;
+ 0.01419528300, -0.00170271310;
+ -0.01832044100, -0.00271804760;
+ 0.00478798390, 0.01451607600;
+ -0.01552720100, 0.00680642330;
+ 0.00557794300, 0.01041172100;
+ 0.00418925410, -0.00240398610;
+ -0.00542981940, -0.01092214200;
+ 0.01256706900, -0.01379128400;
+ -0.01899332900, -0.00274706900;
+ 0.00907149380, -0.00663018700;
+ 0.01506829200, -0.00293179520;
+ -0.01827722500, -0.00283953570;
+ -0.00150598710, 0.00448947120;
+ 0.00689968130, 0.01392841800;
+ 0.00838313220, 0.00056270157;
+ -0.01889400800, 0.00445784700;
+ 0.01083541500, -0.00433810380;
+ 0.00975696280, -0.01959502400;
+ -0.00687770220, -0.01815605200;
+ -0.01112640300, -0.01494359600;
+ -0.00472052850, -0.01500657700;
+ -0.01367499100, -0.00267410440;
+ 0.01489290400, -0.00432076310;
+ -0.00262017640, -0.00290406560;
+ -0.00261222360, 0.00098096246;
+ 0.00452692100, -0.01865841900;
+ 0.00118941740, 0.00323370830;
+ 0.00029588303, 0.00675359970;
+ -0.01081920700, 0.02384338300;
+ ];
dy = bvar_data(:, 1);
dx = bvar_data(:, 2);
diff --git a/tests/conditional_forecasts/2/fsdat_simul.m b/tests/conditional_forecasts/2/fsdat_simul.m
index d4f4a8066f17ba49faad004256693ebc1b9b01e9..159612e577c3b91d585970404c9cf576c0e8a8d6 100644
--- a/tests/conditional_forecasts/2/fsdat_simul.m
+++ b/tests/conditional_forecasts/2/fsdat_simul.m
@@ -1,828 +1,828 @@
gy_obs =[
- 1.0030045
- 0.99990934
- 1.0172778
- 0.99464043
- 1.0253423
- 1.0150215
- 0.97772557
- 0.97832186
- 1.0159561
- 1.0085937
- 1.0102649
- 1.0007604
- 1.0112596
- 1.0163279
- 1.0173204
- 1.0103896
- 1.0006493
- 0.99447124
- 1.0196405
- 1.0089304
- 0.99650737
- 1.0139707
- 0.97865842
- 1.0192225
- 0.99139628
- 1.0141362
- 1.0196612
- 0.97483476
- 0.99686151
- 0.99594464
- 1.0000642
- 1.0172243
- 1.0025773
- 0.97199728
- 1.0217815
- 1.0219949
- 0.99490252
- 1.0190728
- 1.0111337
- 1.0003792
- 0.98969164
- 1.010438
- 1.0216309
- 1.0016671
- 1.0357588
- 0.98803787
- 1.0093457
- 1.0177035
- 0.98548204
- 1.0274294
- 1.0141377
- 1.0091174
- 0.96427632
- 1.0083272
- 1.0007882
- 0.99038262
- 1.0031336
- 0.99500213
- 0.98203716
- 0.9889452
- 1.011632
- 0.99451949
- 0.97291047
- 0.98750871
- 0.99992418
- 0.97657318
- 0.99930448
- 1.0008515
- 1.0044064
- 0.98133792
- 1.0091702
- 1.0087023
- 1.0119876
- 1.0143019
- 1.0311061
- 0.99340471
- 1.0057428
- 0.99197259
- 1.0071019
- 0.99448853
- 1.0061819
- 1.0070088
- 0.9950913
- 1.0302318
- 0.9817693
- 1.0072885
- 0.97355282
- 0.98782586
- 1.0136674
- 0.99863956
- 1.0205668
- 0.99611384
- 1.0073805
- 0.99691529
- 1.0089194
- 1.0030467
- 1.0112006
- 1.0260523
- 0.97803331
- 0.99423374
- 1.0043727
- 1.0140173
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- 0.99524348
- 0.99775943
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- 1.0210212
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- 1.011456
- 1.0124871
- 0.99843599
- 0.99324886
- 0.99912838
- 1.003327
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- 1.0115223
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- 1.0129916
- 1.0053413
- 1.0051638
- 0.99212952
- 1.0214422
- 0.98716707
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- 1.0061791
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- 0.99492949
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- 1.0168619
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- 0.99768475
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- 1.0118678
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- 1.0276965
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- 1.0101918
- 1.011615
- 1.0085286
- 1.0074455
- 0.98866757
- 0.99959012
- 1.0129881
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- 1.0016059
- 1.016556
- 0.99687023
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- 1.0059212
- 0.98083159
- 0.98638851
- 1.0128713
- 1.0096232
- 1.0115891
- 1.0011213
- 1.0147105
- 1.0066344
- 1.0164429
- 0.99825038
- 0.99403411
+ 1.0030045
+ 0.99990934
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+ 1.0253423
+ 1.0150215
+ 0.97772557
+ 0.97832186
+ 1.0159561
+ 1.0085937
+ 1.0102649
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+ 1.0173204
+ 1.0103896
+ 1.0006493
+ 0.99447124
+ 1.0196405
+ 1.0089304
+ 0.99650737
+ 1.0139707
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-];
+ ];
diff --git a/tests/dates/fsdat_simul.m b/tests/dates/fsdat_simul.m
index bc2c5a4fe385847554a2c724d095c71c73b3f69a..6ce6114a74ac15de5a19ef809a7ebff628c420d4 100644
--- a/tests/dates/fsdat_simul.m
+++ b/tests/dates/fsdat_simul.m
@@ -2,830 +2,830 @@ INIT__ = '1950Q1';
FREQ__ = 4;
gy_obs =[
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-];
+ ];
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+ 1.4083656
+ 1.4233002
+ 1.4037932
+ 1.3973604
+ 1.4095657
+ 1.4095764
+ 1.4080055
+ 1.4095882
+ 1.4108374
+ 1.4164143
+ 1.4283402
+ 1.4343939
+ 1.4392909
+ 1.4406097
+ 1.4468355
+ 1.4412132
+ 1.4305562
+ 1.4252445
+ 1.4103094
+ 1.4059847
+ 1.4141106
+ 1.4429769
+ 1.4489679
+ 1.4559263
+ 1.4593079
+ 1.4627911
+ 1.453154
+ 1.4416665
+ 1.4101485
+ 1.4175823
+ 1.4266407
-];
+ ];
diff --git a/tests/decision_rules/third_order/comparison_policy_functions_dynare_mathematica.m b/tests/decision_rules/third_order/comparison_policy_functions_dynare_mathematica.m
index 729fd152fca2185f526f65a96f5a0d3021a830f2..8739e93168092645de4231a347e4aaf956d319d8 100644
--- a/tests/decision_rules/third_order/comparison_policy_functions_dynare_mathematica.m
+++ b/tests/decision_rules/third_order/comparison_policy_functions_dynare_mathematica.m
@@ -1,8 +1,8 @@
%read in the FV et al. policy functions derived from Mathematica
if ~isoctave() && ~matlab_ver_less_than('8.4')
- websave('FV_2011_policyfunctions.mat','http://www.dynare.org/Datasets/FV_2011_policyfunctions.mat', weboptions('Timeout', 30))
+ websave('FV_2011_policyfunctions.mat','http://www.dynare.org/Datasets/FV_2011_policyfunctions.mat', weboptions('Timeout', 30))
else
- urlwrite('http://www.dynare.org/Datasets/FV_2011_policyfunctions.mat','FV_2011_policyfunctions.mat')
+ urlwrite('http://www.dynare.org/Datasets/FV_2011_policyfunctions.mat','FV_2011_policyfunctions.mat')
end
load FV_2011_policyfunctions
@@ -79,9 +79,9 @@ end
gxxx_dyn=zeros(size(gxxx));
for endo_iter_1=1:nx
for endo_iter_2=1:nx
- for endo_iter_3=1:nx
+ for endo_iter_3=1:nx
gxxx_dyn(nu+endo_iter_1,nu+endo_iter_2,nu+endo_iter_3,:)=dr.ghxxx(FV_endo_order,(FV_endo_state_order(endo_iter_1)-1)*nx*nx+(FV_endo_state_order(endo_iter_2)-1)*nx+FV_endo_state_order(endo_iter_3));
- end
+ end
end
end
@@ -95,21 +95,21 @@ end
for endo_iter_1=1:nx
for endo_iter_2=1:nx
- for exo_iter=1:nu
+ for exo_iter=1:nu
gxxx_dyn(nu+endo_iter_1,nu+endo_iter_2,exo_iter,:)=dr.ghxxu(FV_endo_order,(FV_endo_state_order(endo_iter_1)-1)*nx*nu+(FV_endo_state_order(endo_iter_2)-1)*nu+FV_exo_order(exo_iter));
gxxx_dyn(exo_iter,nu+endo_iter_2,nu+endo_iter_1,:)=dr.ghxxu(FV_endo_order,(FV_endo_state_order(endo_iter_1)-1)*nx*nu+(FV_endo_state_order(endo_iter_2)-1)*nu+FV_exo_order(exo_iter));
gxxx_dyn(nu+endo_iter_1,exo_iter,nu+endo_iter_2,:)=dr.ghxxu(FV_endo_order,(FV_endo_state_order(endo_iter_1)-1)*nx*nu+(FV_endo_state_order(endo_iter_2)-1)*nu+FV_exo_order(exo_iter));
- end
+ end
end
end
for endo_iter_1=1:nx
for exo_iter_1=1:nu
- for exo_iter_2=1:nu
+ for exo_iter_2=1:nu
gxxx_dyn(nu+endo_iter_1,exo_iter_1,exo_iter_2,:)=dr.ghxuu(FV_endo_order,(FV_endo_state_order(endo_iter_1)-1)*nu*nu+(FV_exo_order(exo_iter_1)-1)*nu+FV_exo_order(exo_iter_2));
gxxx_dyn(exo_iter_1,nu+endo_iter_1,exo_iter_2,:)=dr.ghxuu(FV_endo_order,(FV_endo_state_order(endo_iter_1)-1)*nu*nu+(FV_exo_order(exo_iter_1)-1)*nu+FV_exo_order(exo_iter_2));
gxxx_dyn(exo_iter_1,exo_iter_2,nu+endo_iter_1,:)=dr.ghxuu(FV_endo_order,(FV_endo_state_order(endo_iter_1)-1)*nu*nu+(FV_exo_order(exo_iter_1)-1)*nu+FV_exo_order(exo_iter_2));
- end
+ end
end
end
diff --git a/tests/ep/ar_steadystate.m b/tests/ep/ar_steadystate.m
index 966331f8e29d9a92e282d4b9b3a179f664956c54..d040472426e0545a67e4a7222ebb7e8ee6f7e654 100644
--- a/tests/ep/ar_steadystate.m
+++ b/tests/ep/ar_steadystate.m
@@ -1,8 +1,8 @@
function [ys, info] = ar_steadystate(ys, exogenous)
% Steady state routine for ar.mod (First order autoregressive process)
-
+
global M_
-
+
info = 0;
ys(1)=M_.params(2);
diff --git a/tests/ep/exact_solution.m b/tests/ep/exact_solution.m
index 5b525797fa2c14cdec7228063d1773f575acff97..64b1aa7dc636fc0719bf9d0b3f3b4b08f714a247 100644
--- a/tests/ep/exact_solution.m
+++ b/tests/ep/exact_solution.m
@@ -1,30 +1,30 @@
function y=exact_solution(M,oo,n)
- beta = M.params(1);
- theta = M.params(2);
- rho = M.params(3);
- xbar = M.params(4);
- sigma2 = M.Sigma_e;
-
- if beta*exp(theta*xbar+.5*theta^2*sigma2/(1-rho)^2)>1-eps
- disp('The model doesn''t have a solution!')
- return
- end
-
- i = 1:n;
- a = theta*xbar*i+(theta^2*sigma2)/(2*(1-rho)^2)*(i-2*rho*(1-rho.^i)/(1-rho)+rho^2*(1-rho.^(2*i))/(1-rho^2));
- b = theta*rho*(1-rho.^i)/(1-rho);
-
- x = oo.endo_simul(2,:);
- xhat = x-xbar;
-
- n2 = size(x,2);
-
- y = zeros(1,n2);
-
-
- for j=1:n2
- y(j) = sum(beta.^i.*exp(a+b*xhat(j)));
- end
-
- disp(sum(beta.^i.*exp(theta*xbar*i)))
- disp(sum(beta.^i.*exp(a)))
\ No newline at end of file
+beta = M.params(1);
+theta = M.params(2);
+rho = M.params(3);
+xbar = M.params(4);
+sigma2 = M.Sigma_e;
+
+if beta*exp(theta*xbar+.5*theta^2*sigma2/(1-rho)^2)>1-eps
+ disp('The model doesn''t have a solution!')
+ return
+end
+
+i = 1:n;
+a = theta*xbar*i+(theta^2*sigma2)/(2*(1-rho)^2)*(i-2*rho*(1-rho.^i)/(1-rho)+rho^2*(1-rho.^(2*i))/(1-rho^2));
+b = theta*rho*(1-rho.^i)/(1-rho);
+
+x = oo.endo_simul(2,:);
+xhat = x-xbar;
+
+n2 = size(x,2);
+
+y = zeros(1,n2);
+
+
+for j=1:n2
+ y(j) = sum(beta.^i.*exp(a+b*xhat(j)));
+end
+
+disp(sum(beta.^i.*exp(theta*xbar*i)))
+disp(sum(beta.^i.*exp(a)))
\ No newline at end of file
diff --git a/tests/ep/rbcii_steady_state.m b/tests/ep/rbcii_steady_state.m
index 5fc2dcb1ae5bb7aa7ef7dcd2db765c1cdbdfec40..dd79d28a4b2131b988965574203710a9d41cd8e2 100644
--- a/tests/ep/rbcii_steady_state.m
+++ b/tests/ep/rbcii_steady_state.m
@@ -1,61 +1,61 @@
-function [ys_, params, info] = rbcii_steadystate2(ys_, exo_, params)
-
- % Flag initialization (equal to zero if the deterministic steady state exists)
- info = 0;
-
- % efficiency
- ys_(13)=0;
-
- % Efficiency
- ys_(12)=params(8);
-
- % Steady state ratios
- Output_per_unit_of_Capital=((1/params(1)-1+params(6))/params(4))^(1/(1-params(5)));
- Consumption_per_unit_of_Capital=Output_per_unit_of_Capital-params(6);
- Labour_per_unit_of_Capital=(((Output_per_unit_of_Capital/ys_(12))^params(5)-params(4))/(1-params(4)))^(1/params(5));
- Output_per_unit_of_Labour=Output_per_unit_of_Capital/Labour_per_unit_of_Capital;
- Consumption_per_unit_of_Labour=Consumption_per_unit_of_Capital/Labour_per_unit_of_Capital;
-
- % Steady state share of capital revenues in total revenues (calibration check)
- ShareOfCapital=params(4)/(params(4)+(1-params(4))*Labour_per_unit_of_Capital^params(5));
-
- % Steady state level of labour
- ys_(3)=1/(1+Consumption_per_unit_of_Labour/((1-params(4))*params(2)/(1-params(2))*Output_per_unit_of_Labour^(1-params(5))));
-
- % Steady state level of consumption
- ys_(4)=Consumption_per_unit_of_Labour*ys_(3);
-
- % Steady state level of physical capital stock
- ys_(1)=ys_(3)/Labour_per_unit_of_Capital;
-
- % Steady state level of output
- ys_(2)=Output_per_unit_of_Capital*ys_(1);
-
- % Steady state level of investment
- ys_(5)=params(6)*ys_(1);
-
- % Steady state level of the expected term appearing in the Euler equation
- ys_(14)=(ys_(4)^params(2)*(1-ys_(3))^(1-params(2)))^(1-params(3))/ys_(4)*(1+params(4)*(ys_(2)/ys_(1))^(1-params(5))-params(6));
-
- % Steady state level of output in the unconstrained regime (positive investment)
- ys_(6)=ys_(2);
-
- % Steady state level of labour in the unconstrained regime
- ys_(7)=ys_(3);
-
- % Steady state level of consumption in the unconstrained regime
- ys_(8)=ys_(4);
-
- % Steady state level of labour in the constrained regime (noinvestment)
- [lss,info] = l_solver(ys_(3),params(4),params(5),params(2),params(8),ys_(1),100);
- if info, return, end
- ys_(10) = lss;
-
- % Steady state level of consumption in the constrained regime
- ys_(11)=params(8)*(params(4)*ys_(1)^params(5)+(1-params(4))*ys_(10)^params(5))^(1/params(5));
-
- % Steady state level of output in the constrained regime
- ys_(9)=ys_(11);
+function [ys_, params, info] = rbcii_steady_state(ys_, exo_, params)
+
+% Flag initialization (equal to zero if the deterministic steady state exists)
+info = 0;
+
+% efficiency
+ys_(13)=0;
+
+% Efficiency
+ys_(12)=params(8);
+
+% Steady state ratios
+Output_per_unit_of_Capital=((1/params(1)-1+params(6))/params(4))^(1/(1-params(5)));
+Consumption_per_unit_of_Capital=Output_per_unit_of_Capital-params(6);
+Labour_per_unit_of_Capital=(((Output_per_unit_of_Capital/ys_(12))^params(5)-params(4))/(1-params(4)))^(1/params(5));
+Output_per_unit_of_Labour=Output_per_unit_of_Capital/Labour_per_unit_of_Capital;
+Consumption_per_unit_of_Labour=Consumption_per_unit_of_Capital/Labour_per_unit_of_Capital;
+
+% Steady state share of capital revenues in total revenues (calibration check)
+ShareOfCapital=params(4)/(params(4)+(1-params(4))*Labour_per_unit_of_Capital^params(5));
+
+% Steady state level of labour
+ys_(3)=1/(1+Consumption_per_unit_of_Labour/((1-params(4))*params(2)/(1-params(2))*Output_per_unit_of_Labour^(1-params(5))));
+
+% Steady state level of consumption
+ys_(4)=Consumption_per_unit_of_Labour*ys_(3);
+
+% Steady state level of physical capital stock
+ys_(1)=ys_(3)/Labour_per_unit_of_Capital;
+
+% Steady state level of output
+ys_(2)=Output_per_unit_of_Capital*ys_(1);
+
+% Steady state level of investment
+ys_(5)=params(6)*ys_(1);
+
+% Steady state level of the expected term appearing in the Euler equation
+ys_(14)=(ys_(4)^params(2)*(1-ys_(3))^(1-params(2)))^(1-params(3))/ys_(4)*(1+params(4)*(ys_(2)/ys_(1))^(1-params(5))-params(6));
+
+% Steady state level of output in the unconstrained regime (positive investment)
+ys_(6)=ys_(2);
+
+% Steady state level of labour in the unconstrained regime
+ys_(7)=ys_(3);
+
+% Steady state level of consumption in the unconstrained regime
+ys_(8)=ys_(4);
+
+% Steady state level of labour in the constrained regime (noinvestment)
+[lss,info] = l_solver(ys_(3),params(4),params(5),params(2),params(8),ys_(1),100);
+if info, return, end
+ys_(10) = lss;
+
+% Steady state level of consumption in the constrained regime
+ys_(11)=params(8)*(params(4)*ys_(1)^params(5)+(1-params(4))*ys_(10)^params(5))^(1/params(5));
+
+% Steady state level of output in the constrained regime
+ys_(9)=ys_(11);
end
@@ -63,26 +63,26 @@ end
function r = p0(labour,alpha,psi,theta,effstar,kstar)
- r = labour * ( alpha*kstar^psi/labour^psi + 1-alpha + theta*(1-alpha)/(1-theta)/effstar^psi ) - theta*(1-alpha)/(1-theta)/effstar^psi;
+r = labour * ( alpha*kstar^psi/labour^psi + 1-alpha + theta*(1-alpha)/(1-theta)/effstar^psi ) - theta*(1-alpha)/(1-theta)/effstar^psi;
end
-
+
function d = p1(labour,alpha,psi,theta,effstar,kstar)
- d = alpha*(1-psi)*kstar^psi/labour^psi + 1-alpha + theta*(1-alpha)/(1-alpha)/effstar^psi;
+d = alpha*(1-psi)*kstar^psi/labour^psi + 1-alpha + theta*(1-alpha)/(1-alpha)/effstar^psi;
end
function [labour,info] = l_solver(labour,alpha,psi,theta,effstar,kstar,maxiter)
- iteration = 1; info = 0;
- r = p0(labour,alpha,psi,theta,effstar,kstar);
- condition = abs(r);
- while condition
- if iteration==maxiter
- info = 1;
- break
- end
- d = p1(labour,alpha,psi,theta,effstar,kstar);
- labour = labour - r/d;
- r = p0(labour,alpha,psi,theta,effstar,kstar);
- condition = abs(r)>1e-9;
- iteration = iteration + 1;
+iteration = 1; info = 0;
+r = p0(labour,alpha,psi,theta,effstar,kstar);
+condition = abs(r);
+while condition
+ if iteration==maxiter
+ info = 1;
+ break
end
+ d = p1(labour,alpha,psi,theta,effstar,kstar);
+ labour = labour - r/d;
+ r = p0(labour,alpha,psi,theta,effstar,kstar);
+ condition = abs(r)>1e-9;
+ iteration = iteration + 1;
+end
end
\ No newline at end of file
diff --git a/tests/estimation/fsdat_simul.m b/tests/estimation/fsdat_simul.m
index d4f4a8066f17ba49faad004256693ebc1b9b01e9..159612e577c3b91d585970404c9cf576c0e8a8d6 100644
--- a/tests/estimation/fsdat_simul.m
+++ b/tests/estimation/fsdat_simul.m
@@ -1,828 +1,828 @@
gy_obs =[
- 1.0030045
- 0.99990934
- 1.0172778
- 0.99464043
- 1.0253423
- 1.0150215
- 0.97772557
- 0.97832186
- 1.0159561
- 1.0085937
- 1.0102649
- 1.0007604
- 1.0112596
- 1.0163279
- 1.0173204
- 1.0103896
- 1.0006493
- 0.99447124
- 1.0196405
- 1.0089304
- 0.99650737
- 1.0139707
- 0.97865842
- 1.0192225
- 0.99139628
- 1.0141362
- 1.0196612
- 0.97483476
- 0.99686151
- 0.99594464
- 1.0000642
- 1.0172243
- 1.0025773
- 0.97199728
- 1.0217815
- 1.0219949
- 0.99490252
- 1.0190728
- 1.0111337
- 1.0003792
- 0.98969164
- 1.010438
- 1.0216309
- 1.0016671
- 1.0357588
- 0.98803787
- 1.0093457
- 1.0177035
- 0.98548204
- 1.0274294
- 1.0141377
- 1.0091174
- 0.96427632
- 1.0083272
- 1.0007882
- 0.99038262
- 1.0031336
- 0.99500213
- 0.98203716
- 0.9889452
- 1.011632
- 0.99451949
- 0.97291047
- 0.98750871
- 0.99992418
- 0.97657318
- 0.99930448
- 1.0008515
- 1.0044064
- 0.98133792
- 1.0091702
- 1.0087023
- 1.0119876
- 1.0143019
- 1.0311061
- 0.99340471
- 1.0057428
- 0.99197259
- 1.0071019
- 0.99448853
- 1.0061819
- 1.0070088
- 0.9950913
- 1.0302318
- 0.9817693
- 1.0072885
- 0.97355282
- 0.98782586
- 1.0136674
- 0.99863956
- 1.0205668
- 0.99611384
- 1.0073805
- 0.99691529
- 1.0089194
- 1.0030467
- 1.0112006
- 1.0260523
- 0.97803331
- 0.99423374
- 1.0043727
- 1.0140173
- 1.0111473
- 0.99524348
- 0.99775943
- 0.9958619
- 0.9982344
- 1.0210212
- 1.0022288
- 1.0014801
- 1.011456
- 1.0124871
- 0.99843599
- 0.99324886
- 0.99912838
- 1.003327
- 1.0072071
- 1.0115223
- 1.009266
- 1.0070554
- 1.0129916
- 1.0053413
- 1.0051638
- 0.99212952
- 1.0214422
- 0.98716707
- 0.99905788
- 0.98877357
- 0.98568476
- 0.99767393
- 1.0061791
- 0.98423439
- 0.99492949
- 0.98786999
- 0.99754239
- 1.0168619
- 0.99472384
- 1.0041658
- 0.98123181
- 1.0112882
- 0.99245422
- 1.0010255
- 1.0017799
- 1.0089968
- 1.0072824
- 0.99768475
- 1.0044726
- 1.0118678
- 1.0056385
- 1.0276965
- 1.0025122
- 1.0065161
- 1.0234338
- 0.99760167
- 0.98922272
- 1.0101918
- 1.011615
- 1.0085286
- 1.0074455
- 0.98866757
- 0.99959012
- 1.0129881
- 0.99127881
- 0.97971901
- 1.0185314
- 1.020054
- 1.0132605
- 0.98063643
- 0.99490253
- 1.0101531
- 1.0004526
- 1.0059109
- 0.98974491
- 1.0062391
- 1.0216488
- 0.99398446
- 0.97786609
- 1.0019274
- 0.99587153
- 1.0095881
- 1.0111887
- 0.99457649
- 0.97896734
- 1.000172
- 1.0142951
- 1.0034224
- 1.0037242
- 1.0016059
- 1.016556
- 0.99687023
- 1.0117844
- 1.0059212
- 0.98083159
- 0.98638851
- 1.0128713
- 1.0096232
- 1.0115891
- 1.0011213
- 1.0147105
- 1.0066344
- 1.0164429
- 0.99825038
- 0.99403411
+ 1.0030045
+ 0.99990934
+ 1.0172778
+ 0.99464043
+ 1.0253423
+ 1.0150215
+ 0.97772557
+ 0.97832186
+ 1.0159561
+ 1.0085937
+ 1.0102649
+ 1.0007604
+ 1.0112596
+ 1.0163279
+ 1.0173204
+ 1.0103896
+ 1.0006493
+ 0.99447124
+ 1.0196405
+ 1.0089304
+ 0.99650737
+ 1.0139707
+ 0.97865842
+ 1.0192225
+ 0.99139628
+ 1.0141362
+ 1.0196612
+ 0.97483476
+ 0.99686151
+ 0.99594464
+ 1.0000642
+ 1.0172243
+ 1.0025773
+ 0.97199728
+ 1.0217815
+ 1.0219949
+ 0.99490252
+ 1.0190728
+ 1.0111337
+ 1.0003792
+ 0.98969164
+ 1.010438
+ 1.0216309
+ 1.0016671
+ 1.0357588
+ 0.98803787
+ 1.0093457
+ 1.0177035
+ 0.98548204
+ 1.0274294
+ 1.0141377
+ 1.0091174
+ 0.96427632
+ 1.0083272
+ 1.0007882
+ 0.99038262
+ 1.0031336
+ 0.99500213
+ 0.98203716
+ 0.9889452
+ 1.011632
+ 0.99451949
+ 0.97291047
+ 0.98750871
+ 0.99992418
+ 0.97657318
+ 0.99930448
+ 1.0008515
+ 1.0044064
+ 0.98133792
+ 1.0091702
+ 1.0087023
+ 1.0119876
+ 1.0143019
+ 1.0311061
+ 0.99340471
+ 1.0057428
+ 0.99197259
+ 1.0071019
+ 0.99448853
+ 1.0061819
+ 1.0070088
+ 0.9950913
+ 1.0302318
+ 0.9817693
+ 1.0072885
+ 0.97355282
+ 0.98782586
+ 1.0136674
+ 0.99863956
+ 1.0205668
+ 0.99611384
+ 1.0073805
+ 0.99691529
+ 1.0089194
+ 1.0030467
+ 1.0112006
+ 1.0260523
+ 0.97803331
+ 0.99423374
+ 1.0043727
+ 1.0140173
+ 1.0111473
+ 0.99524348
+ 0.99775943
+ 0.9958619
+ 0.9982344
+ 1.0210212
+ 1.0022288
+ 1.0014801
+ 1.011456
+ 1.0124871
+ 0.99843599
+ 0.99324886
+ 0.99912838
+ 1.003327
+ 1.0072071
+ 1.0115223
+ 1.009266
+ 1.0070554
+ 1.0129916
+ 1.0053413
+ 1.0051638
+ 0.99212952
+ 1.0214422
+ 0.98716707
+ 0.99905788
+ 0.98877357
+ 0.98568476
+ 0.99767393
+ 1.0061791
+ 0.98423439
+ 0.99492949
+ 0.98786999
+ 0.99754239
+ 1.0168619
+ 0.99472384
+ 1.0041658
+ 0.98123181
+ 1.0112882
+ 0.99245422
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- 1.2909605
- 1.2927921
- 1.288932
- 1.2910852
- 1.3012725
- 1.3048721
- 1.3196515
- 1.3181903
- 1.321309
- 1.3431543
- 1.344136
- 1.3730377
- 1.3773695
- 1.3754742
- 1.3825964
- 1.3985574
- 1.3861412
- 1.3767823
- 1.3764309
- 1.3678747
- 1.3718554
- 1.3768022
- 1.3617199
- 1.3798267
- 1.3863533
- 1.3905803
- 1.4061004
- 1.4202788
- 1.4313191
- 1.4406155
- 1.4444837
- 1.4367244
- 1.4379653
- 1.4371881
- 1.4243012
- 1.41826
- 1.4133617
- 1.40181
- 1.3965683
- 1.3865729
- 1.3855433
- 1.3755111
- 1.3758609
- 1.3962625
- 1.3994012
- 1.4083656
- 1.4233002
- 1.4037932
- 1.3973604
- 1.4095657
- 1.4095764
- 1.4080055
- 1.4095882
- 1.4108374
- 1.4164143
- 1.4283402
- 1.4343939
- 1.4392909
- 1.4406097
- 1.4468355
- 1.4412132
- 1.4305562
- 1.4252445
- 1.4103094
- 1.4059847
- 1.4141106
- 1.4429769
- 1.4489679
- 1.4559263
- 1.4593079
- 1.4627911
- 1.453154
- 1.4416665
- 1.4101485
- 1.4175823
- 1.4266407
+ 1
+ 0.99948573
+ 1.0068249
+ 1.0141211
+ 1.0073149
+ 0.99884398
+ 1.0237035
+ 1.0349636
+ 1.036819
+ 1.0247366
+ 1.0242391
+ 1.0275737
+ 1.0065684
+ 0.99838346
+ 0.97281734
+ 0.95346302
+ 0.9355791
+ 0.9461152
+ 0.94338882
+ 0.92988921
+ 0.9311862
+ 0.93529467
+ 0.93784681
+ 0.91501401
+ 0.92360522
+ 0.91049302
+ 0.90754698
+ 0.91365103
+ 0.91499228
+ 0.92260749
+ 0.92533824
+ 0.90949431
+ 0.91106924
+ 0.90594116
+ 0.90491334
+ 0.9039891
+ 0.91060772
+ 0.92132842
+ 0.91934854
+ 0.92268418
+ 0.92545127
+ 0.91517169
+ 0.90513459
+ 0.90224212
+ 0.87734878
+ 0.88013667
+ 0.86906494
+ 0.84776403
+ 0.83895869
+ 0.81373437
+ 0.78998314
+ 0.77594176
+ 0.77982695
+ 0.77098321
+ 0.76538611
+ 0.76608075
+ 0.77458654
+ 0.78354767
+ 0.81282389
+ 0.83627649
+ 0.82873051
+ 0.83181309
+ 0.83149903
+ 0.83551261
+ 0.83305985
+ 0.84648418
+ 0.86195421
+ 0.88047436
+ 0.90177533
+ 0.93232215
+ 0.94445051
+ 0.9472487
+ 0.94786015
+ 0.95992178
+ 0.95499149
+ 0.95788581
+ 0.9684288
+ 0.97731917
+ 0.98739379
+ 1.0033879
+ 1.0159673
+ 1.0269931
+ 1.0436661
+ 1.0492034
+ 1.0765292
+ 1.0784865
+ 1.0971624
+ 1.1171737
+ 1.1193675
+ 1.1526119
+ 1.1550265
+ 1.1585277
+ 1.1560166
+ 1.1671172
+ 1.1706294
+ 1.1805791
+ 1.1786896
+ 1.1756876
+ 1.1820789
+ 1.171211
+ 1.1637997
+ 1.1636684
+ 1.179719
+ 1.1912538
+ 1.2187959
+ 1.2326986
+ 1.2418602
+ 1.2388704
+ 1.2449963
+ 1.2538678
+ 1.2508929
+ 1.2474781
+ 1.255148
+ 1.274482
+ 1.2862757
+ 1.2813665
+ 1.2888943
+ 1.2787436
+ 1.2678886
+ 1.274325
+ 1.2720952
+ 1.263492
+ 1.2652139
+ 1.2667561
+ 1.264558
+ 1.2680362
+ 1.2660431
+ 1.2909605
+ 1.2927921
+ 1.288932
+ 1.2910852
+ 1.3012725
+ 1.3048721
+ 1.3196515
+ 1.3181903
+ 1.321309
+ 1.3431543
+ 1.344136
+ 1.3730377
+ 1.3773695
+ 1.3754742
+ 1.3825964
+ 1.3985574
+ 1.3861412
+ 1.3767823
+ 1.3764309
+ 1.3678747
+ 1.3718554
+ 1.3768022
+ 1.3617199
+ 1.3798267
+ 1.3863533
+ 1.3905803
+ 1.4061004
+ 1.4202788
+ 1.4313191
+ 1.4406155
+ 1.4444837
+ 1.4367244
+ 1.4379653
+ 1.4371881
+ 1.4243012
+ 1.41826
+ 1.4133617
+ 1.40181
+ 1.3965683
+ 1.3865729
+ 1.3855433
+ 1.3755111
+ 1.3758609
+ 1.3962625
+ 1.3994012
+ 1.4083656
+ 1.4233002
+ 1.4037932
+ 1.3973604
+ 1.4095657
+ 1.4095764
+ 1.4080055
+ 1.4095882
+ 1.4108374
+ 1.4164143
+ 1.4283402
+ 1.4343939
+ 1.4392909
+ 1.4406097
+ 1.4468355
+ 1.4412132
+ 1.4305562
+ 1.4252445
+ 1.4103094
+ 1.4059847
+ 1.4141106
+ 1.4429769
+ 1.4489679
+ 1.4559263
+ 1.4593079
+ 1.4627911
+ 1.453154
+ 1.4416665
+ 1.4101485
+ 1.4175823
+ 1.4266407
-];
+ ];
diff --git a/tests/expectations/expectation_ss_old_steadystate.m b/tests/expectations/expectation_ss_old_steadystate.m
index bfd46d82a7f0eb7c3fbfc4879b029d12b64a648e..1b230e4b02fb22607d799afb4e7be90d11b77c97 100644
--- a/tests/expectations/expectation_ss_old_steadystate.m
+++ b/tests/expectations/expectation_ss_old_steadystate.m
@@ -1,12 +1,12 @@
function [ys_, check_] = expectation_ss_old_steadystate(ys_orig_, exo_)
- ys_=zeros(6,1);
- global M_
- ys_(4)=0;
- ys_(6)=0;
- ys_(5)=0.3333333333333333;
- ys_(3)=((1/M_.params(1)-(1-M_.params(4)))/(M_.params(3)*ys_(5)^(1-M_.params(3))))^(1/(M_.params(3)-1));
- ys_(1)=ys_(5)^(1-M_.params(3))*ys_(3)^M_.params(3);
- ys_(2)=ys_(1)-M_.params(4)*ys_(3);
- M_.params(5)=(1-M_.params(3))*ys_(1)/(ys_(2)*ys_(5)^(1+M_.params(6)));
- check_=0;
+ys_=zeros(6,1);
+global M_
+ys_(4)=0;
+ys_(6)=0;
+ys_(5)=0.3333333333333333;
+ys_(3)=((1/M_.params(1)-(1-M_.params(4)))/(M_.params(3)*ys_(5)^(1-M_.params(3))))^(1/(M_.params(3)-1));
+ys_(1)=ys_(5)^(1-M_.params(3))*ys_(3)^M_.params(3);
+ys_(2)=ys_(1)-M_.params(4)*ys_(3);
+M_.params(5)=(1-M_.params(3))*ys_(1)/(ys_(2)*ys_(5)^(1+M_.params(6)));
+check_=0;
end
diff --git a/tests/fataltest.m b/tests/fataltest.m
index a23db431124a39b27d5d3bde662f34277192058c..1ea213403a72197ffae99d7a74a4442467c35192 100644
--- a/tests/fataltest.m
+++ b/tests/fataltest.m
@@ -1,4 +1,4 @@
-function test(a,b,n)
- if max(max(abs(a)-abs(b))) > 1e-5
+function fataltest(a,b,n)
+if max(max(abs(a)-abs(b))) > 1e-5
error(['Test error in test ' int2str(n)])
- end
\ No newline at end of file
+end
\ No newline at end of file
diff --git a/tests/fs2000/fsdat_simul.m b/tests/fs2000/fsdat_simul.m
index d4f4a8066f17ba49faad004256693ebc1b9b01e9..159612e577c3b91d585970404c9cf576c0e8a8d6 100644
--- a/tests/fs2000/fsdat_simul.m
+++ b/tests/fs2000/fsdat_simul.m
@@ -1,828 +1,828 @@
gy_obs =[
- 1.0030045
- 0.99990934
- 1.0172778
- 0.99464043
- 1.0253423
- 1.0150215
- 0.97772557
- 0.97832186
- 1.0159561
- 1.0085937
- 1.0102649
- 1.0007604
- 1.0112596
- 1.0163279
- 1.0173204
- 1.0103896
- 1.0006493
- 0.99447124
- 1.0196405
- 1.0089304
- 0.99650737
- 1.0139707
- 0.97865842
- 1.0192225
- 0.99139628
- 1.0141362
- 1.0196612
- 0.97483476
- 0.99686151
- 0.99594464
- 1.0000642
- 1.0172243
- 1.0025773
- 0.97199728
- 1.0217815
- 1.0219949
- 0.99490252
- 1.0190728
- 1.0111337
- 1.0003792
- 0.98969164
- 1.010438
- 1.0216309
- 1.0016671
- 1.0357588
- 0.98803787
- 1.0093457
- 1.0177035
- 0.98548204
- 1.0274294
- 1.0141377
- 1.0091174
- 0.96427632
- 1.0083272
- 1.0007882
- 0.99038262
- 1.0031336
- 0.99500213
- 0.98203716
- 0.9889452
- 1.011632
- 0.99451949
- 0.97291047
- 0.98750871
- 0.99992418
- 0.97657318
- 0.99930448
- 1.0008515
- 1.0044064
- 0.98133792
- 1.0091702
- 1.0087023
- 1.0119876
- 1.0143019
- 1.0311061
- 0.99340471
- 1.0057428
- 0.99197259
- 1.0071019
- 0.99448853
- 1.0061819
- 1.0070088
- 0.9950913
- 1.0302318
- 0.9817693
- 1.0072885
- 0.97355282
- 0.98782586
- 1.0136674
- 0.99863956
- 1.0205668
- 0.99611384
- 1.0073805
- 0.99691529
- 1.0089194
- 1.0030467
- 1.0112006
- 1.0260523
- 0.97803331
- 0.99423374
- 1.0043727
- 1.0140173
- 1.0111473
- 0.99524348
- 0.99775943
- 0.9958619
- 0.9982344
- 1.0210212
- 1.0022288
- 1.0014801
- 1.011456
- 1.0124871
- 0.99843599
- 0.99324886
- 0.99912838
- 1.003327
- 1.0072071
- 1.0115223
- 1.009266
- 1.0070554
- 1.0129916
- 1.0053413
- 1.0051638
- 0.99212952
- 1.0214422
- 0.98716707
- 0.99905788
- 0.98877357
- 0.98568476
- 0.99767393
- 1.0061791
- 0.98423439
- 0.99492949
- 0.98786999
- 0.99754239
- 1.0168619
- 0.99472384
- 1.0041658
- 0.98123181
- 1.0112882
- 0.99245422
- 1.0010255
- 1.0017799
- 1.0089968
- 1.0072824
- 0.99768475
- 1.0044726
- 1.0118678
- 1.0056385
- 1.0276965
- 1.0025122
- 1.0065161
- 1.0234338
- 0.99760167
- 0.98922272
- 1.0101918
- 1.011615
- 1.0085286
- 1.0074455
- 0.98866757
- 0.99959012
- 1.0129881
- 0.99127881
- 0.97971901
- 1.0185314
- 1.020054
- 1.0132605
- 0.98063643
- 0.99490253
- 1.0101531
- 1.0004526
- 1.0059109
- 0.98974491
- 1.0062391
- 1.0216488
- 0.99398446
- 0.97786609
- 1.0019274
- 0.99587153
- 1.0095881
- 1.0111887
- 0.99457649
- 0.97896734
- 1.000172
- 1.0142951
- 1.0034224
- 1.0037242
- 1.0016059
- 1.016556
- 0.99687023
- 1.0117844
- 1.0059212
- 0.98083159
- 0.98638851
- 1.0128713
- 1.0096232
- 1.0115891
- 1.0011213
- 1.0147105
- 1.0066344
- 1.0164429
- 0.99825038
- 0.99403411
+ 1.0030045
+ 0.99990934
+ 1.0172778
+ 0.99464043
+ 1.0253423
+ 1.0150215
+ 0.97772557
+ 0.97832186
+ 1.0159561
+ 1.0085937
+ 1.0102649
+ 1.0007604
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+ 0.99930448
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+ 0.99492949
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+ 0.99754239
+ 1.0168619
+ 0.99472384
+ 1.0041658
+ 0.98123181
+ 1.0112882
+ 0.99245422
+ 1.0010255
+ 1.0017799
+ 1.0089968
+ 1.0072824
+ 0.99768475
+ 1.0044726
+ 1.0118678
+ 1.0056385
+ 1.0276965
+ 1.0025122
+ 1.0065161
+ 1.0234338
+ 0.99760167
+ 0.98922272
+ 1.0101918
+ 1.011615
+ 1.0085286
+ 1.0074455
+ 0.98866757
+ 0.99959012
+ 1.0129881
+ 0.99127881
+ 0.97971901
+ 1.0185314
+ 1.020054
+ 1.0132605
+ 0.98063643
+ 0.99490253
+ 1.0101531
+ 1.0004526
+ 1.0059109
+ 0.98974491
+ 1.0062391
+ 1.0216488
+ 0.99398446
+ 0.97786609
+ 1.0019274
+ 0.99587153
+ 1.0095881
+ 1.0111887
+ 0.99457649
+ 0.97896734
+ 1.000172
+ 1.0142951
+ 1.0034224
+ 1.0037242
+ 1.0016059
+ 1.016556
+ 0.99687023
+ 1.0117844
+ 1.0059212
+ 0.98083159
+ 0.98638851
+ 1.0128713
+ 1.0096232
+ 1.0115891
+ 1.0011213
+ 1.0147105
+ 1.0066344
+ 1.0164429
+ 0.99825038
+ 0.99403411
-];
+ ];
gp_obs =[
- 1.0079715
- 1.0074573
- 1.0153107
- 1.0152677
- 1.0011653
- 0.99950061
- 1.0328311
- 1.0192317
- 1.009827
- 0.99588916
- 1.007474
- 1.0113061
- 0.98696624
- 0.99978663
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- 0.98861723
- 0.99008763
- 1.0185076
- 1.0052452
- 0.99447194
- 1.0092685
- 1.01208
- 1.0105237
- 0.98513875
- 1.0165628
- 0.99485934
- 1.0050255
- 1.0140756
- 1.0093128
- 1.0155868
- 1.0107023
- 0.99212762
- 1.0095465
- 1.0028435
- 1.0069437
- 1.0070473
- 1.0145902
- 1.0186922
- 1.0059917
- 1.0113072
- 1.0107386
- 0.99769196
- 0.99793444
- 1.0050791
- 0.98307821
- 1.0107594
- 0.99689982
- 0.98667064
- 0.9991662
- 0.98274722
- 0.98422032
- 0.99393016
- 1.0118567
- 0.99912781
- 1.0023744
- 1.0086662
- 1.0164773
- 1.0169327
- 1.0372478
- 1.0314242
- 1.0004256
- 1.0110541
- 1.0076575
- 1.0119851
- 1.0055188
- 1.0213959
- 1.0234416
- 1.0264917
- 1.0292725
- 1.0385184
- 1.0200999
- 1.0107697
- 1.008583
- 1.0200332
- 1.0030413
- 1.0108659
- 1.0185145
- 1.0168619
- 1.0180462
- 1.0239657
- 1.0205509
- 1.0189973
- 1.0246446
- 1.0135089
- 1.0352973
- 1.0099289
- 1.0266474
- 1.0279829
- 1.0101653
- 1.041216
- 1.0103861
- 1.0114727
- 1.0054605
- 1.0190722
- 1.0114837
- 1.0179213
- 1.006082
- 1.0049696
- 1.0143629
- 0.9971036
- 1.0005602
- 1.0078403
- 1.0240222
- 1.0195063
- 1.0355136
- 1.0218743
- 1.0171331
- 1.0049817
- 1.0140974
- 1.0168431
- 1.0049966
- 1.0045568
- 1.0156414
- 1.0273055
- 1.0197653
- 1.0030624
- 1.0154993
- 0.99782084
- 0.99711648
- 1.014408
- 1.0057417
- 0.99936837
- 1.0096934
- 1.0095138
- 1.0057734
- 1.0114497
- 1.0059784
- 1.0328889
- 1.0098032
- 1.0041114
- 1.0101247
- 1.0181588
- 1.0115712
- 1.0227509
- 1.0065104
- 1.0110902
- 1.0298169
- 1.0089532
- 1.0368733
- 1.0123033
- 1.0060763
- 1.0150937
- 1.0239325
- 0.99555536
- 0.99861271
- 1.0076201
- 0.99941535
- 1.0119522
- 1.0129183
- 0.99288924
- 1.0260784
- 1.0144982
- 1.0121985
- 1.0234916
- 1.02215
- 1.0190118
- 1.0172679
- 1.0118398
- 1.0002123
- 1.0092124
- 1.0071943
- 0.99508468
- 1.0019303
- 1.0030733
- 0.9964198
- 1.0027298
- 0.99797614
- 1.006942
- 0.99793928
- 1.0083214
- 1.0283732
- 1.0111102
- 1.016936
- 1.0229061
- 0.98846454
- 1.0015387
- 1.0201769
- 1.0079822
- 1.0064007
- 1.0095543
- 1.0092207
- 1.0135485
- 1.0198974
- 1.0140252
- 1.0128686
- 1.0092903
- 1.0141974
- 1.0023492
- 0.99731455
- 1.0026598
- 0.99303643
- 1.0036469
- 1.0160975
- 1.0368378
- 1.0139625
- 1.01493
- 1.0113531
- 1.0114548
- 0.99833441
- 0.99648401
- 0.97645361
- 1.0154053
- 1.01703
+ 1.0079715
+ 1.0074573
+ 1.0153107
+ 1.0152677
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+ 1.4627911
+ 1.453154
+ 1.4416665
+ 1.4101485
+ 1.4175823
+ 1.4266407
-];
+ ];
diff --git a/tests/fs2000/fsdat_simul_dseries.m b/tests/fs2000/fsdat_simul_dseries.m
index 2dd9c2e2d68b7943253ac9e03523213fdc62459e..5fa6d19a3b43ee16f7f58da00bd028fe5e195ac1 100644
--- a/tests/fs2000/fsdat_simul_dseries.m
+++ b/tests/fs2000/fsdat_simul_dseries.m
@@ -7,822 +7,822 @@ NAMES__ = {'P_obs'; 'Y_obs'; 'gp_obs'; 'gy_obs'};
TEX__ = {'P\_obs'; 'Y\_obs'; 'gp\_obs'; 'gy\_obs'};
P_obs = [
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gy_obs = [
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diff --git a/tests/fs2000/fsdat_simul_missing_obs.m b/tests/fs2000/fsdat_simul_missing_obs.m
index fe9dc57794097461946cbe594ffdb53e3fac91c3..cc5674e62501d44e7f27e914b11f19cdfcde56dc 100644
--- a/tests/fs2000/fsdat_simul_missing_obs.m
+++ b/tests/fs2000/fsdat_simul_missing_obs.m
@@ -1,416 +1,416 @@
% Generated data, used by fs2000.mod
gy_obs =[
- NaN
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diff --git a/tests/fs2000_ssfile_aux.m b/tests/fs2000_ssfile_aux.m
index c16bfc96daaebc94fb660700145876bb37fd63f3..50a40bcfbaca014162603ec90b7aa01998374597 100644
--- a/tests/fs2000_ssfile_aux.m
+++ b/tests/fs2000_ssfile_aux.m
@@ -1,4 +1,4 @@
function [W, e] = fs2000_ssfile_aux(l, n)
- W = l/n;
- e = 1;
+W = l/n;
+e = 1;
end
diff --git a/tests/gsa/data_ca1.m b/tests/gsa/data_ca1.m
index c28fae1a2800e83eda0e6343196e8aeafad2935f..ca003056bded971e9bf00232802e453a096e9cdf 100644
--- a/tests/gsa/data_ca1.m
+++ b/tests/gsa/data_ca1.m
@@ -1,98 +1,98 @@
data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
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+ 1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
+ 1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
+ 0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
+ 0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
+ 1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
+ 0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
+ 0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
+ 0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
+ 0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
+ -0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
+ -0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
+ -0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
+ -1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
+ 0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
+ 0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
+ 0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
+ -0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
+ 0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
+ 0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
+ 0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
+ 0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
+ 0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
+ 0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
+ 0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
+ 1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
+ 1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
+ 1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
+ 0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
+ 0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
+ -0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
+ 0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
+ 0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
+ 0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
+ 0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
+ 1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
+ 0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
+ 0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
+ 1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
+ 1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
+ 0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
+ 1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
+ 0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
+ 1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
+ 1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
+ 1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
+ 1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
+ 1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
+ 1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
+ 1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
+ 0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
+ 1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
+ 0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
+ 0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
+ 0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
+ -0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
+ 0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
+ 1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
+ 1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
+ 0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
+ ];
+
data = reshape(data,5,86)';
y_obs = data(:,1);
pie_obs = data(:,2);
R_obs = data(:,3);
de = data(:,4);
dq = data(:,5);
-
+
%Country: Canada
%Sample Range: 1981:2 to 2002:3
%Observations: 86
diff --git a/tests/identification/as2007/as2007_steadystate.m b/tests/identification/as2007/as2007_steadystate.m
index 473207a0ba9d26725a741e7463103b05f0b9c874..0f070859dee406e866c37d883460fdcf94339bf4 100644
--- a/tests/identification/as2007/as2007_steadystate.m
+++ b/tests/identification/as2007/as2007_steadystate.m
@@ -3,11 +3,11 @@ function [ys,check1]=as2007_steadystate(ys,exo)
global M_
for j=1:size(M_.param_names,1)
- eval([deblank(M_.param_names(j,:)),' = M_.params(j);'])
- assignin('base',deblank(M_.param_names(j,:)),M_.params(j));
+ eval([deblank(M_.param_names(j,:)),' = M_.params(j);'])
+ assignin('base',deblank(M_.param_names(j,:)),M_.params(j));
end
for j=1:size(M_.endo_names,1)
- eval([deblank(M_.endo_names(j,:)),' = NaN;'])
+ eval([deblank(M_.endo_names(j,:)),' = NaN;'])
end
check1=0;
@@ -24,11 +24,11 @@ INT = pi_steady+rr_steady+4*gam_steady;
%% end own model equations
for iter = 1:length(M_.params) %update parameters set in the file
- eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
+ eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
end
NumberOfEndogenousVariables = M_.orig_endo_nbr; %auxiliary variables are set automatically
for ii = 1:NumberOfEndogenousVariables
- varname = deblank(M_.endo_names(ii,:));
- eval(['ys(' int2str(ii) ') = ' varname ';']);
+ varname = deblank(M_.endo_names(ii,:));
+ eval(['ys(' int2str(ii) ') = ' varname ';']);
end
diff --git a/tests/identification/kim/kim2_steadystate.m b/tests/identification/kim/kim2_steadystate.m
index ffbd641ed6bbd7bd581a6bcedd7c6c60c49afb87..5955fb16fd10423216c5bd7ce33f55e910c647e1 100644
--- a/tests/identification/kim/kim2_steadystate.m
+++ b/tests/identification/kim/kim2_steadystate.m
@@ -3,11 +3,11 @@ function [ys,check1]=kim2_steadystate(ys,exo)
global M_
for j=1:size(M_.param_names,1)
- eval([deblank(M_.param_names(j,:)),' = M_.params(j);'])
- assignin('base',deblank(M_.param_names(j,:)),M_.params(j));
+ eval([deblank(M_.param_names(j,:)),' = M_.params(j);'])
+ assignin('base',deblank(M_.param_names(j,:)),M_.params(j));
end
for j=1:size(M_.endo_names,1)
- eval([deblank(M_.endo_names(j,:)),' = NaN;'])
+ eval([deblank(M_.endo_names(j,:)),' = NaN;'])
end
check1=0;
@@ -22,11 +22,11 @@ lam = (1-s)^theta/c^(1+theta)/(1+theta);
%% end own model equations
for iter = 1:length(M_.params) %update parameters set in the file
- eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
+ eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
end
NumberOfEndogenousVariables = M_.orig_endo_nbr; %auxiliary variables are set automatically
for ii = 1:NumberOfEndogenousVariables
- varname = deblank(M_.endo_names(ii,:));
- eval(['ys(' int2str(ii) ') = ' varname ';']);
+ varname = deblank(M_.endo_names(ii,:));
+ eval(['ys(' int2str(ii) ') = ' varname ';']);
end
diff --git a/tests/kalman/likelihood/compare_kalman_routines.m b/tests/kalman/likelihood/compare_kalman_routines.m
index 13022bbead2f92321008aaaaf6b7a78aa4d4d329..8650db0865d375d7a3bf0a20cd26039332ca4ac9 100644
--- a/tests/kalman/likelihood/compare_kalman_routines.m
+++ b/tests/kalman/likelihood/compare_kalman_routines.m
@@ -187,4 +187,3 @@ else
disp(['percentage dev. = ' num2str((LIK3/LIK2-1)*100)])
end
end
-
\ No newline at end of file
diff --git a/tests/kalman/likelihood/simul_state_space_model.m b/tests/kalman/likelihood/simul_state_space_model.m
index 92d3450e06da8c3f30fd49be25b6a833e06db666..5cc0e336c69c1e9323bbc3c61d8b9ee4bca5f65e 100644
--- a/tests/kalman/likelihood/simul_state_space_model.m
+++ b/tests/kalman/likelihood/simul_state_space_model.m
@@ -1,25 +1,25 @@
function observed_data = simul_state_space_model(T,R,Q,mf,nobs,H)
- pp = length(mf);
- mm = length(T);
- rr = length(Q);
-
- upper_cholesky_Q = chol(Q);
- if nargin>5
- upper_cholesky_H = chol(H);
+pp = length(mf);
+mm = length(T);
+rr = length(Q);
+
+upper_cholesky_Q = chol(Q);
+if nargin>5
+ upper_cholesky_H = chol(H);
+end
+
+state_data = zeros(mm,1);
+
+if (nargin==5)
+ for t = 1:nobs
+ state_data = T*state_data + R* upper_cholesky_Q * randn(rr,1);
+ observed_data(:,t) = state_data(mf);
end
-
- state_data = zeros(mm,1);
-
- if (nargin==5)
- for t = 1:nobs
- state_data = T*state_data + R* upper_cholesky_Q * randn(rr,1);
- observed_data(:,t) = state_data(mf);
- end
- elseif (nargin==6)
- for t = 1:nobs
- state_data = T*state_data + R* upper_cholesky_Q * randn(rr,1);
- observed_data(:,t) = state_data(mf) + upper_cholesky_H * randn(pp,1);
- end
- else
- error('simul_state_space_model:: I don''t understand what you want!!!')
- end
\ No newline at end of file
+elseif (nargin==6)
+ for t = 1:nobs
+ state_data = T*state_data + R* upper_cholesky_Q * randn(rr,1);
+ observed_data(:,t) = state_data(mf) + upper_cholesky_H * randn(pp,1);
+ end
+else
+ error('simul_state_space_model:: I don''t understand what you want!!!')
+end
\ No newline at end of file
diff --git a/tests/kalman_filter_smoother/compare_results_simulation/fsdat_simul_logged.m b/tests/kalman_filter_smoother/compare_results_simulation/fsdat_simul_logged.m
index 3e442115c23401f155bae1e7afa8383b611cea0f..11294674738a923382116ccf5e703ea5f2dda6e9 100644
--- a/tests/kalman_filter_smoother/compare_results_simulation/fsdat_simul_logged.m
+++ b/tests/kalman_filter_smoother/compare_results_simulation/fsdat_simul_logged.m
@@ -1,830 +1,830 @@
gy_obs =[
- 1.0030045
- 0.99990934
- 1.0172778
- 0.99464043
- 1.0253423
- 1.0150215
- 0.97772557
- 0.97832186
- 1.0159561
- 1.0085937
- 1.0102649
- 1.0007604
- 1.0112596
- 1.0163279
- 1.0173204
- 1.0103896
- 1.0006493
- 0.99447124
- 1.0196405
- 1.0089304
- 0.99650737
- 1.0139707
- 0.97865842
- 1.0192225
- 0.99139628
- 1.0141362
- 1.0196612
- 0.97483476
- 0.99686151
- 0.99594464
- 1.0000642
- 1.0172243
- 1.0025773
- 0.97199728
- 1.0217815
- 1.0219949
- 0.99490252
- 1.0190728
- 1.0111337
- 1.0003792
- 0.98969164
- 1.010438
- 1.0216309
- 1.0016671
- 1.0357588
- 0.98803787
- 1.0093457
- 1.0177035
- 0.98548204
- 1.0274294
- 1.0141377
- 1.0091174
- 0.96427632
- 1.0083272
- 1.0007882
- 0.99038262
- 1.0031336
- 0.99500213
- 0.98203716
- 0.9889452
- 1.011632
- 0.99451949
- 0.97291047
- 0.98750871
- 0.99992418
- 0.97657318
- 0.99930448
- 1.0008515
- 1.0044064
- 0.98133792
- 1.0091702
- 1.0087023
- 1.0119876
- 1.0143019
- 1.0311061
- 0.99340471
- 1.0057428
- 0.99197259
- 1.0071019
- 0.99448853
- 1.0061819
- 1.0070088
- 0.9950913
- 1.0302318
- 0.9817693
- 1.0072885
- 0.97355282
- 0.98782586
- 1.0136674
- 0.99863956
- 1.0205668
- 0.99611384
- 1.0073805
- 0.99691529
- 1.0089194
- 1.0030467
- 1.0112006
- 1.0260523
- 0.97803331
- 0.99423374
- 1.0043727
- 1.0140173
- 1.0111473
- 0.99524348
- 0.99775943
- 0.9958619
- 0.9982344
- 1.0210212
- 1.0022288
- 1.0014801
- 1.011456
- 1.0124871
- 0.99843599
- 0.99324886
- 0.99912838
- 1.003327
- 1.0072071
- 1.0115223
- 1.009266
- 1.0070554
- 1.0129916
- 1.0053413
- 1.0051638
- 0.99212952
- 1.0214422
- 0.98716707
- 0.99905788
- 0.98877357
- 0.98568476
- 0.99767393
- 1.0061791
- 0.98423439
- 0.99492949
- 0.98786999
- 0.99754239
- 1.0168619
- 0.99472384
- 1.0041658
- 0.98123181
- 1.0112882
- 0.99245422
- 1.0010255
- 1.0017799
- 1.0089968
- 1.0072824
- 0.99768475
- 1.0044726
- 1.0118678
- 1.0056385
- 1.0276965
- 1.0025122
- 1.0065161
- 1.0234338
- 0.99760167
- 0.98922272
- 1.0101918
- 1.011615
- 1.0085286
- 1.0074455
- 0.98866757
- 0.99959012
- 1.0129881
- 0.99127881
- 0.97971901
- 1.0185314
- 1.020054
- 1.0132605
- 0.98063643
- 0.99490253
- 1.0101531
- 1.0004526
- 1.0059109
- 0.98974491
- 1.0062391
- 1.0216488
- 0.99398446
- 0.97786609
- 1.0019274
- 0.99587153
- 1.0095881
- 1.0111887
- 0.99457649
- 0.97896734
- 1.000172
- 1.0142951
- 1.0034224
- 1.0037242
- 1.0016059
- 1.016556
- 0.99687023
- 1.0117844
- 1.0059212
- 0.98083159
- 0.98638851
- 1.0128713
- 1.0096232
- 1.0115891
- 1.0011213
- 1.0147105
- 1.0066344
- 1.0164429
- 0.99825038
- 0.99403411
+ 1.0030045
+ 0.99990934
+ 1.0172778
+ 0.99464043
+ 1.0253423
+ 1.0150215
+ 0.97772557
+ 0.97832186
+ 1.0159561
+ 1.0085937
+ 1.0102649
+ 1.0007604
+ 1.0112596
+ 1.0163279
+ 1.0173204
+ 1.0103896
+ 1.0006493
+ 0.99447124
+ 1.0196405
+ 1.0089304
+ 0.99650737
+ 1.0139707
+ 0.97865842
+ 1.0192225
+ 0.99139628
+ 1.0141362
+ 1.0196612
+ 0.97483476
+ 0.99686151
+ 0.99594464
+ 1.0000642
+ 1.0172243
+ 1.0025773
+ 0.97199728
+ 1.0217815
+ 1.0219949
+ 0.99490252
+ 1.0190728
+ 1.0111337
+ 1.0003792
+ 0.98969164
+ 1.010438
+ 1.0216309
+ 1.0016671
+ 1.0357588
+ 0.98803787
+ 1.0093457
+ 1.0177035
+ 0.98548204
+ 1.0274294
+ 1.0141377
+ 1.0091174
+ 0.96427632
+ 1.0083272
+ 1.0007882
+ 0.99038262
+ 1.0031336
+ 0.99500213
+ 0.98203716
+ 0.9889452
+ 1.011632
+ 0.99451949
+ 0.97291047
+ 0.98750871
+ 0.99992418
+ 0.97657318
+ 0.99930448
+ 1.0008515
+ 1.0044064
+ 0.98133792
+ 1.0091702
+ 1.0087023
+ 1.0119876
+ 1.0143019
+ 1.0311061
+ 0.99340471
+ 1.0057428
+ 0.99197259
+ 1.0071019
+ 0.99448853
+ 1.0061819
+ 1.0070088
+ 0.9950913
+ 1.0302318
+ 0.9817693
+ 1.0072885
+ 0.97355282
+ 0.98782586
+ 1.0136674
+ 0.99863956
+ 1.0205668
+ 0.99611384
+ 1.0073805
+ 0.99691529
+ 1.0089194
+ 1.0030467
+ 1.0112006
+ 1.0260523
+ 0.97803331
+ 0.99423374
+ 1.0043727
+ 1.0140173
+ 1.0111473
+ 0.99524348
+ 0.99775943
+ 0.9958619
+ 0.9982344
+ 1.0210212
+ 1.0022288
+ 1.0014801
+ 1.011456
+ 1.0124871
+ 0.99843599
+ 0.99324886
+ 0.99912838
+ 1.003327
+ 1.0072071
+ 1.0115223
+ 1.009266
+ 1.0070554
+ 1.0129916
+ 1.0053413
+ 1.0051638
+ 0.99212952
+ 1.0214422
+ 0.98716707
+ 0.99905788
+ 0.98877357
+ 0.98568476
+ 0.99767393
+ 1.0061791
+ 0.98423439
+ 0.99492949
+ 0.98786999
+ 0.99754239
+ 1.0168619
+ 0.99472384
+ 1.0041658
+ 0.98123181
+ 1.0112882
+ 0.99245422
+ 1.0010255
+ 1.0017799
+ 1.0089968
+ 1.0072824
+ 0.99768475
+ 1.0044726
+ 1.0118678
+ 1.0056385
+ 1.0276965
+ 1.0025122
+ 1.0065161
+ 1.0234338
+ 0.99760167
+ 0.98922272
+ 1.0101918
+ 1.011615
+ 1.0085286
+ 1.0074455
+ 0.98866757
+ 0.99959012
+ 1.0129881
+ 0.99127881
+ 0.97971901
+ 1.0185314
+ 1.020054
+ 1.0132605
+ 0.98063643
+ 0.99490253
+ 1.0101531
+ 1.0004526
+ 1.0059109
+ 0.98974491
+ 1.0062391
+ 1.0216488
+ 0.99398446
+ 0.97786609
+ 1.0019274
+ 0.99587153
+ 1.0095881
+ 1.0111887
+ 0.99457649
+ 0.97896734
+ 1.000172
+ 1.0142951
+ 1.0034224
+ 1.0037242
+ 1.0016059
+ 1.016556
+ 0.99687023
+ 1.0117844
+ 1.0059212
+ 0.98083159
+ 0.98638851
+ 1.0128713
+ 1.0096232
+ 1.0115891
+ 1.0011213
+ 1.0147105
+ 1.0066344
+ 1.0164429
+ 0.99825038
+ 0.99403411
-];
+ ];
gp_obs =[
- 1.0079715
- 1.0074573
- 1.0153107
- 1.0152677
- 1.0011653
- 0.99950061
- 1.0328311
- 1.0192317
- 1.009827
- 0.99588916
- 1.007474
- 1.0113061
- 0.98696624
- 0.99978663
- 0.98240542
- 0.98861723
- 0.99008763
- 1.0185076
- 1.0052452
- 0.99447194
- 1.0092685
- 1.01208
- 1.0105237
- 0.98513875
- 1.0165628
- 0.99485934
- 1.0050255
- 1.0140756
- 1.0093128
- 1.0155868
- 1.0107023
- 0.99212762
- 1.0095465
- 1.0028435
- 1.0069437
- 1.0070473
- 1.0145902
- 1.0186922
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-];
+ ];
gp_obs=log(gp_obs);
gy_obs=log(gy_obs);
diff --git a/tests/kalman_filter_smoother/fsdat_simul.m b/tests/kalman_filter_smoother/fsdat_simul.m
index d4f4a8066f17ba49faad004256693ebc1b9b01e9..159612e577c3b91d585970404c9cf576c0e8a8d6 100644
--- a/tests/kalman_filter_smoother/fsdat_simul.m
+++ b/tests/kalman_filter_smoother/fsdat_simul.m
@@ -1,828 +1,828 @@
gy_obs =[
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+ ];
Y_obs =[
- 1
- 0.99690484
- 1.0111781
- 1.0028141
- 1.0251518
- 1.0371688
- 1.0118899
- 0.98720726
- 1.0001589
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- 1.0546356
- 1.0620691
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- 1.1121124
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- 1.1034869
- 1.1181859
- 1.1006634
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- 1.000933
- 0.99636447
- 0.98660883
- 0.98273271
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- 0.98725774
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- 1.002037
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- 0.94639265
- 0.96503697
- 0.95601693
- 0.93087851
- 0.92980141
- 0.92266844
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- 0.93743628
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- 0.9049711
- 0.90213859
- 0.91342916
- 0.91384707
- 0.91456681
- 0.91316822
- 0.92671976
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- 0.91010921
- 0.89349322
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- 0.90997873
- 0.91856328
- 0.91668007
- 0.92838606
- 0.932016
- 0.94545438
- 0.94070026
- 0.93172987
+ 1
+ 0.99690484
+ 1.0111781
+ 1.0028141
+ 1.0251518
+ 1.0371688
+ 1.0118899
+ 0.98720726
+ 1.0001589
+ 1.0057481
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+ 1.0107643
+ 1.0190194
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+ 1.0466587
+ 1.0540438
+ 1.0516886
+ 1.0431553
+ 1.0597913
+ 1.0657172
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+ 1.0504499
+ 1.0615817
+ 1.0782384
+ 1.0500687
+ 1.0439257
+ 1.0368658
+ 1.0339255
+ 1.0481453
+ 1.0477181
+ 1.0167109
+ 1.0354878
+ 1.0544782
+ 1.0463762
+ 1.0624445
+ 1.0705737
+ 1.0679484
+ 1.0546356
+ 1.0620691
+ 1.0806955
+ 1.0793581
+ 1.1121124
+ 1.0971458
+ 1.1034869
+ 1.1181859
+ 1.1006634
+ 1.1250883
+ 1.1362214
+ 1.1423343
+ 1.1036061
+ 1.1089288
+ 1.1067125
+ 1.0940906
+ 1.0942197
+ 1.0862174
+ 1.06525
+ 1.0511907
+ 1.0598182
+ 1.0513331
+ 1.0212391
+ 1.0057433
+ 1.002663
+ 0.97623167
+ 0.97253165
+ 0.97037865
+ 0.97178055
+ 0.95011397
+ 0.95627969
+ 0.96197747
+ 0.97096053
+ 0.98225794
+ 1.0103595
+ 1.0007597
+ 1.003498
+ 0.99246608
+ 0.99656347
+ 0.98804749
+ 0.99122491
+ 0.99522926
+ 0.98731605
+ 1.0145434
+ 0.99330816
+ 0.99759216
+ 0.96814048
+ 0.95296183
+ 0.96362471
+ 0.95925977
+ 0.97682205
+ 0.96993138
+ 0.9743074
+ 0.96821818
+ 0.97413308
+ 0.9741753
+ 0.98237142
+ 1.0054193
+ 0.98044807
+ 0.9716773
+ 0.9730455
+ 0.98405828
+ 0.99220103
+ 0.98444001
+ 0.97919493
+ 0.97205233
+ 0.96728223
+ 0.98529893
+ 0.98452324
+ 0.98299888
+ 0.99145042
+ 1.000933
+ 0.99636447
+ 0.98660883
+ 0.98273271
+ 0.98305518
+ 0.98725774
+ 0.99577549
+ 1.002037
+ 1.0060879
+ 1.016075
+ 1.0184118
+ 1.0205711
+ 1.0096961
+ 1.0281337
+ 1.0122963
+ 1.0083497
+ 0.99411874
+ 0.976799
+ 0.97146842
+ 0.97464304
+ 0.95587292
+ 0.94779791
+ 0.93266339
+ 0.92720128
+ 0.94105864
+ 0.93277798
+ 0.93393927
+ 0.91216657
+ 0.92045028
+ 0.9099
+ 0.90792098
+ 0.90669634
+ 0.91268867
+ 0.91696661
+ 0.91164685
+ 0.91311495
+ 0.92197825
+ 0.92461222
+ 0.94930422
+ 0.9488119
+ 0.95232353
+ 0.97275278
+ 0.96734995
+ 0.95356817
+ 0.96075548
+ 0.96936594
+ 0.97489002
+ 0.97933106
+ 0.96499412
+ 0.96157973
+ 0.97156334
+ 0.95983765
+ 0.93655215
+ 0.95207909
+ 0.96912862
+ 0.97938462
+ 0.95701655
+ 0.94891457
+ 0.95606317
+ 0.95351125
+ 0.95641767
+ 0.94315807
+ 0.94639265
+ 0.96503697
+ 0.95601693
+ 0.93087851
+ 0.92980141
+ 0.92266844
+ 0.92925206
+ 0.93743628
+ 0.92900826
+ 0.9049711
+ 0.90213859
+ 0.91342916
+ 0.91384707
+ 0.91456681
+ 0.91316822
+ 0.92671976
+ 0.92058549
+ 0.92936541
+ 0.93228212
+ 0.91010921
+ 0.89349322
+ 0.90336005
+ 0.90997873
+ 0.91856328
+ 0.91668007
+ 0.92838606
+ 0.932016
+ 0.94545438
+ 0.94070026
+ 0.93172987
-];
+ ];
P_obs =[
- 1
- 0.99948573
- 1.0068249
- 1.0141211
- 1.0073149
- 0.99884398
- 1.0237035
- 1.0349636
- 1.036819
- 1.0247366
- 1.0242391
- 1.0275737
- 1.0065684
- 0.99838346
- 0.97281734
- 0.95346302
- 0.9355791
- 0.9461152
- 0.94338882
- 0.92988921
- 0.9311862
- 0.93529467
- 0.93784681
- 0.91501401
- 0.92360522
- 0.91049302
- 0.90754698
- 0.91365103
- 0.91499228
- 0.92260749
- 0.92533824
- 0.90949431
- 0.91106924
- 0.90594116
- 0.90491334
- 0.9039891
- 0.91060772
- 0.92132842
- 0.91934854
- 0.92268418
- 0.92545127
- 0.91517169
- 0.90513459
- 0.90224212
- 0.87734878
- 0.88013667
- 0.86906494
- 0.84776403
- 0.83895869
- 0.81373437
- 0.78998314
- 0.77594176
- 0.77982695
- 0.77098321
- 0.76538611
- 0.76608075
- 0.77458654
- 0.78354767
- 0.81282389
- 0.83627649
- 0.82873051
- 0.83181309
- 0.83149903
- 0.83551261
- 0.83305985
- 0.84648418
- 0.86195421
- 0.88047436
- 0.90177533
- 0.93232215
- 0.94445051
- 0.9472487
- 0.94786015
- 0.95992178
- 0.95499149
- 0.95788581
- 0.9684288
- 0.97731917
- 0.98739379
- 1.0033879
- 1.0159673
- 1.0269931
- 1.0436661
- 1.0492034
- 1.0765292
- 1.0784865
- 1.0971624
- 1.1171737
- 1.1193675
- 1.1526119
- 1.1550265
- 1.1585277
- 1.1560166
- 1.1671172
- 1.1706294
- 1.1805791
- 1.1786896
- 1.1756876
- 1.1820789
- 1.171211
- 1.1637997
- 1.1636684
- 1.179719
- 1.1912538
- 1.2187959
- 1.2326986
- 1.2418602
- 1.2388704
- 1.2449963
- 1.2538678
- 1.2508929
- 1.2474781
- 1.255148
- 1.274482
- 1.2862757
- 1.2813665
- 1.2888943
- 1.2787436
- 1.2678886
- 1.274325
- 1.2720952
- 1.263492
- 1.2652139
- 1.2667561
- 1.264558
- 1.2680362
- 1.2660431
- 1.2909605
- 1.2927921
- 1.288932
- 1.2910852
- 1.3012725
- 1.3048721
- 1.3196515
- 1.3181903
- 1.321309
- 1.3431543
- 1.344136
- 1.3730377
- 1.3773695
- 1.3754742
- 1.3825964
- 1.3985574
- 1.3861412
- 1.3767823
- 1.3764309
- 1.3678747
- 1.3718554
- 1.3768022
- 1.3617199
- 1.3798267
- 1.3863533
- 1.3905803
- 1.4061004
- 1.4202788
- 1.4313191
- 1.4406155
- 1.4444837
- 1.4367244
- 1.4379653
- 1.4371881
- 1.4243012
- 1.41826
- 1.4133617
- 1.40181
- 1.3965683
- 1.3865729
- 1.3855433
- 1.3755111
- 1.3758609
- 1.3962625
- 1.3994012
- 1.4083656
- 1.4233002
- 1.4037932
- 1.3973604
- 1.4095657
- 1.4095764
- 1.4080055
- 1.4095882
- 1.4108374
- 1.4164143
- 1.4283402
- 1.4343939
- 1.4392909
- 1.4406097
- 1.4468355
- 1.4412132
- 1.4305562
- 1.4252445
- 1.4103094
- 1.4059847
- 1.4141106
- 1.4429769
- 1.4489679
- 1.4559263
- 1.4593079
- 1.4627911
- 1.453154
- 1.4416665
- 1.4101485
- 1.4175823
- 1.4266407
+ 1
+ 0.99948573
+ 1.0068249
+ 1.0141211
+ 1.0073149
+ 0.99884398
+ 1.0237035
+ 1.0349636
+ 1.036819
+ 1.0247366
+ 1.0242391
+ 1.0275737
+ 1.0065684
+ 0.99838346
+ 0.97281734
+ 0.95346302
+ 0.9355791
+ 0.9461152
+ 0.94338882
+ 0.92988921
+ 0.9311862
+ 0.93529467
+ 0.93784681
+ 0.91501401
+ 0.92360522
+ 0.91049302
+ 0.90754698
+ 0.91365103
+ 0.91499228
+ 0.92260749
+ 0.92533824
+ 0.90949431
+ 0.91106924
+ 0.90594116
+ 0.90491334
+ 0.9039891
+ 0.91060772
+ 0.92132842
+ 0.91934854
+ 0.92268418
+ 0.92545127
+ 0.91517169
+ 0.90513459
+ 0.90224212
+ 0.87734878
+ 0.88013667
+ 0.86906494
+ 0.84776403
+ 0.83895869
+ 0.81373437
+ 0.78998314
+ 0.77594176
+ 0.77982695
+ 0.77098321
+ 0.76538611
+ 0.76608075
+ 0.77458654
+ 0.78354767
+ 0.81282389
+ 0.83627649
+ 0.82873051
+ 0.83181309
+ 0.83149903
+ 0.83551261
+ 0.83305985
+ 0.84648418
+ 0.86195421
+ 0.88047436
+ 0.90177533
+ 0.93232215
+ 0.94445051
+ 0.9472487
+ 0.94786015
+ 0.95992178
+ 0.95499149
+ 0.95788581
+ 0.9684288
+ 0.97731917
+ 0.98739379
+ 1.0033879
+ 1.0159673
+ 1.0269931
+ 1.0436661
+ 1.0492034
+ 1.0765292
+ 1.0784865
+ 1.0971624
+ 1.1171737
+ 1.1193675
+ 1.1526119
+ 1.1550265
+ 1.1585277
+ 1.1560166
+ 1.1671172
+ 1.1706294
+ 1.1805791
+ 1.1786896
+ 1.1756876
+ 1.1820789
+ 1.171211
+ 1.1637997
+ 1.1636684
+ 1.179719
+ 1.1912538
+ 1.2187959
+ 1.2326986
+ 1.2418602
+ 1.2388704
+ 1.2449963
+ 1.2538678
+ 1.2508929
+ 1.2474781
+ 1.255148
+ 1.274482
+ 1.2862757
+ 1.2813665
+ 1.2888943
+ 1.2787436
+ 1.2678886
+ 1.274325
+ 1.2720952
+ 1.263492
+ 1.2652139
+ 1.2667561
+ 1.264558
+ 1.2680362
+ 1.2660431
+ 1.2909605
+ 1.2927921
+ 1.288932
+ 1.2910852
+ 1.3012725
+ 1.3048721
+ 1.3196515
+ 1.3181903
+ 1.321309
+ 1.3431543
+ 1.344136
+ 1.3730377
+ 1.3773695
+ 1.3754742
+ 1.3825964
+ 1.3985574
+ 1.3861412
+ 1.3767823
+ 1.3764309
+ 1.3678747
+ 1.3718554
+ 1.3768022
+ 1.3617199
+ 1.3798267
+ 1.3863533
+ 1.3905803
+ 1.4061004
+ 1.4202788
+ 1.4313191
+ 1.4406155
+ 1.4444837
+ 1.4367244
+ 1.4379653
+ 1.4371881
+ 1.4243012
+ 1.41826
+ 1.4133617
+ 1.40181
+ 1.3965683
+ 1.3865729
+ 1.3855433
+ 1.3755111
+ 1.3758609
+ 1.3962625
+ 1.3994012
+ 1.4083656
+ 1.4233002
+ 1.4037932
+ 1.3973604
+ 1.4095657
+ 1.4095764
+ 1.4080055
+ 1.4095882
+ 1.4108374
+ 1.4164143
+ 1.4283402
+ 1.4343939
+ 1.4392909
+ 1.4406097
+ 1.4468355
+ 1.4412132
+ 1.4305562
+ 1.4252445
+ 1.4103094
+ 1.4059847
+ 1.4141106
+ 1.4429769
+ 1.4489679
+ 1.4559263
+ 1.4593079
+ 1.4627911
+ 1.453154
+ 1.4416665
+ 1.4101485
+ 1.4175823
+ 1.4266407
-];
+ ];
diff --git a/tests/kalman_filter_smoother/testsmoother.m b/tests/kalman_filter_smoother/testsmoother.m
index 2e633bc2345c0a612ede5569c1b0a148ac80c9d9..3ec1c8cdff8ea4ce7f39e0b314c9b0dc22fc80ba 100644
--- a/tests/kalman_filter_smoother/testsmoother.m
+++ b/tests/kalman_filter_smoother/testsmoother.m
@@ -9,10 +9,10 @@ Pstar1(1,1) = 0;
Pstar1(4,1) = 0;
Pstar1(1,4) = 0;
[alphahat1,epsilonhat1,etahat1,a11, aK1] = DiffuseKalmanSmootherH1(T,R,Q,H, ...
- Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
+ Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
[alphahat2,epsilonhat2,etahat2,a12, aK2] = DiffuseKalmanSmootherH3(T,R,Q,H, ...
- Pinf1,Pstar1,Y,trend, ...
- pp,mm,smpl,mf);
+ Pinf1,Pstar1,Y,trend, ...
+ pp,mm,smpl,mf);
max(max(abs(alphahat1-alphahat2)))
max(max(abs(epsilonhat1-epsilonhat2)))
max(max(abs(etahat1-etahat2)))
@@ -21,10 +21,10 @@ max(max(abs(aK1-aK2)))
return
[alphahat1,etahat1,a11, aK1] = DiffuseKalmanSmoother1(T,R,Q, ...
- Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
+ Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
[alphahat2,etahat2,a12, aK2] = DiffuseKalmanSmoother3(T,R,Q, ...
- Pinf1,Pstar1,Y,trend, ...
- pp,mm,smpl,mf);
+ Pinf1,Pstar1,Y,trend, ...
+ pp,mm,smpl,mf);
max(max(abs(alphahat1-alphahat2)))
@@ -35,10 +35,10 @@ max(max(abs(a11-a12)))
H = zeros(size(H));
[alphahat1,etahat1,a11, aK1] = DiffuseKalmanSmoother1(T,R,Q, ...
- Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
+ Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
[alphahat2,epsilonhat2,etahat2,a12, aK2] = DiffuseKalmanSmootherH1(T,R,Q,H, ...
- Pinf1,Pstar1,Y,trend, ...
- pp,mm,smpl,mf);
+ Pinf1,Pstar1,Y,trend, ...
+ pp,mm,smpl,mf);
max(max(abs(alphahat1-alphahat2)))
max(max(abs(etahat1-etahat2)))
max(max(abs(a11-a12)))
@@ -46,9 +46,9 @@ max(max(abs(a11-a12)))
[alphahat1,etahat1,a11, aK1] = DiffuseKalmanSmoother3(T,R,Q, ...
- Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
+ Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
[alphahat2,epsilonhat2,etahat2,a12, aK2] = DiffuseKalmanSmootherH3(T,R,Q, H, ...
- Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
+ Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf);
max(max(abs(alphahat1-alphahat2)))
max(max(abs(etahat1-etahat2)))
diff --git a/tests/load_octave_packages.m b/tests/load_octave_packages.m
index 886bf644b0b07cd11e9afcc3e50c813e2af1913e..62b6e3d3c403c5f2d71a81ecd1352bbbd73c9100 100644
--- a/tests/load_octave_packages.m
+++ b/tests/load_octave_packages.m
@@ -11,11 +11,11 @@
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
-##
-## You should have received a copy of the GNU General Public License
-## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+ ##
+ ## You should have received a copy of the GNU General Public License
+ ## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-pkg load io
-pkg load optim
-pkg load control
-pkg load statistics
\ No newline at end of file
+ pkg load io
+ pkg load optim
+ pkg load control
+ pkg load statistics
\ No newline at end of file
diff --git a/tests/ls2003/data_ca1.m b/tests/ls2003/data_ca1.m
index c28fae1a2800e83eda0e6343196e8aeafad2935f..ca003056bded971e9bf00232802e453a096e9cdf 100644
--- a/tests/ls2003/data_ca1.m
+++ b/tests/ls2003/data_ca1.m
@@ -1,98 +1,98 @@
data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
--0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
--0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
--0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
--0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
--0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
--0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
-1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
-2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
-1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
-1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
-1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
-1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
-0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
-1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
-1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
-0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
-1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
-1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
--0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
-0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
-0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
--0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
-2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
-1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
-1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
-1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
-1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
-1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
-0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
-0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
-1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
-0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
-0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
-0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
-0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
--0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
--0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
--0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
--1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
-0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
-0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
-0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
--0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
-0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
-0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
-0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
-0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
-0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
-0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
-0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
-1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
-1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
-1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
-0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
-0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
--0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
-0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
-0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
-0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
-0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
-1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
-0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
-0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
-1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
-1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
-0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
-1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
-0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
-1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
-1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
-1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
-1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
-1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
-1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
-1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
-0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
-1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
-0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
-0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
-0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
--0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
-0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
-1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
-1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
-0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
-];
-
+ -0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
+ -0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
+ -0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
+ -0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
+ -0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
+ -0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
+ 1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
+ 2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
+ 1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
+ 1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
+ 1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
+ 1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
+ 0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
+ 1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
+ 1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
+ 0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
+ 1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
+ 1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
+ -0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
+ 0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
+ 0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
+ -0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
+ 2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
+ 1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
+ 1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
+ 1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
+ 1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
+ 1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
+ 0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
+ 0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
+ 1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
+ 0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
+ 0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
+ 0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
+ 0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
+ -0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
+ -0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
+ -0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
+ -1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
+ 0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
+ 0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
+ 0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
+ -0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
+ 0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
+ 0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
+ 0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
+ 0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
+ 0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
+ 0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
+ 0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
+ 1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
+ 1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
+ 1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
+ 0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
+ 0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
+ -0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
+ 0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
+ 0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
+ 0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
+ 0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
+ 1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
+ 0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
+ 0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
+ 1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
+ 1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
+ 0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
+ 1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
+ 0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
+ 1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
+ 1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
+ 1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
+ 1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
+ 1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
+ 1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
+ 1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
+ 0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
+ 1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
+ 0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
+ 0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
+ 0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
+ -0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
+ 0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
+ 1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
+ 1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
+ 0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
+ ];
+
data = reshape(data,5,86)';
y_obs = data(:,1);
pie_obs = data(:,2);
R_obs = data(:,3);
de = data(:,4);
dq = data(:,5);
-
+
%Country: Canada
%Sample Range: 1981:2 to 2002:3
%Observations: 86
diff --git a/tests/measurement_errors/data_ca1.m b/tests/measurement_errors/data_ca1.m
index c28fae1a2800e83eda0e6343196e8aeafad2935f..ca003056bded971e9bf00232802e453a096e9cdf 100644
--- a/tests/measurement_errors/data_ca1.m
+++ b/tests/measurement_errors/data_ca1.m
@@ -1,98 +1,98 @@
data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
--0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
--0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
--0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
--0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
--0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
--0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
-1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
-2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
-1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
-1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
-1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
-1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
-0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
-1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
-1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
-0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
-1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
-1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
--0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
-0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
-0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
--0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
-2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
-1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
-1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
-1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
-1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
-1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
-0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
-0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
-1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
-0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
-0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
-0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
-0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
--0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
--0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
--0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
--1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
-0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
-0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
-0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
--0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
-0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
-0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
-0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
-0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
-0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
-0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
-0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
-1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
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+ -0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
+ 0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
+ 1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
+ 1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
+ 0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
+ ];
+
data = reshape(data,5,86)';
y_obs = data(:,1);
pie_obs = data(:,2);
R_obs = data(:,3);
de = data(:,4);
dq = data(:,5);
-
+
%Country: Canada
%Sample Range: 1981:2 to 2002:3
%Observations: 86
diff --git a/tests/measurement_errors/fs2000_corr_me_ml_mcmc/fsdat_simul.m b/tests/measurement_errors/fs2000_corr_me_ml_mcmc/fsdat_simul.m
index 56c0e4cd56a8bb96fb579587fb4218afb626c56e..f6ad30c85be4e71227913664bd14cce25ac504eb 100644
--- a/tests/measurement_errors/fs2000_corr_me_ml_mcmc/fsdat_simul.m
+++ b/tests/measurement_errors/fs2000_corr_me_ml_mcmc/fsdat_simul.m
@@ -1,416 +1,416 @@
% Generated data, used by fs2000.mod
gy_obs =[
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-];
+ ];
gp_obs =[
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- 1.0156316
- 0.99818589
- 0.9894107
- 1.0127584
- 1.0146882
- 1.0136529
- 1.0340107
- 1.0343652
- 1.02971
- 1.0077932
- 1.0198114
- 1.013971
- 1.0061083
- 1.0089573
- 1.0037926
- 1.0082071
- 0.99498155
- 0.99735772
- 0.98765026
- 1.006465
- 1.0196088
- 1.0053233
- 1.0119974
- 1.0188066
- 1.0029302
- 1.0183459
- 1.0034218
- 1.0158799
- 0.98824798
- 1.0274357
- 1.0168832
- 1.0180641
- 1.0294657
- 0.98864091
- 1.0358326
- 0.99889969
- 1.0178322
- 0.99813566
- 1.0073549
- 1.0215985
- 1.0084245
- 1.0080939
- 1.0157021
- 1.0075815
- 1.0032633
- 1.0117871
- 1.0209276
- 1.0077569
- 0.99680958
- 1.0120266
- 1.0017625
- 1.0138811
- 1.0198358
- 1.0059629
- 1.0115416
- 1.0319473
- 1.0167074
- 1.0116111
- 1.0048627
- 1.0217622
- 1.0125221
- 1.0142045
- 0.99792469
- 0.99823971
- 0.99561547
- 0.99850373
- 0.9898464
- 1.0030963
- 1.0051373
- 1.0004213
- 1.0144117
- 0.97185592
- 0.9959518
- 1.0073529
- 1.0051603
- 0.98642572
- 0.99433423
- 1.0112131
- 1.0007695
- 1.0176867
- 1.0134363
- 0.99926191
- 0.99879835
- 0.99878754
- 1.0331374
- 1.0077797
- 1.0127221
- 1.0047393
- 1.0074106
- 0.99784213
- 1.0056495
- 1.0057708
- 0.98817494
- 0.98742176
- 0.99930555
- 1.0000687
- 1.0129754
- 1.009529
- 1.0226731
- 1.0149534
- 1.0164295
- 1.0239469
- 1.0293458
- 1.026199
- 1.0197525
- 1.0126818
- 1.0054473
- 1.0254423
- 1.0069461
- 1.0153135
- 1.0337515
- 1.0178187
- 1.0240469
- 1.0079489
- 1.0186953
- 1.0008628
- 1.0113799
- 1.0140118
- 1.0168007
- 1.011441
- 0.98422774
- 0.98909729
- 1.0157859
- 1.0151586
- 0.99756232
- 0.99497777
- 1.0102841
- 1.0221659
- 0.9937759
- 0.99877193
- 1.0079433
- 0.99667692
- 1.0095959
- 1.0128804
- 1.0156949
- 1.0111951
- 1.0228887
- 1.0122083
- 1.0190197
- 1.0074927
- 1.0268096
- 0.99689352
- 0.98948474
- 1.0024938
- 1.0105543
- 1.014116
- 1.0141217
- 1.0056504
- 1.0101026
- 1.0105069
- 0.99619053
- 1.0059439
- 0.99449473
- 0.99482458
- 1.0037702
- 1.0068087
- 0.99575975
- 1.0030815
- 1.0334014
- 0.99879386
- 0.99625634
- 1.0171195
- 0.99233844
+ 1.0079715
+ 1.0115853
+ 1.0167502
+ 1.0068957
+ 1.0138189
+ 1.0258364
+ 1.0243817
+ 1.017373
+ 1.0020171
+ 1.0003742
+ 1.0008974
+ 1.0104804
+ 1.0116393
+ 1.0114294
+ 0.99932124
+ 0.99461459
+ 1.0170349
+ 1.0051446
+ 1.020639
+ 1.0051964
+ 1.0093042
+ 1.007068
+ 1.01086
+ 0.99590086
+ 1.0014883
+ 1.0117332
+ 0.9990095
+ 1.0108284
+ 1.0103672
+ 1.0036722
+ 1.0005124
+ 1.0190331
+ 1.0130978
+ 1.007842
+ 1.0285436
+ 1.0322054
+ 1.0213403
+ 1.0246486
+ 1.0419306
+ 1.0258867
+ 1.0156316
+ 0.99818589
+ 0.9894107
+ 1.0127584
+ 1.0146882
+ 1.0136529
+ 1.0340107
+ 1.0343652
+ 1.02971
+ 1.0077932
+ 1.0198114
+ 1.013971
+ 1.0061083
+ 1.0089573
+ 1.0037926
+ 1.0082071
+ 0.99498155
+ 0.99735772
+ 0.98765026
+ 1.006465
+ 1.0196088
+ 1.0053233
+ 1.0119974
+ 1.0188066
+ 1.0029302
+ 1.0183459
+ 1.0034218
+ 1.0158799
+ 0.98824798
+ 1.0274357
+ 1.0168832
+ 1.0180641
+ 1.0294657
+ 0.98864091
+ 1.0358326
+ 0.99889969
+ 1.0178322
+ 0.99813566
+ 1.0073549
+ 1.0215985
+ 1.0084245
+ 1.0080939
+ 1.0157021
+ 1.0075815
+ 1.0032633
+ 1.0117871
+ 1.0209276
+ 1.0077569
+ 0.99680958
+ 1.0120266
+ 1.0017625
+ 1.0138811
+ 1.0198358
+ 1.0059629
+ 1.0115416
+ 1.0319473
+ 1.0167074
+ 1.0116111
+ 1.0048627
+ 1.0217622
+ 1.0125221
+ 1.0142045
+ 0.99792469
+ 0.99823971
+ 0.99561547
+ 0.99850373
+ 0.9898464
+ 1.0030963
+ 1.0051373
+ 1.0004213
+ 1.0144117
+ 0.97185592
+ 0.9959518
+ 1.0073529
+ 1.0051603
+ 0.98642572
+ 0.99433423
+ 1.0112131
+ 1.0007695
+ 1.0176867
+ 1.0134363
+ 0.99926191
+ 0.99879835
+ 0.99878754
+ 1.0331374
+ 1.0077797
+ 1.0127221
+ 1.0047393
+ 1.0074106
+ 0.99784213
+ 1.0056495
+ 1.0057708
+ 0.98817494
+ 0.98742176
+ 0.99930555
+ 1.0000687
+ 1.0129754
+ 1.009529
+ 1.0226731
+ 1.0149534
+ 1.0164295
+ 1.0239469
+ 1.0293458
+ 1.026199
+ 1.0197525
+ 1.0126818
+ 1.0054473
+ 1.0254423
+ 1.0069461
+ 1.0153135
+ 1.0337515
+ 1.0178187
+ 1.0240469
+ 1.0079489
+ 1.0186953
+ 1.0008628
+ 1.0113799
+ 1.0140118
+ 1.0168007
+ 1.011441
+ 0.98422774
+ 0.98909729
+ 1.0157859
+ 1.0151586
+ 0.99756232
+ 0.99497777
+ 1.0102841
+ 1.0221659
+ 0.9937759
+ 0.99877193
+ 1.0079433
+ 0.99667692
+ 1.0095959
+ 1.0128804
+ 1.0156949
+ 1.0111951
+ 1.0228887
+ 1.0122083
+ 1.0190197
+ 1.0074927
+ 1.0268096
+ 0.99689352
+ 0.98948474
+ 1.0024938
+ 1.0105543
+ 1.014116
+ 1.0141217
+ 1.0056504
+ 1.0101026
+ 1.0105069
+ 0.99619053
+ 1.0059439
+ 0.99449473
+ 0.99482458
+ 1.0037702
+ 1.0068087
+ 0.99575975
+ 1.0030815
+ 1.0334014
+ 0.99879386
+ 0.99625634
+ 1.0171195
+ 0.99233844
-];
+ ];
diff --git a/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol3v.m b/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol3v.m
index ce6156c7b99c9fcc358c986ec4a71c98e2323277..11a132b64874a0076fb2a8d4196aa6caf51b086d 100644
--- a/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol3v.m
+++ b/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol3v.m
@@ -49,7 +49,7 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free D+ parameters in ith equation in all states.
if (nargin==3)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
end
@@ -59,10 +59,10 @@ k = kvar*nStates; % Maximum number of lagged and exogenous variables in each eq
Qi = zeros(n,n,nvar); % 3rd dim: nvar contemporaneous equations.
Ri = zeros(k,k,nvar); % 1st and 2nd dims: lagged and exogenous equations.
- % Row corresponds to equation with nvar variables for state 1, ..., nvar variables for state nState.
- % 0 means no restriction.
- % 1 and -1 or any other number means the linear combination of the corresponding parameters is restricted to 0.
- % 1 (only 1) means that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation with nvar variables for state 1, ..., nvar variables for state nState.
+ % 0 means no restriction.
+ % 1 and -1 or any other number means the linear combination of the corresponding parameters is restricted to 0.
+ % 1 (only 1) means that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -77,47 +77,47 @@ Ri = zeros(k,k,nvar); % 1st and 2nd dims: lagged and exogenous equations.
eqninx = 1;
nreseqn = 2; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 -1 0 0
- 0 1 0 0 -1 0
- 0 0 1 0 0 -1
-
- 0 0 0 0 1 0
- 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 1 0 0 0 0
- 0 0 1 0 0 0
-
- 0 0 0 0 1 0
- 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 -1 0 0
+ 0 1 0 0 -1 0
+ 0 0 1 0 0 -1
+
+ 0 0 0 0 1 0
+ 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_*.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 1 0 0 0 0
+ 0 0 1 0 0 0
+
+ 0 0 0 0 1 0
+ 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_*.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -125,61 +125,61 @@ end
eqninx = 2;
nreseqn = 1; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 -1 0 0
- 0 1 0 0 -1 0
- 0 0 1 0 0 -1
-
- 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 1 0 0 0
-
- 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 -1 0 0
+ 0 1 0 0 -1 0
+ 0 0 1 0 0 -1
+
+ 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_3s_case3a.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
-
- %==== For freely time-varying A+ for only the first 6 lags.
- %==== Lagged restrictions: zeros on all lags except the first 6 lags in the MS equation.
- % nlagsno0 = 6; % Number of lags to be nonzero.
- % for si=1:nStates
- % for ki = 1:lags-nlagsno0
- % for kj=1:nvar
- % Ri(kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,2) = 1;
- % end
- % end
- % end
- %**** For constant D+_s except the first two lags and the constant term. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- % for si=1:nStates-1
- % for ki=[2*nvar+1:kvar-1]
- % Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- % end
- % end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 1 0 0 0
+
+ 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_3s_case3a.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
+
+ %==== For freely time-varying A+ for only the first 6 lags.
+ %==== Lagged restrictions: zeros on all lags except the first 6 lags in the MS equation.
+ % nlagsno0 = 6; % Number of lags to be nonzero.
+ % for si=1:nStates
+ % for ki = 1:lags-nlagsno0
+ % for kj=1:nvar
+ % Ri(kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,2) = 1;
+ % end
+ % end
+ % end
+ %**** For constant D+_s except the first two lags and the constant term. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ % for si=1:nStates-1
+ % for ki=[2*nvar+1:kvar-1]
+ % Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ % end
+ % end
end
@@ -187,42 +187,42 @@ end
eqninx = 3;
nreseqn = 0; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 -1 0 0
- 0 1 0 0 -1 0
- 0 0 1 0 0 -1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 -1 0 0
+ 0 1 0 0 -1 0
+ 0 0 1 0 0 -1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_*.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_*.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
for ki=1:nvar % initializing loop for each equation
- Ui{ki} = null(Qi(:,:,ki));
- Vi{ki} = null(Ri(:,:,ki));
- n0(ki) = size(Ui{ki},2);
- np(ki) = size(Vi{ki},2);
+ Ui{ki} = null(Qi(:,:,ki));
+ Vi{ki} = null(Ri(:,:,ki));
+ n0(ki) = size(Ui{ki},2);
+ np(ki) = size(Vi{ki},2);
end
diff --git a/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol4v.m b/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol4v.m
index bc3215cdeb318494043b56b4ed1302f44b2e1081..7af810db3a0ef771692274b45c0570840393b1b7 100644
--- a/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol4v.m
+++ b/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol4v.m
@@ -49,7 +49,7 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free D+ parameters in ith equation in all states.
if (nargin==3)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
end
@@ -59,10 +59,10 @@ k = kvar*nStates; % Maximum number of lagged and exogenous variables in each eq
Qi = zeros(n,n,nvar); % 3rd dim: nvar contemporaneous equations.
Ri = zeros(k,k,nvar); % 1st and 2nd dims: lagged and exogenous equations.
- % Row corresponds to equation with nvar variables for state 1, ..., nvar variables for state nState.
- % 0 means no restriction.
- % 1 and -1 or any other number means the linear combination of the corresponding parameters is restricted to 0.
- % 1 (only 1) means that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation with nvar variables for state 1, ..., nvar variables for state nState.
+ % 0 means no restriction.
+ % 1 and -1 or any other number means the linear combination of the corresponding parameters is restricted to 0.
+ % 1 (only 1) means that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -77,51 +77,51 @@ Ri = zeros(k,k,nvar); % 1st and 2nd dims: lagged and exogenous equations.
eqninx = 1;
nreseqn = 3; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 -1 0 0 0
- 0 1 0 0 0 -1 0 0
- 0 0 1 0 0 0 -1 0
- 0 0 0 1 0 0 0 -1
-
- 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 1 0 0 0 0 0 0
- 0 0 1 0 0 0 0 0
- 0 0 0 1 0 0 0 0
-
- 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 -1 0 0 0
+ 0 1 0 0 0 -1 0 0
+ 0 0 1 0 0 0 -1 0
+ 0 0 0 1 0 0 0 -1
+
+ 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 1 0 0 0 0 0 0
+ 0 0 1 0 0 0 0 0
+ 0 0 0 1 0 0 0 0
+
+ 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -129,65 +129,65 @@ end
eqninx = 2;
nreseqn = 2; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 -1 0 0 0
- 0 1 0 0 0 -1 0 0
- 0 0 1 0 0 0 -1 0
- 0 0 0 1 0 0 0 -1
-
- 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 1 0 0 0 0 0
- 0 0 0 1 0 0 0 0
-
- 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 -1 0 0 0
+ 0 1 0 0 0 -1 0 0
+ 0 0 1 0 0 0 -1 0
+ 0 0 0 1 0 0 0 -1
+
+ 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_3s_case3a.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
-
- %==== For freely time-varying A+ for only the first 6 lags.
- %==== Lagged restrictions: zeros on all lags except the first 6 lags in the MS equation.
- % nlagsno0 = 6; % Number of lags to be nonzero.
- % for si=1:nStates
- % for ki = 1:lags-nlagsno0
- % for kj=1:nvar
- % Ri(kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,2) = 1;
- % end
- % end
- % end
- %**** For constant D+_s except the first two lags and the constant term. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- % for si=1:nStates-1
- % for ki=[2*nvar+1:kvar-1]
- % Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- % end
- % end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 1 0 0 0 0 0
+ 0 0 0 1 0 0 0 0
+
+ 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_3s_case3a.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
+
+ %==== For freely time-varying A+ for only the first 6 lags.
+ %==== Lagged restrictions: zeros on all lags except the first 6 lags in the MS equation.
+ % nlagsno0 = 6; % Number of lags to be nonzero.
+ % for si=1:nStates
+ % for ki = 1:lags-nlagsno0
+ % for kj=1:nvar
+ % Ri(kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,2) = 1;
+ % end
+ % end
+ % end
+ %**** For constant D+_s except the first two lags and the constant term. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ % for si=1:nStates-1
+ % for ki=[2*nvar+1:kvar-1]
+ % Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ % end
+ % end
end
@@ -195,44 +195,44 @@ end
eqninx = 3;
nreseqn = 1; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 -1 0 0 0
- 0 1 0 0 0 -1 0 0
- 0 0 1 0 0 0 -1 0
- 0 0 0 1 0 0 0 -1
-
- 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 0 1 0 0 0 0
-
- 0 0 0 0 0 0 0 1
- ];
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 -1 0 0 0
+ 0 1 0 0 0 -1 0 0
+ 0 0 1 0 0 0 -1 0
+ 0 0 0 1 0 0 0 -1
+
+ 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 0 1 0 0 0 0
+
+ 0 0 0 0 0 0 0 1
+ ];
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -240,36 +240,36 @@ end
eqninx = 4;
nreseqn = 0; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 -1 0 0 0
- 0 1 0 0 0 -1 0 0
- 0 0 1 0 0 0 -1 0
- 0 0 0 1 0 0 0 -1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 -1 0 0 0
+ 0 1 0 0 0 -1 0 0
+ 0 0 1 0 0 0 -1 0
+ 0 0 0 1 0 0 0 -1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -324,8 +324,8 @@ end
for ki=1:nvar % initializing loop for each equation
- Ui{ki} = null(Qi(:,:,ki));
- Vi{ki} = null(Ri(:,:,ki));
- n0(ki) = size(Ui{ki},2);
- np(ki) = size(Vi{ki},2);
+ Ui{ki} = null(Qi(:,:,ki));
+ Vi{ki} = null(Ri(:,:,ki));
+ n0(ki) = size(Ui{ki},2);
+ np(ki) = size(Vi{ki},2);
end
diff --git a/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol6v.m b/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol6v.m
index 389109df7adc60a840dff7574284435d98d6fb7d..932e927454417857a536b4c22e3f0a773d4edcdf 100644
--- a/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol6v.m
+++ b/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol6v.m
@@ -49,7 +49,7 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free D+ parameters in ith equation in all states.
if (nargin==3)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
end
@@ -59,10 +59,10 @@ k = kvar*nStates; % Maximum number of lagged and exogenous variables in each eq
Qi = zeros(n,n,nvar); % 3rd dim: nvar contemporaneous equations.
Ri = zeros(k,k,nvar); % 1st and 2nd dims: lagged and exogenous equations.
- % Row corresponds to equation with nvar variables for state 1, ..., nvar variables for state nState.
- % 0 means no restriction.
- % 1 and -1 or any other number means the linear combination of the corresponding parameters is restricted to 0.
- % 1 (only 1) means that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation with nvar variables for state 1, ..., nvar variables for state nState.
+ % 0 means no restriction.
+ % 1 and -1 or any other number means the linear combination of the corresponding parameters is restricted to 0.
+ % 1 (only 1) means that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -77,59 +77,59 @@ Ri = zeros(k,k,nvar); % 1st and 2nd dims: lagged and exogenous equations.
eqninx = 1;
nreseqn = 5; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 -1 0 0 0 0 0
- 0 1 0 0 0 0 0 -1 0 0 0 0
- 0 0 1 0 0 0 0 0 -1 0 0 0
- 0 0 0 1 0 0 0 0 0 -1 0 0
- 0 0 0 0 1 0 0 0 0 0 -1 0
- 0 0 0 0 0 1 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 1 0 0 0 0
- 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 1 0 0 0 0 0 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 1 0 0 0 0
- 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 -1 0 0 0 0 0
+ 0 1 0 0 0 0 0 -1 0 0 0 0
+ 0 0 1 0 0 0 0 0 -1 0 0 0
+ 0 0 0 1 0 0 0 0 0 -1 0 0
+ 0 0 0 0 1 0 0 0 0 0 -1 0
+ 0 0 0 0 0 1 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 1 0 0 0 0
+ 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 1 0 0 0 0 0 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 1 0 0 0 0
+ 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -138,56 +138,56 @@ end
eqninx = 2;
nreseqn = 4; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 -1 0 0 0 0 0
- 0 1 0 0 0 0 0 -1 0 0 0 0
- 0 0 1 0 0 0 0 0 -1 0 0 0
- 0 0 0 1 0 0 0 0 0 -1 0 0
- 0 0 0 0 1 0 0 0 0 0 -1 0
- 0 0 0 0 0 1 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 1 0 0 0 0 0 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 -1 0 0 0 0 0
+ 0 1 0 0 0 0 0 -1 0 0 0 0
+ 0 0 1 0 0 0 0 0 -1 0 0 0
+ 0 0 0 1 0 0 0 0 0 -1 0 0
+ 0 0 0 0 1 0 0 0 0 0 -1 0
+ 0 0 0 0 0 1 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 1 0 0 0 0 0 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -195,70 +195,70 @@ end
eqninx = 3;
nreseqn = 3; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 -1 0 0 0 0 0
- 0 1 0 0 0 0 0 -1 0 0 0 0
- 0 0 1 0 0 0 0 0 -1 0 0 0
- 0 0 0 1 0 0 0 0 0 -1 0 0
- 0 0 0 0 1 0 0 0 0 0 -1 0
- 0 0 0 0 0 1 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 0 1 0 0 0 0 0 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 -1 0 0 0 0 0
+ 0 1 0 0 0 0 0 -1 0 0 0 0
+ 0 0 1 0 0 0 0 0 -1 0 0 0
+ 0 0 0 1 0 0 0 0 0 -1 0 0
+ 0 0 0 0 1 0 0 0 0 0 -1 0
+ 0 0 0 0 0 1 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_3s_case3a.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
-
- %==== For freely time-varying A+ for only the first 6 lags.
- %==== Lagged restrictions: zeros on all lags except the first 6 lags in the MS equation.
- % nlagsno0 = 6; % Number of lags to be nonzero.
- % for si=1:nStates
- % for ki = 1:lags-nlagsno0
- % for kj=1:nvar
- % Ri(kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,2) = 1;
- % end
- % end
- % end
- %**** For constant D+_s except the first two lags and the constant term. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- % for si=1:nStates-1
- % for ki=[2*nvar+1:kvar-1]
- % Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- % end
- % end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 0 1 0 0 0 0 0 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_3s_case3a.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
+
+ %==== For freely time-varying A+ for only the first 6 lags.
+ %==== Lagged restrictions: zeros on all lags except the first 6 lags in the MS equation.
+ % nlagsno0 = 6; % Number of lags to be nonzero.
+ % for si=1:nStates
+ % for ki = 1:lags-nlagsno0
+ % for kj=1:nvar
+ % Ri(kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,2) = 1;
+ % end
+ % end
+ % end
+ %**** For constant D+_s except the first two lags and the constant term. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ % for si=1:nStates-1
+ % for ki=[2*nvar+1:kvar-1]
+ % Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ % end
+ % end
end
@@ -266,49 +266,49 @@ end
eqninx = 4;
nreseqn = 2; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 -1 0 0 0 0 0
- 0 1 0 0 0 0 0 -1 0 0 0 0
- 0 0 1 0 0 0 0 0 -1 0 0 0
- 0 0 0 1 0 0 0 0 0 -1 0 0
- 0 0 0 0 1 0 0 0 0 0 -1 0
- 0 0 0 0 0 1 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 0 0 1 0 0 0 0 0 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 -1 0 0 0 0 0
+ 0 1 0 0 0 0 0 -1 0 0 0 0
+ 0 0 1 0 0 0 0 0 -1 0 0 0
+ 0 0 0 1 0 0 0 0 0 -1 0 0
+ 0 0 0 0 1 0 0 0 0 0 -1 0
+ 0 0 0 0 0 1 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 0 0 1 0 0 0 0 0 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -316,46 +316,46 @@ end
eqninx = 5;
nreseqn = 1; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 -1 0 0 0 0 0
- 0 1 0 0 0 0 0 -1 0 0 0 0
- 0 0 1 0 0 0 0 0 -1 0 0 0
- 0 0 0 1 0 0 0 0 0 -1 0 0
- 0 0 0 0 1 0 0 0 0 0 -1 0
- 0 0 0 0 0 1 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 0 0 0 1 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 -1 0 0 0 0 0
+ 0 1 0 0 0 0 0 -1 0 0 0 0
+ 0 0 1 0 0 0 0 0 -1 0 0 0
+ 0 0 0 1 0 0 0 0 0 -1 0 0
+ 0 0 0 0 1 0 0 0 0 0 -1 0
+ 0 0 0 0 0 1 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 0 0 0 1 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -363,38 +363,38 @@ end
eqninx = 6;
nreseqn = 0; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 -1 0 0 0 0 0
- 0 1 0 0 0 0 0 -1 0 0 0 0
- 0 0 1 0 0 0 0 0 -1 0 0 0
- 0 0 0 1 0 0 0 0 0 -1 0 0
- 0 0 0 0 1 0 0 0 0 0 -1 0
- 0 0 0 0 0 1 0 0 0 0 0 -1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 -1 0 0 0 0 0
+ 0 1 0 0 0 0 0 -1 0 0 0 0
+ 0 0 1 0 0 0 0 0 -1 0 0 0
+ 0 0 0 1 0 0 0 0 0 -1 0 0
+ 0 0 0 0 1 0 0 0 0 0 -1 0
+ 0 0 0 0 0 1 0 0 0 0 0 -1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -448,8 +448,8 @@ end
for ki=1:nvar % initializing loop for each equation
- Ui{ki} = null(Qi(:,:,ki));
- Vi{ki} = null(Ri(:,:,ki));
- n0(ki) = size(Ui{ki},2);
- np(ki) = size(Vi{ki},2);
+ Ui{ki} = null(Qi(:,:,ki));
+ Vi{ki} = null(Ri(:,:,ki));
+ n0(ki) = size(Ui{ki},2);
+ np(ki) = size(Vi{ki},2);
end
diff --git a/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol7v.m b/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol7v.m
index de818ab9051c8f35e4e54a92619eced157cefb1f..eb2e80c69513abdb45a4c50f5835c35f9a057b83 100644
--- a/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol7v.m
+++ b/tests/ms-sbvar/archive-files/ftd_2s_caseall_upperchol7v.m
@@ -49,7 +49,7 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free D+ parameters in ith equation in all states.
if (nargin==3)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
end
@@ -59,10 +59,10 @@ k = kvar*nStates; % Maximum number of lagged and exogenous variables in each eq
Qi = zeros(n,n,nvar); % 3rd dim: nvar contemporaneous equations.
Ri = zeros(k,k,nvar); % 1st and 2nd dims: lagged and exogenous equations.
- % Row corresponds to equation with nvar variables for state 1, ..., nvar variables for state nState.
- % 0 means no restriction.
- % 1 and -1 or any other number means the linear combination of the corresponding parameters is restricted to 0.
- % 1 (only 1) means that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation with nvar variables for state 1, ..., nvar variables for state nState.
+ % 0 means no restriction.
+ % 1 and -1 or any other number means the linear combination of the corresponding parameters is restricted to 0.
+ % 1 (only 1) means that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -77,63 +77,63 @@ Ri = zeros(k,k,nvar); % 1st and 2nd dims: lagged and exogenous equations.
eqninx = 1;
nreseqn = 6; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
- 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 1 0 0 0 0 0
- 0 0 0 0 0 0 0 0 0 1 0 0 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 1 0 0 0 0 0 0 0 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 0 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 0 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 0 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 0 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 1 0 0 0 0 0
- 0 0 0 0 0 0 0 0 0 1 0 0 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
+ 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 1 0 0 0 0 0
+ 0 0 0 0 0 0 0 0 0 1 0 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_*.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 1 0 0 0 0 0 0 0 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 0 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 1 0 0 0 0 0
+ 0 0 0 0 0 0 0 0 0 1 0 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_*.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -141,60 +141,60 @@ end
eqninx = 2;
nreseqn = 5; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
- 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 0 1 0 0 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 1 0 0 0 0 0 0 0 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 0 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 0 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 0 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 0 1 0 0 0 0
- 0 0 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
+ 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 0 1 0 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_*.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 1 0 0 0 0 0 0 0 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 0 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 0 1 0 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_*.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -202,57 +202,57 @@ end
eqninx = 3;
nreseqn = 4; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
- 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 0 1 0 0 0 0 0 0 0 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 0 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 0 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 0 0 1 0 0 0
- 0 0 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
+ 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 0 1 0 0 0 0 0 0 0 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -261,71 +261,71 @@ end
eqninx = 4;
nreseqn = 3; % Number of linear restrictions for A0(:,eqninx) for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
- 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 0 0 1 0 0 0 0 0 0 0 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 0 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 0 0 0 1 0 0
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
-
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
+ 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_3s_case3a.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
-
- %==== For freely time-varying A+ for only the first 6 lags.
- %==== Lagged restrictions: zeros on all lags except the first 6 lags in the MS equation.
- % nlagsno0 = 6; % Number of lags to be nonzero.
- % for si=1:nStates
- % for ki = 1:lags-nlagsno0
- % for kj=1:nvar
- % Ri(kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,2) = 1;
- % end
- % end
- % end
- %**** For constant D+_s except the first two lags and the constant term. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- % for si=1:nStates-1
- % for ki=[2*nvar+1:kvar-1]
- % Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- % end
- % end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 0 0 1 0 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 0 0 0 1 0 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_3s_case3a.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
+
+ %==== For freely time-varying A+ for only the first 6 lags.
+ %==== Lagged restrictions: zeros on all lags except the first 6 lags in the MS equation.
+ % nlagsno0 = 6; % Number of lags to be nonzero.
+ % for si=1:nStates
+ % for ki = 1:lags-nlagsno0
+ % for kj=1:nvar
+ % Ri(kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,kvar*(si-1)+nlagsno0*nvar+nvar*(ki-1)+kj,2) = 1;
+ % end
+ % end
+ % end
+ %**** For constant D+_s except the first two lags and the constant term. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ % for si=1:nStates-1
+ % for ki=[2*nvar+1:kvar-1]
+ % Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ % end
+ % end
end
@@ -333,50 +333,50 @@ end
eqninx = 5;
nreseqn = 2; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
- 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 0 0 0 1 0 0 0 0 0 0 0 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 0 0 0 0 1 0
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
+ 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 0 0 0 1 0 0 0 0 0 0 0 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 0 0 0 0 1 0
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -384,47 +384,47 @@ end
eqninx = 6;
nreseqn = 1; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
- 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
-
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:nreseqn*nStates,:,eqninx) = [
- 0 0 0 0 0 0 1 0 0 0 0 0 0 0
-
- 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- ];
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
+ 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
+
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For time-varying A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:nreseqn*nStates,:,eqninx) = [
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 0
+
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+ ];
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -432,39 +432,39 @@ end
eqninx = 7;
nreseqn = 0; % Number of linear restrictions for the equation for each state.
if (indxEqnTv_m(eqninx, 2)<=2)
- %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
- Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
- 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
- 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
- 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
- 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
- 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
- 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
- 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
- ];
- %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
-else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
- %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
- if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ %**** For constant A0_s. In the order of [a0j(1),...,a0j(nStates)] for the 2nd dim of Qi.
+ Qi(1:(nStates-1)*nvar+nreseqn,:,eqninx) = [
+ 1 0 0 0 0 0 0 -1 0 0 0 0 0 0
+ 0 1 0 0 0 0 0 0 -1 0 0 0 0 0
+ 0 0 1 0 0 0 0 0 0 -1 0 0 0 0
+ 0 0 0 1 0 0 0 0 0 0 -1 0 0 0
+ 0 0 0 0 1 0 0 0 0 0 0 -1 0 0
+ 0 0 0 0 0 1 0 0 0 0 0 0 -1 0
+ 0 0 0 0 0 0 1 0 0 0 0 0 0 -1
+ ];
+ %**** For constant D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
- for si=1:nStates-1
- for ki=1:kvar
- Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
- end
- end
- else
- error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
- end
+ end
+ end
+else % Time-varying equations at least for A0_s. For D+_s, constant-parameter equations in general.
+ %**** For D+_s. In the order of [aj+(1),...,aj+(nStates)] for the 2nd dim of Ri.
+ if (indxEqnTv_m(eqninx, 2)==3) % For constant D+** except the constant term. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar-1 % -1: no restrictions on the constant term, which is freely time-varying.
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ elseif (indxEqnTv_m(eqninx, 2)==4) % For constant D+**. In the order of [dj**(1),...,dj**(nStates)] for the 2nd dim of Ri.
+ for si=1:nStates-1
+ for ki=1:kvar
+ Ri(kvar*(si-1)+ki,[kvar*(si-1)+ki si*kvar+ki],eqninx) = [1 -1];
+ end
+ end
+ else
+ error('.../ftd_2s_caseall_simszha5v.m: Have not got time to deal with the simple case indxEqnTv_m(eqninx, 2)=5.')
+ end
end
@@ -518,8 +518,8 @@ end
for ki=1:nvar % initializing loop for each equation
- Ui{ki} = null(Qi(:,:,ki));
- Vi{ki} = null(Ri(:,:,ki));
- n0(ki) = size(Ui{ki},2);
- np(ki) = size(Vi{ki},2);
+ Ui{ki} = null(Qi(:,:,ki));
+ Vi{ki} = null(Ri(:,:,ki));
+ n0(ki) = size(Ui{ki},2);
+ np(ki) = size(Vi{ki},2);
end
diff --git a/tests/ms-sbvar/archive-files/ftd_RSvensson_4v.m b/tests/ms-sbvar/archive-files/ftd_RSvensson_4v.m
index 2b24a786ab780f51242114d45a7cf0c4a02a4fcf..2c9f4344752c289e321c3f1891abf839e7c47ad9 100644
--- a/tests/ms-sbvar/archive-files/ftd_RSvensson_4v.m
+++ b/tests/ms-sbvar/archive-files/ftd_RSvensson_4v.m
@@ -1,4 +1,4 @@
-function [Ui,Vi,n0,np,ixmC0Pres] = ftd_reac_function_4v(lags,nvar,nexo,indxC0Pres)
+function [Ui,Vi,n0,np,ixmC0Pres] = ftd_RSvensson_4v(lags,nvar,nexo,indxC0Pres)
% vlist = [ff+ch fh dpgdp ffr)
%
% Exporting orthonormal matrices for the deterministic linear restrictions (equation by equation)
@@ -50,17 +50,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -75,13 +75,13 @@ Qi(1:3,:,1) = [
0 1 0 0
0 0 1 0
0 0 0 1
- ];
+ ];
%======== The second equation ===========
Qi(1:2,:,2) = [
0 0 1 0
0 0 0 1
- ];
+ ];
%======== The third equation =========== NOTE THAT WE FORBID A
%CONTEMPORANEOUS IMPACT OF OUTPUTON PRICES TO AVOID A CONSTRAINT THAT
@@ -90,7 +90,7 @@ Qi(1:3,:,3) = [
1 0 0 0
0 1 0 0
0 0 0 1
- ];
+ ];
%======== The fourth equation ===========
@@ -98,34 +98,34 @@ Qi(1:3,:,3) = [
% Restrictions on the A+ in order to focus strictly on the reaction fucntion
% indicates free parameterers X i
-% Ap = [
+% Ap = [
% X X X X
-% X X X X
+% X X X X
% -a1 -b1 X X
% a1 b1 0 X (1st lag)
% X X X X
-% X X X X
+% X X X X
% -a2 -b2 X X
% b2 b2 0 X (2nd lag)
% X 0 X X
-% X X X X
+% X X X X
% -a3 -b3 X X
% a3 a3 0 X (3rd lag)
% X X X X
-% X X X X
+% X X X X
% -a4 -b4 X X
% a4 b4 0 X (4th lag)
% X X X X (constant terms)
-% ];
+% ];
k=nvar*lags+nexo;
Ri = zeros(k,k,nvar);
% constraints on IS curve /conso+corporate investment
for nv=1:2
-for ll=1:lags
-Ri(ll,3+lags*(ll-1),nv)=1;
-Ri(ll,4+lags*(ll-1),nv)=1;
-end
+ for ll=1:lags
+ Ri(ll,3+lags*(ll-1),nv)=1;
+ Ri(ll,4+lags*(ll-1),nv)=1;
+ end
end
% constraints on IS curve /conso+corporate investment only on the long run
@@ -140,15 +140,15 @@ end
% constraints on Ph curve / inflation does not react to interest rates
for ll=1:lags
-Ri(ll,4+lags*(ll-1),3)=1;
+ Ri(ll,4+lags*(ll-1),3)=1;
end
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -159,30 +159,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/archive-files/ftd_cholesky.m b/tests/ms-sbvar/archive-files/ftd_cholesky.m
index 42126015abf5d6a6f8e2bee19450f05aeb53f3de..7c89c38eabb1d556a182cda765dd7de31b7fe242 100644
--- a/tests/ms-sbvar/archive-files/ftd_cholesky.m
+++ b/tests/ms-sbvar/archive-files/ftd_cholesky.m
@@ -47,17 +47,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -69,146 +69,146 @@ Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
%The restrictions considered here are in the following form where X means unrestricted:
% A0 = [
% X 0 X X
-% 0 X X X
-% 0 0 X X
-% 0 0 0 X
-% ];
-% Ap = [
+% 0 X X X
+% 0 0 X X
+% 0 0 0 X
+% ];
+% Ap = [
% X 0 X X
-% 0 X X X
+% 0 X X X
% 0 0 X X
% 0 0 X X (1st lag)
% X 0 X X
-% 0 X X X
+% 0 X X X
% 0 0 X X
% 0 0 X X (2nd lag)
% X 0 X X
-% 0 X X X
+% 0 X X X
% 0 0 X X
% 0 0 X X (3rd lag)
% X 0 X X
-% 0 X X X
+% 0 X X X
% 0 0 X X
% 0 0 X X (4th lag)
% 0 X 0 0 (constant terms)
-% ];
+% ];
if (0)
- %------------------------ Lower triangular A0 ------------------------------
- %======== The first equation ===========
-
-
- %======== The second equation ===========
- Qi(1:1,:,2) = [
- 1 0 0 0
- ];
-
- %======== The third equation ===========
- Qi(1:2,:,3) = [
- 1 0 0 0
- 0 1 0 0
- ];
-
- %======== The fourth equation ===========
- Qi(1:3,:,4) = [
- 1 0 0 0
- 0 1 0 0
- 0 0 1 0
- ];
+ %------------------------ Lower triangular A0 ------------------------------
+ %======== The first equation ===========
+
+
+ %======== The second equation ===========
+ Qi(1:1,:,2) = [
+ 1 0 0 0
+ ];
+
+ %======== The third equation ===========
+ Qi(1:2,:,3) = [
+ 1 0 0 0
+ 0 1 0 0
+ ];
+
+ %======== The fourth equation ===========
+ Qi(1:3,:,4) = [
+ 1 0 0 0
+ 0 1 0 0
+ 0 0 1 0
+ ];
else
- %------------------------ Upper triangular A0 ------------------------------
- %======== The first equation ===========
- Qi(2:4,:,1) = [
- 0 1 0 0
- 0 0 1 0
- 0 0 0 1
- ];
-
- %======== The second equation ===========
- Qi([1 3:4],:,2) = [
- 1 0 0 0
- 0 0 1 0
- 0 0 0 1
- ];
-
- %======== The third equation ===========
- Qi(4:4,:,3) = [
- 0 0 0 1
- ];
-
- %======== The fourth equation ===========
+ %------------------------ Upper triangular A0 ------------------------------
+ %======== The first equation ===========
+ Qi(2:4,:,1) = [
+ 0 1 0 0
+ 0 0 1 0
+ 0 0 0 1
+ ];
+
+ %======== The second equation ===========
+ Qi([1 3:4],:,2) = [
+ 1 0 0 0
+ 0 0 1 0
+ 0 0 0 1
+ ];
+
+ %======== The third equation ===========
+ Qi(4:4,:,3) = [
+ 0 0 0 1
+ ];
+
+ %======== The fourth equation ===========
end
%-------------------------- Lag restrictions. ------------------------------------------
if (1)
- %--- Lag restrictions.
- indxeqn = 1; %Which equation.
- nrestrs = (nvar-1)*lags+1; %Number of restrictions.
- vars_restr = [2:nvar]; %Variables that are restricted: id, ik, and y.
- blags = zeros(nrestrs,k); %k=nvar*lags+1
- cnt = 0;
- for ki = 1:lags
- for kj=vars_restr
- cnt = cnt+1;
- blags(cnt,nvar*(ki-1)+kj) = 1;
- end
- end
- %--- Keep constant zero.
- cnt = cnt+1;
- blags(cnt,end) = 1; %Constant = 0.
- if cnt~=nrestrs
- error('Check lagged restrictions in 1st equation!')
- end
- Ri(1:nrestrs,:,indxeqn) = blags;
-
- %--- Lag restrictions.
- indxeqn = 2; %Which equation.
- nrestrs = (nvar-1)*lags; %Number of restrictions.
- vars_restr = [1 3:nvar]; %Variables that are restricted: id, ik, and y.
- blags = zeros(nrestrs,k); %k=nvar*lags+1
- cnt = 0;
- for ki = 1:lags
- for kj=vars_restr
- cnt = cnt+1;
- blags(cnt,nvar*(ki-1)+kj) = 1;
- end
- end
- Ri(1:nrestrs,:,indxeqn) = blags;
-
- %--- Lag restrictions.
- indxeqn = 3; %Which equation.
- nrestrs = 1; %Number of restrictions.
- blags = zeros(nrestrs,k);
- cnt = 0;
- %--- Keep constant zero.
- cnt = cnt+1;
- blags(cnt,end) = 1; %Constant = 0.
- if cnt~=nrestrs
- error('Check lagged restrictions in 1st equation!')
- end
- Ri(1:nrestrs,:,indxeqn) = blags;
-
- %--- Lag restrictions.
- indxeqn = 4; %Which equation.
- nrestrs = 1; %Number of restrictions.
- blags = zeros(nrestrs,k);
- cnt = 0;
- %--- Keep constant zero.
- cnt = cnt+1;
- blags(cnt,end) = 1; %Constant = 0.
- if cnt~=nrestrs
- error('Check lagged restrictions in 1st equation!')
- end
- Ri(1:nrestrs,:,indxeqn) = blags;
+ %--- Lag restrictions.
+ indxeqn = 1; %Which equation.
+ nrestrs = (nvar-1)*lags+1; %Number of restrictions.
+ vars_restr = [2:nvar]; %Variables that are restricted: id, ik, and y.
+ blags = zeros(nrestrs,k); %k=nvar*lags+1
+ cnt = 0;
+ for ki = 1:lags
+ for kj=vars_restr
+ cnt = cnt+1;
+ blags(cnt,nvar*(ki-1)+kj) = 1;
+ end
+ end
+ %--- Keep constant zero.
+ cnt = cnt+1;
+ blags(cnt,end) = 1; %Constant = 0.
+ if cnt~=nrestrs
+ error('Check lagged restrictions in 1st equation!')
+ end
+ Ri(1:nrestrs,:,indxeqn) = blags;
+
+ %--- Lag restrictions.
+ indxeqn = 2; %Which equation.
+ nrestrs = (nvar-1)*lags; %Number of restrictions.
+ vars_restr = [1 3:nvar]; %Variables that are restricted: id, ik, and y.
+ blags = zeros(nrestrs,k); %k=nvar*lags+1
+ cnt = 0;
+ for ki = 1:lags
+ for kj=vars_restr
+ cnt = cnt+1;
+ blags(cnt,nvar*(ki-1)+kj) = 1;
+ end
+ end
+ Ri(1:nrestrs,:,indxeqn) = blags;
+
+ %--- Lag restrictions.
+ indxeqn = 3; %Which equation.
+ nrestrs = 1; %Number of restrictions.
+ blags = zeros(nrestrs,k);
+ cnt = 0;
+ %--- Keep constant zero.
+ cnt = cnt+1;
+ blags(cnt,end) = 1; %Constant = 0.
+ if cnt~=nrestrs
+ error('Check lagged restrictions in 1st equation!')
+ end
+ Ri(1:nrestrs,:,indxeqn) = blags;
+
+ %--- Lag restrictions.
+ indxeqn = 4; %Which equation.
+ nrestrs = 1; %Number of restrictions.
+ blags = zeros(nrestrs,k);
+ cnt = 0;
+ %--- Keep constant zero.
+ cnt = cnt+1;
+ blags(cnt,end) = 1; %Constant = 0.
+ if cnt~=nrestrs
+ error('Check lagged restrictions in 1st equation!')
+ end
+ Ri(1:nrestrs,:,indxeqn) = blags;
end
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -222,30 +222,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/archive-files/ftd_non_rec_5v.m b/tests/ms-sbvar/archive-files/ftd_non_rec_5v.m
index e9fbeb4099a5f9e7a43a39e8de43d90b7b4aada6..ba328cb3de65b42b1ea58a50f3fd9622709cf9d8 100644
--- a/tests/ms-sbvar/archive-files/ftd_non_rec_5v.m
+++ b/tests/ms-sbvar/archive-files/ftd_non_rec_5v.m
@@ -1,4 +1,4 @@
-function [Ui,Vi,n0,np,ixmC0Pres] = ftd_upperchol5v(lags,nvar,nexo,indxC0Pres)
+function [Ui,Vi,n0,np,ixmC0Pres] = ftd_non_rec_5v(lags,nvar,nexo,indxC0Pres)
% vlist = [127 124 93 141 21]; % 1: GDP; 2: GDP deflator 124 (consumption deflator 79); 3: R; 4: M3 141 (M2 140); 5: exchange rate 21.
% varlist={'y', 'P', 'R', 'M3', 'Ex'};
%
@@ -45,17 +45,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -71,20 +71,20 @@ Qi(1:4,:,1) = [
0 0 1 0 0
0 0 0 1 0
0 0 0 0 1
- ];
+ ];
%======== The second equation ===========
Qi(1:3,:,2) = [
0 0 1 0 0
0 0 0 1 0
0 0 0 0 1
- ];
+ ];
%======== The third equation ===========
Qi(1:2,:,3) = [
0 0 0 1 0
0 0 0 0 1
- ];
+ ];
%======== The fourth equation ===========
@@ -99,7 +99,7 @@ Qi(1:3,:,5) = [
1 0 0 0 0
0 1 0 0 0
0 0 1 0 0
- ];
+ ];
@@ -149,10 +149,10 @@ Qi(1:3,:,5) = [
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -163,30 +163,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/archive-files/ftd_simszha5v.m b/tests/ms-sbvar/archive-files/ftd_simszha5v.m
index 0a0034a9d0298e983674cc113fae1b28a8311c70..a48168fc6a16d886e6c5c7ba0c8c9b365b562ac7 100644
--- a/tests/ms-sbvar/archive-files/ftd_simszha5v.m
+++ b/tests/ms-sbvar/archive-files/ftd_simszha5v.m
@@ -45,17 +45,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -75,30 +75,30 @@ Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
% 0 0 0 0 1
% ]; % Respond to Pcom.
Qi(1:3,:,2) = [
- 1 0 0 0 0
- 0 0 0 1 0
- 0 0 0 0 1
- ]; % Not respond to Pcom.
+ 1 0 0 0 0
+ 0 0 0 1 0
+ 0 0 0 0 1
+ ]; % Not respond to Pcom.
%======== The third equation: money demand ===========
Qi(1,:,3) = [
- 1 0 0 0 0
- ];
+ 1 0 0 0 0
+ ];
%======== The fourth equation: y equation ===========
Qi(1:4,:,4) = [
- 1 0 0 0 0
- 0 1 0 0 0
- 0 0 1 0 0
- 0 0 0 0 1
- ];
+ 1 0 0 0 0
+ 0 1 0 0 0
+ 0 0 1 0 0
+ 0 0 0 0 1
+ ];
%======== The fifth equation: p equation ===========
Qi(1:3,:,5) = [
- 1 0 0 0 0
- 0 1 0 0 0
- 0 0 1 0 0
- ];
+ 1 0 0 0 0
+ 0 1 0 0 0
+ 0 0 1 0 0
+ ];
%===== Lagged restrictions in foreign (Granger causing) block
@@ -147,10 +147,10 @@ Qi(1:3,:,5) = [
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -161,30 +161,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/archive-files/ftd_upperchol3v.m b/tests/ms-sbvar/archive-files/ftd_upperchol3v.m
index a5c19f79e8ef575e6e0ff1480593e73b84ebf19e..4c221df74fc460b5764781cf22d22bbcd5a65b62 100644
--- a/tests/ms-sbvar/archive-files/ftd_upperchol3v.m
+++ b/tests/ms-sbvar/archive-files/ftd_upperchol3v.m
@@ -44,17 +44,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -69,12 +69,12 @@ Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
Qi(1:2,:,1) = [
0 1 0
0 0 1
- ];
+ ];
%======== The second equation ===========
Qi(1:1,:,2) = [
0 0 1
- ];
+ ];
%======== The third equation ===========
@@ -127,10 +127,10 @@ Qi(1:1,:,2) = [
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -141,30 +141,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/archive-files/ftd_upperchol4v.m b/tests/ms-sbvar/archive-files/ftd_upperchol4v.m
index aadac9512f31724b77225a07edc5ede987795aa4..db0b9c371af64021f0ffe676b90d01c8e10d3890 100644
--- a/tests/ms-sbvar/archive-files/ftd_upperchol4v.m
+++ b/tests/ms-sbvar/archive-files/ftd_upperchol4v.m
@@ -45,17 +45,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -70,18 +70,18 @@ Qi(1:3,:,1) = [
0 1 0 0
0 0 1 0
0 0 0 1
- ];
+ ];
%======== The second equation ===========
Qi(1:2,:,2) = [
0 0 1 0
0 0 0 1
- ];
+ ];
%======== The third equation ===========
Qi(1:1,:,3) = [
0 0 0 1
- ];
+ ];
%======== The fourth equation ===========
@@ -135,10 +135,10 @@ Qi(1:1,:,3) = [
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -149,30 +149,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/archive-files/ftd_upperchol5v.m b/tests/ms-sbvar/archive-files/ftd_upperchol5v.m
index b41a60c1747dde3075a4d4ae7cac3cc1e3a8cdab..948d0c0d19e17b069aa0d9165876f8219c79e503 100644
--- a/tests/ms-sbvar/archive-files/ftd_upperchol5v.m
+++ b/tests/ms-sbvar/archive-files/ftd_upperchol5v.m
@@ -45,17 +45,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -71,26 +71,26 @@ Qi(1:4,:,1) = [
0 0 1 0 0
0 0 0 1 0
0 0 0 0 1
- ];
+ ];
%======== The second equation ===========
Qi(1:3,:,2) = [
0 0 1 0 0
0 0 0 1 0
0 0 0 0 1
- ];
+ ];
%======== The third equation ===========
Qi(1:2,:,3) = [
0 0 0 1 0
0 0 0 0 1
- ];
+ ];
%======== The fourth equation ===========
Qi(1:1,:,4) = [
0 0 0 0 1
- ];
+ ];
%======== The fifth equation ===========
@@ -144,10 +144,10 @@ Qi(1:1,:,4) = [
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -158,30 +158,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/archive-files/ftd_upperchol6v.m b/tests/ms-sbvar/archive-files/ftd_upperchol6v.m
index c6560ffd986fccb13b4e8e499f434512d612b16e..462704c240d05115a8f9c6ee00e41954a2d8fb19 100644
--- a/tests/ms-sbvar/archive-files/ftd_upperchol6v.m
+++ b/tests/ms-sbvar/archive-files/ftd_upperchol6v.m
@@ -45,17 +45,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -72,7 +72,7 @@ Qi(1:5,:,1) = [
0 0 0 1 0 0
0 0 0 0 1 0
0 0 0 0 0 1
- ];
+ ];
%======== The second equation ===========
Qi(1:4,:,2) = [
@@ -80,27 +80,27 @@ Qi(1:4,:,2) = [
0 0 0 1 0 0
0 0 0 0 1 0
0 0 0 0 0 1
- ];
+ ];
%======== The third equation ===========
Qi(1:3,:,3) = [
0 0 0 1 0 0
0 0 0 0 1 0
0 0 0 0 0 1
- ];
+ ];
%======== The fourth equation ===========
Qi(1:2,:,4) = [
0 0 0 0 1 0
0 0 0 0 0 1
- ];
+ ];
%======== The fifth equation ===========
Qi(1:1,:,5) = [
0 0 0 0 0 1
- ];
+ ];
%======== The sixth equation ===========
@@ -151,10 +151,10 @@ Qi(1:1,:,5) = [
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -165,30 +165,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/archive-files/ftd_upperchol7v.m b/tests/ms-sbvar/archive-files/ftd_upperchol7v.m
index d0dc7969c4bc3d435bc427a6c62300a24584a9c9..dc7db34accef8103be6ab8153aac3012ccee0604 100644
--- a/tests/ms-sbvar/archive-files/ftd_upperchol7v.m
+++ b/tests/ms-sbvar/archive-files/ftd_upperchol7v.m
@@ -45,17 +45,17 @@ n0 = zeros(nvar,1); % ith element represents the number of free A0 parameters in
np = zeros(nvar,1); % ith element represents the number of free A+ parameters in ith equation
if (nargin==2)
- nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
+ nexo = 1; % 1: constant as default where nexo must be a nonnegative integer
elseif (nargin==3)
- indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
+ indxC0Pres = 0; % default is no cross-A0-and-A+ restrictions.
end
k = lags*nvar+nexo; % maximum number of lagged and exogenous variables in each equation
Qi = zeros(nvar,nvar,nvar); % for nvar contemporaneous equations
Ri = zeros(k,k,nvar); % for nvar lagged and exogenous equations
- % Row corresponds to equation. 0 means no restriction.
- % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
+ % Row corresponds to equation. 0 means no restriction.
+ % 1 means exclusion restriction such that the corresponding parameter is restricted to 0.
%nfvar = 6; % number of foreign (Granger causing) variables
%nhvar = nvar-nfvar; % number of home (affected) variables.
@@ -73,7 +73,7 @@ Qi(1:6,:,1) = [
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 1
- ];
+ ];
%======== The second equation ===========
Qi(1:5,:,2) = [
@@ -82,7 +82,7 @@ Qi(1:5,:,2) = [
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 1
- ];
+ ];
%======== The third equation ===========
Qi(1:4,:,3) = [
@@ -90,27 +90,27 @@ Qi(1:4,:,3) = [
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 1
- ];
+ ];
%======== The fourth equation ===========
Qi(1:3,:,4) = [
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 1
- ];
+ ];
%======== The fifth equation ===========
Qi(1:2,:,5) = [
0 0 0 0 0 1 0
0 0 0 0 0 0 1
- ];
+ ];
%======== The sixth equation ===========
Qi(1:1,:,6) = [
0 0 0 0 0 0 1
- ];
+ ];
%======== The seventh equation ===========
@@ -161,10 +161,10 @@ Qi(1:1,:,6) = [
for n=1:nvar % initializing loop for each equation
- Ui{n} = null(Qi(:,:,n));
- Vi{n} = null(Ri(:,:,n));
- n0(n) = size(Ui{n},2);
- np(n) = size(Vi{n},2);
+ Ui{n} = null(Qi(:,:,n));
+ Vi{n} = null(Ri(:,:,n));
+ n0(n) = size(Ui{n},2);
+ np(n) = size(Vi{n},2);
end
@@ -175,30 +175,30 @@ end
%(2)-------------------------------------------------------------
%
if indxC0Pres
- neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
- ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
- % 1st: the jth column (equation) of A+ or A0: f_j or a_j
- % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
- % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
- % 4th: the number s such that f_j(i) = s * a_j(h) holds.
- %** 1st equation
- ixmC0Pres{1} = [1 2 2 1
- 1 7 1 1];
- %** 2nd equation
- ixmC0Pres{2} = [2 2 2 2];
- %** 3rd equation
- ixmC0Pres{3} = [3 7 1 1
- 3 2 2 1];
-
-
-% % 4 columns.
-% ncres = 5; % manually key in the number of cross-A0-A+ restrictions
-
-% % 1st: the jth column (equation) of A+ or A0: f_j or a_j
-% % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
-% % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
-% % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ neq_cres = 3; % the number of equations in which cross-A0-A+ restrictions occur.
+ ixmC0Pres = cell(neq_cres,1); % in each cell representing equation, we have 4 columns:
+ % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % 4th: the number s such that f_j(i) = s * a_j(h) holds.
+ %** 1st equation
+ ixmC0Pres{1} = [1 2 2 1
+ 1 7 1 1];
+ %** 2nd equation
+ ixmC0Pres{2} = [2 2 2 2];
+ %** 3rd equation
+ ixmC0Pres{3} = [3 7 1 1
+ 3 2 2 1];
+
+
+ % % 4 columns.
+ % ncres = 5; % manually key in the number of cross-A0-A+ restrictions
+
+ % % 1st: the jth column (equation) of A+ or A0: f_j or a_j
+ % % 2nd: the ith element f_j(i) -- the ith element in the jth column of A+
+ % % 3rd: the hth element a_j(h) -- the hth element in the jth column of A0
+ % % 4th: the number s such that f_j(i) = s * a_j(h) holds.
else
- ixmC0Pres = NaN;
+ ixmC0Pres = NaN;
end
diff --git a/tests/ms-sbvar/data.m b/tests/ms-sbvar/data.m
index 6f738015c7dfd1d207906084dca5781f0c7508ee..d8a7c941254756b2753b5457b493b0579ee65aab 100644
--- a/tests/ms-sbvar/data.m
+++ b/tests/ms-sbvar/data.m
@@ -1,193 +1,193 @@
sbvar_data = [
--9.3174834887745916e-003, 1.7994658843431877e-002, 2.5699999999999997e-002;
-7.7668705855149511e-003, 6.0096276044880881e-003, 3.0800000000000001e-002;
--1.9541593158383108e-003, 1.1443694953360728e-002, 3.5799999999999998e-002;
--7.3230760374594084e-003, 1.6080663886388402e-002, 3.9900000000000005e-002;
-5.7366104256297845e-003, 9.6254961625830138e-003, 3.9300000000000002e-002;
--8.3093609995312789e-003, 1.7721697565065142e-002, 3.7000000000000005e-002;
--1.5818734568909143e-002, 1.8802248364432783e-002, 2.9399999999999999e-002;
--3.8114188274117389e-002, 1.7753163941062411e-002, 2.3000000000000000e-002;
--4.1399862204639426e-002, 4.5389998028741996e-003, 2.0000000000000000e-002;
--3.2217707697825837e-002, 7.3753322217300354e-003, 1.7299999999999999e-002;
--2.5646357007195419e-002, 1.0583418386522991e-002, 1.6799999999999999e-002;
--1.4897222570872337e-002, 1.0366269881014523e-002, 2.4000000000000000e-002;
--6.6220480083236666e-003, 2.3042923285839567e-002, 2.4600000000000000e-002;
--5.3027079623060303e-003, 1.0468178907987236e-002, 2.6099999999999998e-002;
--5.7275387773225717e-003, 1.0815248301383029e-002, 2.8500000000000001e-002;
--1.2909019643277730e-002, 1.3963993831495269e-002, 2.9200000000000000e-002;
--9.6082193296807006e-003, 1.1306915202373702e-002, 2.9700000000000001e-002;
--6.9847294194245180e-003, 4.0554812275257479e-003, 2.9600000000000001e-002;
-1.8176103434601742e-003, 7.3752799189321649e-003, 3.3300000000000003e-002;
--4.5038023245602687e-004, 2.3887283546807359e-002, 3.4500000000000003e-002;
-1.1624668564948593e-002, 1.4307761419874110e-002, 3.4599999999999999e-002;
-1.2948656776092804e-002, 1.3154713006571006e-002, 3.4900000000000000e-002;
-1.6160285046599832e-002, 1.9531653948000383e-002, 3.4599999999999999e-002;
-8.4081398395898788e-003, 1.8522230201726275e-002, 3.5799999999999998e-002;
-2.2153370885423129e-002, 1.7709079726716315e-002, 3.9699999999999999e-002;
-2.4844201757035833e-002, 1.7812125625833675e-002, 4.0800000000000003e-002;
-3.4050690186470334e-002, 1.7733161216544779e-002, 4.0700000000000000e-002;
-4.6893307071320223e-002, 2.4854086852623247e-002, 4.1700000000000001e-002;
-5.9972460768834779e-002, 2.4879959563927745e-002, 4.5599999999999995e-002;
-5.2289186415585220e-002, 3.7979469553559353e-002, 4.9100000000000005e-002;
-4.7741188658148914e-002, 3.9049003040727781e-002, 5.4100000000000002e-002;
-4.4667561574096126e-002, 3.5671179948047138e-002, 5.5599999999999997e-002;
-4.2427836565945398e-002, 1.9374879269963063e-002, 4.8200000000000000e-002;
-3.1462874033119093e-002, 2.5309792721300628e-002, 3.9900000000000005e-002;
-2.8437659950142802e-002, 3.7210113920888466e-002, 3.8900000000000004e-002;
-2.5156025048538311e-002, 4.4947363315081201e-002, 4.1700000000000001e-002;
-3.4855619579102992e-002, 4.3766256282161686e-002, 4.7899999999999998e-002;
-4.1146105898716812e-002, 4.5485089147871749e-002, 5.9800000000000006e-002;
-3.7608522339491302e-002, 3.9312213398265738e-002, 5.9400000000000001e-002;
-3.1755688168807694e-002, 5.7147340097736921e-002, 5.9200000000000003e-002;
-3.7547536338742304e-002, 4.0820102882030529e-002, 6.5700000000000008e-002;
-3.0780798807969134e-002, 5.4795099957268389e-002, 8.3299999999999999e-002;
-2.7622883356809069e-002, 5.9674785474016057e-002, 8.9800000000000005e-002;
-1.3687491471252144e-002, 5.1526594947709725e-002, 8.9399999999999993e-002;
-3.0365204590552253e-003, 5.7110106004252703e-002, 8.5699999999999998e-002;
--3.8946120840908094e-003, 5.8310720503999880e-002, 7.8799999999999995e-002;
--3.7031729362304588e-003, 3.2162694194911579e-002, 6.7000000000000004e-002;
--2.2953853215847531e-002, 5.2193859691229916e-002, 5.5700000000000000e-002;
--3.9774834192911612e-003, 6.1343390594280400e-002, 3.8599999999999995e-002;
--6.6430088990969693e-003, 5.4548116487401987e-002, 4.5599999999999995e-002;
--6.9966828696923500e-003, 4.0591135320590110e-002, 5.4699999999999999e-002;
--1.2347397716578001e-002, 3.2276797966984239e-002, 4.7500000000000001e-002;
--2.9473495209533240e-003, 6.7805039825567626e-002, 3.5400000000000001e-002;
-1.2120764500071601e-002, 2.3686434724627725e-002, 4.2999999999999997e-002;
-1.3231348379735053e-002, 3.7187744116042420e-002, 4.7400000000000005e-002;
-2.0987028138604202e-002, 4.7889363970077925e-002, 5.1399999999999994e-002;
-3.7485754706574781e-002, 5.3965548807981989e-002, 6.5400000000000000e-002;
-4.0318879693293397e-002, 6.8340638829176292e-002, 7.8200000000000006e-002;
-2.6218511286559831e-002, 7.8958874043481897e-002, 1.0560000000000000e-001;
-2.6929695576288992e-002, 7.0997794665009550e-002, 1.0000000000000001e-001;
-9.4554586277908470e-003, 8.4242699131246379e-002, 9.3200000000000005e-002;
-3.6174737897027853e-003, 9.1565984601668537e-002, 1.1250000000000000e-001;
--1.4685635040370570e-002, 1.2944791465588246e-001, 1.2089999999999999e-001;
--2.7095820218557165e-002, 1.2813135610460602e-001, 9.3500000000000000e-002;
--4.7490291499844517e-002, 9.5634229266530868e-002, 6.3000000000000000e-002;
--4.8493379593802288e-002, 6.0105697293320492e-002, 5.4199999999999998e-002;
--3.9943449805699416e-002, 7.6752303729665350e-002, 6.1600000000000002e-002;
--3.5077206071779443e-002, 7.2995258807648344e-002, 5.4100000000000002e-002;
--2.0906071356066036e-002, 4.5679585226099162e-002, 4.8300000000000003e-002;
--2.1531096410072337e-002, 4.3592360792875207e-002, 5.2000000000000005e-002;
--2.4735476775209264e-002, 5.5187881222506396e-002, 5.2800000000000000e-002;
--2.5561529099840996e-002, 7.0182306554444240e-002, 4.8700000000000000e-002;
--2.1575901985043444e-002, 6.8358747781264828e-002, 4.6600000000000003e-002;
--1.0282812897440152e-002, 6.5803889922906311e-002, 5.1600000000000000e-002;
--9.1324207260257140e-004, 5.6172786341162295e-002, 5.8200000000000002e-002;
--9.5486836624303351e-003, 6.9205174325260410e-002, 6.5099999999999991e-002;
--1.4957543819619445e-002, 6.8508819756844419e-002, 6.7599999999999993e-002;
-1.5069561708809687e-002, 7.9300571687745292e-002, 7.2800000000000004e-002;
-1.6283475252537372e-002, 7.0872150059167804e-002, 8.1000000000000003e-002;
-2.0908466837013862e-002, 8.4120663761548808e-002, 9.5799999999999996e-002;
-1.4559374240283418e-002, 7.4654989747748868e-002, 1.0070000000000000e-001;
-7.4026792768986382e-003, 1.0065048845414548e-001, 1.0180000000000000e-001;
-6.7867658044900026e-003, 8.4869122045493794e-002, 1.0949999999999999e-001;
-2.0964569874966088e-003, 8.1073829867721159e-002, 1.3580000000000000e-001;
--2.1618734445638665e-003, 9.0701460926355892e-002, 1.5049999999999999e-001;
--2.9866760868227260e-002, 9.1306883112545645e-002, 1.2689999999999999e-001;
--3.8807200394211705e-002, 9.3833166941218682e-002, 9.8400000000000001e-002;
--2.7491967650325577e-002, 1.1718934484063248e-001, 1.5850000000000000e-001;
--1.4366396848604523e-002, 1.0830156525255896e-001, 1.6570000000000001e-001;
--2.8990249638850329e-002, 7.2488303659308695e-002, 1.7780000000000001e-001;
--2.3603799101664436e-002, 7.5735091281379452e-002, 1.7579999999999998e-001;
--4.2733757910307091e-002, 7.1783638615472212e-002, 1.3589999999999999e-001;
--6.5834256612443909e-002, 5.7815346934783074e-002, 1.4230000000000001e-001;
--6.7076173517195414e-002, 5.0774215309779880e-002, 1.4510000000000001e-001;
--7.7493754839396800e-002, 5.6543508350202609e-002, 1.1010000000000000e-001;
--8.3437100867300273e-002, 4.3285023548542245e-002, 9.2899999999999996e-002;
--7.8140443582185526e-002, 3.4701884333945499e-002, 8.6500000000000007e-002;
--6.2904972370690260e-002, 2.9380728193572736e-002, 8.8000000000000009e-002;
--5.0575674226140066e-002, 4.1378527908603857e-002, 9.4600000000000004e-002;
--3.7530293571547801e-002, 2.9492818368749285e-002, 9.4299999999999995e-002;
--2.5480519753907416e-002, 5.0489471212566306e-002, 9.6900000000000000e-002;
--1.5811147128429681e-002, 3.6455602629870576e-002, 1.0560000000000000e-001;
--1.3623195024511148e-002, 3.3023322354348572e-002, 1.1390000000000000e-001;
--1.3078242370475834e-002, 2.3921358528453451e-002, 9.2699999999999991e-002;
--1.1665978412656486e-002, 4.6889910860992590e-002, 8.4800000000000000e-002;
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+ 5.6961778759188064e-003, 4.7444796184034521e-002, 8.2400000000000001e-002;
+ -1.1072568495222868e-003, 3.6239655982325480e-002, 8.1600000000000006e-002;
+ -1.5465707409310525e-002, 3.1366693341789098e-002, 7.7399999999999997e-002;
+ -2.7250024246535887e-002, 4.7905236749817171e-002, 6.4299999999999996e-002;
+ -2.7337568911169896e-002, 2.5679327033720556e-002, 5.8600000000000006e-002;
+ -2.8996765457870666e-002, 2.7744462882228538e-002, 5.6399999999999999e-002;
+ -3.0694988523064737e-002, 2.0348807487869491e-002, 4.8200000000000000e-002;
+ -2.6687542665930764e-002, 2.6838736648956640e-002, 4.0199999999999993e-002;
+ -2.3361909698373040e-002, 2.0963598977361553e-002, 3.7699999999999997e-002;
+ -1.9843866905633334e-002, 1.7512821090635011e-002, 3.2599999999999997e-002;
+ -1.5118603774070039e-002, 2.1185582236595835e-002, 3.0400000000000000e-002;
+ -2.0197613265910519e-002, 3.1946708550473213e-002, 3.0400000000000000e-002;
+ -2.1520678025641615e-002, 2.1834134877041667e-002, 2.9999999999999999e-002;
+ -2.2826650301176699e-002, 1.8033708065957166e-002, 3.0600000000000002e-002;
+ -1.5976648136103222e-002, 1.9600906791856332e-002, 2.9900000000000003e-002;
+ -1.2481565520511495e-002, 2.5764744780397253e-002, 3.2099999999999997e-002;
+ -6.2498609089072232e-003, 1.7163326403677015e-002, 3.9399999999999998e-002;
+ -7.5419440421207184e-003, 2.4448612633015232e-002, 4.4900000000000002e-002;
+ -2.9008641302628035e-003, 1.9270549031769058e-002, 5.1699999999999996e-002;
+ -7.2102329848391378e-003, 2.6468635791329520e-002, 5.8099999999999999e-002;
+ -1.2589423111688092e-002, 1.4805044409490042e-002, 6.0199999999999997e-002;
+ -1.1715387895728568e-002, 1.7085018789666284e-002, 5.7999999999999996e-002;
+ -1.1777024741238762e-002, 1.9780736678506994e-002, 5.7200000000000001e-002;
+ -1.2215155411272605e-002, 2.4726200999461767e-002, 5.3600000000000002e-002;
+ -3.5465932809213285e-003, 1.3486465713533846e-002, 5.2400000000000002e-002;
+ -2.9219038194341351e-003, 1.9323425037825803e-002, 5.3099999999999994e-002;
+ 8.2672938771111149e-004, 1.6730936106534644e-002, 5.2800000000000000e-002;
+ 5.2731183513543556e-004, 2.1687064498104203e-002, 5.2800000000000000e-002;
+ 7.4708897268216390e-003, 1.3887651948481405e-002, 5.5199999999999999e-002;
+ 1.1578449231922860e-002, 1.0535540622131023e-002, 5.5300000000000002e-002;
+ 1.0543713785281739e-002, 1.3591721544186308e-002, 5.5099999999999996e-002;
+ 1.3043222430857426e-002, 9.7155784328055717e-003, 5.5199999999999999e-002;
+ 1.1021797245557963e-002, 7.8485022563632434e-003, 5.5000000000000000e-002;
+ 1.3766304579396760e-002, 1.4031942678612408e-002, 5.5300000000000002e-002;
+ 2.0010289782806723e-002, 1.1684049976040223e-002, 4.8600000000000004e-002;
+ 1.9500810360241871e-002, 1.5486288460806463e-002, 4.7300000000000002e-002;
+ 1.8677267947765586e-002, 1.7674602281525287e-002, 4.7500000000000001e-002;
+ 2.1068187519647452e-002, 1.3207048148448308e-002, 5.0900000000000001e-002;
+ 2.9432867931319606e-002, 1.8614186008366396e-002, 5.3099999999999994e-002;
+ 2.2709401609937174e-002, 3.3601370511199269e-002, 5.6799999999999996e-002;
+ 2.9063996825298588e-002, 1.9804593863093523e-002, 6.2699999999999992e-002;
+ 1.8810297095397388e-002, 1.8609127901011213e-002, 6.5199999999999994e-002;
+ 1.4978576794066001e-002, 1.7916238079900726e-002, 6.4699999999999994e-002;
+ 4.8316137761403866e-003, 3.2976319868455617e-002, 5.5899999999999998e-002;
+ -9.1822274865016595e-004, 3.1213866380320532e-002, 4.3299999999999998e-002;
+ -1.3163778876048582e-002, 1.5733791887268644e-002, 3.5000000000000003e-002;
+ -1.7841900605217731e-002, 1.6933827369602694e-002, 2.1299999999999999e-002;
+ -1.9532762689722816e-002, 1.6823164543461777e-002, 1.7299999999999999e-002;
+ -2.2376267503108949e-002, 1.5189134545742444e-002, 1.7500000000000002e-002;
+ -2.4570058045892296e-002, 1.5598774847326746e-002, 1.7399999999999999e-002;
+ -3.1885812767447064e-002, 2.2380594713903079e-002, 1.4400000000000000e-002;
+ -3.5301487936340692e-002, 3.0770251840726015e-002, 1.2500000000000001e-002;
+ -3.3809664438850362e-002, 1.1207937615285157e-002, 1.2500000000000001e-002;
+ -2.3738888747095288e-002, 1.8271566479553414e-002, 1.0200000000000001e-002;
+ -2.2389486776477341e-002, 1.8759653895370487e-002, 1.0000000000000000e-002;
+ -1.9372963882339889e-002, 3.6183114349394030e-002, 1.0000000000000000e-002;
+ -1.8172640165300180e-002, 3.8524562683139418e-002, 1.0100000000000000e-002;
+ -1.5851276113677315e-002, 1.4577624436418635e-002, 1.4300000000000000e-002;
+ -1.5145664166732686e-002, 2.7339757365790307e-002, 1.9500000000000000e-002;
+ -1.3284941407389894e-002, 3.0828456732055809e-002, 2.4700000000000003e-002;
+ -1.2679438144379773e-002, 2.5660138484441486e-002, 2.9399999999999999e-002;
+ -1.0133886633141742e-002, 3.3074553498490200e-002, 3.4599999999999999e-002;
+ -1.5055016783550812e-002, 3.0184663811322121e-002, 3.9800000000000002e-002;
+ ];
Y = sbvar_data(:, 1);
Pie = sbvar_data(:, 2);
diff --git a/tests/parallel/data_ca1.m b/tests/parallel/data_ca1.m
index c28fae1a2800e83eda0e6343196e8aeafad2935f..ca003056bded971e9bf00232802e453a096e9cdf 100644
--- a/tests/parallel/data_ca1.m
+++ b/tests/parallel/data_ca1.m
@@ -1,98 +1,98 @@
data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
--0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
--0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
--0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
--0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
--0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
--0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
-1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
-2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
-1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
-1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
-1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
-1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
-0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
-1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
-1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
-0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
-1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
-1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
--0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
-0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
-0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
--0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
-2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
-1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
-1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
-1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
-1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
-1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
-0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
-0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
-1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
-0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
-0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
-0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
-0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
--0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
--0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
--0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
--1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
-0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
-0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
-0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
--0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
-0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
-0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
-0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
-0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
-0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
-0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
-0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
-1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
-1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
-1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
-0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
-0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
--0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
-0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
-0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
-0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
-0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
-1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
-0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
-0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
-1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
-1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
-0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
-1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
-0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
-1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
-1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
-1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
-1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
-1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
-1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
-1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
-0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
-1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
-0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
-0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
-0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
--0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
-0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
-1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
-1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
-0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
-];
-
+ -0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
+ -0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
+ -0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
+ -0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
+ -0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
+ -0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
+ 1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
+ 2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
+ 1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
+ 1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
+ 1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
+ 1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
+ 0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
+ 1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
+ 1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
+ 0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
+ 1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
+ 1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
+ -0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
+ 0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
+ 0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
+ -0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
+ 2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
+ 1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
+ 1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
+ 1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
+ 1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
+ 1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
+ 0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
+ 0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
+ 1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
+ 0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
+ 0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
+ 0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
+ 0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
+ -0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
+ -0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
+ -0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
+ -1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
+ 0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
+ 0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
+ 0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
+ -0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
+ 0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
+ 0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
+ 0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
+ 0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
+ 0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
+ 0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
+ 0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
+ 1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
+ 1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
+ 1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
+ 0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
+ 0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
+ -0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
+ 0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
+ 0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
+ 0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
+ 0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
+ 1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
+ 0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
+ 0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
+ 1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
+ 1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
+ 0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
+ 1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
+ 0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
+ 1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
+ 1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
+ 1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
+ 1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
+ 1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
+ 1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
+ 1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
+ 0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
+ 1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
+ 0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
+ 0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
+ 0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
+ -0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
+ 0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
+ 1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
+ 1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
+ 0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
+ ];
+
data = reshape(data,5,86)';
y_obs = data(:,1);
pie_obs = data(:,2);
R_obs = data(:,3);
de = data(:,4);
dq = data(:,5);
-
+
%Country: Canada
%Sample Range: 1981:2 to 2002:3
%Observations: 86
diff --git a/tests/particle/benchmark.m b/tests/particle/benchmark.m
index 6531fa3bca537185b504c8e278b3e01b878e3964..ddf74133cd257a001e17f522ebede00afaf105a2 100644
--- a/tests/particle/benchmark.m
+++ b/tests/particle/benchmark.m
@@ -1,153 +1,153 @@
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set_dynare_seed('default');
diff --git a/tests/particle/dsge_base2_steadystate.m b/tests/particle/dsge_base2_steadystate.m
index e714180ecf95a8cf8450118e0d9fd71db3099ee9..ae947de5b6a2e45ac80e99c7b0d0cbc6993fcf36 100644
--- a/tests/particle/dsge_base2_steadystate.m
+++ b/tests/particle/dsge_base2_steadystate.m
@@ -1,4 +1,4 @@
-function [ys,check,penlt] = mze_steadystate(ys,exe)
+function [ys,check,penlt] = dsge_base2_steadystate(ys,exe)
global M_
persistent idx NumberOfParameters NumberOfEndogenousVariables
diff --git a/tests/particle/extreme.m b/tests/particle/extreme.m
index c6a7dba26e612eadb4cd10bbce51b978cf5d3a2c..cf22bc161c3731ff22542627449cbb175b03a427 100644
--- a/tests/particle/extreme.m
+++ b/tests/particle/extreme.m
@@ -1,153 +1,153 @@
series = [ 1.831805242058402 0.326183687045750 0.571394980772413
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set_dynare_seed('default');
diff --git a/tests/particle/risky.m b/tests/particle/risky.m
index 4d0b7a829971b6f2f937d3c4a47f9b3d31a6992a..33c64573ad46b7ea79cbea3d486e56a384400196 100644
--- a/tests/particle/risky.m
+++ b/tests/particle/risky.m
@@ -1,153 +1,153 @@
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+ 2.545923232397798 0.353562161906674 1.000133847612044
+ 2.516609766492527 0.351998960811952 0.979615171231177
+ 2.612598123816773 0.356735709969249 1.047580818676070
+ 2.514581877733644 0.351736314350723 0.978472497403178
+ 2.346814819442599 0.343151537387364 0.859593502999736
+ 2.190061053436186 0.334783668048696 0.749123665788591
+ 2.248248190346710 0.337975537211722 0.790133451151325
+ 2.231245577955952 0.337099249591117 0.778120306643938
+ 2.213620813638347 0.336172963701584 0.765787919679999
+ 2.372368622485876 0.344669139219734 0.877475808694303
+ 2.402394532412963 0.346236714114806 0.898578702308663
+ 2.421632200543405 0.347211723112796 0.912182166100545
+ 2.600814823475984 0.356206951763316 1.039020125139189
+ 2.499717430182296 0.351010191098421 0.967897548419094
+ 2.572211042595633 0.354659119526649 1.018967108776286
+ 2.663557373274198 0.359030754462987 1.084127294941044
+ 2.624508384540186 0.356972637699486 1.056848324055082
+ 2.638578391882532 0.357631581496546 1.066764536958948
+ 2.490619544707827 0.350219619250241 0.961836430307891
+ 2.351499685312523 0.343126531712676 0.862944429125821
+ 2.361744409273265 0.343693557597678 0.870045243822348
+ 2.442494968080141 0.347873202461086 0.927160968018594
+ 2.745123805313629 0.362770643020118 1.142349343426111
+ 2.655499503705102 0.358132929197389 1.079779897215503
+ 2.663850696079126 0.358574973979539 1.085379958290025
+ 2.642540272757401 0.357462408799286 1.070401889504760
+ 2.404037994048185 0.345491588561572 0.900884395443159
+ 2.348769349405146 0.342712258600463 0.861219163642359
+ 2.151645849089095 0.332135094769575 0.722080451054128
+ 2.252343203542463 0.337640454116440 0.793256172150181
+ 2.158313350236808 0.332590653949928 0.726803768436609
+ 2.177375300770910 0.333675009914873 0.740364253592691
+ 2.125333870619463 0.330857624626525 0.703750112206580
+ 2.166601866570710 0.333192210611845 0.732859178568121
+ 2.108843923653465 0.330052027456209 0.692243810014626
+ 2.170141182101418 0.333505871231270 0.735409067431057
+ 2.254451075620278 0.338167643418567 0.794516594921980
+ 2.446506792318999 0.348291161505242 0.929849297341202
+ 2.420231514001146 0.346916032088861 0.911318264635804
+ 2.181800682169253 0.334237425269950 0.743319742477540
+ 2.099742314313267 0.329709128744430 0.685953130712018
+ 2.253483605428042 0.338209143902201 0.794027010792921
+ 2.139849820112224 0.332063175615470 0.714018808416182
+ 2.250143469020041 0.338136253085361 0.791600043424550
+ 2.264735780708348 0.338979274263224 0.801680191863518
+ 2.425121660100526 0.347436401511180 0.914625338218002
+ 2.206590644219017 0.335830510968584 0.760892479739662
+ 2.189826738892797 0.334948331690477 0.749202347672420
+ 2.069051029835663 0.328235256714593 0.664845519474291
+ 2.391186717627713 0.345755663706005 0.891100361136644
+ 2.478723828708481 0.350304667654797 0.952470631759343
+ 2.633589418757750 0.357943374909926 1.062259709272963
+ 2.501720459872961 0.351218582106388 0.969405568955396
+ 2.438825464623843 0.348074452736294 0.924486170126454
+ 2.402956953910124 0.346226899680829 0.899047137304136
+ 2.524642865536631 0.352419486777799 0.985020882838327
+ 2.486592708507011 0.350410839822111 0.958347818799192
+ 2.561198294838873 0.354135408220830 1.011115336262727
+ 2.320611782924737 0.341730318797737 0.841256200313256
+ 2.279686641907904 0.339623952830723 0.812097446193594
+ 2.192677970879956 0.334934939278205 0.750983611681066
+ 2.034253696137675 0.326074943710870 0.640293522184461
+ 2.100411653364924 0.329874859982984 0.687004183670506
+ 2.136363142365890 0.331978934678318 0.712025267361496
+ 2.145765618417357 0.332571647651874 0.718576738965439
+ 2.015355353786295 0.325223748826715 0.627682513187217
+ 2.144497911280442 0.332597163880719 0.718235199174144
+ 2.184419901142909 0.334907791330299 0.745855884353715
+ 2.156941988546521 0.333440304455754 0.726637490761326
+ 2.164896092557094 0.333922888423381 0.732317236030551
+ 2.142495889557134 0.332723978543999 0.716747302318746
+ 1.972335146672318 0.323009944472129 0.598611382636265
+ 1.960032978731420 0.322337700909600 0.590822458953444
+ 1.892736021185325 0.318399928224863 0.544712037197812
+ 1.817727518077365 0.313890597043592 0.493800641801013
+ 1.732818354432900 0.308638151751227 0.436587334970866
+ 1.709712053275818 0.307294092052731 0.421979569450315
+ 1.560777278572816 0.297457158767192 0.322157189475494
+ 1.678355925543266 0.305618851041591 0.403484108249424] ;
set_dynare_seed('default');
diff --git a/tests/practicing/datasaver.m b/tests/practicing/datasaver.m
index 0ac532a4aea7bf5c042c21fe43001a563784be48..11a7ae861f35bd8c9a29c907bf60c2bea4070f47 100644
--- a/tests/practicing/datasaver.m
+++ b/tests/practicing/datasaver.m
@@ -9,19 +9,19 @@ function datasaver (s,var_list)
% This is part of the examples included in F. Barillas, R. Colacito,
% S. Kitao, C. Matthes, T. Sargent and Y. Shin (2007) "Practicing
% Dynare".
-
+
% Modified by M. Juillard to make it also compatible with Dynare
% version 4 (12/4/07)
-
+
global lgy_ lgx_ y_ endo_nbr M_ oo_
% test and adapt for Dynare version 4
if isempty(lgy_)
- lgy_ = M_.endo_names;
- lgx_ + M_.exo_names;
- y_ = oo_.endo_simul;
- endo_nbr = M_.endo_nbr;
+ lgy_ = M_.endo_names;
+ lgx_ + M_.exo_names;
+ y_ = oo_.endo_simul;
+ endo_nbr = M_.endo_nbr;
end
sm=[s,'.m'];
@@ -29,29 +29,29 @@ fid=fopen(sm,'w') ;
n = size(var_list,1);
if n == 0
- n = endo_nbr;
- ivar = [1:n]';
- var_list = lgy_;
+ n = endo_nbr;
+ ivar = [1:n]';
+ var_list = lgy_;
else
- ivar=zeros(n,1);
- for i=1:n
- i_tmp = strmatch(var_list(i,:),lgy_,'exact');
- if isempty(i_tmp)
- error (['One of the specified variables does not exist']) ;
- else
- ivar(i) = i_tmp;
+ ivar=zeros(n,1);
+ for i=1:n
+ i_tmp = strmatch(var_list(i,:),lgy_,'exact');
+ if isempty(i_tmp)
+ error (['One of the specified variables does not exist']) ;
+ else
+ ivar(i) = i_tmp;
+ end
end
- end
end
for i = 1:n
- fprintf(fid,[lgy_(ivar(i),:), '=['],'\n') ;
- fprintf(fid,'\n') ;
- fprintf(fid,'%15.8g\n',y_(ivar(i),:)') ;
- fprintf(fid,'\n') ;
- fprintf(fid,'];\n') ;
- fprintf(fid,'\n') ;
+ fprintf(fid,[lgy_(ivar(i),:), '=['],'\n') ;
+ fprintf(fid,'\n') ;
+ fprintf(fid,'%15.8g\n',y_(ivar(i),:)') ;
+ fprintf(fid,'\n') ;
+ fprintf(fid,'];\n') ;
+ fprintf(fid,'\n') ;
end
fclose(fid) ;
diff --git a/tests/printMakeCheckMatlabErrMsg.m b/tests/printMakeCheckMatlabErrMsg.m
index a72b55401be47b9e76475d9c69f66f18d809dfb2..ef9895d85332a5a0f6735db0f04d6871fc26e62b 100644
--- a/tests/printMakeCheckMatlabErrMsg.m
+++ b/tests/printMakeCheckMatlabErrMsg.m
@@ -1,9 +1,9 @@
function printMakeCheckMatlabErrMsg(modfilename, exception)
- fprintf('\n********************************************\n');
- disp('*** DYNARE-TEST-MATLAB ERROR ENCOUNTERED ***');
- disp('********************************************');
- disp([' WHILE RUNNING MODFILE: ' modfilename]);
- fprintf('\n');
- disp(getReport(exception));
- fprintf('*************************************\n\n\n');
+fprintf('\n********************************************\n');
+disp('*** DYNARE-TEST-MATLAB ERROR ENCOUNTERED ***');
+disp('********************************************');
+disp([' WHILE RUNNING MODFILE: ' modfilename]);
+fprintf('\n');
+disp(getReport(exception));
+fprintf('*************************************\n\n\n');
end
diff --git a/tests/printMakeCheckOctaveErrMsg.m b/tests/printMakeCheckOctaveErrMsg.m
index 84e19d00edf1827559ee954ca16bc9eda6875e3e..b64bb4bb0e224cb787c347bff5ba61fd892cc18f 100644
--- a/tests/printMakeCheckOctaveErrMsg.m
+++ b/tests/printMakeCheckOctaveErrMsg.m
@@ -1,14 +1,14 @@
function printMakeCheckOctaveErrMsg(modfilename, err)
- printf("\n");
- printf("********************************************\n");
- printf("*** DYNARE-TEST-OCTAVE ERROR ENCOUNTERED ***\n");
- printf("********************************************\n");
- printf(" WHILE RUNNING MODFILE: %s\n", modfilename);
- printf(" MSG: %s\n", err.message);
- if (isfield(err, 'stack'))
- printf(" IN FILE: %s\n", err.stack(1).file);
- printf(" IN FUNCTION: %s\n", err.stack(1).name);
- printf(" ON LINE and COLUMN: %d and %d\n",err.stack(1).line,err.stack(1).column);
- end
- printf("*************************************\n\n\n");
+printf("\n");
+printf("********************************************\n");
+printf("*** DYNARE-TEST-OCTAVE ERROR ENCOUNTERED ***\n");
+printf("********************************************\n");
+printf(" WHILE RUNNING MODFILE: %s\n", modfilename);
+printf(" MSG: %s\n", err.message);
+if (isfield(err, 'stack'))
+ printf(" IN FILE: %s\n", err.stack(1).file);
+ printf(" IN FUNCTION: %s\n", err.stack(1).name);
+ printf(" ON LINE and COLUMN: %d and %d\n",err.stack(1).line,err.stack(1).column);
+end
+printf("*************************************\n\n\n");
end
diff --git a/tests/recursive/data_ca1.m b/tests/recursive/data_ca1.m
index c28fae1a2800e83eda0e6343196e8aeafad2935f..ca003056bded971e9bf00232802e453a096e9cdf 100644
--- a/tests/recursive/data_ca1.m
+++ b/tests/recursive/data_ca1.m
@@ -1,98 +1,98 @@
data = [0.928467646476 11.8716889412 20 0.418037507392 0.227382377518 ...
--0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
--0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
--0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
--0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
--0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
--0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
-1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
-2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
-1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
-1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
-1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
-1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
-0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
-1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
-1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
-0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
-1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
-1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
--0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
-0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
-0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
--0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
-2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
-1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
-1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
-1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
-1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
-1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
-0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
-0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
-1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
-0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
-0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
-0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
-0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
--0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
--0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
--0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
--1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
-0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
-0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
-0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
--0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
-0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
-0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
-0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
-0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
-0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
-0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
-0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
-1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
-1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
-1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
-0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
-0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
--0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
-0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
-0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
-0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
-0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
-1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
-0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
-0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
-1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
-1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
-0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
-1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
-0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
-1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
-1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
-1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
-1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
-1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
-1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
-1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
-0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
-1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
-0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
-0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
-0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
--0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
-0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
-1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
-1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
-0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
-];
-
+ -0.705994063083 11.7522582094 21.25 1.09254424511 -1.29488274994 ...
+ -0.511895351926 9.68144025625 17.25 -1.66150408407 0.331508393098 ...
+ -0.990955971267 10.0890781236 17 1.43016275252 -2.43589670141 ...
+ -0.981233061806 12.1094840679 18.25 2.91293288733 -0.790246576864 ...
+ -0.882182844512 8.54559460406 15 0.419579139481 0.358729719566 ...
+ -0.930893002836 6.19238374422 12.5 -1.48847457959 0.739779938797 ...
+ 1.53158206947 2.76544271886 11.5 -0.336216769682 0.455559918769 ...
+ 2.2659052834 5.47418162513 11 0.306436789767 -0.0707985731221 ...
+ 1.05419803797 6.35698426189 11 0.140700250477 0.620401487202 ...
+ 1.20161076793 3.4253301593 11 0.461296492351 0.14354323987 ...
+ 1.73934077971 4.70926070322 11.5 1.35798282982 0.38564694435 ...
+ 1.71735262584 3.54232079749 12.5 2.9097529155 -0.804308583301 ...
+ 0.426343657844 3.32719108897 13 1.64214862652 -1.18214664701 ...
+ 1.67751812324 2.93444727338 11.25 0.344434910651 -1.6529373719 ...
+ 1.37013301099 4.72303361923 11.75 2.61511526582 0.327684243041 ...
+ 0.281231073781 4.4893853071 10.5 1.17043449257 1.12855106649 ...
+ 1.53638992834 3.7325309699 10.25 -0.683947046728 0.11943538737 ...
+ 1.68081431462 3.34729969129 10 1.41159342106 -1.59065680853 ...
+ -0.343321601133 5.05563513564 12 1.75117366498 -2.40127764642 ...
+ 0.873415608666 3.2779996255 10.25 -1.39895866711 0.0971444398216 ...
+ 0.26399696544 4.78229419828 9.75 0.0914692438124 0.299310457612 ...
+ -0.562233624818 3.88598638237 9.75 -0.0505384765105 0.332826708151 ...
+ 2.15161914936 3.84859710132 8.75 -3.44811080489 0.789138678784 ...
+ 1.2345093726 5.62225030942 9.5 -0.366945407434 2.32974981198 ...
+ 1.62554967459 4.24667132831 10 -0.800958371402 0.0293183770935 ...
+ 1.33035402527 2.75248979249 9.75 -0.855723113225 0.852493939813 ...
+ 1.52078814077 3.53415985826 9.75 -3.37963469203 -1.05133958119 ...
+ 1.16704983697 4.92754079464 10.75 -3.0142303324 0.459907431978 ...
+ 0.277213572101 4.55532133037 11.75 -0.851995599415 2.03242034852 ...
+ 0.842215068977 3.11164509647 12.25 -1.08290421696 0.014323281961 ...
+ 1.05325028606 4.92882647578 13.5 -1.1953883867 0.706764750654 ...
+ 0.453051253568 6.82998950103 13.5 0.111803656462 0.088462593153 ...
+ 0.199885995525 5.82643354662 13.5 -0.920501518421 -0.26504958666 ...
+ 0.137907999624 2.66076369132 13.5 -1.17122929812 -0.995642430514 ...
+ 0.721949686709 5.70497876823 14.25 1.19378169018 -1.10644839651 ...
+ -0.418465249225 3.75861110232 14.75 -1.03131674824 0.188507675831 ...
+ -0.644028342116 4.15104788154 13.75 -1.48911756546 0.204560913792 ...
+ -0.848213852668 5.65580324027 12.75 0.677011703877 -0.849628054542 ...
+ -1.51954076928 11.4866911266 11.25 -0.446024680774 -0.456342350765 ...
+ 0.265275055215 2.85472749592 9.75 -0.598778202436 -0.907311640831 ...
+ 0.356162529063 2.29614015658 9.5 -0.46820788432 -1.22130883441 ...
+ 0.368308864363 -0.539083504685 8 -0.781333991956 0.374007246518 ...
+ -0.145751412732 1.61507621789 8.25 3.68291932628 1.32438399845 ...
+ 0.285457283664 2.14334055993 7 1.42819405379 -0.00818660844123 ...
+ 0.372390129412 1.60000213334 6.25 0.626106424052 -0.10136772765 ...
+ 0.382720203063 1.72614243263 7.25 4.89631941021 -1.10060711916 ...
+ 0.737957515573 2.90430582851 6 -0.0422721010314 0.4178952497 ...
+ 0.649532581668 0.657135682543 6 0.692066153971 0.422299120276 ...
+ 0.627159201987 1.70352689913 5.75 2.62066711305 -1.29237304034 ...
+ 0.905441299817 1.95663197267 5.5 1.5949697565 -0.27115830703 ...
+ 1.49322577898 -2.08741765309 6.25 1.23027694802 0.418336889527 ...
+ 1.48750731567 -1.57274121871 8 3.01660550994 -0.893958254365 ...
+ 1.39783858087 2.22623066426 7 -0.80842319214 1.47625453886 ...
+ 0.89274836317 1.30378081742 8 -0.249485058661 0.159871204185 ...
+ 0.920652246088 4.1437741965 9.75 2.8204453623 0.178149239655 ...
+ -0.00264276644799 3.07989972052 8.75 -2.56342461535 2.105998353 ...
+ 0.0198190461681 0.766283759256 8 -1.15838865989 1.56888883418 ...
+ 0.440050515311 0.127570085801 7.5 0.0400753569995 0.028914333532 ...
+ 0.129536637901 1.78174141526 6.75 0.959943962785 0.307781224401 ...
+ 0.398549827172 3.03606770667 6.5 -0.340209794742 0.100979469478 ...
+ 1.17174775425 0.629625188037 5.75 0.403003686814 0.902394579377 ...
+ 0.991163981251 2.50862910684 4.75 -1.44963996982 1.16150986945 ...
+ 0.967603566096 2.12003739013 4.75 0.610846030775 -0.889994896068 ...
+ 1.14689383604 1.24185011459 4.75 2.01098091308 -1.73846431001 ...
+ 1.32593824054 0.990713820685 4.75 -0.0955142989332 -0.0369257308362 ...
+ 0.861135002644 -0.24744943605 6 1.72793107135 -0.691506789639 ...
+ 1.26870850151 2.09844764887 6.5 1.50720217572 -1.31399187077 ...
+ 0.260364987715 1.10650139716 6.5 1.13659047496 0.0720441664643 ...
+ 1.09731242214 0.490796381346 7.25 4.59123894147 -2.14073070763 ...
+ 1.63792841781 0.612652594286 6.75 1.79604605035 -0.644363995357 ...
+ 1.48465576034 0.978295808687 6.75 -2.00753620902 1.39437534964 ...
+ 1.0987608663 4.25212569087 6.25 -2.58901196498 2.56054320803 ...
+ 1.42592178132 2.76984518311 6.25 0.888195752358 1.03114549274 ...
+ 1.52958239462 1.31795955491 6.5 -0.902907564082 -0.0952198893776 ...
+ 1.0170168994 2.14733589918 7 -1.3054866978 2.68803738466 ...
+ 0.723253652257 3.43552889347 7.5 1.8213700853 0.592593586195 ...
+ 1.24720806008 3.87383806577 7.5 0.0522300654168 0.988871238698 ...
+ 0.482531471239 2.67793287032 7.5 2.9693944293 -0.108591166081 ...
+ 0.154056100439 0.927269031704 6.75 0.119222057561 3.30489209451 ...
+ 0.0694865769274 6.65916526788 6.25 0.889014476084 -2.83976849035 ...
+ -0.121267434867 0.341442615696 5.25 0.323053239216 -3.49289229012 ...
+ 0.726473690375 -3.5423730964 4 2.19149290449 -3.20855054004 ...
+ 1.39271709108 2.63121085718 3.75 0.88406577736 0.75622580197 ...
+ 1.07502077727 5.88578836799 4.25 -2.55088273352 2.89018116374 ...
+ 0.759049251607 4.24703604223 4.5 0.575687665685 -0.388292506167 ...
+ ];
+
data = reshape(data,5,86)';
y_obs = data(:,1);
pie_obs = data(:,2);
R_obs = data(:,3);
de = data(:,4);
dq = data(:,5);
-
+
%Country: Canada
%Sample Range: 1981:2 to 2002:3
%Observations: 86
diff --git a/tests/reporting/ResidTablePage.m b/tests/reporting/ResidTablePage.m
index c283429a0db649ba90ed408de7ce11cfffccdc5d..c4e25e3051db45828fdc9c55987b7456f7be883d 100644
--- a/tests/reporting/ResidTablePage.m
+++ b/tests/reporting/ResidTablePage.m
@@ -50,7 +50,7 @@ rep = rep.addTable('title', countryName, ...
for i=1:length(seriesNames)
if (any(strcmp(countryAbbr, otherThree)) && ...
- any(strcmp(seriesNames{i}{1}, notForOtherThree))) || ...
+ any(strcmp(seriesNames{i}{1}, notForOtherThree))) || ...
(any(strcmp(countryAbbr, 'US')) && any(strcmp(seriesNames{i}{1}, notForUS))) || ...
(any(strcmp(countryAbbr, firstThree)) && any(strcmp(seriesNames{i}{1}, notForFirstThree)))
continue
diff --git a/tests/reporting/runDynareReport.m b/tests/reporting/runDynareReport.m
index eab6eb234d551a29057a627b23b0ff1a809994c2..ba67cec76f5c874a1662c1867bac568b4432702d 100644
--- a/tests/reporting/runDynareReport.m
+++ b/tests/reporting/runDynareReport.m
@@ -202,13 +202,13 @@ rep = rep.addPage('title', {'Jan1 vs Jan2', 'World Oil and Food Prices'}, ...
'titleFormat', {'\large\bfseries', '\large'});
rep = rep.addSection('cols', 1);
rep = rep.addParagraph('text', 'Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.', ...
- 'cols', 2, ...
- 'heading', '\textbf{My First Paragraph Has Two Columns}');
+ 'cols', 2, ...
+ 'heading', '\textbf{My First Paragraph Has Two Columns}');
rep = rep.addSection('cols', 1);
rep = rep.addParagraph('text', 'Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\newline', ...
- 'heading', '\textbf{My Next Paragraphs Only Have One}', ...
- 'indent', false);
+ 'heading', '\textbf{My Next Paragraphs Only Have One}', ...
+ 'indent', false);
rep = rep.addParagraph('text', 'Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\newline');
rep = rep.addSection('cols', 2);
diff --git a/tests/run_all_unitary_tests.m b/tests/run_all_unitary_tests.m
index b31f9d8adabc51673eaf518e67d53f95fe96f0e2..7200f40b36578d69424781e76746eed829f89848 100644
--- a/tests/run_all_unitary_tests.m
+++ b/tests/run_all_unitary_tests.m
@@ -73,14 +73,14 @@ else
fid = fopen('run_all_unitary_tests.m.trs', 'w+');
end
if length(failedtests) > 0
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: %d\n', counter);
- fprintf(fid,':number-failed-tests: %d\n', length(failedtests));
- fprintf(fid,':list-of-failed-tests: %s\n', failedtests{:});
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: %d\n', counter);
+ fprintf(fid,':number-failed-tests: %d\n', length(failedtests));
+ fprintf(fid,':list-of-failed-tests: %s\n', failedtests{:});
else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: %d\n', counter);
- fprintf(fid,':number-failed-tests: 0\n');
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: %d\n', counter);
+ fprintf(fid,':number-failed-tests: 0\n');
end
fprintf(fid,':elapsed-time: %f\n',0.0);
fclose(fid);
diff --git a/tests/run_block_byte_tests_matlab.m b/tests/run_block_byte_tests_matlab.m
index 846beadecf1403dcc6cfdd82aa7819c669757db1..3675c0da7e8b2bd22a3534a0318f48b2853a4c87 100644
--- a/tests/run_block_byte_tests_matlab.m
+++ b/tests/run_block_byte_tests_matlab.m
@@ -29,7 +29,7 @@ addpath([top_test_dir filesep '..' filesep 'matlab']);
% Test Dynare Version
if ~strcmp(dynare_version(), getenv('DYNARE_VERSION'))
- error('Incorrect version of Dynare is being tested')
+ error('Incorrect version of Dynare is being tested')
end
% Test block_bytecode/ls2003.mod with various combinations of
@@ -134,14 +134,14 @@ delete('wsMat.mat')
cd(getenv('TOP_TEST_DIR'));
fid = fopen('run_block_byte_tests_matlab.m.trs', 'w+');
if size(failedBlock,2) > 0
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: %d\n', num_block_tests);
- fprintf(fid,':number-failed-tests: %d\n', size(failedBlock,2));
- fprintf(fid,':list-of-failed-tests: %s\n', failedBlock{:});
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: %d\n', num_block_tests);
+ fprintf(fid,':number-failed-tests: %d\n', size(failedBlock,2));
+ fprintf(fid,':list-of-failed-tests: %s\n', failedBlock{:});
else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: %d\n', num_block_tests);
- fprintf(fid,':number-failed-tests: 0\n');
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: %d\n', num_block_tests);
+ fprintf(fid,':number-failed-tests: 0\n');
end
fprintf(fid,':elapsed-time: %f\n', ecput);
fclose(fid);
diff --git a/tests/run_block_byte_tests_octave.m b/tests/run_block_byte_tests_octave.m
index 288a17e9428d44164c28c79861fd050f3fd13b35..d930e8028dca46ed79ed3c0a8b9d519542709ed2 100644
--- a/tests/run_block_byte_tests_octave.m
+++ b/tests/run_block_byte_tests_octave.m
@@ -11,142 +11,142 @@
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
-##
-## You should have received a copy of the GNU General Public License
-## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+ ##
+ ## You should have received a copy of the GNU General Public License
+ ## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-## Implementation notes:
-##
-## Before every call to Dynare, the contents of the workspace is saved in
-## 'wsOct', and reloaded after Dynare has finished (this is necessary since
-## Dynare does a 'clear -all').
+ ## Implementation notes:
+ ##
+ ## Before every call to Dynare, the contents of the workspace is saved in
+ ## 'wsOct', and reloaded after Dynare has finished (this is necessary since
+ ## Dynare does a 'clear -all').
-top_test_dir = getenv('TOP_TEST_DIR');
-addpath(top_test_dir);
-addpath([top_test_dir filesep '..' filesep 'matlab']);
+ top_test_dir = getenv('TOP_TEST_DIR');
+ addpath(top_test_dir);
+ addpath([top_test_dir filesep '..' filesep 'matlab']);
-## Test Dynare Version
-if !strcmp(dynare_version(), getenv("DYNARE_VERSION"))
- error("Incorrect version of Dynare is being tested")
-endif
+ ## Test Dynare Version
+ if !strcmp(dynare_version(), getenv("DYNARE_VERSION"))
+ error("Incorrect version of Dynare is being tested")
+ endif
-## Ask gnuplot to create graphics in text mode
-## Note that setenv() was introduced in Octave 3.0.2, for compatibility
-## with MATLAB
-putenv("GNUTERM", "dumb")
+ ## Ask gnuplot to create graphics in text mode
+ ## Note that setenv() was introduced in Octave 3.0.2, for compatibility
+ ## with MATLAB
+ putenv("GNUTERM", "dumb")
-## Test block_bytecode/ls2003.mod with various combinations of
-## block/bytecode/solve_algo/stack_solve_algo
-failedBlock = {};
-num_block_tests = 0;
-cd([top_test_dir filesep 'block_bytecode']);
-tic;
-for blockFlag = 0:1
- for bytecodeFlag = 0:1
- ## Recall that solve_algo=7 and stack_solve_algo=2 are not supported
- ## under Octave
- default_solve_algo = 2;
- default_stack_solve_algo = 0;
- if !blockFlag && !bytecodeFlag
- solve_algos = 0:4;
- stack_solve_algos = [0 6];
- elseif blockFlag && !bytecodeFlag
- solve_algos = [0:4 6 8];
- stack_solve_algos = [0 1 3 4];
- else
- solve_algos = [0:6 8];
- stack_solve_algos = [0 1 3:5];
- endif
+ ## Test block_bytecode/ls2003.mod with various combinations of
+ ## block/bytecode/solve_algo/stack_solve_algo
+ failedBlock = {};
+ num_block_tests = 0;
+ cd([top_test_dir filesep 'block_bytecode']);
+ tic;
+ for blockFlag = 0:1
+ for bytecodeFlag = 0:1
+ ## Recall that solve_algo=7 and stack_solve_algo=2 are not supported
+ ## under Octave
+ default_solve_algo = 2;
+ default_stack_solve_algo = 0;
+ if !blockFlag && !bytecodeFlag
+ solve_algos = 0:4;
+ stack_solve_algos = [0 6];
+ elseif blockFlag && !bytecodeFlag
+ solve_algos = [0:4 6 8];
+ stack_solve_algos = [0 1 3 4];
+ else
+ solve_algos = [0:6 8];
+ stack_solve_algos = [0 1 3:5];
+ endif
- sleep(1) # Workaround for strange race condition related to the _static.m file
+ sleep(1) # Workaround for strange race condition related to the _static.m file
- for i = 1:length(solve_algos)
- num_block_tests = num_block_tests + 1;
- if !blockFlag && !bytecodeFlag && (i == 1)
- ## This is the reference simulation path against which all
- ## other simulations will be tested
- try
- old_path = path;
- save wsOct
- run_ls2003(blockFlag, bytecodeFlag, solve_algos(i), default_stack_solve_algo)
- load wsOct
- path(old_path);
- y_ref = oo_.endo_simul;
- save('test.mat','y_ref');
- catch
- load wsOct
- path(old_path);
- failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'];
- printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'], lasterror);
- end_try_catch
- else
- try
- old_path = path;
- save wsOct
- run_ls2003(blockFlag, bytecodeFlag, solve_algos(i), default_stack_solve_algo)
- load wsOct
- path(old_path);
- ## Test against the reference simulation path
- load('test.mat','y_ref');
- diff = oo_.endo_simul - y_ref;
- if(abs(diff) > options_.dynatol.x)
- failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'];
- differr.message = ["ERROR: simulation path differs from the reference path" ];
- printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'], differr);
- endif
- catch
- load wsOct
- e = lasterror(); # The path() command alters the lasterror, because of io package
- path(old_path);
- lasterror(e);
- failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'];
- printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'], lasterror);
- end_try_catch
- endif
- endfor
- for i = 1:length(stack_solve_algos)
- num_block_tests = num_block_tests + 1;
- try
- old_path = path;
- save wsOct
- run_ls2003(blockFlag, bytecodeFlag, default_solve_algo, stack_solve_algos(i))
- load wsOct
- path(old_path);
- ## Test against the reference simulation path
- load('test.mat','y_ref');
- diff = oo_.endo_simul - y_ref;
- if(abs(diff) > options_.dynatol.x)
- failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(default_solve_algo) ',' num2str(stack_solve_algos(i)) ')'];
- differr.message = ["ERROR: simulation path differs from the reference path" ];
- printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(default_solve_algo) ',' num2str(stack_solve_algos(i)) ')'], differr);
- endif
- catch
- load wsOct
- e = lasterror(); # The path() command alters the lasterror, because of io package
- path(old_path);
- lasterror(e);
- failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(default_solve_algo) ',' num2str(stack_solve_algos(i)) ')'];
- printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(default_solve_algo) ',' num2str(stack_solve_algos(i)) ')'], lasterror);
- end_try_catch
- endfor
- endfor
-endfor
-ecput = toc;
-delete('wsOct');
-cd(getenv('TOP_TEST_DIR'));
-fid = fopen('run_block_byte_tests_octave.o.trs', 'w+');
-if size(failedBlock,2) > 0
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: %d\n', num_block_tests);
- fprintf(fid,':number-failed-tests: %d\n', size(failedBlock,2));
- fprintf(fid,':list-of-failed-tests: %s\n', failedBlock{:});
-else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: %d\n', num_block_tests);
- fprintf(fid,':number-failed-tests: 0\n');
-end
-fprintf(fid,':elapsed-time: %f\n', ecput);
-fclose(fid);
-## Local variables:
-## mode: Octave
-## End:
+ for i = 1:length(solve_algos)
+ num_block_tests = num_block_tests + 1;
+ if !blockFlag && !bytecodeFlag && (i == 1)
+ ## This is the reference simulation path against which all
+ ## other simulations will be tested
+ try
+ old_path = path;
+ save wsOct
+ run_ls2003(blockFlag, bytecodeFlag, solve_algos(i), default_stack_solve_algo)
+ load wsOct
+ path(old_path);
+ y_ref = oo_.endo_simul;
+ save('test.mat','y_ref');
+ catch
+ load wsOct
+ path(old_path);
+ failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'];
+ printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'], lasterror);
+ end_try_catch
+ else
+ try
+ old_path = path;
+ save wsOct
+ run_ls2003(blockFlag, bytecodeFlag, solve_algos(i), default_stack_solve_algo)
+ load wsOct
+ path(old_path);
+ ## Test against the reference simulation path
+ load('test.mat','y_ref');
+ diff = oo_.endo_simul - y_ref;
+ if(abs(diff) > options_.dynatol.x)
+ failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'];
+ differr.message = ["ERROR: simulation path differs from the reference path" ];
+ printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'], differr);
+ endif
+ catch
+ load wsOct
+ e = lasterror(); # The path() command alters the lasterror, because of io package
+ path(old_path);
+ lasterror(e);
+ failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'];
+ printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(solve_algos(i)) ',' num2str(default_stack_solve_algo) ')'], lasterror);
+ end_try_catch
+ endif
+ endfor
+ for i = 1:length(stack_solve_algos)
+ num_block_tests = num_block_tests + 1;
+ try
+ old_path = path;
+ save wsOct
+ run_ls2003(blockFlag, bytecodeFlag, default_solve_algo, stack_solve_algos(i))
+ load wsOct
+ path(old_path);
+ ## Test against the reference simulation path
+ load('test.mat','y_ref');
+ diff = oo_.endo_simul - y_ref;
+ if(abs(diff) > options_.dynatol.x)
+ failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(default_solve_algo) ',' num2str(stack_solve_algos(i)) ')'];
+ differr.message = ["ERROR: simulation path differs from the reference path" ];
+ printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(default_solve_algo) ',' num2str(stack_solve_algos(i)) ')'], differr);
+ endif
+ catch
+ load wsOct
+ e = lasterror(); # The path() command alters the lasterror, because of io package
+ path(old_path);
+ lasterror(e);
+ failedBlock{size(failedBlock,2)+1} = ['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(default_solve_algo) ',' num2str(stack_solve_algos(i)) ')'];
+ printMakeCheckOctaveErrMsg(['block_bytecode' filesep 'run_ls2003.m(' num2str(blockFlag) ',' num2str(bytecodeFlag) ',' num2str(default_solve_algo) ',' num2str(stack_solve_algos(i)) ')'], lasterror);
+ end_try_catch
+ endfor
+ endfor
+ endfor
+ ecput = toc;
+ delete('wsOct');
+ cd(getenv('TOP_TEST_DIR'));
+ fid = fopen('run_block_byte_tests_octave.o.trs', 'w+');
+ if size(failedBlock,2) > 0
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: %d\n', num_block_tests);
+ fprintf(fid,':number-failed-tests: %d\n', size(failedBlock,2));
+ fprintf(fid,':list-of-failed-tests: %s\n', failedBlock{:});
+ else
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: %d\n', num_block_tests);
+ fprintf(fid,':number-failed-tests: 0\n');
+ end
+ fprintf(fid,':elapsed-time: %f\n', ecput);
+ fclose(fid);
+ ## Local variables:
+ ## mode: Octave
+ ## End:
diff --git a/tests/run_m_script.m b/tests/run_m_script.m
index f40fc242c098cb1420fe5615862f4478c6752b53..09840efcad0673f11573f629886b5745f95fe5d0 100644
--- a/tests/run_m_script.m
+++ b/tests/run_m_script.m
@@ -22,31 +22,31 @@ top_test_dir = getenv('TOP_TEST_DIR');
cd(directory);
try
- mscript;
- testFailed = false;
+ mscript;
+ testFailed = false;
catch exception
- printMakeCheckMatlabErrMsg(strtok(getenv('FILESTEM')), exception);
- testFailed = true;
+ printMakeCheckMatlabErrMsg(strtok(getenv('FILESTEM')), exception);
+ testFailed = true;
end
cd(top_test_dir);
name = strtok(getenv('FILESTEM'));
fid = fopen([name '.m.tls'], 'w');
if fid < 0
- wd = pwd
- filestep = getenv('FILESTEM')
- error(['ERROR: problem opening file ' name '.m.tls for writing....']);
+ wd = pwd
+ filestep = getenv('FILESTEM')
+ error(['ERROR: problem opening file ' name '.m.tls for writing....']);
end
if testFailed
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 1\n');
- fprintf(fid,':list-of-failed-tests: %s\n', [name '.m']);
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 1\n');
+ fprintf(fid,':list-of-failed-tests: %s\n', [name '.m']);
else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 0\n');
- fprintf(fid,':list-of-passed-tests: %s\n', [name '.m']);
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 0\n');
+ fprintf(fid,':list-of-passed-tests: %s\n', [name '.m']);
end
fclose(fid);
exit;
\ No newline at end of file
diff --git a/tests/run_o_script.m b/tests/run_o_script.m
index 3edba058292e8cf02c4b4a6c89963a4cf6d770e7..723df2e1b16174b5c17f8ffea50dfa43b931cb6a 100644
--- a/tests/run_o_script.m
+++ b/tests/run_o_script.m
@@ -11,42 +11,42 @@
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
-##
-## You should have received a copy of the GNU General Public License
-## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+ ##
+ ## You should have received a copy of the GNU General Public License
+ ## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-load_octave_packages
+ load_octave_packages
-top_test_dir = getenv('TOP_TEST_DIR');
-[mfile, name] = strtok(getenv('FILESTEM'));
+ top_test_dir = getenv('TOP_TEST_DIR');
+ [mfile, name] = strtok(getenv('FILESTEM'));
-[directory, mscript, ext] = fileparts([top_test_dir '/' mfile]);
-cd(directory);
+ [directory, mscript, ext] = fileparts([top_test_dir '/' mfile]);
+ cd(directory);
-try
- mscript;
- testFailed = false;
-catch
- printMakeCheckOctaveErrMsg(getenv('FILESTEM'), lasterror);
- testFailed = true;
-end_try_catch
+ try
+ mscript;
+ testFailed = false;
+ catch
+ printMakeCheckOctaveErrMsg(getenv('FILESTEM'), lasterror);
+ testFailed = true;
+ end_try_catch
-cd(top_test_dir);
-name = strtok(getenv('FILESTEM'));
-fid = fopen([name '.o.tls'], 'w+');
-if testFailed
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 1\n');
- fprintf(fid,':list-of-failed-tests: %s\n', [name '.m']);
-else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 0\n');
- fprintf(fid,':list-of-passed-tests: %s\n', [name '.m']);
-end
-fclose(fid);
+ cd(top_test_dir);
+ name = strtok(getenv('FILESTEM'));
+ fid = fopen([name '.o.tls'], 'w+');
+ if testFailed
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 1\n');
+ fprintf(fid,':list-of-failed-tests: %s\n', [name '.m']);
+ else
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 0\n');
+ fprintf(fid,':list-of-passed-tests: %s\n', [name '.m']);
+ end
+ fclose(fid);
-## Local variables:
-## mode: Octave
-## End:
+ ## Local variables:
+ ## mode: Octave
+ ## End:
diff --git a/tests/run_reporting_test_matlab.m b/tests/run_reporting_test_matlab.m
index 3c5ccb95deae5d07f2c3814e856cf109034af2e4..bd8a738d4f03f3baccb875d71027c8f546101180 100644
--- a/tests/run_reporting_test_matlab.m
+++ b/tests/run_reporting_test_matlab.m
@@ -21,7 +21,7 @@ addpath([top_test_dir filesep '..' filesep 'matlab']);
% Test Dynare Version
if ~strcmp(dynare_version(), getenv('DYNARE_VERSION'))
- error('Incorrect version of Dynare is being tested')
+ error('Incorrect version of Dynare is being tested')
end
% To add default directories, empty dseries objects
@@ -44,15 +44,15 @@ end
cd(getenv('TOP_TEST_DIR'));
fid = fopen('run_reporting_test_matlab.m.trs', 'w+');
if testFailed
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 1\n');
- fprintf(fid,':list-of-failed-tests: run_reporting_test_matlab.m\n');
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 1\n');
+ fprintf(fid,':list-of-failed-tests: run_reporting_test_matlab.m\n');
else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 0\n');
- fprintf(fid,':list-of-passed-tests: run_reporting_test_matlab.m\n');
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 0\n');
+ fprintf(fid,':list-of-passed-tests: run_reporting_test_matlab.m\n');
end
fprintf(fid,':elapsed-time: %f\n',0.0);
fclose(fid);
diff --git a/tests/run_reporting_test_octave.m b/tests/run_reporting_test_octave.m
index 32edcfb630f147e4bb2b2388f3be38e7d0035bf2..d47fb64a66591877b6c23789ecaa4ca3d70ecab3 100644
--- a/tests/run_reporting_test_octave.m
+++ b/tests/run_reporting_test_octave.m
@@ -11,58 +11,58 @@
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
-##
-## You should have received a copy of the GNU General Public License
-## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+ ##
+ ## You should have received a copy of the GNU General Public License
+ ## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-load_octave_packages
+ load_octave_packages
-top_test_dir = getenv('TOP_TEST_DIR');
-addpath(top_test_dir);
-addpath([top_test_dir filesep '..' filesep 'matlab']);
+ top_test_dir = getenv('TOP_TEST_DIR');
+ addpath(top_test_dir);
+ addpath([top_test_dir filesep '..' filesep 'matlab']);
-## Test Dynare Version
-if !strcmp(dynare_version(), getenv("DYNARE_VERSION"))
- error("Incorrect version of Dynare is being tested")
-endif
+ ## Test Dynare Version
+ if !strcmp(dynare_version(), getenv("DYNARE_VERSION"))
+ error("Incorrect version of Dynare is being tested")
+ endif
-## Ask gnuplot to create graphics in text mode
-## Note that setenv() was introduced in Octave 3.0.2, for compatibility
-## with MATLAB
-putenv("GNUTERM", "dumb")
+ ## Ask gnuplot to create graphics in text mode
+ ## Note that setenv() was introduced in Octave 3.0.2, for compatibility
+ ## with MATLAB
+ putenv("GNUTERM", "dumb")
-## To add default directories, empty dseries objects
-dynare_config([], 0);
+ ## To add default directories, empty dseries objects
+ dynare_config([], 0);
-printf("\n*** TESTING: run_reporting_test_octave.m ***\n");
-try
- cd([top_test_dir filesep 'reporting']);
- db_a = dseries('db_a.csv');
- db_q = dseries('db_q.csv');
- dc_a = dseries('dc_a.csv');
- dc_q = dseries('dc_q.csv');
- runDynareReport(dc_a, dc_q, db_a, db_q);
- testFailed = false;
-catch
- testFailed = true;
-end
+ printf("\n*** TESTING: run_reporting_test_octave.m ***\n");
+ try
+ cd([top_test_dir filesep 'reporting']);
+ db_a = dseries('db_a.csv');
+ db_q = dseries('db_q.csv');
+ dc_a = dseries('dc_a.csv');
+ dc_q = dseries('dc_q.csv');
+ runDynareReport(dc_a, dc_q, db_a, db_q);
+ testFailed = false;
+ catch
+ testFailed = true;
+ end
-cd(getenv('TOP_TEST_DIR'));
-fid = fopen('run_reporting_test_octave.o.trs', 'w+');
-if testFailed
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 1\n');
- fprintf(fid,':list-of-failed-tests: run_reporting_test_octave.m\n');
-else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 0\n');
- fprintf(fid,':list-of-passed-tests: run_reporting_test_octave.m\n');
-end
-fprintf(fid,':elapsed-time: %f\n',0.0);
-fclose(fid);
+ cd(getenv('TOP_TEST_DIR'));
+ fid = fopen('run_reporting_test_octave.o.trs', 'w+');
+ if testFailed
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 1\n');
+ fprintf(fid,':list-of-failed-tests: run_reporting_test_octave.m\n');
+ else
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 0\n');
+ fprintf(fid,':list-of-passed-tests: run_reporting_test_octave.m\n');
+ end
+ fprintf(fid,':elapsed-time: %f\n',0.0);
+ fclose(fid);
-## Local variables:
-## mode: Octave
-## End:
+ ## Local variables:
+ ## mode: Octave
+ ## End:
diff --git a/tests/run_test_matlab.m b/tests/run_test_matlab.m
index fb1b487a0861f2dc2e09325dd310fd99c7c8056b..c7735dc1689f25afe9d9a0da320e5a5b53455d5a 100644
--- a/tests/run_test_matlab.m
+++ b/tests/run_test_matlab.m
@@ -21,7 +21,7 @@ addpath([top_test_dir filesep '..' filesep 'matlab']);
% Test Dynare Version
if ~strcmp(dynare_version(), getenv('DYNARE_VERSION'))
- error('Incorrect version of Dynare is being tested')
+ error('Incorrect version of Dynare is being tested')
end
% Test MOD files listed in Makefile.am
@@ -35,11 +35,11 @@ disp(['*** TESTING: ' modfile ' ***']);
tic;
save(['wsMat' testfile '.mat']);
try
- dynare([testfile ext], 'console')
- testFailed = false;
+ dynare([testfile ext], 'console')
+ testFailed = false;
catch exception
- printMakeCheckMatlabErrMsg(strtok(getenv('FILESTEM')), exception);
- testFailed = true;
+ printMakeCheckMatlabErrMsg(strtok(getenv('FILESTEM')), exception);
+ testFailed = true;
end
top_test_dir = getenv('TOP_TEST_DIR');
[modfile, name] = strtok(getenv('FILESTEM'));
@@ -52,20 +52,20 @@ cd(top_test_dir);
name = strtok(getenv('FILESTEM'));
fid = fopen([name '.m.trs'], 'w');
if fid < 0
- wd = pwd
- filestep = getenv('FILESTEM')
- error(['ERROR: problem opening file ' name '.m.trs for writing....']);
+ wd = pwd
+ filestep = getenv('FILESTEM')
+ error(['ERROR: problem opening file ' name '.m.trs for writing....']);
end
if testFailed
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 1\n');
- fprintf(fid,':list-of-failed-tests: %s\n', [name '.mod']);
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 1\n');
+ fprintf(fid,':list-of-failed-tests: %s\n', [name '.mod']);
else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 0\n');
- fprintf(fid,':list-of-passed-tests: %s\n', [name '.mod']);
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 0\n');
+ fprintf(fid,':list-of-passed-tests: %s\n', [name '.mod']);
end
fprintf(fid,':elapsed-time: %f\n', ecput);
fclose(fid);
diff --git a/tests/run_test_octave.m b/tests/run_test_octave.m
index 12cff43516e7f8dcf47c8bb4d4725fae40279b4c..12ff117ad68ee353432ce52703b22bdedef3005f 100644
--- a/tests/run_test_octave.m
+++ b/tests/run_test_octave.m
@@ -11,71 +11,71 @@
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
-##
-## You should have received a copy of the GNU General Public License
-## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+ ##
+ ## You should have received a copy of the GNU General Public License
+ ## along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-## Implementation notes:
-##
-## Before every call to Dynare, the contents of the workspace is saved in
-## 'wsOct', and reloaded after Dynare has finished (this is necessary since
-## Dynare does a 'clear -all').
+ ## Implementation notes:
+ ##
+ ## Before every call to Dynare, the contents of the workspace is saved in
+ ## 'wsOct', and reloaded after Dynare has finished (this is necessary since
+ ## Dynare does a 'clear -all').
-load_octave_packages
+ load_octave_packages
-top_test_dir = getenv('TOP_TEST_DIR');
-addpath(top_test_dir);
-addpath('.'); % workaround for bug in Octave 4.2.0-rc2: https://savannah.gnu.org/bugs/?49334
-addpath([top_test_dir filesep '..' filesep 'matlab']);
+ top_test_dir = getenv('TOP_TEST_DIR');
+ addpath(top_test_dir);
+ addpath('.'); % workaround for bug in Octave 4.2.0-rc2: https://savannah.gnu.org/bugs/?49334
+ addpath([top_test_dir filesep '..' filesep 'matlab']);
-## Test Dynare Version
-if !strcmp(dynare_version(), getenv("DYNARE_VERSION"))
- error("Incorrect version of Dynare is being tested")
-endif
+ ## Test Dynare Version
+ if !strcmp(dynare_version(), getenv("DYNARE_VERSION"))
+ error("Incorrect version of Dynare is being tested")
+ endif
-## Ask gnuplot to create graphics in text mode
-graphics_toolkit gnuplot;
-setenv("GNUTERM", "dumb");
+ ## Ask gnuplot to create graphics in text mode
+ graphics_toolkit gnuplot;
+ setenv("GNUTERM", "dumb");
-## Test MOD files listed in Makefile.am
-name = getenv("FILESTEM");
-[directory, testfile, ext] = fileparts([top_test_dir '/' name]);
-cd(directory);
+ ## Test MOD files listed in Makefile.am
+ name = getenv("FILESTEM");
+ [directory, testfile, ext] = fileparts([top_test_dir '/' name]);
+ cd(directory);
-printf("\n*** TESTING: %s ***\n", name);
+ printf("\n*** TESTING: %s ***\n", name);
-tic;
-save(['wsOct' testfile '.mat']);
-try
- dynare([testfile ext])
- testFailed = false;
-catch
- printMakeCheckOctaveErrMsg(getenv("FILESTEM"), lasterror);
- testFailed = true;
-end_try_catch
-top_test_dir = getenv('TOP_TEST_DIR');
-name = getenv("FILESTEM");
-[directory, testfile, ext] = fileparts([top_test_dir '/' name]);
-load(['wsOct' testfile '.mat']);
-ecput = toc;
-delete(['wsOct' testfile '.mat']);
+ tic;
+ save(['wsOct' testfile '.mat']);
+ try
+ dynare([testfile ext])
+ testFailed = false;
+ catch
+ printMakeCheckOctaveErrMsg(getenv("FILESTEM"), lasterror);
+ testFailed = true;
+ end_try_catch
+ top_test_dir = getenv('TOP_TEST_DIR');
+ name = getenv("FILESTEM");
+ [directory, testfile, ext] = fileparts([top_test_dir '/' name]);
+ load(['wsOct' testfile '.mat']);
+ ecput = toc;
+ delete(['wsOct' testfile '.mat']);
-cd(top_test_dir);
-fid = fopen([name '.o.trs'], 'w+');
-if testFailed
- fprintf(fid,':test-result: FAIL\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 1\n');
- fprintf(fid,':list-of-failed-tests: %s\n', [name '.mod']);
-else
- fprintf(fid,':test-result: PASS\n');
- fprintf(fid,':number-tests: 1\n');
- fprintf(fid,':number-failed-tests: 0\n');
- fprintf(fid,':list-of-passed-tests: %s\n', [name '.mod']);
-end
-fprintf(fid,':elapsed-time: %f\n', ecput);
-fclose(fid);
+ cd(top_test_dir);
+ fid = fopen([name '.o.trs'], 'w+');
+ if testFailed
+ fprintf(fid,':test-result: FAIL\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 1\n');
+ fprintf(fid,':list-of-failed-tests: %s\n', [name '.mod']);
+ else
+ fprintf(fid,':test-result: PASS\n');
+ fprintf(fid,':number-tests: 1\n');
+ fprintf(fid,':number-failed-tests: 0\n');
+ fprintf(fid,':list-of-passed-tests: %s\n', [name '.mod']);
+ end
+ fprintf(fid,':elapsed-time: %f\n', ecput);
+ fclose(fid);
-## Local variables:
-## mode: Octave
-## End:
+ ## Local variables:
+ ## mode: Octave
+ ## End:
diff --git a/tests/shock_decomposition/fsdat_simul.m b/tests/shock_decomposition/fsdat_simul.m
index d4f4a8066f17ba49faad004256693ebc1b9b01e9..159612e577c3b91d585970404c9cf576c0e8a8d6 100644
--- a/tests/shock_decomposition/fsdat_simul.m
+++ b/tests/shock_decomposition/fsdat_simul.m
@@ -1,828 +1,828 @@
gy_obs =[
- 1.0030045
- 0.99990934
- 1.0172778
- 0.99464043
- 1.0253423
- 1.0150215
- 0.97772557
- 0.97832186
- 1.0159561
- 1.0085937
- 1.0102649
- 1.0007604
- 1.0112596
- 1.0163279
- 1.0173204
- 1.0103896
- 1.0006493
- 0.99447124
- 1.0196405
- 1.0089304
- 0.99650737
- 1.0139707
- 0.97865842
- 1.0192225
- 0.99139628
- 1.0141362
- 1.0196612
- 0.97483476
- 0.99686151
- 0.99594464
- 1.0000642
- 1.0172243
- 1.0025773
- 0.97199728
- 1.0217815
- 1.0219949
- 0.99490252
- 1.0190728
- 1.0111337
- 1.0003792
- 0.98969164
- 1.010438
- 1.0216309
- 1.0016671
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diff --git a/tests/smoother2histval/fsdat_simul.m b/tests/smoother2histval/fsdat_simul.m
index face0f579b63c4363107e7403a3edf416f31e3f6..ed7853c80b3eca4adae18e61cce7dfe0ea4a3aeb 100644
--- a/tests/smoother2histval/fsdat_simul.m
+++ b/tests/smoother2histval/fsdat_simul.m
@@ -1,390 +1,390 @@
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- 0.96109456
- 0.98850646
- 1.0115635
- 1.0010548
- 0.98951687
- 0.98151347
- 1.0106021
- 1.0310697
- 0.990769
- 0.97940286
- 1.0279158
- 1.0070844
- 0.97456591
- 1.0235486
- 0.99211813
- 0.99808011
- 1.0038972
- 1.0178385
- 1.0008656
- 1.0012176
- 1.0120603
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- 0.95512181
- 1.0341867
- 1.0291133
- 1.0062875
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- 1.0518127
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- 1.0324251
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- 0.98698556
- 0.97985038
- 1.0220522
- 0.98358428
- 1.0085008
- 1.0095106
- 0.96544852
- 1.0014508
- 0.99673838
- 0.9703847
- 1.0245765
- 1.0031506
- 1.009074
- 0.98601129
- 0.99799441
- 1.0078514
- 0.98192982
- 1.0371426
- 0.97563731
- 0.99473616
- 0.99510009
- 0.98135322
- 1.0224481
- 0.99779603
- 0.98590478
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- 0.99767204
- 1.0208174
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- 1.0138123
- 1.0032682
- 0.99039426
- 1.0087413
- 1.0285208
- 0.98783907
- 1.0007856
- 1.0265034
- 0.99713746
- 1.0032946
- 1.0027628
- 0.99316893
- 0.99241067
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- 1.0057718
- 1.029354
- 0.9717329
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- 0.98185255
- 0.99861261
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- 1.0127951
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- 1.0268872
- 1.0048837
- 1.0124301
- 1.0020776
- 0.95526625
- 0.98592847
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- 1.0041718
- 1.0039668
- 1.0119603
- 1.0153073
- 0.99318888
- 0.96711969
- 0.99946578
- 1.0307262
- 0.97737468
- 1.0029169
- 1.0148043
- 0.97950296
- 0.97038701
- 1.010492
- 1.0087364
- 0.99717614
- 1.0375848
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- 0.98325812
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- 0.98795832
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- 1.0395356
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-];
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+ 0.96646656
+ 1.0575586
+ 0.98945919
+ ];
diff --git a/tests/steady_state/walsh1_old_ss_steadystate.m b/tests/steady_state/walsh1_old_ss_steadystate.m
index 9530862643e15467daf9179101cceec2650c362c..e419a00aced8bee224aea8b55eadf3353ce9a272 100644
--- a/tests/steady_state/walsh1_old_ss_steadystate.m
+++ b/tests/steady_state/walsh1_old_ss_steadystate.m
@@ -4,42 +4,42 @@ global M_
% read out parameters to access them with their name
NumberOfParameters = M_.param_nbr;
for ii = 1:NumberOfParameters
- paramname = deblank(M_.param_names(ii,:));
- eval([ paramname ' = M_.params(' int2str(ii) ');']);
+ paramname = deblank(M_.param_names(ii,:));
+ eval([ paramname ' = M_.params(' int2str(ii) ');']);
end
% initialize indicator
check = 0;
%% Enter model equations here
-
- pi = thetass-1;
- en = 1/3;
- eR = 1/betta;
- y_k = (1/alphha)*(1/betta-1+delta);
- ek = en*y_k^(-1/(1-alphha));
- ec = ek*(y_k-delta);
- em = ec*(a/(1-a))^(-1/b)*((thetass-betta)/thetass)^(-1/b);
- ey = ek*y_k;
- Xss = a*ec^(1-b)*(1+(a/(1-a))^(-1/b)*((thetass-betta)/thetass)^((b-1)/b));
- Psi = (1-alphha)*(ey/en)*Xss^((b-phi1)/(1-b))*a*ec^(-b)*(1-en)^eta;
- n = log(en);
- k = log(ek);
- m = log(em);
- c = log(ec);
- y = log(ey);
- R = log(eR);
- z = 0;
- u = 0;
-
+
+pi = thetass-1;
+en = 1/3;
+eR = 1/betta;
+y_k = (1/alphha)*(1/betta-1+delta);
+ek = en*y_k^(-1/(1-alphha));
+ec = ek*(y_k-delta);
+em = ec*(a/(1-a))^(-1/b)*((thetass-betta)/thetass)^(-1/b);
+ey = ek*y_k;
+Xss = a*ec^(1-b)*(1+(a/(1-a))^(-1/b)*((thetass-betta)/thetass)^((b-1)/b));
+Psi = (1-alphha)*(ey/en)*Xss^((b-phi1)/(1-b))*a*ec^(-b)*(1-en)^eta;
+n = log(en);
+k = log(ek);
+m = log(em);
+c = log(ec);
+y = log(ey);
+R = log(eR);
+z = 0;
+u = 0;
+
%% end own model equations
for iter = 1:length(M_.params) %update parameters set in the file
- eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
+ eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
end
NumberOfEndogenousVariables = M_.orig_endo_nbr; %auxiliary variables are set automatically
for ii = 1:NumberOfEndogenousVariables
- varname = deblank(M_.endo_names(ii,:));
- eval(['ys(' int2str(ii) ') = ' varname ';']);
+ varname = deblank(M_.endo_names(ii,:));
+ eval(['ys(' int2str(ii) ') = ' varname ';']);
end