diff --git a/doc/dynare.texi b/doc/dynare.texi
index 7265b4aeeb0cac4dad8ce097a6f08b1ecec03ab2..d31f2be5794eabcca5d73e0d1ca8292c383db225 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -9733,7 +9733,7 @@ Instantiates an empty @dseries object, with, if defined, an initial date given b
@sp 1
-@deftypefn {dseries} dseries (@var{FILENAME})
+@deftypefn {dseries} dseries (@var{FILENAME}[, @var{INITIAL_DATE}])
Instantiates and populates a @dseries object with a data file specified by @var{FILENAME}, a string passed as input. Valid file types are @file{.m} file, @file{.mat} file, @file{.csv} file, and @file{.xls} file. A typical @file{.m} file will have the following form:
@@ -9746,7 +9746,7 @@ azert = randn(100,1);
yuiop = randn(100,1);
@end example
-If a @file{.mat} file is used instead, it should provide the same informations. Note that the @code{INIT__} variable can be either a @dates object or a string which could be used to instantiate the same @dates object.
+If a @file{.mat} file is used instead, it should provide the same informations. Note that the @code{INIT__} variable can be either a @dates object or a string which could be used to instantiate the same @dates object. If @code{INIT__} is not provided in the @file{.mat} or @file{.m} file, the initial is by default set equal to @code{dates('1Y')}. If a second input argument is passed to the constructor, @dates object @var{INITIAL_DATE}, the initial date defined in @var{FILENAME} is reset to @var{INITIAL_DATE}. This is typically usefull if @code{INIT__} is not provided in the data file.
@end deftypefn
diff --git a/matlab/@dates/dates.m b/matlab/@dates/dates.m
index 99093bfe7aa26564ecabebce63368007d1ead316..c1c80fea2fb156bbf67a35e6ad4fdd27ff79ccab 100644
--- a/matlab/@dates/dates.m
+++ b/matlab/@dates/dates.m
@@ -45,7 +45,7 @@ function dd = dates(varargin) % --*-- Unitary tests --*--
%! @end deftypefn
%@eod:
-% Copyright (C) 2011-2013 Dynare Team
+% Copyright (C) 2011-2014 Dynare Team
%
% This file is part of Dynare.
%
@@ -62,6 +62,7 @@ function dd = dates(varargin) % --*-- Unitary tests --*--
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+% Initialization.
if nargin>0 && ischar(varargin{1}) && isequal(varargin{1},'initialize')
dd = struct('ndat', 0, 'freq', NaN(0), 'time', NaN(0,2));
dd = class(dd,'dates');
@@ -71,61 +72,98 @@ end
dd = evalin('base','emptydatesobject');
-switch nargin
- case 0
- % Returns an empty object
+if isequal(nargin, 0)
+ % Return an empty dates obect
return
- case 1
- if isa(varargin{1},'dates')
- % Returns a copy of the input argument
- dd = varargin{1};
- elseif isdate(varargin{1})
- date = string2date(varargin{1});
- dd.ndat = 1;
- dd.freq = date.freq;
- dd.time = date.time;
- elseif isfreq(varargin{1})
- % Instantiate an empty dates object (only set frequency)
- if ischar(varargin{1})
- dd.freq = string2freq(varargin{1});
+end
+
+if all(cellfun(@isdates, varargin))
+ % Concatenates dates in a dates object.
+ dd = horzcat(varargin{:});
+ return
+end
+
+if all(cellfun(@isstringdate,varargin))
+ % Concatenates dates in a dates object.
+ tmp = cellfun(@string2date,varargin);
+ if all([tmp.freq]-tmp(1).freq==0)
+ dd.freq = tmp(1).freq;
+ else
+ error('dates::dates: Wrong calling sequence of the constructor! All dates must have common frequency.')
+ end
+ dd.ndat = length(tmp);
+ dd.time = transpose(reshape([tmp.time],2,dd.ndat));
+ return
+end
+
+if isequal(nargin,1) && isfreq(varargin{1})
+ % Instantiate an empty dates object (only set frequency)
+ if ischar(varargin{1})
+ dd.freq = string2freq(varargin{1});
+ else
+ dd.freq = varargin{1};
+ end
+ return
+end
+
+if isequal(nargin,3) && isfreq(varargin{1})
+ if ischar(varargin{1})
+ dd.freq = string2freq(varargin{1});
+ else
+ dd.freq = varargin{1};
+ end
+ if (isnumeric(varargin{2}) && isvector(varargin{2}) && isint(varargin{2}))
+ if isnumeric(varargin{3}) && isvector(varargin{3}) && isint(varargin{3})
+ if all(varargin{3}>=1) && all(varargin{3}<=dd.freq)
+ dd.time = [varargin{2}(:), varargin{3}(:)];
+ dd.ndat = size(dd.time,1);
+ else
+ error(sprintf('dates::dates: Wrong calling sequence of the constructor! Third input must contain integers between 1 and %i.',dd.freq))
+ end
else
- dd.freq = varargin{1};
+ error('dates::dates: Wrong calling sequence of the constructor! Third input must be a vector of integers.')
end
else
- error('dates:: Wrong calling sequence of the constructor!')
+ error('dates::dates: Wrong calling sequence of the constructor! Second input must be a vector of integers.')
end
- otherwise
- if isdate(varargin{1})
- dd.ndat = nargin;
- dd.time = NaN(dd.ndat,2);
- date = string2date(varargin{1});
- dd.freq = date.freq;
- dd.time(1,:) = date.time;
- elseif isdates(varargin{1})
- dd = horzcat(varargin{:});
- return
- elseif isfreq(varargin{1})
- S.type = '()';
- S.subs = varargin;
- dd = subsref(dd,S);
- return
+ return
+end
+
+if isequal(nargin,2) && isfreq(varargin{1})
+ if ischar(varargin{1})
+ dd.freq = string2freq(varargin{1});
else
- error('dates::dates: Wrong calling sequence!')
+ dd.freq = varargin{1};
end
- for i=2:dd.ndat
- if isdate(varargin{i})
- date = string2date(varargin{i});
- if isequal(date.freq,dd.freq)
- dd.time(i,:) = date.time;
+ if isequal(dd.freq, 1)
+ if (isnumeric(varargin{2}) && isvector(varargin{2}) && isint(varargin{2}))
+ dd.time = [varargin{2}, ones(length(varargin{2}),1)];
+ dd.ndat = size(dd.time,1);
+ return
+ else
+ error('dates::dates: Wrong calling sequence of the constructor! Second input must be a vector of integers.')
+ end
+ else
+ if isequal(size(varargin{2},2), 2)
+ if all(isint(varargin{2}(:,1))) && all(isint(varargin{2}(:,1)))
+ if all(varargin{2}(:,2)>=1) && all(varargin{2}(:,2)<=dd.freq)
+ dd.time = [varargin{2}(:,1), varargin{2}(:,2)];
+ dd.ndat = size(dd.time,1);
+ else
+ error(sprintf('dates::dates: Wrong calling sequence of the constructor! Second column of the last input must contain integers between 1 and %i.',dd.freq))
+ end
else
- error(sprintf('dates::dates: Check that all the inputs have the same frequency (see input number %i)!',i))
+ error('dates::dates: Wrong calling sequence! Second input argument must be an array of integers.')
end
else
- error(sprintf('dates::dates: Input %i has to be a string date!',i))
+ error('dates::dates: Wrong calling sequence!')
end
end
+ return
end
+error('dates::dates: Wrong calling sequence!')
+
%@test:1
%$ % Define some dates
%$ B1 = '1945Q3';
diff --git a/matlab/@dseries/dseries.m b/matlab/@dseries/dseries.m
index 6bcfd2820ad0fbd2e73b4c962965bea049d47223..502445275849c660bcf535e633a2f7e4aa098637 100644
--- a/matlab/@dseries/dseries.m
+++ b/matlab/@dseries/dseries.m
@@ -10,7 +10,7 @@ function ts = dseries(varargin) % --*-- Unitary tests --*--
%! @sp 2
%! If @code{nargin==0} then an empty dseries object is created. The object can be populated with data subsequently using the overloaded subsref method.
%! @sp 2
-%! If @code{nargin==1} and if the input argument is a @ref{dynDate} object, then a dseries object without data is created. This object can be populated with the overload subsref method.
+%! If @code{nargin==1} and if the input argument is a @ref{dates} object, then a dseries object without data is created. This object can be populated with the overload subsref method.
%! @sp 2
%! If @code{nargin==1} and if the input argument is a string for the name of a csv, m or mat file containing data, then a dseries object is created from these data.
%! @sp 2
@@ -54,7 +54,7 @@ function ts = dseries(varargin) % --*-- Unitary tests --*--
%! frequency is unspecified. @var{freq} is equal to 4 if data are on a quaterly basis. @var{freq} is equal to
%! 12 if data are on a monthly basis. @var{freq} is equal to 52 if data are on a weekly basis.
%! @item init
-%! @ref{dynDate} object, initial date of the dataset.
+%! @ref{dates} object, initial date of the dataset.
%! @end table
%! @end deftypefn
%@eod:
@@ -153,6 +153,12 @@ switch nargin
ts.dates = dates(1,1):dates(1,1)+(ts.nobs-1);
end
case {2,3,4}
+ if isequal(nargin,2) && ischar(varargin{1}) && isdates(varargin{2})
+ % Instantiate dseries object with a data file and force the initial date to be as given by the second input argument.
+ ds = dseries(varargin{1});
+ ts = dseries(ds.data, varargin{2}, ds.name, ds.tex);
+ return
+ end
a = varargin{1};
b = varargin{2};
if nargin<4
diff --git a/matlab/@dseries/ydiff.m b/matlab/@dseries/ydiff.m
index 49b94dc6f61aedbb2a2e8be4d1695cfccd5d2e4c..beee58c8ac649511949712b59b357e1d7388b184 100644
--- a/matlab/@dseries/ydiff.m
+++ b/matlab/@dseries/ydiff.m
@@ -151,7 +151,7 @@ end
%$
%$ try
%$ data = transpose(1:100);
-%$ ts = dseries(data,1950,{'A1'},{'A_1'});
+%$ ts = dseries(data,'1950Y',{'A1'},{'A_1'});
%$ ts = ts.ydiff;
%$ t(1) = 1;
%$ catch
diff --git a/matlab/@dynTimeIndex/subsasgn.m b/matlab/@dynTimeIndex/subsasgn.m
index b03987c866a4cf7cbb56d9a3463aed084921e226..1014680622ce5bab3410c8b8ba0a31f032eb6fc5 100644
--- a/matlab/@dynTimeIndex/subsasgn.m
+++ b/matlab/@dynTimeIndex/subsasgn.m
@@ -17,4 +17,4 @@ function val = subsasgn(val, idx, rhs)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-error('dynTimeIndex::subsasgn: Members of dynDate class are private!')
\ No newline at end of file
+error('dynTimeIndex::subsasgn: Members of dynTimeIndex class are private!')
\ No newline at end of file
diff --git a/matlab/DsgeSmoother.m b/matlab/DsgeSmoother.m
index e8e76f97e02015bf064a2959463cbadbe7df9b2b..d66e77a69b60fa9380bd5d5a49e322aac66f001b 100644
--- a/matlab/DsgeSmoother.m
+++ b/matlab/DsgeSmoother.m
@@ -59,7 +59,7 @@ R = [];
P = [];
PK = [];
decomp = [];
-nobs = size(options_.varobs,1);
+nobs = length(options_.varobs);
smpl = size(Y,2);
M_ = set_all_parameters(xparam1,estim_params_,M_);
diff --git a/matlab/GetPosteriorParametersStatistics.m b/matlab/GetPosteriorParametersStatistics.m
index 437f8008ac28d8a629f3d8a72f56c14282b51a71..f92c2629ec4286316f796f1db242aab11eb25eef 100644
--- a/matlab/GetPosteriorParametersStatistics.m
+++ b/matlab/GetPosteriorParametersStatistics.m
@@ -180,17 +180,17 @@ if nvn
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(Draws,1,options_.mh_conf_sig);
- name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+ name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
else
try
- name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+ name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
catch
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(Draws,1,options_.mh_conf_sig);
- name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+ name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
end
end
diff --git a/matlab/PlotPosteriorDistributions.m b/matlab/PlotPosteriorDistributions.m
index 5a622272b56bec12f656179e2a589067008112e7..4321103b8a31189d5064d051ff1e7444aa800e50 100644
--- a/matlab/PlotPosteriorDistributions.m
+++ b/matlab/PlotPosteriorDistributions.m
@@ -89,7 +89,7 @@ for i=1:npar
eval(['pmod = oo_.posterior_mode.shocks_std.' name ';'])
end
elseif i <= nvx+nvn
- name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i-nvx,1),:));
+ name = options_.varobs{estim_params_.nvn_observable_correspondence(i-nvx,1)};
eval(['x1 = oo_.posterior_density.measurement_errors_std.' name '(:,1);'])
eval(['f1 = oo_.posterior_density.measurement_errors_std.' name '(:,2);'])
eval(['oo_.prior_density.mearsurement_errors_std.' name '(:,1) = x2;'])
diff --git a/matlab/PosteriorFilterSmootherAndForecast.m b/matlab/PosteriorFilterSmootherAndForecast.m
index 0b3bd1e35075aa96e2c8dbd3ad62d516dbbe540c..b636565633d0c722330bd42bf132107d1eb8969a 100644
--- a/matlab/PosteriorFilterSmootherAndForecast.m
+++ b/matlab/PosteriorFilterSmootherAndForecast.m
@@ -48,7 +48,7 @@ MaxNumberOfPlotPerFigure = 4;% The square root must be an integer!
MaxNumberOfBytes=options_.MaxNumberOfBytes;
endo_nbr=M_.endo_nbr;
exo_nbr=M_.exo_nbr;
-nvobs = size(options_.varobs,1);
+nvobs = length(options_.varobs);
nn = sqrt(MaxNumberOfPlotPerFigure);
iendo = 1:endo_nbr;
i_last_obs = gend+(1-M_.maximum_endo_lag:0);
@@ -74,9 +74,9 @@ B = 200;
MAX_nruns = min(B,ceil(options_.MaxNumberOfBytes/(npar+2)/8));
MAX_nsmoo = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*gend)/8));
MAX_ninno = min(B,ceil(MaxNumberOfBytes/(exo_nbr*gend)/8));
-MAX_nerro = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)*gend)/8));
+MAX_nerro = min(B,ceil(MaxNumberOfBytes/(length(options_.varobs)*gend)/8));
if naK
- MAX_naK = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)* ...
+ MAX_naK = min(B,ceil(MaxNumberOfBytes/(length(options_.varobs)* ...
length(options_.filter_step_ahead)*gend)/8));
end
if horizon
@@ -272,4 +272,4 @@ if options_.forecast
pm3(endo_nbr,horizon+maxlag,ifil6,B,'Forecasted variables (total)',...
M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
'names2','smooth',MetropolisFolder,'_forc_total')
-end
\ No newline at end of file
+end
diff --git a/matlab/PosteriorIRF.m b/matlab/PosteriorIRF.m
index cf9737f0f842dae5bc370ac043842071a7abc509..fc1c3bcf58ce1b158458f244062f46433da3188f 100644
--- a/matlab/PosteriorIRF.m
+++ b/matlab/PosteriorIRF.m
@@ -1,11 +1,11 @@
function PosteriorIRF(type)
-% Builds posterior IRFs after the MH algorithm.
-%
-% INPUTS
+% Builds posterior IRFs after the MH algorithm.
+%
+% INPUTS
% o type [char] string specifying the joint density of the
-% deep parameters ('prior','posterior').
-%
-% OUTPUTS
+% deep parameters ('prior','posterior').
+%
+% OUTPUTS
% None (oo_ and plots).
%
% SPECIAL REQUIREMENTS
@@ -34,15 +34,16 @@ function PosteriorIRF(type)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-global options_ estim_params_ oo_ M_ bayestopt_ dataset_
+global options_ estim_params_ oo_ M_ bayestopt_ dataset_ dataset_info
+
% Set the number of periods
-if isempty(options_.irf) || ~options_.irf
+if isempty(options_.irf) || ~options_.irf
options_.irf = 40;
end
% Set varlist if necessary
varlist = options_.varlist;
if isempty(varlist)
- varlist = options_.varobs;
+ varlist = char(options_.varobs);
end
options_.varlist = varlist;
nvar = size(varlist,1);
@@ -58,7 +59,7 @@ end
% Get index of shocks for requested IRFs
irf_shocks_indx = getIrfShocksIndx();
-
+
% Set various parameters & Check or create directories
nvx = estim_params_.nvx;
nvn = estim_params_.nvn;
@@ -68,8 +69,8 @@ np = estim_params_.np ;
npar = nvx+nvn+ncx+ncn+np;
offset = npar-np; clear('nvx','nvn','ncx','ncn','np');
-nvobs = dataset_.info.nvobs;
-gend = dataset_.info.ntobs;
+nvobs = dataset_.vobs;
+gend = dataset_.nobs;
MaxNumberOfPlotPerFigure = 9;
nn = sqrt(MaxNumberOfPlotPerFigure);
MAX_nirfs_dsge = ceil(options_.MaxNumberOfBytes/(options_.irf*nvar*M_.exo_nbr)/8);
@@ -111,14 +112,14 @@ else% type = 'prior'
B = options_.prior_draws;
options_.B = B;
end
-try
+try
delete([MhDirectoryName filesep M_.fname '_irf_dsge*.mat'])
-catch
+catch
disp('No _IRFs (dsge) files to be deleted!')
end
-try
+try
delete([MhDirectoryName filesep M_.fname '_irf_bvardsge*.mat'])
-catch
+catch
disp('No _IRFs (bvar-dsge) files to be deleted!')
end
irun = 0;
@@ -144,10 +145,6 @@ if MAX_nirfs_dsgevar
else
stock_irf_bvardsge = zeros(options_.irf,nvobs,M_.exo_nbr,B);
end
- [mYY,mXY,mYX,mXX,Ydata,Xdata] = ...
- var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,...
- options_.dsge_varlag,-1,options_.datafile,options_.varobs,...
- options_.xls_sheet,options_.xls_range);
NumberOfLags = options_.dsge_varlag;
NumberOfLagsTimesNvobs = NumberOfLags*nvobs;
if options_.noconstant
@@ -174,7 +171,7 @@ localVars.npar = npar;
localVars.type=type;
if strcmpi(type,'posterior')
- while b<B
+ while b<B
b = b + 1;
x(b,:) = GetOneDraw(type);
end
@@ -192,7 +189,7 @@ if options_.dsge_var
localVars.NumberOfLags = options_.dsge_varlag;
localVars.NumberOfLagsTimesNvobs = NumberOfLags*nvobs;
localVars.Companion_matrix = diag(ones(nvobs*(NumberOfLags-1),1),-nvobs);
-end
+end
localVars.nvar=nvar;
localVars.IndxVariables=IndxVariables;
localVars.MAX_nirfs_dsgevar=MAX_nirfs_dsgevar;
@@ -225,14 +222,15 @@ else
localVars.NumberOfIRFfiles_dsge=NumberOfIRFfiles_dsge;
localVars.NumberOfIRFfiles_dsgevar=NumberOfIRFfiles_dsgevar;
localVars.ifil2=ifil2;
-
+
globalVars = struct('M_',M_, ...
'options_', options_, ...
'bayestopt_', bayestopt_, ...
'estim_params_', estim_params_, ...
'oo_', oo_, ...
- 'dataset_',dataset_);
-
+ 'dataset_',dataset_, ...
+ 'dataset_info',dataset_info);
+
% which files have to be copied to run remotely
NamFileInput(1,:) = {'',[M_.fname '_static.m']};
NamFileInput(2,:) = {'',[M_.fname '_dynamic.m']};
@@ -244,13 +242,13 @@ else
for j=1:length(fout),
nosaddle = nosaddle + fout(j).nosaddle;
end
-
+
end
% END first parallel section!
if nosaddle
- disp(['PosteriorIRF :: Percentage of discarded posterior draws = ' num2str(nosaddle/(B+nosaddle))])
+ disp(['PosteriorIRF :: Percentage of discarded posterior draws = ' num2str(nosaddle/(B+nosaddle))])
end
ReshapeMatFiles('irf_dsge',type)
@@ -323,7 +321,7 @@ if MAX_nirfs_dsgevar
MedianIRFdsgevar = zeros(options_.irf,nvar,M_.exo_nbr);
VarIRFdsgevar = zeros(options_.irf,nvar,M_.exo_nbr);
DistribIRFdsgevar = zeros(options_.irf,9,nvar,M_.exo_nbr);
- HPDIRFdsgevar = zeros(options_.irf,2,nvar,M_.exo_nbr);
+ HPDIRFdsgevar = zeros(options_.irf,2,nvar,M_.exo_nbr);
fprintf('Estimation::mcmc: Posterior (bvar-dsge) IRFs...\n');
tit(M_.exo_names_orig_ord,:) = M_.exo_names;
kdx = 0;
@@ -341,7 +339,7 @@ if MAX_nirfs_dsgevar
end
kdx = kdx + size(STOCK_IRF_BVARDSGE,1);
end
- clear STOCK_IRF_BVARDSGE;
+ clear STOCK_IRF_BVARDSGE;
for i = irf_shocks_indx
for j = 1:nvar
name = [deblank(M_.endo_names(IndxVariables(j),:)) '_' deblank(tit(i,:))];
@@ -384,7 +382,7 @@ localVars.MaxNumberOfPlotPerFigure=MaxNumberOfPlotPerFigure;
if options_.dsge_var
localVars.HPDIRFdsgevar=HPDIRFdsgevar;
localVars.MeanIRFdsgevar = MeanIRFdsgevar;
-end
+end
% The files .TeX are genereted in sequential way always!
@@ -394,14 +392,14 @@ if options_.TeX
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
-
+
for i=irf_shocks_indx
NAMES = [];
TEXNAMES = [];
-
+
for j=1:nvar
- if max(abs(MeanIRF(:,j,i))) > options_.impulse_responses.plot_threshold
-
+ if max(abs(MeanIRF(:,j,i))) > options_.impulse_responses.plot_threshold
+
name = deblank(varlist(j,:));
texname = deblank(varlist_TeX(j,:));
@@ -413,7 +411,7 @@ if options_.TeX
TEXNAMES = char(TEXNAMES,['$' texname '$']);
end
end
-
+
end
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:size(TEXNAMES,1)
@@ -430,10 +428,10 @@ if options_.TeX
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
-
+
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
-
+
end
% The others file format are generated in parallel by PosteriorIRF_core2!
@@ -453,7 +451,7 @@ if ~isoctave
else
globalVars = struct('M_',M_, ...
'options_', options_);
-
+
[fout] = masterParallel(options_.parallel, 1, M_.exo_nbr,NamFileInput,'PosteriorIRF_core2', localVars, globalVars, options_.parallel_info);
end
end
@@ -463,4 +461,4 @@ end
% END parallel code!
-fprintf('Estimation::mcmc: Posterior IRFs, done!\n');
\ No newline at end of file
+fprintf('Estimation::mcmc: Posterior IRFs, done!\n');
diff --git a/matlab/PosteriorIRF_core1.m b/matlab/PosteriorIRF_core1.m
index 1eff387ad64115fcce0e12710c2136ebc612bbc5..0d0a1a752e4ba9921bc39a74ffb61344b9e02167 100644
--- a/matlab/PosteriorIRF_core1.m
+++ b/matlab/PosteriorIRF_core1.m
@@ -41,7 +41,7 @@ function myoutput=PosteriorIRF_core1(myinputs,fpar,B,whoiam, ThisMatlab)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-global options_ estim_params_ oo_ M_ bayestopt_ dataset_
+global options_ estim_params_ oo_ M_ bayestopt_ dataset_ dataset_info
if nargin<4,
whoiam=0;
@@ -189,24 +189,24 @@ while fpar<B
end
if MAX_nirfs_dsgevar
IRUN = IRUN+1;
- [fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
- DSGE_PRIOR_WEIGHT = floor(dataset_.info.ntobs*(1+dsge_prior_weight));
- SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.info.ntobs*(dsge_prior_weight+1)));
+ DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
+ SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));
explosive_var = 1;
while explosive_var
% draw from the marginal posterior of SIGMA
- SIGMAu_draw = rand_inverse_wishart(dataset_.info.nvobs, DSGE_PRIOR_WEIGHT-NumberOfParametersPerEquation, ...
+ SIGMAu_draw = rand_inverse_wishart(dataset_.vobs, DSGE_PRIOR_WEIGHT-NumberOfParametersPerEquation, ...
SIGMA_inv_upper_chol);
% draw from the conditional posterior of PHI
- PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,dataset_.info.nvobs, PHI, ...
+ PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,dataset_.vobs, PHI, ...
chol(SIGMAu_draw)', chol(iXX)');
- Companion_matrix(1:dataset_.info.nvobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
+ Companion_matrix(1:dataset_.vobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
% Check for stationarity
explosive_var = any(abs(eig(Companion_matrix))>1.000000001);
end
% Get the mean
- mu = zeros(1,dataset_.info.nvobs);
+ mu = zeros(1,dataset_.vobs);
% Get rotation
if dsge_prior_weight > 0
Atheta(oo_.dr.order_var,M_.exo_names_orig_ord) = oo_.dr.ghu*sqrt(M_.Sigma_e);
@@ -216,24 +216,24 @@ while fpar<B
SIGMAu_chol = chol(SIGMAu_draw)';
SIGMAtrOMEGA = SIGMAu_chol*OMEGAstar';
PHIpower = eye(NumberOfLagsTimesNvobs);
- irfs = zeros (options_.irf,dataset_.info.nvobs*M_.exo_nbr);
- tmp3 = PHIpower(1:dataset_.info.nvobs,1:dataset_.info.nvobs)*SIGMAtrOMEGA;
+ irfs = zeros (options_.irf,dataset_.vobs*M_.exo_nbr);
+ tmp3 = PHIpower(1:dataset_.vobs,1:dataset_.vobs)*SIGMAtrOMEGA;
irfs(1,:) = tmp3(:)';
for t = 2:options_.irf
PHIpower = Companion_matrix*PHIpower;
- tmp3 = PHIpower(1:dataset_.info.nvobs,1:dataset_.info.nvobs)*SIGMAtrOMEGA;
+ tmp3 = PHIpower(1:dataset_.vobs,1:dataset_.vobs)*SIGMAtrOMEGA;
irfs(t,:) = tmp3(:)'+kron(ones(1,M_.exo_nbr),mu);
end
tmp_dsgevar = kron(ones(options_.irf,1),mu);
- for j = 1:(dataset_.info.nvobs*M_.exo_nbr)
+ for j = 1:(dataset_.vobs*M_.exo_nbr)
if max(irfs(:,j)) - min(irfs(:,j)) > 1e-10
tmp_dsgevar(:,j) = (irfs(:,j));
end
end
if IRUN < MAX_nirfs_dsgevar
- stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.info.nvobs,M_.exo_nbr);
+ stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.vobs,M_.exo_nbr);
else
- stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.info.nvobs,M_.exo_nbr);
+ stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.vobs,M_.exo_nbr);
instr = [MhDirectoryName '/' M_.fname '_irf_bvardsge' ...
int2str(NumberOfIRFfiles_dsgevar) '.mat stock_irf_bvardsge;'];,
eval(['save ' instr]);
@@ -242,7 +242,7 @@ while fpar<B
end
NumberOfIRFfiles_dsgevar = NumberOfIRFfiles_dsgevar+1;
IRUN =0;
- stock_irf_dsgevar = zeros(options_.irf,dataset_.info.nvobs,M_.exo_nbr,MAX_nirfs_dsgevar);
+ stock_irf_dsgevar = zeros(options_.irf,dataset_.vobs,M_.exo_nbr,MAX_nirfs_dsgevar);
end
end
if irun == MAX_nirfs_dsge || irun == B || fpar == B
@@ -279,20 +279,6 @@ while fpar<B
ifil2 = ifil2 + 1;
irun2 = 0;
end
-% if isoctave
-% if (whoiam==0),
-% printf(['Posterior IRF %3.f%% done\r'],(fpar/B*100));
-% end
-% elseif ~whoiam,
-% waitbar(fpar/B,h);
-% end
-% if whoiam,
-% if ~isoctave
-% fprintf('Done! \n');
-% end
-% waitbarString = [ 'Subdraw ' int2str(fpar) '/' int2str(B) ' done.'];
-% fMessageStatus((fpar-fpar0)/(B-fpar0),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
-% end
dyn_waitbar((fpar-fpar0)/(B-fpar0),h);
end
@@ -310,9 +296,5 @@ end
myoutput.OutputFileName = [OutputFileName_dsge;
OutputFileName_param;
OutputFileName_bvardsge];
-
-myoutput.nosaddle = nosaddle;
-
-
-
+myoutput.nosaddle = nosaddle;
diff --git a/matlab/PosteriorIRF_core2.m b/matlab/PosteriorIRF_core2.m
index f33fbc2832ac0529a3146fe4bed8713adc552b90..c20702eb5f4658996bb7aa9a0690a6e49ba7724d 100644
--- a/matlab/PosteriorIRF_core2.m
+++ b/matlab/PosteriorIRF_core2.m
@@ -5,7 +5,7 @@ function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
% Perform in parallel execution a portion of the PosteriorIRF.m code.
% See also the comment in random_walk_metropolis_hastings_core.m funtion.
%
-% INPUTS
+% INPUTS
% See the comment in random_walk_metropolis_hastings_core.m funtion.
%
% OUTPUTS
@@ -13,8 +13,8 @@ function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
% Contained:
% OutputFileName (i.e. the figures without the file .txt).
%
-% ALGORITHM
-% Portion of PosteriorIRF.m function code. Specifically the last 'for' cycle.
+% ALGORITHM
+% Portion of PosteriorIRF.m function code. Specifically the last 'for' cycle.
%
% SPECIAL REQUIREMENTS.
% None.
@@ -36,7 +36,7 @@ function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-global options_ M_
+global options_ M_
if nargin<4,
whoiam=0;
@@ -87,7 +87,7 @@ OutputFileName={};
subplotnum = 0;
for i=fpar:npar,
figunumber = 0;
-
+
for j=1:nvar
if max(abs(MeanIRF(:,j,i))) > options_.impulse_responses.plot_threshold
subplotnum = subplotnum+1;
@@ -96,7 +96,7 @@ for i=fpar:npar,
elseif subplotnum == 1 && ~options_.relative_irf
hh = dyn_figure(options_,'Name',['Orthogonalized shock to ' tit(i,:)]);
end
-
+
set(0,'CurrentFigure',hh)
subplot(nn,nn,subplotnum);
if ~MAX_nirfs_dsgevar
@@ -108,12 +108,12 @@ for i=fpar:npar,
set(h2,'FaceColor',[1 1 1]);
set(h2,'BaseValue',min(HPDIRF(:,1,j,i)));
plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',3)
- % 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]);
hold off
- else
+ else
h1 = area(1:options_.irf,HPDIRF(:,2,j,i));
set(h1,'FaceColor',[.9 .9 .9]);
set(h1,'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))]));
@@ -136,9 +136,9 @@ for i=fpar:npar,
else
if options_.debug
fprintf('POSTERIOR_IRF: The IRF of %s to %s is smaller than the irf_plot_threshold of %4.3f and will not be displayed.\n',deblank(varlist(j,:)),tit(i,:),options_.impulse_responses.plot_threshold)
- end
+ end
end
-
+
if subplotnum == MaxNumberOfPlotPerFigure || (j == nvar && subplotnum> 0)
figunumber = figunumber+1;
dyn_saveas(hh,[DirectoryName '/' M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber)],options_);
@@ -154,9 +154,8 @@ for i=fpar:npar,
% 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
+end% loop over exo_var
myoutput.OutputFileName = OutputFileName;
-
diff --git a/matlab/ReshapeMatFiles.m b/matlab/ReshapeMatFiles.m
index 06acd73722da37d768502f447384d7e93a871c7f..1ad90fce11f9d7fa0bd6d3f0a1b66ea094c115ff 100644
--- a/matlab/ReshapeMatFiles.m
+++ b/matlab/ReshapeMatFiles.m
@@ -70,7 +70,7 @@ switch type
TYPEarray = 4;
case 'irf_bvardsge'
CAPtype = 'IRF_BVARDSGE';
- TYPEsize = [ options_.irf , size(options_.varobs,1) , M_.exo_nbr ];
+ TYPEsize = [ options_.irf , length(options_.varobs) , M_.exo_nbr ];
TYPEarray = 4;
case 'smooth'
CAPtype = 'SMOOTH';
@@ -82,7 +82,7 @@ switch type
TYPEarray = 3;
case 'error'
CAPtype = 'ERROR';
- TYPEsize = [ size(options_.varobs,1) , options_.nobs ];
+ TYPEsize = [ length(options_.varobs) , options_.nobs ];
TYPEarray = 3;
case 'innov'
CAPtype = 'INNOV';
diff --git a/matlab/bvar_forecast.m b/matlab/bvar_forecast.m
index 03100c053000b06ec27695773110a76e7500f5de..1b88e0ae61552c32fad4d0c79ee976a3be268f89 100644
--- a/matlab/bvar_forecast.m
+++ b/matlab/bvar_forecast.m
@@ -122,7 +122,7 @@ 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, :));
+ options_.varobs{i});
end
dyn_saveas(dyn_graph.fh,[OutputDirectoryName '/' M_.fname '_BVAR_forecast_',num2str(nlags)],options_)
@@ -146,8 +146,8 @@ if ~isempty(forecast_data.realized_val)
fprintf('RMSE of BVAR(%d):\n', nlags);
- for i = 1:size(options_.varobs, 1)
- fprintf('%s: %10.4f\n', options_.varobs(i, :), rmse(i));
+ for i = 1:length(options_.varobs)
+ fprintf('%s: %10.4f\n', options_.varobs{i}, rmse(i));
end
end
@@ -162,8 +162,8 @@ if ~isdir(DirectoryName)
end
save([ DirectoryName '/simulations.mat'], 'sims_no_shock', 'sims_with_shocks');
-for i = 1:size(options_.varobs, 1)
- name = options_.varobs(i, :);
+for i = 1:length(options_.varobs)
+ name = options_.varobs{i};
sims = squeeze(sims_with_shocks(:,i,:));
eval(['oo_.bvar.forecast.with_shocks.Mean.' name ' = mean(sims, 2);']);
diff --git a/matlab/bvar_irf.m b/matlab/bvar_irf.m
index d742915d7c7ce01ff1791969f4dc4d6db91030a9..d7528f79bce6acfb6a844fcc2abcdda4661571f1 100644
--- a/matlab/bvar_irf.m
+++ b/matlab/bvar_irf.m
@@ -138,9 +138,9 @@ save([ DirectoryName '/simulations.mat'], 'sampled_irfs');
% Save results in oo_
for i=1:ny
- shock_name = options_.varobs(i, :);
+ shock_name = options_.varobs{i};
for j=1:ny
- variable_name = options_.varobs(j, :);
+ variable_name = options_.varobs{j};
eval(['oo_.bvar.irf.Mean.' variable_name '.' shock_name ' = posterior_mean_irfs(' int2str(j) ',' int2str(i) ',:);'])
eval(['oo_.bvar.irf.Median.' variable_name '.' shock_name ' = posterior_median_irfs(' int2str(j) ',' int2str(i) ',:);'])
eval(['oo_.bvar.irf.Var.' variable_name '.' shock_name ' = posterior_variance_irfs(' int2str(j) ',' int2str(i) ',:);'])
diff --git a/matlab/check_dsge_var_model.m b/matlab/check_dsge_var_model.m
index b10815ac81617ae3c2b7ebebfd4c5782bd151640..8b16ec09dac452bf45e97317d15742224db77e49 100644
--- a/matlab/check_dsge_var_model.m
+++ b/matlab/check_dsge_var_model.m
@@ -20,25 +20,25 @@ function check_dsge_var_model(Model, EstimatedParameters, BayesInfo)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if EstimatedParameters.nvn
- error('DsgeVarLikelihood:: Measurement errors are not allowed!')
+ error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
end
if EstimatedParameters.ncn
- error('DsgeVarLikelihood:: Measurement errors are not allowed!')
+ error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
end
if any(vec(Model.H))
- error('DsgeVarLikelihood:: Measurement errors are not allowed!')
+ error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
end
if EstimatedParameters.ncx
- error('DsgeVarLikelihood:: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
+ error('Estimation::DsgeVarLikelihood: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
end
if Model.exo_nbr>1 && any(vec(tril(Model.Sigma_e,-1)))
- error('DsgeVarLikelihood:: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
+ error('Estimation::DsgeVarLikelihood: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
end
if isequal(BayesInfo.with_trend,1)
- error('DsgeVarLikelihood :: Linear trend is not yet implemented!')
+ error('Estimation::DsgeVarLikelihood: Linear trend is not yet implemented!')
end
diff --git a/matlab/check_file_extension.m b/matlab/check_file_extension.m
index 63d627f0b0f3428f57695ee9999d4dad48d3b8f8..10d4c1c9b3d6c1197599d921b72dfa396f9e6c24 100644
--- a/matlab/check_file_extension.m
+++ b/matlab/check_file_extension.m
@@ -19,6 +19,10 @@ function b = check_file_extension(file,type)
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
+% Clean-up path
+file = strrep(file, '../', '');
+file = strrep(file, './', '');
+
remain = file;
while ~isempty(remain)
[ext, remain] = strtok(remain,'.');
diff --git a/matlab/check_list_of_variables.m b/matlab/check_list_of_variables.m
index 48a3064a1a5186ca25ebc9259d301b2710ce00f1..30805becb342bb327b1c7f0f6923d18042f09371 100644
--- a/matlab/check_list_of_variables.m
+++ b/matlab/check_list_of_variables.m
@@ -46,7 +46,7 @@ if options_.dsge_var && options_.bayesian_irf
msg = 1;
end
end
- if size(varlist,1)~=size(options_.varobs)
+ if size(varlist,1)~=length(options_.varobs)
msg = 1;
end
if msg
@@ -55,7 +55,7 @@ if options_.dsge_var && options_.bayesian_irf
skipline()
end
end
- varlist = options_.varobs;
+ varlist = char(options_.varobs);
return
end
@@ -128,7 +128,7 @@ elseif isempty(varlist) && isempty(options_.endo_vars_for_moment_computations_in
if choice==1
varlist = M_.endo_names(1:M_.orig_endo_nbr, :);
elseif choice==2
- varlist = options_.varobs;
+ varlist = char(options_.varobs);
elseif choice==3
varlist = NaN;
else
@@ -138,15 +138,14 @@ elseif isempty(varlist) && isempty(options_.endo_vars_for_moment_computations_in
end
end
end
+ if isnan(varlist)
+ edit([M_.fname '.mod'])
+ end
+ skipline()
end
- if isnan(varlist)
- edit([M_.fname '.mod'])
- end
- skipline()
end
-
function format_text(remain, max_number_of_words_per_line)
index = 0;
line_of_text = [];
diff --git a/matlab/compute_moments_varendo.m b/matlab/compute_moments_varendo.m
index 5093ffc52ccba8f1e85de7de2f0913da834332b6..6482afb746b403f7480344aabdc80ab1fa17618d 100644
--- a/matlab/compute_moments_varendo.m
+++ b/matlab/compute_moments_varendo.m
@@ -36,14 +36,14 @@ function oo_ = compute_moments_varendo(type,options_,M_,oo_,var_list_)
if strcmpi(type,'posterior')
posterior = 1;
if nargin==4
- var_list_ = options_.varobs;
+ var_list_ = char(options_.varobs);
end
elseif strcmpi(type,'prior')
posterior = 0;
if nargin==4
var_list_ = options_.prior_analysis_endo_var_list;
if isempty(var_list_)
- options_.prior_analysis_var_list = options_.varobs;
+ options_.prior_analysis_var_list = char(options_.varobs);
end
end
else
diff --git a/matlab/display_estimation_results_table.m b/matlab/display_estimation_results_table.m
index 0fecc8f38c4e778c7cc80a0f06e90409ec81ab0e..334182bcb37c2d32b9bfe1bc27b3b1aab40b335d 100644
--- a/matlab/display_estimation_results_table.m
+++ b/matlab/display_estimation_results_table.m
@@ -105,7 +105,7 @@ if nvx
disp(tit1)
ip = nvx+1;
for i=1:nvn
- name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+ 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), ...
@@ -276,7 +276,7 @@ if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior
fprintf(fidTeX,'\\hline \\hline \\endlastfoot \n');
ip = nvx+1;
for i=1:nvn
- idx = strmatch(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:),M_.endo_names);
+ idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)},M_.endo_names);
fprintf(fidTeX,'$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
deblank(M_.endo_names_tex(idx,:)), ...
deblank(pnames(bayestopt_.pshape(ip)+1,:)), ...
@@ -469,7 +469,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
fprintf(fidTeX,'\\hline \\hline \\endlastfoot \n');
ip = nvx+1;
for i=1:nvn
- idx = strmatch(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:),M_.endo_names);
+ 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),...
diff --git a/matlab/dsge_likelihood.m b/matlab/dsge_likelihood.m
index e3ffc7ceacfdf590397c4594b562f0f1c29e0979..ee0c606a3f2a7440d6106ce8400ec18d2a648d7a 100644
--- a/matlab/dsge_likelihood.m
+++ b/matlab/dsge_likelihood.m
@@ -1,4 +1,4 @@
-function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults,derivatives_info)
+function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults,derivatives_info)
% Evaluates the posterior kernel of a dsge model using the specified
% kalman_algo; the resulting posterior includes the 2*pi constant of the
% likelihood function
@@ -295,7 +295,7 @@ BayesInfo.mf = BayesInfo.mf1;
% Define the constant vector of the measurement equation.
if DynareOptions.noconstant
- constant = zeros(DynareDataset.info.nvobs,1);
+ constant = zeros(DynareDataset.vobs,1);
else
if DynareOptions.loglinear
constant = log(SteadyState(BayesInfo.mfys));
@@ -306,28 +306,28 @@ end
% Define the deterministic linear trend of the measurement equation.
if BayesInfo.with_trend
- trend_coeff = zeros(DynareDataset.info.nvobs,1);
+ trend_coeff = zeros(DynareDataset.vobs,1);
t = DynareOptions.trend_coeffs;
for i=1:length(t)
if ~isempty(t{i})
trend_coeff(i) = evalin('base',t{i});
end
end
- trend = repmat(constant,1,DynareDataset.info.ntobs)+trend_coeff*[1:DynareDataset.info.ntobs];
+ trend = repmat(constant,1,DynareDataset.nobs)+trend_coeff*[1:DynareDataset.nobs];
else
- trend = repmat(constant,1,DynareDataset.info.ntobs);
+ trend = repmat(constant,1,DynareDataset.nobs);
end
% Get needed informations for kalman filter routines.
start = DynareOptions.presample+1;
-Z = BayesInfo.mf; % old mf
-no_missing_data_flag = ~DynareDataset.missing.state;
-mm = length(T); % old np
-pp = DynareDataset.info.nvobs;
+Z = BayesInfo.mf;
+no_missing_data_flag = ~DatasetInfo.missing.state;
+mm = length(T);
+pp = DynareDataset.vobs;
rr = length(Q);
kalman_tol = DynareOptions.kalman_tol;
riccati_tol = DynareOptions.riccati_tol;
-Y = DynareDataset.data-trend;
+Y = transpose(DynareDataset.data)-trend;
%------------------------------------------------------------------------------
% 3. Initial condition of the Kalman filter
@@ -406,7 +406,7 @@ switch DynareOptions.lik_init
kalman_tol, riccati_tol, DynareOptions.presample, ...
T,R,Q,H,Z,mm,pp,rr);
else
- [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
+ [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), ...
zeros(mm,1), Pinf, Pstar, ...
kalman_tol, riccati_tol, DynareOptions.presample, ...
@@ -441,9 +441,9 @@ switch DynareOptions.lik_init
% no need to test again for correlation elements
correlated_errors_have_been_checked = 1;
- [dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
- DynareDataset.missing.number_of_observations,...
- DynareDataset.missing.no_more_missing_observations, ...
+ [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, riccati_tol, DynareOptions.presample, ...
@@ -523,7 +523,7 @@ if analytic_derivation,
DLIK = [];
AHess = [];
iv = DynareResults.dr.restrict_var_list;
- if nargin<8 || isempty(derivatives_info)
+ if nargin<9 || isempty(derivatives_info)
[A,B,nou,nou,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
if ~isempty(EstimatedParameters.var_exo)
indexo=EstimatedParameters.var_exo(:,1);
@@ -655,10 +655,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
end
else
if 0 %DynareOptions.block
- [err, LIK,lik] = block_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,...
+ [err, LIK,lik] = block_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,...
T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
else
- [LIK,lik] = missing_observations_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
+ [LIK,lik] = missing_observations_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
a, Pstar, ...
kalman_tol, DynareOptions.riccati_tol, ...
DynareOptions.presample, ...
@@ -733,7 +733,7 @@ if (kalman_algo==2) || (kalman_algo==4)
end
end
- [LIK, lik] = univariate_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
+ [LIK, lik] = univariate_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
a,Pstar, ...
DynareOptions.kalman_tol, ...
DynareOptions.riccati_tol, ...
diff --git a/matlab/dsge_var_likelihood.m b/matlab/dsge_var_likelihood.m
index 6c37fd1a468cc7ac4e586075e727ffbc059950fd..f4cf0a8b2fa2e30aa2a1721760597b6ac22a6930 100644
--- a/matlab/dsge_var_likelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -1,4 +1,4 @@
-function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
% Evaluates the posterior kernel of the bvar-dsge model.
%
% INPUTS
@@ -55,7 +55,11 @@ end
nx = EstimatedParameters.nvx + EstimatedParameters.np;
% Get the number of observed variables in the VAR model.
-NumberOfObservedVariables = DynareDataset.info.nvobs;
+NumberOfObservedVariables = DynareDataset.vobs;
+
+% Get the number of observations.
+NumberOfObservations = DynareDataset.nobs;
+
% Get the number of lags in the VAR model.
NumberOfLags = DynareOptions.dsge_varlag;
@@ -110,8 +114,8 @@ Model.Sigma_e = Q;
dsge_prior_weight = Model.params(dsge_prior_weight_idx);
% Is the dsge prior proper?
-if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.info.ntobs;
- fval = objective_function_penalty_base+abs(DynareDataset.info.ntobs*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
+if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/NumberOfObservations;
+ fval = objective_function_penalty_base+abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
exit_flag = 0;
info = 51;
return
@@ -197,9 +201,9 @@ assignin('base','GXX',GXX);
assignin('base','GYX',GYX);
if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
- tmp0 = dsge_prior_weight*DynareDataset.info.ntobs*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
- tmp1 = dsge_prior_weight*DynareDataset.info.ntobs*GYX + mYX;
- tmp2 = inv(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX);
+ tmp0 = dsge_prior_weight*NumberOfObservations*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
+ tmp1 = dsge_prior_weight*NumberOfObservations*GYX + mYX;
+ tmp2 = inv(dsge_prior_weight*NumberOfObservations*GXX+mXX);
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
[SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
if ~SIGMAu_is_positive_definite
@@ -208,28 +212,28 @@ if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model
exit_flag = 0;
return;
end
- SIGMAu = SIGMAu / (DynareDataset.info.ntobs*(1+dsge_prior_weight));
+ SIGMAu = SIGMAu / (NumberOfObservations*(1+dsge_prior_weight));
PHI = tmp2*tmp1'; clear('tmp1');
- prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*DynareDataset.info.ntobs- ...
+ prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*NumberOfObservations- ...
NumberOfObservedVariables*NumberOfLags ...
+1-(1:NumberOfObservedVariables)')));
- prodlng2 = sum(gammaln(.5*(dsge_prior_weight*DynareDataset.info.ntobs- ...
+ prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
NumberOfObservedVariables*NumberOfLags ...
+1-(1:NumberOfObservedVariables)')));
- lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX)) ...
- + .5*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters)*log(det((dsge_prior_weight+1)*DynareDataset.info.ntobs*SIGMAu)) ...
- - .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX)) ...
- - .5*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters)*log(det(dsge_prior_weight*DynareDataset.info.ntobs*(GYY-GYX*inv(GXX)*GYX'))) ...
- + .5*NumberOfObservedVariables*DynareDataset.info.ntobs*log(2*pi) ...
- - .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters) ...
- + .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters) ...
+ lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ...
+ + .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMAu)) ...
+ - .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ...
+ - .5*(dsge_prior_weight*NumberOfObservations-NumberOfParameters)*log(det(dsge_prior_weight*NumberOfObservations*(GYY-GYX*inv(GXX)*GYX'))) ...
+ + .5*NumberOfObservedVariables*NumberOfObservations*log(2*pi) ...
+ - .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters) ...
+ + .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*NumberOfObservations-NumberOfParameters) ...
- prodlng1 + prodlng2;
else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
iGXX = inv(GXX);
SIGMAu = GYY - GYX*iGXX*transpose(GYX);
PHI = iGXX*transpose(GYX);
- lik = DynareDataset.info.ntobs * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
- trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/DynareDataset.info.ntobs));
+ lik = NumberOfObservations * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) + ...
+ trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/NumberOfObservations));
lik = .5*lik;% Minus likelihood
end
@@ -254,7 +258,7 @@ if (nargout==9)
iGXX = inv(GXX);
prior.SIGMAstar = GYY - GYX*iGXX*GYX';
prior.PHIstar = iGXX*transpose(GYX);
- prior.ArtificialSampleSize = fix(dsge_prior_weight*DynareDataset.info.ntobs);
+ prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
prior.iGXX = iGXX;
end
\ No newline at end of file
diff --git a/matlab/dsgevar_posterior_density.m b/matlab/dsgevar_posterior_density.m
index eca70f743903bc6961f3c217b9f152555b684eb6..8e359f3ce447c6417b2055c9f1107d511b3b81a2 100644
--- a/matlab/dsgevar_posterior_density.m
+++ b/matlab/dsgevar_posterior_density.m
@@ -1,4 +1,4 @@
-function bvar = dsgevar_posterior_density(deep,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function bvar = dsgevar_posterior_density(deep,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
% This function characterizes the posterior distribution of a bvar with
% a dsge prior (as in Del Negro and Schorfheide 2003) for a given value
% of the deep parameters (structural parameters + the size of the
@@ -38,7 +38,7 @@ dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
bvar.NumberOfLags = options_.varlag;
-bvar.NumberOfVariables = size(options_.varobs,1);
+bvar.NumberOfVariables = length(options_.varobs);
bvar.Constant = 'no';
bvar.NumberOfEstimatedParameters = bvar.NumberOfLags*bvar.NumberOfVariables;
if ~options_.noconstant
@@ -47,7 +47,7 @@ if ~options_.noconstant
bvar.NumberOfVariables;
end
-[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(deep',DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(deep',DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
% Conditionnal posterior density of the lagged matrices (given Sigma) ->
% Matric-variate normal distribution.
diff --git a/matlab/dyn_forecast.m b/matlab/dyn_forecast.m
index bea68db53b3e60014fdf574863fe94ee3f56d11f..5df0edbf80d1e62ea262ddaf1559952f4b2593f4 100644
--- a/matlab/dyn_forecast.m
+++ b/matlab/dyn_forecast.m
@@ -81,8 +81,8 @@ switch task
order_var = oo_.dr.order_var;
i_var_obs = [];
trend_coeffs = [];
- for i=1:size(var_obs,1)
- tmp = strmatch(var_obs(i,:),endo_names(i_var,:),'exact');
+ for i=1:length(var_obs)
+ tmp = strmatch(var_obs{i},endo_names(i_var,:),'exact');
if ~isempty(tmp)
i_var_obs = [ i_var_obs; tmp];
trend_coeffs = [trend_coeffs; oo_.Smoother.TrendCoeffs(i)];
diff --git a/matlab/dynare_estimation.m b/matlab/dynare_estimation.m
index 52d42aa0f65b7a94892f08e2db50b8e17ade1226..ed2e3d84a4b2b81fc36eb0ce579d0c0ef2a56258 100644
--- a/matlab/dynare_estimation.m
+++ b/matlab/dynare_estimation.m
@@ -105,7 +105,7 @@ if nnobs > 1 && horizon > 0
end
endo_names = M_.endo_names;
- n_varobs = size(options_.varobs,1);
+ n_varobs = length(options_.varobs);
if isempty(var_list)
var_list = endo_names;
@@ -125,7 +125,7 @@ if nnobs > 1 && horizon > 0
IdObs = zeros(n_varobs,1);
for j=1:n_varobs
- iobs = strmatch(options_.varobs(j,:),var_list,'exact');
+ iobs = strmatch(options_.varobs{j},var_list,'exact');
if ~isempty(iobs)
IdObs(j,1) = iobs;
end
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index df152bc30297d7f0e6b554465bc75a602cc9748f..a84f4a7fd371b2cf0dc19c167158b2f4eb18972c 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -29,7 +29,7 @@ function dynare_estimation_1(var_list_,dname)
% 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_ options_ oo_ estim_params_ bayestopt_ dataset_
+global M_ options_ oo_ estim_params_ bayestopt_ dataset_ dataset_info
% Set particle filter flag.
if options_.order > 1
@@ -78,10 +78,20 @@ else
objective_function = str2func('dsge_var_likelihood');
end
-[dataset_,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
+[dataset_,dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_] = ...
+ dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
if options_.dsge_var
check_dsge_var_model(M_, estim_params_, bayestopt_);
+ if dataset_info.missing.state
+ error('Estimation::DsgeVarLikelihood: I cannot estimate a DSGE-VAR model with missing observations!')
+ end
+ if options_.noconstant
+ var_sample_moments(options_.dsge_varlag, -1, dataset_);
+ else
+ % The steady state is non zero ==> a constant in the VAR is needed!
+ var_sample_moments(options_.dsge_varlag, 0, dataset_);
+ end
end
%check for calibrated covariances before updating parameters
@@ -114,14 +124,14 @@ if estim_params_.ncx || any(nnz(tril(M_.Correlation_matrix,-1))) || isfield(esti
end
data = dataset_.data;
-rawdata = dataset_.rawdata;
-data_index = dataset_.missing.aindex;
-missing_value = dataset_.missing.state;
+rawdata = dataset_.data;
+data_index = dataset_info.missing.aindex;
+missing_value = dataset_info.missing.state;
% Set number of observations
-gend = options_.nobs;
+gend = dataset_.nobs;
% Set the number of observed variables.
-n_varobs = size(options_.varobs,1);
+n_varobs = length(options_.varobs);
% Get the number of parameters to be estimated.
nvx = estim_params_.nvx; % Variance of the structural innovations (number of parameters).
nvn = estim_params_.nvn; % Variance of the measurement innovations (number of parameters).
@@ -154,29 +164,10 @@ if ~isempty(estim_params_)
M_ = set_all_parameters(xparam1,estim_params_,M_);
end
-% compute sample moments if needed (bvar-dsge)
-if options_.dsge_var
- if dataset_.missing.state
- error('I cannot estimate a DSGE-VAR model with missing observations!')
- end
- if options_.noconstant
- evalin('base',...
- ['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
- 'var_sample_moments(options_.first_obs,' ...
- 'options_.first_obs+options_.nobs-1,options_.dsge_varlag,-1,' ...
- 'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
- else% The steady state is non zero ==> a constant in the VAR is needed!
- evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
- 'var_sample_moments(options_.first_obs,' ...
- 'options_.first_obs+options_.nobs-1,options_.dsge_varlag,0,' ...
- 'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
- end
-end
-
%% perform initial estimation checks;
try
- oo_ = initial_estimation_checks(objective_function,xparam1,dataset_,M_,estim_params_,options_,bayestopt_,oo_);
+ oo_ = initial_estimation_checks(objective_function,xparam1,dataset_,dataset_info,M_,estim_params_,options_,bayestopt_,oo_);
catch % if check fails, provide info on using calibration if present
e = lasterror();
if full_calibration_detected %calibrated model present and no explicit starting values
@@ -246,7 +237,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
optim_options = optimset(optim_options,'GradObj','on','TolX',1e-7);
end
[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
- fmincon(objective_function,xparam1,[],[],[],[],lb,ub,[],optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ fmincon(objective_function,xparam1,[],[],[],[],lb,ub,[],optim_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
case 2
error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
case 3
@@ -264,10 +255,10 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
optim_options = optimset(optim_options,'GradObj','on');
end
if ~isoctave
- [xparam1,fval,exitflag] = fminunc(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [xparam1,fval,exitflag] = fminunc(objective_function,xparam1,optim_options,dataset_,dataset_info_,options_,M_,estim_params_,bayestopt_,oo_);
else
% Under Octave, use a wrapper, since fminunc() does not have a 4th arg
- func = @(x) objective_function(x, dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ func = @(x) objective_function(x, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
[xparam1,fval,exitflag] = fminunc(func,xparam1,optim_options);
end
case 4
@@ -306,7 +297,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
% Call csminwell.
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
- csminwel1(objective_function, xparam1, H0, analytic_grad, crit, nit, numgrad, epsilon, dataset_, options_, M_, estim_params_, bayestopt_, oo_);
+ csminwel1(objective_function, xparam1, H0, analytic_grad, crit, nit, numgrad, epsilon, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_);
% Disp value at the mode.
disp(sprintf('Objective function at mode: %f',fval))
case 5
@@ -334,7 +325,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
else
nit=1000;
end
- [xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,analytic_grad,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,analytic_grad,crit,nit,flag,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,oo_);
if options_.analytic_derivation,
options_.analytic_derivation = ana_deriv;
end
@@ -384,7 +375,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
end
% Evaluate the objective function.
- fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ fval = feval(objective_function,xparam1,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,oo_);
OldMode = fval;
if ~exist('MeanPar','var')
MeanPar = xparam1;
@@ -416,8 +407,8 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
flag = 'LastCall';
end
[xparam1,PostVar,Scale,PostMean] = ...
- gmhmaxlik(objective_function,xparam1,[lb ub],gmhmaxlikOptions,Scale,flag,MeanPar,CovJump,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
- fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ gmhmaxlik(objective_function,xparam1,[lb ub],gmhmaxlikOptions,Scale,flag,MeanPar,CovJump,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
+ fval = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
skipline()
@@ -436,11 +427,11 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
[xparam1,PostVar,Scale,PostMean] = ...
gmhmaxlik(objective_function,xparam1,[lb ub],...
- gmhmaxlikOptions,Scale,flag,PostMean,PostVar,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
- fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ gmhmaxlikOptions,Scale,flag,PostMean,PostVar,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
+ fval = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
- fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ fval = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
skipline()
disp('========================================================== ')
disp([' Change in the covariance matrix = ' num2str(mouvement) '.'])
@@ -473,7 +464,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
if isfield(options_,'optim_opt')
eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
end
- [xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
case 8
% Dynare implementation of the simplex algorithm.
simplexOptions = options_.simplex;
@@ -498,7 +489,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
end
end
- [xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,simplexOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,simplexOptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
case 9
% Set defaults
H0 = 1e-4*ones(nx,1);
@@ -523,7 +514,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
warning('off','CMAES:NonfinitenessRange');
warning('off','CMAES:InitialSigma');
- [x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),xparam1,H0,cmaesOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),xparam1,H0,cmaesOptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
xparam1=BESTEVER.x;
disp(sprintf('\n Objective function at mode: %f',fval))
case 10
@@ -554,16 +545,16 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
end
simpsaOptionsList = options2cell(simpsaOptions);
simpsaOptions = simpsaset(simpsaOptionsList{:});
- [xparam1, fval, exitflag] = simpsa(func2str(objective_function),xparam1,lb,ub,simpsaOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [xparam1, fval, exitflag] = simpsa(func2str(objective_function),xparam1,lb,ub,simpsaOptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
case 11
options_.cova_compute = 0 ;
- [xparam1,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_) ;
+ [xparam1,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_) ;
case 101
myoptions=soptions;
myoptions(2)=1e-6; %accuracy of argument
myoptions(3)=1e-6; %accuracy of function (see Solvopt p.29)
myoptions(5)= 1.0;
- [xparam1,fval]=solvopt(xparam1,objective_function,[],myoptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [xparam1,fval]=solvopt(xparam1,objective_function,[],myoptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
case 102
%simulating annealing
% LB=zeros(size(xparam1))-20;
@@ -600,10 +591,10 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
disp(['c vector ' num2str(c')]);
[xparam1, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(objective_function,xparam1,maxy,rt_,epsilon,ns,nt ...
- ,neps,maxevl,LB,UB,c,idisp ,t,vm,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ ,neps,maxevl,LB,UB,c,idisp ,t,vm,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
otherwise
if ischar(options_.mode_compute)
- [xparam1, fval, retcode ] = feval(options_.mode_compute,objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ [xparam1, fval, retcode ] = feval(options_.mode_compute,objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
else
error(['dynare_estimation:: mode_compute = ' int2str(options_.mode_compute) ' option is unknown!'])
end
@@ -614,11 +605,11 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
ana_deriv = options_.analytic_derivation;
options_.analytic_derivation = 2;
[junk1, junk2, hh] = feval(objective_function,xparam1, ...
- dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = ana_deriv;
else
hh = reshape(hessian(objective_function,xparam1, ...
- options_.gstep,dataset_,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
+ options_.gstep,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
end
end
end
@@ -698,7 +689,7 @@ end
if options_.mode_check.status == 1 && ~options_.mh_posterior_mode_estimation
ana_deriv = options_.analytic_derivation;
options_.analytic_derivation = 0;
- mode_check(objective_function,xparam1,hh,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ mode_check(objective_function,xparam1,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = ana_deriv;
end
@@ -725,14 +716,14 @@ if ~options_.cova_compute
end
if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
- %% display results table and store parameter estimates and standard errors in results
+ % display results table and store parameter estimates and standard errors in results
oo_=display_estimation_results_table(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_,pnames,'Posterior','posterior');
- %% Laplace approximation to the marginal log density:
+ % Laplace approximation to the marginal log density:
if options_.cova_compute
estim_params_nbr = size(xparam1,1);
scale_factor = -sum(log10(diag(invhess)));
log_det_invhess = -estim_params_nbr*log(scale_factor)+log(det(scale_factor*invhess));
- likelihood = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ likelihood = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
oo_.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
skipline()
disp(sprintf('Log data density [Laplace approximation] is %f.',oo_.MarginalDensity.LaplaceApproximation))
@@ -779,7 +770,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
ana_deriv = options_.analytic_derivation;
options_.analytic_derivation = 0;
if options_.cova_compute
- feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
else
error('I Cannot start the MCMC because the Hessian of the posterior kernel at the mode was not computed.')
end
@@ -827,7 +818,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
if error_flag
error('Estimation::mcmc: I cannot compute the posterior statistics!')
end
- prior_posterior_statistics('posterior',dataset_);
+ prior_posterior_statistics('posterior',dataset_,dataset_info);
end
xparam = get_posterior_parameters('mean');
M_ = set_all_parameters(xparam,estim_params_,M_);
@@ -842,7 +833,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
> 0) && options_.load_mh_file)) ...
|| ~options_.smoother ) && options_.partial_information == 0 % to be fixed
%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
- [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,dataset_.info.ntobs,dataset_.data,dataset_.missing.aindex,dataset_.missing.state);
+ [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
oo_.Smoother.SteadyState = ys;
oo_.Smoother.TrendCoeffs = trend_coeff;
oo_.Smoother.Variance = P;
@@ -949,8 +940,11 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
%% Smooth observational errors...
%%
if options_.prefilter == 1
- yf = atT(bayestopt_.mf,:)+repmat(dataset_.descriptive.mean',1,gend);
+ yf = atT(bayestopt_.mf,:)+repmat(dataset_info.descriptive.mean',1,gend);
elseif options_.loglinear == 1
+ gend
+ bayestopt_.mfys
+ ys
yf = atT(bayestopt_.mf,:)+repmat(log(ys(bayestopt_.mfys)),1,gend)+...
trend_coeff*[1:gend];
else
@@ -965,8 +959,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
number_of_plots_to_draw = number_of_plots_to_draw + 1;
index = cat(1,index,i);
end
- eval(['oo_.SmoothedMeasurementErrors.' deblank(options_.varobs(i,:)) ...
- ' = measurement_error(i,:)'';']);
+ eval(['oo_.SmoothedMeasurementErrors.' options_.varobs{i} ' = measurement_error(i,:)'';']);
end
if ~options_.nograph
[nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_plots_to_draw);
@@ -995,7 +988,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
hold on
plot(1:gend,measurement_error(index(k),:),marker_string{2,1},'linewidth',1)
hold off
- name = deblank(options_.varobs(index(k),:));
+ name = options_.varobs{index(k)};
if gend>1
xlim([1 gend])
end
@@ -1009,7 +1002,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
set(gca,'XTickLabel',options_.XTickLabel)
end
if options_.TeX
- idx = strmatch(options_.varobs(index(k),:),M_.endo_names,'exact');
+ idx = strmatch(options_.varobs{index(k)},M_.endo_names,'exact');
texname = M_.endo_names_tex(idx,:);
if isempty(TeXNAMES)
TeXNAMES = ['$ ' deblank(texname) ' $'];
@@ -1070,7 +1063,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
hold on
plot(1:gend,rawdata(:,k),marker_string{2,1},'linewidth',1)
hold off
- name = deblank(options_.varobs(k,:));
+ name = options_.varobs{k};
if isempty(NAMES)
NAMES = name;
else
@@ -1084,7 +1077,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
xlim([1 gend])
end
if options_.TeX
- idx = strmatch(options_.varobs(k,:),M_.endo_names,'exact');
+ idx = strmatch(options_.varobs{k},M_.endo_names,'exact');
texname = M_.endo_names_tex(idx,:);
if isempty(TeXNAMES)
TeXNAMES = ['$ ' deblank(texname) ' $'];
diff --git a/matlab/dynare_estimation_init.m b/matlab/dynare_estimation_init.m
index 5359470baedc81d5f6180d622f0448f2218e6be6..874f9642249e42dfd53ec95fbfeff3954dd91acd 100644
--- a/matlab/dynare_estimation_init.m
+++ b/matlab/dynare_estimation_init.m
@@ -1,4 +1,4 @@
-function [dataset_, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
+function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
% function dynare_estimation_init(var_list_, gsa_flag)
% preforms initialization tasks before estimation or
@@ -41,47 +41,61 @@ hh = [];
if isempty(gsa_flag)
gsa_flag = 0;
-else% Decide if a DSGE or DSGE-VAR has to be estimated.
+else
+ % Decide if a DSGE or DSGE-VAR has to be estimated.
if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
options_.dsge_var = 1;
end
+ % Get the list of the endogenous variables for which posterior statistics wil be computed
var_list_ = check_list_of_variables(options_, M_, var_list_);
options_.varlist = var_list_;
end
-% Get the indices of the observed variables in M_.endo_names.
-options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
-for i = 1:size(M_.endo_names,1)
- tmp = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
- if ~isempty(tmp)
- if length(tmp)>1
- skipline()
- error(['Multiple declarations of ' deblank(M_.endo_names(i,:)) ' as an observed variable is not allowed!'])
+% Set the number of observed variables.
+options_.number_of_observed_variables = length(options_.varobs);
+
+% Test if observed variables are declared.
+if ~options_.number_of_observed_variables
+ error('VAROBS is missing!')
+end
+
+% Check that each declared observed variable is also an endogenous variable.
+for i = 1:options_.number_of_observed_variables
+ id = strmatch(options_.varobs{i}, M_.endo_names, 'exact');
+ if isempty(id)
+ error(['Unknown variable (' options_.varobs{i} ')!'])
+ end
+end
+
+% Check that a variable is not declared as observed more than once.
+if ~isequal(options_.varobs,unique(options_.varobs))
+ for i = 1:options_.number_of_observed_variables
+ if length(strmatch(options_.varobs{i},options_.varobs))>1
+ error(['A variable cannot be declared as observed more than once (' options_.varobs{i} ')!'])
end
- options_.lgyidx2varobs(i) = tmp;
end
end
+% Check the perturbation order (nonlinear filters with third order perturbation, or higher order, are not yet implemented).
if options_.order>2
error(['I cannot estimate a model with a ' int2str(options_.order) ' order approximation of the model!'])
end
-% Set options_.lik_init equal to 3 if diffuse filter is used or
-% kalman_algo refers to a diffuse filter algorithm.
-if (options_.diffuse_filter==1) || (options_.kalman_algo > 2)
- if options_.lik_init == 2
- error(['options diffuse_filter, lik_init and/or kalman_algo have ' ...
- 'contradictory settings'])
+% Set options_.lik_init equal to 3 if diffuse filter is used or kalman_algo refers to a diffuse filter algorithm.
+if isequal(options_.diffuse_filter,1) || (options_.kalman_algo>2)
+ if isequal(options_.lik_init,2)
+ error(['options diffuse_filter, lik_init and/or kalman_algo have contradictory settings'])
else
options_.lik_init = 3;
end
end
% If options_.lik_init == 1
-% set by default options_.qz_criterium to 1-1e-6
-% and check options_.qz_criterium < 1-eps if options_.lik_init == 1
-% Else set by default options_.qz_criterium to 1+1e-6
-if options_.lik_init == 1
+% set by default options_.qz_criterium to 1-1e-6
+% and check options_.qz_criterium < 1-eps if options_.lik_init == 1
+% Else
+% set by default options_.qz_criterium to 1+1e-6
+if isequal(options_.lik_init,1)
if isempty(options_.qz_criterium)
options_.qz_criterium = 1-1e-6;
elseif options_.qz_criterium > 1-eps
@@ -111,9 +125,6 @@ else
M_.dname = dname;
end
-% Set the number of observed variables.
-n_varobs = size(options_.varobs,1);
-
% Set priors over the estimated parameters.
if ~isempty(estim_params_)
[xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
@@ -297,7 +308,10 @@ end
% storing prior parameters in results
oo_.prior.mean = bayestopt_.p1;
+oo_.prior.mode = bayestopt_.p5;
oo_.prior.variance = diag(bayestopt_.p2.^2);
+oo_.prior.hyperparameters.first = bayestopt_.p6;
+oo_.prior.hyperparameters.second = bayestopt_.p7;
% Is there a linear trend in the measurement equation?
if ~isfield(options_,'trend_coeffs') % No!
@@ -307,7 +321,7 @@ else% Yes!
bayestopt_.trend_coeff = {};
trend_coeffs = options_.trend_coeffs;
nt = length(trend_coeffs);
- for i=1:n_varobs
+ for i=1:options_.number_of_observed_variables
if i > length(trend_coeffs)
bayestopt_.trend_coeff{i} = '0';
else
@@ -326,17 +340,12 @@ nstatic = M_.nstatic; % Number of static variables.
npred = M_.nspred; % Number of predetermined variables.
nspred = M_.nspred; % Number of predetermined variables in the state equation.
-% Test if observed variables are declared.
-if isempty(options_.varobs)
- error('VAROBS is missing')
-end
-
% Setting resticted state space (observed + predetermined variables)
var_obs_index = [];
k1 = [];
-for i=1:n_varobs
- var_obs_index = [var_obs_index; strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:),'exact')];
- k1 = [k1; strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
+for i=1:options_.number_of_observed_variables
+ var_obs_index = [var_obs_index; strmatch(options_.varobs{i},M_.endo_names(dr.order_var,:),'exact')];
+ k1 = [k1; strmatch(options_.varobs{i},M_.endo_names, 'exact')];
end
% Define union of observed and state variables
@@ -359,10 +368,8 @@ k3 = [];
k3p = [];
if options_.selected_variables_only
for i=1:size(var_list_,1)
- k3 = [k3; strmatch(var_list_(i,:),M_.endo_names(dr.order_var,:), ...
- 'exact')];
- k3p = [k3; strmatch(var_list_(i,:),M_.endo_names, ...
- 'exact')];
+ k3 = [k3; strmatch(var_list_(i,:),M_.endo_names(dr.order_var,:), 'exact')];
+ k3p = [k3; strmatch(var_list_(i,:),M_.endo_names, 'exact')];
end
else
k3 = (1:M_.endo_nbr)';
@@ -383,14 +390,12 @@ if options_.block == 1
bayestopt_.mf2 = var_obs_index;
bayestopt_.mfys = k1;
oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))];
-
[k2, i_posA, i_posB] = union(k3p, M_.state_var', 'rows');
bayestopt_.smoother_var_list = [k3p(i_posA); M_.state_var(sort(i_posB))'];
[junk,junk,bayestopt_.smoother_saved_var_list] = intersect(k3p,bayestopt_.smoother_var_list(:));
[junk,ic] = intersect(bayestopt_.smoother_var_list,M_.state_var);
bayestopt_.smoother_restrict_columns = ic;
- [junk,bayestopt_.smoother_mf] = ismember(k1, ...
- bayestopt_.smoother_var_list);
+ [junk,bayestopt_.smoother_mf] = ismember(k1, bayestopt_.smoother_var_list);
else
k2 = union(var_obs_index,[M_.nstatic+1:M_.nstatic+M_.nspred]', 'rows');
% Set restrict_state to postion of observed + state variables in expanded state vector.
@@ -404,13 +409,11 @@ else
bayestopt_.mfys = k1;
[junk,ic] = intersect(k2,nstatic+(1:npred)');
oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
-
bayestopt_.smoother_var_list = union(k2,k3);
[junk,junk,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:));
[junk,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)');
bayestopt_.smoother_restrict_columns = ic;
- [junk,bayestopt_.smoother_mf] = ismember(var_obs_index, ...
- bayestopt_.smoother_var_list);
+ [junk,bayestopt_.smoother_mf] = ismember(var_obs_index, bayestopt_.smoother_var_list);
end;
if options_.analytic_derivation,
@@ -440,37 +443,12 @@ if options_.analytic_derivation,
end
end
-% Test if the data file is declared.
-if isempty(options_.datafile)
- if gsa_flag
- dataset_ = [];
-% rawdata = [];
-% data_info = [];
- return
- else
- error('datafile option is missing')
- end
-end
-
% If jscale isn't specified for an estimated parameter, use global option options_.jscale, set to 0.2, by default.
k = find(isnan(bayestopt_.jscale));
bayestopt_.jscale(k) = options_.mh_jscale;
-% Load and transform data.
-transformation = [];
-if options_.loglinear && ~options_.logdata
- transformation = @log;
-end
-xls.sheet = options_.xls_sheet;
-xls.range = options_.xls_range;
-
-if ~isfield(options_,'nobs')
- options_.nobs = [];
-end
-
-dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
-
-options_.nobs = dataset_.info.ntobs;
+% Build the dataset
+[dataset_, dataset_info] = makedataset(options_, options_.dsge_var*options_.dsge_varlag, gsa_flag);
% setting steadystate_check_flag option
if options_.diffuse_filter
@@ -479,6 +457,7 @@ else
steadystate_check_flag = 1;
end
+% If the steady state of the observed variables is non zero, set noconstant equal 0 ()
M = M_;
nvx = estim_params_.nvx;
ncx = estim_params_.ncx;
@@ -502,5 +481,4 @@ else
disp('variables in the model block, or drop the prefiltering.')
error('The option "prefilter" is inconsistent with the non-zero mean measurement equations in the model.')
end
-end
-
+end
\ No newline at end of file
diff --git a/matlab/dynare_identification.m b/matlab/dynare_identification.m
index bec4abe11235f4c6758b5a052b0cc5661ce92c34..69fde13075e5423becd5d2f7285b067485e1b7cc 100644
--- a/matlab/dynare_identification.m
+++ b/matlab/dynare_identification.m
@@ -128,11 +128,11 @@ options_ = set_default_option(options_,'datafile','');
options_.mode_compute = 0;
options_.plot_priors = 0;
options_.smoother=1;
-[dataset_,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
+[dataset_,dataset_info,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
options_ident.analytic_derivation_mode = options_.analytic_derivation_mode;
if isempty(dataset_),
dataset_.info.ntobs = periods;
- dataset_.info.nvobs = rows(options_.varobs);
+ dataset_.info.nvobs = length(options_.varobs);
dataset_.info.varobs = options_.varobs;
dataset_.rawdata = [];
dataset_.missing.state = 0;
@@ -145,17 +145,8 @@ if isempty(dataset_),
dataset_.missing.no_more_missing_observations = 1;
dataset_.descriptive.mean = [];
dataset_.data = [];
-
-% data_info.gend = periods;
-% data_info.data = [];
-% data_info.data_index = [];
-% data_info.number_of_observations = periods*size(options_.varobs,1);
-% data_info.no_more_missing_observations = 0;
-% data_info.missing_value = 0;
end
-% results = prior_sampler(0,M_,bayestopt_,options_,oo_);
-
if prior_exist
if any(bayestopt_.pshape > 0)
if options_ident.prior_range
@@ -278,7 +269,7 @@ if iload <=0,
disp('Testing current parameter values')
end
[idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info] = ...
- identification_analysis(params,indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+ identification_analysis(params,indx,indexo,options_ident,dataset_, dataset_info, prior_exist, name_tex,1);
if info(1)~=0,
skipline()
disp('----------- ')
@@ -321,7 +312,7 @@ if iload <=0,
kk=kk+1;
params = prior_draw();
[idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info] = ...
- identification_analysis(params,indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+ identification_analysis(params,indx,indexo,options_ident,dataset_,dataset_info, prior_exist, name_tex,1);
end
end
if info(1)
@@ -375,7 +366,7 @@ if iload <=0,
params = prior_draw();
end
[dum1, ideJ, ideH, ideGP, dum2 , info] = ...
- identification_analysis(params,indx,indexo,options_MC,dataset_, prior_exist, name_tex,0);
+ identification_analysis(params,indx,indexo,options_MC,dataset_, dataset_info, prior_exist, name_tex,0);
if iteration==0 && info(1)==0,
MAX_tau = min(SampleSize,ceil(MaxNumberOfBytes/(size(ideH.siH,1)*nparam)/8));
stoH = zeros([size(ideH.siH,1),nparam,MAX_tau]);
@@ -546,7 +537,7 @@ if SampleSize > 1,
disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
if ~iload,
[idehess_max, idemoments_max, idemodel_max, idelre_max, derivatives_info_max] = ...
- identification_analysis(pdraws(jmax,:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+ identification_analysis(pdraws(jmax,:),indx,indexo,options_ident,dataset_,dataset_info, prior_exist, name_tex,1);
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_max', 'idemoments_max','idemodel_max', 'idelre_max', 'jmax', '-append');
end
disp_identification(pdraws(jmax,:), idemodel_max, idemoments_max, name,1);
@@ -561,7 +552,7 @@ if SampleSize > 1,
disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
if ~iload,
[idehess_min, idemoments_min, idemodel_min, idelre_min, derivatives_info_min] = ...
- identification_analysis(pdraws(jmin,:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+ identification_analysis(pdraws(jmin,:),indx,indexo,options_ident,dataset_, dataset_info, prior_exist, name_tex,1);
save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_min', 'idemoments_min','idemodel_min', 'idelre_min', 'jmin', '-append');
end
disp_identification(pdraws(jmin,:), idemodel_min, idemoments_min, name,1);
@@ -576,7 +567,7 @@ if SampleSize > 1,
disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
if ~iload,
[idehess_(j), idemoments_(j), idemodel_(j), idelre_(j), derivatives_info_(j)] = ...
- identification_analysis(pdraws(jcrit(j),:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+ identification_analysis(pdraws(jcrit(j),:),indx,indexo,options_ident,dataset_, dataset_info, prior_exist, name_tex,1);
end
disp_identification(pdraws(jcrit(j),:), idemodel_(j), idemoments_(j), name,1);
close all,
diff --git a/matlab/dynare_sensitivity.m b/matlab/dynare_sensitivity.m
index 623f80ec50f955cbab5a6c6268a97583255a67f3..1d9eeef7f5fadf1f3e8187bdcc3a074753ef61f8 100644
--- a/matlab/dynare_sensitivity.m
+++ b/matlab/dynare_sensitivity.m
@@ -84,7 +84,7 @@ if ~isempty(options_gsa.datafile) || isempty(bayestopt_) || options_gsa.rmse,
options_.mode_compute = 0;
options_.filtered_vars = 1;
options_.plot_priors = 0;
- [dataset_,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
+ [dataset_,dataset_info,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
% computes a first linear solution to set up various variables
else
if isempty(options_.qz_criterium)
@@ -154,7 +154,7 @@ options_gsa = set_default_option(options_gsa,'namlagendo',[]);
options_gsa = set_default_option(options_gsa,'namexo',[]);
% RMSE mapping
options_gsa = set_default_option(options_gsa,'lik_only',0);
-options_gsa = set_default_option(options_gsa,'var_rmse',options_.varobs);
+options_gsa = set_default_option(options_gsa,'var_rmse',char(options_.varobs));
options_gsa = set_default_option(options_gsa,'pfilt_rmse',0.1);
options_gsa = set_default_option(options_gsa,'istart_rmse',options_.presample+1);
options_gsa = set_default_option(options_gsa,'alpha_rmse',0.001);
@@ -343,7 +343,7 @@ if options_gsa.rmse,
end
end
- prior_posterior_statistics('gsa',dataset_);
+ prior_posterior_statistics('gsa',dataset_, dataset_info);
if options_.bayesian_irf
PosteriorIRF('gsa');
end
@@ -366,7 +366,7 @@ if options_gsa.rmse,
end
clear a;
% filt_mc_(OutputDirectoryName,data_info);
- filt_mc_(OutputDirectoryName,options_gsa,dataset_);
+ filt_mc_(OutputDirectoryName,options_gsa,dataset_,dataset_info);
end
options_.opt_gsa = options_gsa;
@@ -405,9 +405,9 @@ if options_gsa.glue,
Obs.data = data;
Obs.time = [1:gend];
Obs.num = gend;
- for j=1:size(options_.varobs,1)
- Obs.name{j} = deblank(options_.varobs(j,:));
- vj=deblank(options_.varobs(j,:));
+ for j=1:length(options_.varobs)
+ Obs.name{j} = options_.varobs{j};
+ vj = options_.varobs{j};
jxj = strmatch(vj,lgy_(dr_.order_var,:),'exact');
js = strmatch(vj,lgy_,'exact');
@@ -445,7 +445,7 @@ if options_gsa.glue,
ismoo(j)=jxj;
end
- jsmoo = size(options_.varobs,1);
+ jsmoo = length(options_.varobs);
for j=1:M_.endo_nbr,
if ~ismember(j,ismoo),
jsmoo=jsmoo+1;
@@ -470,10 +470,10 @@ if options_gsa.glue,
Exo(j).name = deblank(tit(j,:));
end
if ~options_gsa.ppost
- Lik(size(options_.varobs,1)+1).name = 'logpo';
- Lik(size(options_.varobs,1)+1).ini = 'yes';
- Lik(size(options_.varobs,1)+1).isam = 1;
- Lik(size(options_.varobs,1)+1).data = -logpo2;
+ Lik(length(options_.varobs)+1).name = 'logpo';
+ Lik(length(options_.varobs)+1).ini = 'yes';
+ Lik(length(options_.varobs)+1).isam = 1;
+ Lik(length(options_.varobs)+1).data = -logpo2;
end
Sam.name = bayestopt_.name;
Sam.dim = [size(x) 0];
diff --git a/matlab/evaluate_likelihood.m b/matlab/evaluate_likelihood.m
index 76fe3b42713cfbbc094b51b170f45c744dd02858..14abbd0d4b8fbab6dac06d59c0bd637dd4228075 100644
--- a/matlab/evaluate_likelihood.m
+++ b/matlab/evaluate_likelihood.m
@@ -37,7 +37,7 @@ function [llik,parameters] = evaluate_likelihood(parameters)
global options_ M_ bayestopt_ oo_ estim_params_
-persistent dataset
+persistent dataset dataset_info
if nargin==0
parameters = 'posterior mode';
@@ -67,22 +67,9 @@ if ischar(parameters)
end
if isempty(dataset)
- % Load and transform data.
- transformation = [];
- if options_.loglinear && ~options_.logdata
- transformation = @log;
- end
- xls.sheet = options_.xls_sheet;
- xls.range = options_.xls_range;
-
- if ~isfield(options_,'nobs')
- options_.nobs = [];
- end
-
- dataset = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
+ [dataset, dataset_info] = makedataset(options_);
end
-llik = -dsge_likelihood(parameters,dataset,options_,M_,estim_params_,bayestopt_,oo_);
+llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
ldens = evaluate_prior(parameters);
-llik = llik - ldens;
-
+llik = llik - ldens;
\ No newline at end of file
diff --git a/matlab/evaluate_smoother.m b/matlab/evaluate_smoother.m
index 6b640b7b4580134daca61ef39d2d9cf7782e87e5..76b8444c0a4bd3149c8df2ba2d69b39795ab456a 100644
--- a/matlab/evaluate_smoother.m
+++ b/matlab/evaluate_smoother.m
@@ -44,14 +44,14 @@ function oo_=evaluate_smoother(parameters,var_list)
global options_ M_ bayestopt_ oo_ estim_params_ % estim_params_ may be emty
-persistent dataset_
+persistent dataset_ dataset_info
if ischar(parameters) && strcmp(parameters,'calibration')
options_.smoother=1;
end
if isempty(dataset_) || isempty(bayestopt_)
- [dataset_,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list, M_.fname, [], M_, options_, oo_, estim_params_, bayestopt_);
+ [dataset_,dataset_info,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list, M_.fname, [], M_, options_, oo_, estim_params_, bayestopt_);
end
if nargin==0
@@ -88,7 +88,7 @@ if ischar(parameters)
end
[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = ...
- DsgeSmoother(parameters,dataset_.info.ntobs,dataset_.data,dataset_.missing.aindex,dataset_.missing.state);
+ DsgeSmoother(parameters,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
oo_.Smoother.SteadyState = ys;
oo_.Smoother.TrendCoeffs = trend_coeff;
diff --git a/matlab/forecast_graphs.m b/matlab/forecast_graphs.m
index f95b77541d7a49bfce842b56c3b562a10320ea32..739e229d6ebce1d3c2e2081babacc0505bcd5c28 100644
--- a/matlab/forecast_graphs.m
+++ b/matlab/forecast_graphs.m
@@ -24,10 +24,6 @@ nr = 3;
exo_nbr = M_.exo_nbr;
endo_names = M_.endo_names;
fname = M_.fname;
-% $$$ varobs = options_.varobs;
-% $$$ y = oo_.SmoothedVariables;
-% $$$ ys = oo_.dr.ys;
-% $$$ gend = size(y,2);
yf = oo_.forecast.Mean;
hpdinf = oo_.forecast.HPDinf;
hpdsup = oo_.forecast.HPDsup;
@@ -44,13 +40,6 @@ for i = 1:size(var_list)
end
nvar = length(i_var);
-% $$$ % build trend for smoothed variables if necessary
-% $$$ trend = zeros(size(varobs,1),10);
-% $$$ if isfield(oo_.Smoother,'TrendCoeffs')
-% $$$ trend_coeffs = oo_.Smoother.TrendCoeffs;
-% $$$ trend = trend_coeffs*(gend-9:gend);
-% $$$ end
-
% create subdirectory <fname>/graphs if id doesn't exist
if ~exist(fname, 'dir')
mkdir('.',fname);
diff --git a/matlab/get_file_extension.m b/matlab/get_file_extension.m
new file mode 100644
index 0000000000000000000000000000000000000000..56c140d838a8d845bc1f7ef040adce9080f317e1
--- /dev/null
+++ b/matlab/get_file_extension.m
@@ -0,0 +1,42 @@
+function ext = get_file_extension(file)
+
+% returns the extension of a file.
+%
+% INPUTS
+% o file string, name of the file
+%
+% OUTPUTS
+% o ext string, extension.
+%
+% REMARKS
+% If the provided file name has no extension, the routine will return an empty array.
+
+% 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/>.
+
+% Clean-up path
+file = strrep(file, '../', '');
+file = strrep(file, './', '');
+
+remain = file;
+while ~isempty(remain)
+ [ext, remain] = strtok(remain,'.');
+end
+
+if strcmp(ext,file)
+ ext = [];
+end
\ No newline at end of file
diff --git a/matlab/get_posterior_parameters.m b/matlab/get_posterior_parameters.m
index 8c4a9c8ebb713d5ca3c26ec5d17d54f467814beb..b8676c2d40d0507d0312f1e3d238e382117cc23f 100644
--- a/matlab/get_posterior_parameters.m
+++ b/matlab/get_posterior_parameters.m
@@ -50,7 +50,7 @@ end
for i=1:nvn
k1 = estim_params_.nvn_observable_correspondence(i,1);
- name1 = deblank(options_.varobs(k1,:));
+ name1 = options_.varobs{k1};
xparam(m) = eval(['oo_.posterior_' type '.measurement_errors_std.' name1]);
m = m+1;
end
@@ -69,8 +69,8 @@ end
for i=1:ncn
k1 = estim_params_.corrn_observable_correspondence(i,1);
k2 = estim_params_.corrn_observable_correspondence(i,2);
- name1 = deblank(options_.varobs(k1,:));
- name2 = deblank(options_.varobs(k2,:));
+ name1 = options_.varobs{k1};
+ name2 = options_.varobs{k2};
xparam(m) = eval(['oo_.posterior_' type '.measurement_errors_corr.' name1 '_' name2]);
m = m+1;
end
diff --git a/matlab/get_the_name.m b/matlab/get_the_name.m
index bd6172a223e6d3e752d36cc5c26ed34e198bc731..9251dd2f967acfe5f539e4a6a215719c6dd0f354 100644
--- a/matlab/get_the_name.m
+++ b/matlab/get_the_name.m
@@ -73,7 +73,7 @@ if k <= nvx
texnam = ['$ SE_{' tname '} $'];
end
elseif k <= (nvx+nvn)
- vname = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(k-estim_params_.nvx,1),:));
+ vname = options_.varobs{estim_params_.nvn_observable_correspondence(k-estim_params_.nvx,1)};
nam=['SE_EOBS_',vname];
if TeX
tname = deblank(M_.endo_names_tex(estim_params_.var_endo(k-estim_params_.nvx,1),:));
diff --git a/matlab/get_variables_list.m b/matlab/get_variables_list.m
index 853b1169f905bba2345468587a02ac32e3903d5a..5d6a93c84f17e42913eee8d244a2f6e900828cbd 100644
--- a/matlab/get_variables_list.m
+++ b/matlab/get_variables_list.m
@@ -35,7 +35,7 @@ function [ivar,vartan,options_] = get_variables_list(options_,M_)
varlist = options_.varlist;
if isempty(varlist)
- varlist = options_.varobs;
+ varlist = char(options_.varobs);
options_.varlist = varlist;
end
nvar = rows(varlist);
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index f9c9c10fa142d14170f6f37769c7605fe65f3554..e9ee6c88d7777919597593f79596ff802391f57e 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -33,6 +33,7 @@ global oo_ M_ options_ estim_params_ bayestopt_ estimation_info ex0_ ys0_
estim_params_ = [];
bayestopt_ = [];
options_.datafile = '';
+options_.dataset = [];
options_.verbosity = 1;
options_.terminal_condition = 0;
options_.rplottype = 0;
@@ -349,11 +350,13 @@ estimation_info.structural_innovation_corr_prior_index = {};
estimation_info.structural_innovation_corr_options_index = {};
estimation_info.structural_innovation_corr.range_index = {};
options_.initial_period = dates(1,1);
-options_.dataset.firstobs = options_.initial_period;
-options_.dataset.lastobs = NaN;
+options_.dataset.file = [];
+options_.dataset.series = [];
+options_.dataset.firstobs = dates();
+options_.dataset.lastobs = dates();
options_.dataset.nobs = NaN;
-options_.dataset.xls_sheet = NaN;
-options_.dataset.xls_range = NaN;
+options_.dataset.xls_sheet = [];
+options_.dataset.xls_range = [];
options_.Harvey_scale_factor = 10;
options_.MaxNumberOfBytes = 1e6;
options_.MaximumNumberOfMegaBytes = 111;
@@ -364,7 +367,8 @@ options_.bayesian_th_moments = 0;
options_.diffuse_filter = 0;
options_.filter_step_ahead = [];
options_.filtered_vars = 0;
-options_.first_obs = 1;
+options_.first_obs = NaN;
+options_.nobs = NaN;
options_.kalman_algo = 0;
options_.fast_kalman = 0;
options_.kalman_tol = 1e-10;
diff --git a/matlab/gsa/filt_mc_.m b/matlab/gsa/filt_mc_.m
index ed6729362b5f194544a0ef4d7f84d5834ff07fe8..329c6919d5ebac6a8edc359103efa597c804ea5f 100644
--- a/matlab/gsa/filt_mc_.m
+++ b/matlab/gsa/filt_mc_.m
@@ -1,4 +1,4 @@
-function [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_,dataset_)
+function [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_,dataset_,dataset_info)
% function [rmse_MC, ixx] = filt_mc_(OutDir)
% inputs (from opt_gsa structure)
% vvarvecm = options_gsa_.var_rmse;
@@ -120,10 +120,10 @@ if ~loadSA,
ys_mean=steady_(M_,options_,oo_);
end
% eval(options_.datafile)
- Y = dataset_.data;
- gend = dataset_.info.ntobs;
- data_index = dataset_.missing.aindex;
- missing_value = dataset_.missing.state;
+ Y = transpose(dataset_.data);
+ gend = dataset_.nobs;
+ data_index = dataset_info.missing.aindex;
+ missing_value = dataset_info.missing.state;
for jx=1:gend, data_indx(jx,data_index{jx})=true; end
%stock_gend=data_info.gend;
%stock_data = data_info.data;
diff --git a/matlab/identification_analysis.m b/matlab/identification_analysis.m
index c30ed8a19666a56c6683f1c6959308f92efc66ab..ce39b30cc8b0ffec5c347031792f409ed51dfb1e 100644
--- a/matlab/identification_analysis.m
+++ b/matlab/identification_analysis.m
@@ -1,4 +1,4 @@
-function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info] = identification_analysis(params,indx,indexo,options_ident,data_info, prior_exist, name_tex, init)
+function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info] = identification_analysis(params,indx,indexo,options_ident,dataset_,dataset_info, prior_exist, name_tex, init)
% function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info] = identification_analysis(params,indx,indexo,options_ident,data_info, prior_exist, name_tex, init)
% given the parameter vector params, wraps all identification analyses
%
@@ -134,17 +134,18 @@ if info(1)==0,
options_.noprint = 1;
options_.order = 1;
options_.SpectralDensity.trigger = 0;
- options_.periods = data_info.info.ntobs+100;
+ options_.periods = dataset_.nobs+100;
if options_.kalman_algo > 2,
options_.kalman_algo = 1;
end
analytic_derivation = options_.analytic_derivation;
options_.analytic_derivation = -2;
- info = stoch_simul(options_.varobs);
- data_info.data=oo_.endo_simul(options_.varobs_id,100+1:end);
+ info = stoch_simul(char(options_.varobs));
+ dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end),dataset_.dates(1),dataset_.names,dataset_.tex);
+ %data_info.data=oo_.endo_simul(options_.varobs_id,100+1:end);
% datax=data;
derivatives_info.no_DLIK=1;
- [fval,DLIK,AHess,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',data_info,options_,M_,estim_params_,bayestopt_,oo_,derivatives_info);
+ [fval,DLIK,AHess,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,derivatives_info);
% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = analytic_derivation;
AHess=-AHess;
diff --git a/matlab/imcforecast.m b/matlab/imcforecast.m
index 2fa56a2bac68b75b37021d179883b1e6f044f748..766394fbc4ec00fb92b15a815db95a2ec93b12de 100644
--- a/matlab/imcforecast.m
+++ b/matlab/imcforecast.m
@@ -102,30 +102,13 @@ if estimated_model
error('imcforecast:: The dimension of the vector of parameters doesn''t match the number of estimated parameters!')
end
end
-
set_parameters(xparam);
-
- % Load and transform data.
- transformation = [];
- if options_.loglinear && ~options_.logdata
- transformation = @log;
- end
- xls.sheet = options_.xls_sheet;
- xls.range = options_.xls_range;
-
- if ~isfield(options_,'nobs')
- options_.nobs = [];
- end
-
- dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
-
- data = dataset_.data;
- data_index = dataset_.missing.aindex;
- gend = options_.nobs;
- missing_value = dataset_.missing.state;
-
+ [dataset_,dataset_info] = makedataset(options_);
+ data = transpose(dataset_.data);
+ data_index = dataset_info.missing.aindex;
+ gend = dataset_.nobs;
+ missing_value = dataset_info.missing.state;
[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam,gend,data,data_index,missing_value);
-
trend = repmat(ys,1,options_cond_fcst.periods+1);
for i=1:M_.endo_nbr
j = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
@@ -134,7 +117,6 @@ if estimated_model
end
end
trend = trend(oo_.dr.order_var,:);
-
InitState(:,1) = atT(:,end);
else
InitState(:,1) = zeros(M_.endo_nbr,1);
diff --git a/matlab/initial_estimation_checks.m b/matlab/initial_estimation_checks.m
index 9dee5461e9047eb67e73beb29ee1fce5d2d88744..c4cbf5ab96966991f47adfb95b929fbdeb6762e0 100644
--- a/matlab/initial_estimation_checks.m
+++ b/matlab/initial_estimation_checks.m
@@ -1,11 +1,12 @@
-function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,Model,EstimatedParameters,DynareOptions,BayesInfo,DynareResults)
+function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,DynareResults)
% function initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
% Checks data (complex values, ML evaluation, initial values, BK conditions,..)
%
% INPUTS
% objective_function [function handle] of the objective function
% xparam1: [vector] of parameters to be estimated
-% DynareDataset: [structure] storing the dataset information
+% DynareDataset: [dseries] object storing the dataset
+% DataSetInfo: [structure] storing informations about the sample.
% Model: [structure] decribing the model
% EstimatedParameters [structure] characterizing parameters to be estimated
% DynareOptions [structure] describing the options
@@ -35,11 +36,11 @@ function DynareResults = initial_estimation_checks(objective_function,xparam1,Dy
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-if DynareDataset.info.nvobs>Model.exo_nbr+EstimatedParameters.nvn
+if DynareDataset.vobs>Model.exo_nbr+EstimatedParameters.nvn
error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables!'])
end
-
-if DynareDataset.info.nvobs>length(find(diag(Model.Sigma_e)))+EstimatedParameters.nvn
+
+if DynareDataset.vobs>length(find(diag(Model.Sigma_e)))+EstimatedParameters.nvn
error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!'])
end
@@ -72,7 +73,7 @@ end
% Evaluate the likelihood.
ana_deriv = DynareOptions.analytic_derivation;
DynareOptions.analytic_derivation=0;
-[fval,junk1,junk2,a,b,c,d] = feval(objective_function,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval,junk1,junk2,a,b,c,d] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
DynareOptions.analytic_derivation=ana_deriv;
if DynareOptions.dsge_var || strcmp(func2str(objective_function),'non_linear_dsge_likelihood')
diff --git a/matlab/load_m_file_data.m b/matlab/load_m_file_data.m
index b692585834f0ff937a0400abf375ed57ad7dddde..20e1b28a77b57a1748abb9575af1db29c33e305b 100644
--- a/matlab/load_m_file_data.m
+++ b/matlab/load_m_file_data.m
@@ -36,7 +36,7 @@ function [freq,init,data,varlist,tex] = load_m_file_data(file)
if isoctave
run(file);
else
- [basename, ext] = strtok(file,'.');
+ basename = file(1:end-2);
run(basename);
end
diff --git a/matlab/metropolis_hastings_initialization.m b/matlab/metropolis_hastings_initialization.m
index 8c6ed5a9f6cefcca4c2f5da15304560cf55dc24e..7fb056553842a8387c9c1a110d22ea7bc32dfd25 100644
--- a/matlab/metropolis_hastings_initialization.m
+++ b/matlab/metropolis_hastings_initialization.m
@@ -1,7 +1,7 @@
function [ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
- metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
+ metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
%function [ ix2, ilogpo2, ModelName, MhDirectoryName, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] =
-% metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds, dataset_,options_,M_,estim_params_,bayestopt_,oo_)
+% metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds, dataset_,dataset_info,,options_,M_,estim_params_,bayestopt_,oo_)
% Metropolis-Hastings initialization.
%
% INPUTS
@@ -110,7 +110,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
candidate = rand_multivariate_normal( transpose(xparam1), d * options_.mh_init_scale, npar);
if all(candidate(:) > mh_bounds(:,1)) && all(candidate(:) < mh_bounds(:,2))
ix2(j,:) = candidate;
- ilogpo2(j) = - feval(TargetFun,ix2(j,:)',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ ilogpo2(j) = - feval(TargetFun,ix2(j,:)',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
if ~isfinite(ilogpo2(j)) % if returned log-density is
% Inf or Nan (penalized value)
validate = 0;
@@ -152,7 +152,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
candidate = transpose(xparam1(:));%
if all(candidate(:) > mh_bounds(:,1)) && all(candidate(:) < mh_bounds(:,2))
ix2 = candidate;
- ilogpo2 = - feval(TargetFun,ix2',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
disp('Estimation::mcmc: Initialization at the posterior mode.')
skipline()
fprintf(fidlog,[' Blck ' int2str(1) 'params:\n']);
diff --git a/matlab/mode_check.m b/matlab/mode_check.m
index 786acb7daba832115ce983ca09c089b1b40cf92f..2fa6f4a9d145b1d8bcbd35fd8e538fab02f25e00 100644
--- a/matlab/mode_check.m
+++ b/matlab/mode_check.m
@@ -1,4 +1,4 @@
-function mode_check(fun,x,hessian,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function mode_check(fun,x,hessian,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
% Checks the estimated ML mode or Posterior mode.
%@info:
@@ -62,7 +62,7 @@ if ~isempty(hessian);
[ s_min, k ] = min(diag(hessian));
end
-fval = feval(fun,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+fval = feval(fun,x,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
if ~isempty(hessian);
skipline()
@@ -138,7 +138,7 @@ for plt = 1:nbplt,
end
for i=1:length(z)
xx(kk) = z(i);
- [fval, junk1, junk2, exit_flag] = feval(fun,xx,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fval, junk1, junk2, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
if exit_flag
y(i,1) = fval;
else
diff --git a/matlab/mr_gstep.m b/matlab/mr_gstep.m
index 690ffa01bd57d7f3513ba2853afac4fc5026f42c..82c8d70f42cd3e2e15a2e4e2d6aefa103fcc8db0 100644
--- a/matlab/mr_gstep.m
+++ b/matlab/mr_gstep.m
@@ -1,9 +1,17 @@
-function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,varargin)
% function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,varargin)
%
% Gibbs type step in optimisation
+%
+% varargin{1} --> DynareDataset
+% varargin{2} --> DatasetInfo
+% varargin{3} --> DynareOptions
+% varargin{4} --> Model
+% varargin{5} --> EstimatedParameters
+% varargin{6} --> BayesInfo
+% varargin{1} --> DynareResults
-% Copyright (C) 2006-2012 Dynare Team
+% Copyright (C) 2006-2014 Dynare Team
%
% This file is part of Dynare.
%
@@ -22,7 +30,7 @@ function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,DynareDataset,DynareOptions,Mod
n=size(x,1);
if isempty(h1),
- h1=DynareOptions.gradient_epsilon*ones(n,1);
+ h1=varargin{3}.gradient_epsilon*ones(n,1);
end
@@ -31,7 +39,7 @@ if isempty(htol0)
else
htol = htol0;
end
-f0=feval(func0,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+f0=feval(func0,x,varargin{:});
xh1=x;
f1=zeros(size(f0,1),n);
@@ -45,10 +53,10 @@ while i<n
hcheck=0;
dx=[];
xh1(i)=x(i)+h1(i);
- fx = feval(func0,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ fx = feval(func0,xh1,varargin{:});
f1(:,i)=fx;
xh1(i)=x(i)-h1(i);
- fx = feval(func0,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ fx = feval(func0,xh1,varargin{:});
f_1(:,i)=fx;
if hcheck && htol<1
htol=min(1,max(min(abs(dx))*2,htol*10));
@@ -61,9 +69,9 @@ while i<n
gg(i)=(f1(i)'-f_1(i)')./(2.*h1(i));
hh(i) = 1/max(1.e-9,abs( (f1(i)+f_1(i)-2*f0)./(h1(i)*h1(i)) ));
if gg(i)*(hh(i)*gg(i))/2 > htol
- [f0 x fc retcode] = csminit1(func0,x,f0,gg,0,diag(hh),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [f0 x fc retcode] = csminit1(func0,x,f0,gg,0,diag(hh),varargin{:});
ig(i)=1;
- fprintf(['Done for param %s = %8.4f\n'],BayesInfo.name{i},x(i))
+ fprintf(['Done for param %s = %8.4f\n'],varargin{6}.name{i},x(i))
end
xh1=x;
end
diff --git a/matlab/mr_hessian.m b/matlab/mr_hessian.m
index a428a3c92369e62f5d00b2ccf685100cd64f32b5..1cdbb05b1736ca95f885e1d228044a13317f3dbd 100644
--- a/matlab/mr_hessian.m
+++ b/matlab/mr_hessian.m
@@ -1,10 +1,10 @@
-function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1] = mr_hessian(init,x,func,hflag,htol0,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1] = mr_hessian(init,x,func,hflag,htol0,varargin)
% [hessian_mat, gg, htol1, ihh, hh_mat0, hh1] = mr_hessian(init,x,func,hflag,htol0,varargin)
%
% numerical gradient and Hessian, with 'automatic' check of numerical
% error
%
-% adapted from Michel Juillard original rutine hessian.m
+% adapted from Michel Juillard original routine hessian.m
%
% func = function handle. The function must give two outputs:
% - the log-likelihood AND the single contributions at times t=1,...,T
@@ -22,9 +22,17 @@ function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1] = mr_hessian(init,x,func,hf
% htol0 = 'precision' of increment of function values for numerical
% derivatives
%
-% varargin: other parameters of func
+% varargin{1} --> DynareDataset
+% varargin{2} --> DatasetInfo
+% varargin{3} --> DynareOptions
+% varargin{4} --> Model
+% varargin{5} --> EstimatedParameters
+% varargin{6} --> BayesInfo
+% varargin{1} --> DynareResults
-% Copyright (C) 2004-2012 Dynare Team
+
+
+% Copyright (C) 2004-2014 Dynare Team
%
% This file is part of Dynare.
%
@@ -45,16 +53,16 @@ persistent h1 htol
n=size(x,1);
if init
- gstep_=DynareOptions.gstep;
+ gstep_=varargin{3}.gstep;
htol = 1.e-4;
- h1=DynareOptions.gradient_epsilon*ones(n,1);
+ h1=varargin{3}.gradient_epsilon*ones(n,1);
return
end
-[f0, ff0]=feval(func,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
-h2=BayesInfo.ub-BayesInfo.lb;
-hmax=BayesInfo.ub-x;
-hmax=min(hmax,x-BayesInfo.lb);
+[f0, ff0]=feval(func,x,varargin{:});
+h2=varargin{6}.ub-varargin{6}.lb;
+hmax=varargin{6}.ub-x;
+hmax=min(hmax,x-varargin{6}.lb);
if isempty(ff0),
outer_product_gradient=0;
else
@@ -83,7 +91,7 @@ while i<n
hcheck=0;
xh1(i)=x(i)+h1(i);
try
- [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fx, ffx]=feval(func,xh1,varargin{:});
catch
fx=1.e8;
end
@@ -104,7 +112,7 @@ while i<n
h1(i) = max(h1(i),1.e-10);
xh1(i)=x(i)+h1(i);
try
- [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fx, ffx]=feval(func,xh1,varargin{:});
catch
fx=1.e8;
end
@@ -113,21 +121,21 @@ while i<n
h1(i)= htol/abs(dx(it))*h1(i);
xh1(i)=x(i)+h1(i);
try
- [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fx, ffx]=feval(func,xh1,varargin{:});
catch
fx=1.e8;
end
while (fx-f0)==0
h1(i)= h1(i)*2;
xh1(i)=x(i)+h1(i);
- [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fx, ffx]=feval(func,xh1,varargin{:});
ic=1;
end
end
it=it+1;
dx(it)=(fx-f0);
h0(it)=h1(i);
- if (h1(i)<1.e-12*min(1,h2(i)) && h1(i)<0.5*hmax(i))% || (icount==10 && abs(dx(it))>(3*htol)),
+ if (h1(i)<1.e-12*min(1,h2(i)) && h1(i)<0.5*hmax(i))
ic=1;
hcheck=1;
end
@@ -141,7 +149,7 @@ while i<n
end
end
xh1(i)=x(i)-h1(i);
- [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fx, ffx]=feval(func,xh1,varargin{:});
f_1(:,i)=fx;
if outer_product_gradient,
if any(isnan(ffx)) || isempty(ffx),
@@ -181,8 +189,8 @@ if outer_product_gradient,
xh1(j)=x(j)+h_1(j);
xh_1(i)=x(i)-h1(i);
xh_1(j)=x(j)-h_1(j);
- temp1 = feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
- temp2 = feval(func,xh_1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ temp1 = feval(func,xh1,varargin{:});
+ temp2 = feval(func,xh_1,varargin{:});
hessian_mat(:,(i-1)*n+j)=-(-temp1 -temp2+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j));
xh1(i)=x(i);
xh1(j)=x(j);
@@ -203,7 +211,7 @@ if outer_product_gradient,
end
end
end
-
+
gga=ggh.*kron(ones(size(ff1)),2.*h1'); % re-scaled gradient
hh_mat=gga'*gga; % rescaled outer product hessian
hh_mat0=ggh'*ggh; % outer product hessian
@@ -222,7 +230,7 @@ if outer_product_gradient,
sd=sqrt(diag(ihh)); %standard errors
sdh=sqrt(1./diag(hh)); %diagonal standard errors
for j=1:length(sd)
- sd0(j,1)=min(BayesInfo.p2(j), sd(j)); %prior std
+ sd0(j,1)=min(varargin{6}.p2(j), sd(j)); %prior std
sd0(j,1)=10^(0.5*(log10(sd0(j,1))+log10(sdh(j,1))));
end
ihh=ihh./(sd*sd').*(sd0*sd0'); %inverse outer product with modified std's
@@ -239,7 +247,7 @@ if outer_product_gradient,
if hflag<2
hessian_mat=hh_mat0(:);
end
-
+
if any(isnan(hessian_mat))
hh_mat0=eye(length(hh_mat0));
ihh=hh_mat0;
diff --git a/matlab/ms-sbvar/ms_sbvar_setup.m b/matlab/ms-sbvar/ms_sbvar_setup.m
index 2d6375a2c920ff9af869eef35ec2057db7041c35..181a6f7d2e54029b363ec2896756374a56403f57 100644
--- a/matlab/ms-sbvar/ms_sbvar_setup.m
+++ b/matlab/ms-sbvar/ms_sbvar_setup.m
@@ -122,7 +122,7 @@ pctindx = [];
% Select the variable to use and rearrange columns if desired
%vlist = [3 1 2];
%options_.ms.vlist = [1 2 3];
-options_.ms.vlist = 1:size(options_.varobs,1);
+options_.ms.vlist = 1:length(options_.varobs);
vlist1=options_.ms.vlist;
%==========================================================================
diff --git a/matlab/ms-sbvar/ms_write_markov_file.m b/matlab/ms-sbvar/ms_write_markov_file.m
index c04051f62ed81ede835f8051ab346decc4775804..02b934a3e79ed358bff1baa051a9ec7c2c83b0f5 100644
--- a/matlab/ms-sbvar/ms_write_markov_file.m
+++ b/matlab/ms-sbvar/ms_write_markov_file.m
@@ -28,7 +28,7 @@ function ms_write_markov_file(fname, options)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
n_chains = length(options.ms.ms_chain);
- nvars = size(options.varobs,1);
+ nvars = length(options.varobs);
fh = fopen(fname,'w');
%/******************************************************************************/
diff --git a/matlab/ms-sbvar/msstart_setup.m b/matlab/ms-sbvar/msstart_setup.m
index 2d322211d84a5dad7928a2c5d2b465c90c73b50d..37b6e27e9e05e5825264b3f40741adc40ffb73db 100644
--- a/matlab/ms-sbvar/msstart_setup.m
+++ b/matlab/ms-sbvar/msstart_setup.m
@@ -48,16 +48,16 @@ end
%1 CBO output gap -- log(x_t)-log(x_t potential)
%2 GDP deflator -- (P_t/P_{t-1})^4-1.0
%2 FFR/100.
-options_.ms.vlist = [1:size(options_.varobs,1)]; % 1: U; 4: PCE inflation.
-options_.ms.varlist=cellstr(options_.varobs);
-options_.ms.log_var = sort(varlist_indices(options_.ms.vlistlog,options_.varobs)); % subset of "options_.ms.vlist. Variables in log level so that differences are in **monthly** growth, unlike R and U which are in annual percent (divided by 100 already).
+options_.ms.vlist = [1:length(options_.varobs)]; % 1: U; 4: PCE inflation.
+options_.ms.varlist=cellstr(options_.varobs');
+options_.ms.log_var = sort(varlist_indices(options_.ms.vlistlog,char(options_.varobs))); % subset of "options_.ms.vlist. Variables in log level so that differences are in **monthly** growth, unlike R and U which are in annual percent (divided by 100 already).
options_.ms.percent_var =setdiff(options_.ms.vlist,options_.ms.log_var);
%options_.ms.restriction_fname='ftd_upperchol3v'; %Only used by msstart2.m.
ylab = options_.ms.varlist;
xlab = options_.ms.varlist;
%----------------
-nvar = size(options_.varobs,1); % number of endogenous variables
+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))
diff --git a/matlab/newrat.m b/matlab/newrat.m
index 1df015776643ad76b136b0bc98861c57b9b4d979..424db0c1c4f5c410ed1c3551db6ae72a10b1a7bb 100644
--- a/matlab/newrat.m
+++ b/matlab/newrat.m
@@ -1,4 +1,4 @@
-function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ftol0, nit, flagg, DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ftol0, nit, flagg, varargin)
% [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, varargin)
%
% Optimiser with outer product gradient and with sequences of univariate steps
@@ -21,9 +21,16 @@ function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ft
% with correlation structure as from outer product gradient,
% flagg = 2, full numerical Hessian
%
-% varargin = list of parameters for func0
+% varargin{1} --> DynareDataset
+% varargin{2} --> DatasetInfo
+% varargin{3} --> DynareOptions
+% varargin{4} --> Model
+% varargin{5} --> EstimatedParameters
+% varargin{6} --> BayesInfo
+% varargin{1} --> DynareResults
-% Copyright (C) 2004-2013 Dynare Team
+
+% Copyright (C) 2004-2014 Dynare Team
%
% This file is part of Dynare.
%
@@ -52,19 +59,19 @@ ftol=ftol0;
gtol=1.e-3;
htol=htol_base;
htol0=htol_base;
-gibbstol=length(BayesInfo.pshape)/50; %25;
+gibbstol=length(varargin{6}.pshape)/50; %25;
% func0 = str2func([func2str(func0),'_hh']);
% func0 = func0;
-[fval0,gg,hh]=feval(func0,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval0,gg,hh]=feval(func0,x,varargin{:});
fval=fval0;
% initialize mr_gstep and mr_hessian
outer_product_gradient=1;
if isempty(hh)
- mr_hessian(1,x,[],[],[],DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
- [dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,x,func0,flagit,htol,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ mr_hessian(1,x,[],[],[],varargin{:});
+ [dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,x,func0,flagit,htol,varargin{:});
if isempty(dum),
outer_product_gradient=0;
igg = 1e-4*eye(nx);
@@ -111,9 +118,9 @@ while norm(gg)>gtol && check==0 && jit<nit
objective_function_penalty_base = fval0(icount);
disp([' '])
disp(['Iteration ',num2str(icount)])
- [fval,x0,fc,retcode] = csminit1(func0,xparam1,fval0(icount),gg,0,H,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fval,x0,fc,retcode] = csminit1(func0,xparam1,fval0(icount),gg,0,H,varargin{:});
if igrad
- [fval1,x01,fc,retcode1] = csminit1(func0,x0,fval,gg,0,inx,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fval1,x01,fc,retcode1] = csminit1(func0,x0,fval,gg,0,inx,varargin{:});
if (fval-fval1)>1
disp('Gradient step!!')
else
@@ -122,28 +129,26 @@ while norm(gg)>gtol && check==0 && jit<nit
fval=fval1;
x0=x01;
end
-% if icount==1 || (icount>1 && (fval0(icount-1)-fval0(icount))>1) || ((fval0(icount)-fval)<1.e-2*(gg'*(H*gg))/2 && igibbs),
- if length(find(ig))<nx
- ggx=ggx*0;
- ggx(find(ig))=gg(find(ig));
- if analytic_derivation,
- hhx=hh;
- else
- hhx = reshape(dum,nx,nx);
- end
- iggx=eye(length(gg));
- iggx(find(ig),find(ig)) = inv( hhx(find(ig),find(ig)) );
- [fvala,x0,fc,retcode] = csminit1(func0,x0,fval,ggx,0,iggx,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ if length(find(ig))<nx
+ ggx=ggx*0;
+ ggx(find(ig))=gg(find(ig));
+ if analytic_derivation,
+ hhx=hh;
+ else
+ hhx = reshape(dum,nx,nx);
end
- [fvala, x0, ig] = mr_gstep(h1,x0,func0,htol,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
- nig=[nig ig];
- disp('Sequence of univariate steps!!')
- fval=fvala;
-% end
+ iggx=eye(length(gg));
+ iggx(find(ig),find(ig)) = inv( hhx(find(ig),find(ig)) );
+ [fvala,x0,fc,retcode] = csminit1(func0,x0,fval,ggx,0,iggx,varargin{:});
+ end
+ [fvala, x0, ig] = mr_gstep(h1,x0,func0,htol,varargin{:});
+ nig=[nig ig];
+ disp('Sequence of univariate steps!!')
+ fval=fvala;
if (fval0(icount)-fval)<ftol && flagit==0
disp('Try diagonal Hessian')
ihh=diag(1./(diag(hhg)));
- [fval2,x0,fc,retcode2] = csminit1(func0,x0,fval,gg,0,ihh,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fval2,x0,fc,retcode2] = csminit1(func0,x0,fval,gg,0,ihh,varargin{:});
if (fval-fval2)>=ftol
disp('Diagonal Hessian successful')
end
@@ -152,7 +157,7 @@ while norm(gg)>gtol && check==0 && jit<nit
if (fval0(icount)-fval)<ftol && flagit==0
disp('Try gradient direction')
ihh0=inx.*1.e-4;
- [fval3,x0,fc,retcode3] = csminit1(func0,x0,fval,gg,0,ihh0,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fval3,x0,fc,retcode3] = csminit1(func0,x0,fval,gg,0,ihh0,varargin{:});
if (fval-fval3)>=ftol
disp('Gradient direction successful')
end
@@ -165,14 +170,14 @@ while norm(gg)>gtol && check==0 && jit<nit
disp('No further improvement is possible!')
check=1;
if analytic_derivation,
- [fvalx,gg,hh]=feval(func0,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fvalx,gg,hh]=feval(func0,xparam1,varargin{:});
hhg=hh;
- H = inv(hh);
+ H = inv(hh);
else
if flagit==2
hh=hh0;
elseif flagg>0
- [dum, gg, htol0, igg, hhg,h1]=mr_hessian(0,xparam1,func0,flagg,ftol0,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [dum, gg, htol0, igg, hhg,h1]=mr_hessian(0,xparam1,func0,flagg,ftol0,varargin{:});
if flagg==2
hh = reshape(dum,nx,nx);
ee=eig(hh);
@@ -208,7 +213,7 @@ while norm(gg)>gtol && check==0 && jit<nit
catch
save m1.mat x fval0 nig
end
- [dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,xparam1,func0,flagit,htol,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,xparam1,func0,flagit,htol,varargin{:});
if isempty(dum),
outer_product_gradient=0;
end
@@ -237,7 +242,7 @@ while norm(gg)>gtol && check==0 && jit<nit
H = igg;
end
elseif analytic_derivation,
- [fvalx,gg,hh]=feval(func0,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+ [fvalx,gg,hh]=feval(func0,xparam1,varargin{:});
hhg=hh;
H = inv(hh);
end
@@ -276,6 +281,4 @@ if check==1,
disp(['Estimation successful.'])
end
-return
-
-
+return
\ No newline at end of file
diff --git a/matlab/osr.m b/matlab/osr.m
index d48cd3e679e032ca62931387ea1d44ceca58d0f5..8d03864ed65f88dc5b970461737a91a7e80199dd 100644
--- a/matlab/osr.m
+++ b/matlab/osr.m
@@ -41,7 +41,6 @@ function osr_res = osr(var_list,params,i_var,W)
global M_ options_ oo_
options_.order = 1;
-options_ = set_default_option(options_,'simul',0);
if isempty(options_.qz_criterium)
options_.qz_criterium = 1+1e-6;
diff --git a/matlab/particle/solve_model_for_online_filter.m b/matlab/particle/solve_model_for_online_filter.m
index 575d0f16b01d2b0c5a78cc529352a43bf2bdbe90..bdcf5b12a63a36bfaaa37c10038137a5b1dc531e 100644
--- a/matlab/particle/solve_model_for_online_filter.m
+++ b/matlab/particle/solve_model_for_online_filter.m
@@ -201,9 +201,10 @@ end
% Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite.
if EstimatedParameters.ncn
+ corrn_observable_correspondence = EstimatedParameters.corrn_observable_correspondence;
for i=1:EstimatedParameters.ncn
- k1 = DynareOptions.lgyidx2varobs(EstimatedParameters.corrn(i,1));
- k2 = DynareOptions.lgyidx2varobs(EstimatedParameters.corrn(i,2));
+ k1 = corrn_observable_correspondence(i,1);
+ k2 = corrn_observable_correspondence(i,2);
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
H(k2,k1) = H(k1,k2);
end
diff --git a/matlab/prior_posterior_statistics.m b/matlab/prior_posterior_statistics.m
index f9192164eefc9fa719d4da8d6bfc69c5ba385df5..f06e0d433ea024755cd1d0069a03c935d2269685 100644
--- a/matlab/prior_posterior_statistics.m
+++ b/matlab/prior_posterior_statistics.m
@@ -1,4 +1,4 @@
-function prior_posterior_statistics(type,dataset)
+function prior_posterior_statistics(type,dataset,dataset_info)
% function prior_posterior_statistics(type,dataset)
% Computes Monte Carlo filter smoother and forecasts
@@ -40,11 +40,11 @@ global options_ estim_params_ oo_ M_ bayestopt_
localVars=[];
-Y = dataset.data;
-gend = dataset.info.ntobs;
-data_index = dataset.missing.aindex;
-missing_value = dataset.missing.state;
-bayestopt_.mean_varobs = dataset.descriptive.mean';
+Y = transpose(dataset.data);
+gend = dataset.nobs;
+data_index = dataset_info.missing.aindex;
+missing_value = dataset_info.missing.state;
+bayestopt_.mean_varobs = dataset_info.descriptive.mean';
nvx = estim_params_.nvx;
nvn = estim_params_.nvn;
@@ -58,7 +58,7 @@ naK = length(options_.filter_step_ahead);
MaxNumberOfBytes=options_.MaxNumberOfBytes;
endo_nbr=M_.endo_nbr;
exo_nbr=M_.exo_nbr;
-nvobs = size(options_.varobs,1);
+nvobs = length(options_.varobs);
iendo = 1:endo_nbr;
horizon = options_.forecast;
filtered_vars = options_.filtered_vars;
@@ -97,7 +97,7 @@ MAX_ninno = min(B,ceil(MaxNumberOfBytes/(exo_nbr*gend)/8));
MAX_nerro = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)*gend)/8));
if naK
- MAX_naK = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)* ...
+ MAX_naK = min(B,ceil(MaxNumberOfBytes/(length(options_.varobs)* ...
length(options_.filter_step_ahead)*gend)/8));
end
@@ -106,7 +106,7 @@ if horizon
MAX_nforc2 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/ ...
8));
IdObs = bayestopt_.mfys;
-
+
end
MAX_momentsno = min(B,ceil(MaxNumberOfBytes/(get_moments_size(options_)*8)));
@@ -232,7 +232,7 @@ else
'options_', options_, ...
'bayestopt_', bayestopt_, ...
'estim_params_', estim_params_, ...
- 'oo_', oo_);
+ 'oo_', oo_);
% which files have to be copied to run remotely
NamFileInput(1,:) = {'',[M_.fname '_static.m']};
NamFileInput(2,:) = {'',[M_.fname '_dynamic.m']};
@@ -240,11 +240,10 @@ else
NamFileInput(length(NamFileInput)+1,:)={'',[M_.fname '_steadystate.m']};
end
[fout] = masterParallel(options_.parallel, 1, B,NamFileInput,'prior_posterior_statistics_core', localVars,globalVars, options_.parallel_info);
-
+
end
ifil = fout(end).ifil;
-% ??????????
stock_gend=gend;
stock_data=Y;
save([DirectoryName '/' M_.fname '_data.mat'],'stock_gend','stock_data');
@@ -301,4 +300,4 @@ if ~isnumeric(options_.parallel),
if leaveSlaveOpen == 0,
closeSlave(options_.parallel,options_.parallel_info.RemoteTmpFolder),
end
-end
+end
\ No newline at end of file
diff --git a/matlab/prior_posterior_statistics_core.m b/matlab/prior_posterior_statistics_core.m
index 41b5b6d33f579d4d8b342999e162083e371b2076..63443a6ae12bb054cd793df6e51bc64a110ac97e 100644
--- a/matlab/prior_posterior_statistics_core.m
+++ b/matlab/prior_posterior_statistics_core.m
@@ -133,14 +133,14 @@ end
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_innov=NaN(M_.exo_nbr,gend,MAX_ninno);
if horizon
stock_forcst_mean= NaN(endo_nbr,horizon+maxlag,MAX_nforc1);
stock_forcst_point = NaN(endo_nbr,horizon+maxlag,MAX_nforc2);
end
end
if nvn
- stock_error = NaN(size(varobs,1),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);
@@ -150,10 +150,6 @@ stock_logpo = NaN(MAX_nruns,1);
stock_ys = NaN(MAX_nruns,endo_nbr);
for b=fpar:B
-
- % [deep, logpo] = GetOneDraw(type);
- % set_all_parameters(deep);
- % dr = resol(oo_.steady_state,0);
if strcmpi(type,'prior')
[deep, logpo] = GetOneDraw(type);
@@ -304,22 +300,7 @@ for b=fpar:B
end
irun(7) = 1;
end
-
- % if moments_varendo && (irun(8) > MAX_momentsno || b == B)
- % stock = stock_moments(1:irun(8)-1);
- % ifil(8) = ifil(8) + 1;
- % save([DirectoryName '/' M_.fname '_moments' int2str(ifil(8)) '.mat'],'stock');
- % if RemoteFlag==1,
- % OutputFileName_moments = [OutputFileName_moments; {[DirectoryName filesep], [M_.fname '_moments' int2str(ifil(8)) '.mat']}];
- % end
- % irun(8) = 1;
- % end
-
- % DirectoryName=TempPath;
-
-
dyn_waitbar((b-fpar+1)/(B-fpar+1),h);
-
end
myoutput.ifil=ifil;
@@ -332,7 +313,6 @@ if RemoteFlag==1,
OutputFileName_param;
OutputFileName_forc_mean;
OutputFileName_forc_point];
- % OutputFileName_moments];
end
dyn_waitbar_close(h);
diff --git a/matlab/random_walk_metropolis_hastings.m b/matlab/random_walk_metropolis_hastings.m
index f524b44c13095a93b22b3a2bde06ac681c8d2b6c..c2a49e7a29e22b194ee7ee4debfa0a2cdc725eb6 100644
--- a/matlab/random_walk_metropolis_hastings.m
+++ b/matlab/random_walk_metropolis_hastings.m
@@ -1,4 +1,4 @@
-function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
+function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
%function record=random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
% Random walk Metropolis-Hastings algorithm.
%
@@ -60,7 +60,7 @@ objective_function_penalty_base = Inf;
% Initialization of the random walk metropolis-hastings chains.
[ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
- metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
InitSizeArray = min([repmat(MAX_nruns,nblck,1) fline+nruns-1],[],2);
@@ -101,6 +101,7 @@ localVars = struct('TargetFun', TargetFun, ...
'InitSizeArray',InitSizeArray, ...
'record', record, ...
'dataset_', dataset_, ...
+ 'dataset_info', dataset_info, ...
'options_', options_, ...
'M_',M_, ...
'bayestopt_', bayestopt_, ...
diff --git a/matlab/random_walk_metropolis_hastings_core.m b/matlab/random_walk_metropolis_hastings_core.m
index 481e92b77a32235bdf9c5bd01a8c1006fb951875..62e1728eb9790d3bbe01b00fdc069ec74c867948 100644
--- a/matlab/random_walk_metropolis_hastings_core.m
+++ b/matlab/random_walk_metropolis_hastings_core.m
@@ -88,6 +88,7 @@ d=myinputs.d;
InitSizeArray=myinputs.InitSizeArray;
record=myinputs.record;
dataset_ = myinputs.dataset_;
+dataset_info = myinputs.dataset_info;
bayestopt_ = myinputs.bayestopt_;
estim_params_ = myinputs.estim_params_;
options_ = myinputs.options_;
@@ -164,7 +165,7 @@ for b = fblck:nblck,
par = feval(ProposalFun, ix2(b,:), proposal_covariance_Cholesky_decomposition, n);
if all( par(:) > mh_bounds(:,1) ) && all( par(:) < mh_bounds(:,2) )
try
- logpost = - feval(TargetFun, par(:),dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ logpost = - feval(TargetFun, par(:),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
catch
logpost = -inf;
end
diff --git a/matlab/read_variables.m b/matlab/read_variables.m
index 23d7fe635d865bb2fa395ccba5a7ab7fc477e5f5..6859a679574c2c557934ac7ab37c7f42e58db9a0 100644
--- a/matlab/read_variables.m
+++ b/matlab/read_variables.m
@@ -42,7 +42,7 @@ if ~isempty(directory)
end
dyn_size_01 = size(dyn_data_01,1);
-var_size_01 = size(var_names_01,1);
+var_size_01 = length(var_names_01);
% Auto-detect extension if not provided
if isempty(extension)
@@ -71,7 +71,7 @@ switch (extension)
case '.m'
eval(basename);
for dyn_i_01=1:var_size_01
- dyn_tmp_01 = eval(var_names_01(dyn_i_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')
@@ -81,7 +81,7 @@ switch (extension)
case '.mat'
s = load(basename);
for dyn_i_01=1:var_size_01
- dyn_tmp_01 = s.(deblank(var_names_01(dyn_i_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')
@@ -106,9 +106,8 @@ switch (extension)
end
case '.csv'
[freq,init,data,varlist] = load_csv_file_data(fullname);
- %var_names_01 = deblank(var_names_01);
for dyn_i_01=1:var_size_01
- iv = strmatch(strtrim(var_names_01(dyn_i_01,:)),varlist,'exact');
+ 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
@@ -118,7 +117,7 @@ switch (extension)
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])
+ error([var_names_01{dyn_i_01} ' not found in ' fullname])
end
end
otherwise
diff --git a/matlab/set_default_option.m b/matlab/set_default_option.m
index 3c8303f76bacde13ef4fd6c02a060b20d03b801a..760d74fa86f8ed428a064fc57998033a43e064c8 100644
--- a/matlab/set_default_option.m
+++ b/matlab/set_default_option.m
@@ -33,6 +33,17 @@ function options=set_default_option(options,field,default)
if ~isfield(options,field)
options.(field) = default;
+ return
+end
+
+if isempty(options.(field))
+ options.(field) = default;
+ return
+end
+
+if isnan(options.(field))
+ options.(field) = default;
+ return
end
% 06/07/03 MJ added ; to eval expression
\ No newline at end of file
diff --git a/matlab/set_prior.m b/matlab/set_prior.m
index c438b2d3b2ae7339c7f43f804bd613b82f95fcc7..253b51c242f6b0102c001304d09856b25f8a6175 100644
--- a/matlab/set_prior.m
+++ b/matlab/set_prior.m
@@ -76,12 +76,12 @@ end
if nvn
estim_params_.nvn_observable_correspondence=NaN(nvn,1); % stores number of corresponding observable
if isequal(M_.H,0) %if no previously set measurement error, initialize H
- nvarobs = size(options_.varobs,1);
+ nvarobs = length(options_.varobs);
M_.H = zeros(nvarobs,nvarobs);
M_.Correlation_matrix_ME = eye(nvarobs);
end
for i=1:nvn
- obsi_ = strmatch(deblank(M_.endo_names(estim_params_.var_endo(i,1),:)),deblank(options_.varobs),'exact');
+ obsi_ = strmatch(deblank(M_.endo_names(estim_params_.var_endo(i,1),:)),options_.varobs,'exact');
if isempty(obsi_)
error(['The variable ' deblank(M_.endo_names(estim_params_.var_endo(i,1),:)) ' has to be declared as observable since you assume a measurement error on it.'])
end
@@ -96,7 +96,7 @@ if nvn
bayestopt_.p3 = [ bayestopt_.p3; estim_params_.var_endo(:,8)]; %take generalized distribution into account
bayestopt_.p4 = [ bayestopt_.p4; estim_params_.var_endo(:,9)]; %take generalized distribution into account
bayestopt_.jscale = [ bayestopt_.jscale; estim_params_.var_endo(:,10)];
- bayestopt_.name = [ bayestopt_.name; cellstr(options_.varobs(estim_params_.nvn_observable_correspondence,:))];
+ bayestopt_.name = [ bayestopt_.name; transpose(options_.varobs(estim_params_.nvn_observable_correspondence))];
end
if ncx
xparam1 = [xparam1; estim_params_.corrx(:,3)];
@@ -115,7 +115,7 @@ end
if ncn
estim_params_.corrn_observable_correspondence=NaN(ncn,2);
if isequal(M_.H,0)
- nvarobs = size(options_.varobs,1);
+ nvarobs = length(options_.varobs);
M_.H = zeros(nvarobs,nvarobs);
M_.Correlation_matrix_ME = eye(nvarobs);
end
@@ -134,8 +134,8 @@ if ncn
for i=1:ncn
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
- obsi1 = strmatch(deblank(M_.endo_names(k1,:)),deblank(options_.varobs),'exact'); %find correspondence to varobs to construct H in set_all_paramters
- obsi2 = strmatch(deblank(M_.endo_names(k2,:)),deblank(options_.varobs),'exact');
+ obsi1 = strmatch(deblank(M_.endo_names(k1,:)),options_.varobs,'exact'); %find correspondence to varobs to construct H in set_all_paramters
+ obsi2 = strmatch(deblank(M_.endo_names(k2,:)),options_.varobs,'exact');
estim_params_.corrn_observable_correspondence(i,:)=[obsi1,obsi2]; %save correspondence
end
end
diff --git a/matlab/simplex_optimization_routine.m b/matlab/simplex_optimization_routine.m
index da073cb19a2595fc64a1ed6f34494b4001eec6be..9d60b5774ead3d38ad97b9b3576f2271a37aa517 100644
--- a/matlab/simplex_optimization_routine.m
+++ b/matlab/simplex_optimization_routine.m
@@ -11,8 +11,15 @@ function [x,fval,exitflag] = simplex_optimization_routine(objective_function,x,o
% o objective_function [string] Name of the objective function to be minimized.
% o x [double] n*1 vector, starting guess of the optimization routine.
% o options [structure] Options of this implementation of the simplex algorithm.
-% o varargin [cell of structures] Structures to be passed to the objective function: dataset_,
-% options_, M_, estim_params_, bayestopt_, and oo_.
+% o varargin [cell of structures] Structures to be passed to the objective function.
+%
+% varargin{1} --> DynareDataset
+% varargin{2} --> DatasetInfo
+% varargin{3} --> DynareOptions
+% varargin{4} --> Model
+% varargin{5} --> EstimatedParameters
+% varargin{6} --> BayesInfo
+% varargin{1} --> DynareResults
%
% OUTPUTS
% o x [double] n*1 vector, estimate of the optimal inputs.
@@ -187,7 +194,7 @@ else
disp(['Current parameter values: '])
fprintf(1,'%s: \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \n','Names','Best point', 'Worst point', 'Mean values', 'Min values', 'Max values');
for i=1:number_of_variables
- fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',varargin{5}.name{i},v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
+ fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',varargin{6}.name{i},v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
end
skipline()
end
@@ -399,7 +406,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
disp(['Current parameter values: '])
fprintf(1,'%s: \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \n','Names','Best point', 'Worst point', 'Mean values', 'Min values', 'Max values');
for i=1:number_of_variables
- fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',varargin{5}.name{i}, v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
+ fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',varargin{6}.name{i}, v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
end
skipline()
end
@@ -425,7 +432,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
disp(['values for the control variables. '])
disp(['New value of delta (size of the new simplex) is: '])
for i=1:number_of_variables
- fprintf(1,'%s: \t\t\t %+8.6f \n',varargin{5}.name{i}, delta(i));
+ fprintf(1,'%s: \t\t\t %+8.6f \n',varargin{6}.name{i}, delta(i));
end
end
% Reset counters
@@ -472,7 +479,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
disp(['values for the control variables. '])
disp(['New value of delta (size of the new simplex) is: '])
for i=1:number_of_variables
- fprintf(1,'%s: \t\t\t %+8.6f \n',varargin{5}.name{i}, delta(i));
+ fprintf(1,'%s: \t\t\t %+8.6f \n',varargin{6}.name{i}, delta(i));
end
end
% Reset counters
diff --git a/matlab/simulated_moment_uncertainty.m b/matlab/simulated_moment_uncertainty.m
index 0815c7a6b00cb8c71c333b7a90df366275b1454b..d41f138359ac80d1af4cc4dbe7fb2ae0773b44b4 100644
--- a/matlab/simulated_moment_uncertainty.m
+++ b/matlab/simulated_moment_uncertainty.m
@@ -30,7 +30,7 @@ for j=1:replic;
options_.noprint = 1;
options_.order = 1;
options_.periods = periods;
- info = stoch_simul(options_.varobs);
+ info = stoch_simul(char(options_.varobs));
dum=[oo_.mean; dyn_vech(oo_.var)];
sd = sqrt(diag(oo_.var));
for i=1:options_.ar;
diff --git a/matlab/stoch_simul.m b/matlab/stoch_simul.m
index 8b2144651c4ec37ef0bc2343c464df1a87604bb5..2009ca08dd886c72b7aa35de89cfed0a3da54e6f 100644
--- a/matlab/stoch_simul.m
+++ b/matlab/stoch_simul.m
@@ -115,7 +115,7 @@ if ~options_.noprint
skipline()
if isfield(options_,'varobs')&& ~isempty(options_.varobs)
- PCL_varobs=options_.varobs;
+ PCL_varobs=char(options_.varobs);
disp('OBSERVED VARIABLES')
else
PCL_varobs=M_.endo_names;
diff --git a/matlab/subset.m b/matlab/subset.m
index 8e419581c869f8cd51a62fbd8e0262b198554c0a..dedbdbfba2ec4a5997c1b80b6b0444199ab6ce67 100644
--- a/matlab/subset.m
+++ b/matlab/subset.m
@@ -20,8 +20,6 @@ function jndx = subset()
global options_ estim_params_ M_
ExcludedParamNames = options_.ExcludedParams;
-VarObs = options_.varobs;
-VarExo = M_.exo_names;
info = options_.ParamSubSet;
nvx = estim_params_.nvx;
diff --git a/matlab/utilities/dataset/compute_corr.m b/matlab/utilities/dataset/compute_corr.m
deleted file mode 100644
index d6469baefd5e4ecc6413394f73190e5ce9fb8f58..0000000000000000000000000000000000000000
--- a/matlab/utilities/dataset/compute_corr.m
+++ /dev/null
@@ -1,59 +0,0 @@
-function dataset_ = compute_corr(dataset_)
-% Computes the correlation matrix of the sample (possibly with missing observations).
-
-%@info:
-%! @deftypefn {Function File} {@var{dataset_} =} compute_corr(@var{dataset_})
-%! @anchor{compute_corr}
-%! This function computes covariance matrix of the sample (possibly with missing observations).
-%!
-%! @strong{Inputs}
-%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @end table
-%!
-%! @strong{Outputs}
-%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @end table
-%!
-%! @strong{This function is called by:}
-%! @ref{descriptive_statistics}.
-%!
-%! @strong{This function calls:}
-%! @ref{ndim}, @ref{compute_cova}.
-%!
-%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.corr} is a
-%! @tex{n\times n} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
-%!
-%! @strong{Remark 2.} If @code{dataset_.descriptive.cova} does not exist, the covariance matrix is computed prior to the
-%! computation of the correlation matrix.
-%!
-%! @end deftypefn
-%@eod:
-
-% Copyright (C) 2011-2012 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/>.
-
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
-if ~isfield(dataset_.descriptive,'cova')
- dataset_ = compute_cova(dataset_);
-end
-normalization_matrix = diag(1./sqrt(diag(dataset_.descriptive.cova)));
-dataset_.descriptive.corr = normalization_matrix*dataset_.descriptive.cova*normalization_matrix;
\ No newline at end of file
diff --git a/matlab/utilities/dataset/compute_cova.m b/matlab/utilities/dataset/compute_cova.m
deleted file mode 100644
index 51f68981ec81ea428899c714104c8861ec475e95..0000000000000000000000000000000000000000
--- a/matlab/utilities/dataset/compute_cova.m
+++ /dev/null
@@ -1,67 +0,0 @@
-function dataset_ = compute_cova(dataset_)
-% Computes the covariance matrix of the sample (possibly with missing observations).
-
-%@info:
-%! @deftypefn {Function File} {@var{dataset_} =} compute_corr(@var{dataset_})
-%! @anchor{compute_corr}
-%! This function computes covariance matrix of the sample (possibly with missing observations).
-%!
-%! @strong{Inputs}
-%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @end table
-%!
-%! @strong{Outputs}
-%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @end table
-%!
-%! @strong{This function is called by:}
-%! @ref{descriptive_statistics}.
-%!
-%! @strong{This function calls:}
-%! @ref{ndim}, @ref{demean}, @ref{nandemean}.
-%!
-%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.cova} is a
-%! @tex{n\times n} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
-%!
-%! @end deftypefn
-%@eod:
-
-% Copyright (C) 2011-2012 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/>.
-
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
-dataset_.descriptive.cova = zeros(dataset_.nvobs);
-
-data = transpose(dataset_.data);
-
-for i=1:dataset_.info.nvobs
- for j=i:dataset_.info.nvobs
- if dataset_.missing.state
- dataset_.descriptive.cova(i,j) = nanmean(nandemean(data(:,i)).*nandemean(data(:,j)));
- else
- dataset_.descriptive.cova(i,j) = mean(demean(data(:,i)).*demean(data(:,j)));
- end
- if j>i
- dataset_.descriptive.cova(j,i) = dataset_.descriptive.cova(i,j);
- end
- end
-end
\ No newline at end of file
diff --git a/matlab/utilities/dataset/describe_missing_data.m b/matlab/utilities/dataset/describe_missing_data.m
index 5506da2be802e8e9c2ec39ed2b78958e40677a69..69589933ee0cdadd7e57b93344f11d04edb36445 100644
--- a/matlab/utilities/dataset/describe_missing_data.m
+++ b/matlab/utilities/dataset/describe_missing_data.m
@@ -26,9 +26,9 @@ function [i,n,s,j] = describe_missing_data(data)
%!
%! @end deftypefn
%@eod:
-
-% Copyright (C) 2008-2012 Dynare Team
-%
+
+% Copyright (C) 2008-2014 Dynare Team
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
@@ -85,7 +85,7 @@ end
%@test:1
-%$ % Define a data set.
+%$ % Define a data set.
%$ A = [ 1 1 ; ...
%$ 1 NaN ; ...
%$ NaN 1 ; ...
@@ -99,7 +99,7 @@ end
%$ 1 1 ];
%$
%$ % Define expected results.
-%$ eB = cell(1,11);
+%$ eB = cell(1,11);
%$ eB(1) = { transpose(1:2) };
%$ eB(2) = { 1 };
%$ eB(3) = { 2 };
@@ -107,7 +107,7 @@ end
%$ eB(5) = { [] };
%$ eB(6) = { 1 };
%$ eB(7) = { 1 };
-%$ eB(8) = { transpose(1:2) };
+%$ eB(8) = { transpose(1:2) };
%$ eB(9) = { transpose(1:2) };
%$ eB(10) = { transpose(1:2) };
%$ eB(11) = { transpose(1:2) };
@@ -117,7 +117,7 @@ end
%$ eE(1) = { [1; 2; 4; transpose(6:11)] };
%$ eE(2) = { [1; 3; 4; transpose(8:11)] };
%$
-%$ % Call the tested routine.
+%$ % Call the tested routine.
%$ [B,C,D,E] = describe_missing_data(transpose(A));
%$
%$ % Check the results.
diff --git a/matlab/utilities/dataset/initialize_dataset.m b/matlab/utilities/dataset/initialize_dataset.m
index 0b65cb8f3ae1456fee14cecc732f46f843a45ded..5d992a288955d728734b7b5f216fe03f371ef0fc 100644
--- a/matlab/utilities/dataset/initialize_dataset.m
+++ b/matlab/utilities/dataset/initialize_dataset.m
@@ -1,4 +1,4 @@
-function dataset_ = initialize_dataset(datafile,varobs,first,nobs,transformation,prefilter,xls)
+function dataset_ = initialize_dataset(datafile,varobs,first,nobs,logged_data_flag,prefilter,xls)
% Initializes a structure describing the dataset.
% Copyright (C) 2011-2013 Dynare Team
@@ -18,8 +18,6 @@ function dataset_ = initialize_dataset(datafile,varobs,first,nobs,transformation
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
if isempty(datafile)
error('Estimation::initialize_dataset: You have to declare a dataset file!')
end
@@ -43,7 +41,7 @@ end
% Fill the dataset structure
dataset_.info.ntobs = nobs;
-dataset_.info.nvobs = rows(varobs);
+dataset_.info.nvobs = length(varobs);
dataset_.info.varobs = varobs;
% Test the number of variables in the database.
@@ -61,9 +59,9 @@ if size(rawdata,1)~=dataset_.info.ntobs
end
rawdata = rawdata(first:(first+dataset_.info.ntobs-1),:);
-% Take the log (or anything else) of the variables if needed
-if isempty(transformation)
- dataset_.rawdata = rawdata;
+% Take the log of the variables if needed
+if logged_data_flag
+ dataset_.rawdata = log(rawdata);
else
if isequal(transformation,@log)
if ~isreal(rawdata)
@@ -101,4 +99,4 @@ if prefilter == 1
dataset_.data = transpose(bsxfun(@minus,dataset_.rawdata,dataset_.descriptive.mean));
else
dataset_.data = transpose(dataset_.rawdata);
-end
\ No newline at end of file
+end
diff --git a/matlab/utilities/dataset/makedataset.m b/matlab/utilities/dataset/makedataset.m
new file mode 100644
index 0000000000000000000000000000000000000000..9eb4cb39682d406a7da92ea13667ada3733fa876
--- /dev/null
+++ b/matlab/utilities/dataset/makedataset.m
@@ -0,0 +1,241 @@
+function [DynareDataset, DatasetInfo] = makedataset(DynareOptions, initialconditions, gsa_flag)
+
+% Initialize a dataset as a dseries object.
+%
+%
+% INPUTS
+% ======
+%
+% DynareOptions [struct] Structure of options built by Dynare's preprocessor.
+%
+%
+% OUTPUTS
+% =======
+%
+% DynareDataset [dseries] The dataset.
+% DatasetInfo [struct] Various informations about the dataset (descriptive statistics and missing observations).
+%
+% EXAMPLE
+% =======
+%
+% [dataset_, dataset_info] = makedataset(options_) ;
+%
+%
+% See also dynare_estimation_init
+
+if nargin<3
+ gsa_flag = 0;
+end
+
+if nargin<2 || isempty(initialconditions)
+ % If a the sample is to be used for the estimation of a VAR or DSGE-VAR model
+ % the second argument must be a strictly positive integer (the number of lags).
+ initialconditions = 0;
+end
+
+if isempty(DynareOptions.datafile) && isempty(DynareOptions.dataset.file) && isempty(DynareOptions.dataset.series)
+ if gsa_flag
+ DynareDataset = dseries();
+ DatasetInfo = [];
+ return
+ else
+ error('makedataset: datafile option is missing!')
+ end
+end
+
+if isempty(DynareOptions.datafile) && ~isempty(DynareOptions.dataset.file)
+ datafile = DynareOptions.dataset.file;
+ newdatainterface = 1;
+elseif isempty(DynareOptions.datafile) && ~isempty(DynareOptions.dataset.series)
+ try
+ dseriesobjectforuserdataset = evalin('base', DynareOptions.dataset.series);
+ catch
+ error(sprintf('makedataset: %s is unknown!', DynareOptions.dataset.series))
+ end
+ if ~isdseries(dseriesobjectforuserdataset)
+ error(sprintf('makedataset: %s has to be a dseries object!', DynareOptions.dataset.series))
+ end
+ datafile = [];
+ newdatainterface = 1;
+elseif ~isempty(DynareOptions.datafile) && isempty(DynareOptions.dataset.file)
+ datafile = DynareOptions.datafile;
+ newdatainterface = 0;
+elseif isempty(DynareOptions.datafile) && ~isempty(DynareOptions.dataset.file)
+ error('You cannot use simultaneously the data command and the datafile option (in the estimation command)!')
+else
+ error('You have to specify the datafile!')
+end
+
+% Check extension.
+if ~isempty(datafile)
+ allowed_extensions = {'m','mat','csv','xls','xlsx'};
+ datafile_extension = get_file_extension(datafile);
+ if isempty(datafile_extension)
+ available_extensions = {}; j = 1;
+ for i=1:length(allowed_extensions)
+ if exist([datafile '.' allowed_extensions{i}])
+ available_extensions(j) = {allowed_extensions{i}};
+ j = j+1;
+ end
+ end
+ if isempty(available_extensions)
+ error(['I can''t find a datafile (with allowed extension)!'])
+ end
+ if length(available_extensions)>1
+ error(sprintf(['You did not specify an extension for the datafile, but more than one candidate ' ...
+ 'are available in the designed folder!\nPlease, add an extension to the datafile ' ...
+ '(m, mat, csv, xls or xlsx are legal extensions).']));
+ end
+ datafile = [datafile '.' available_extensions{1}];
+ end
+end
+
+% Load the data in a dseries object.
+if ~isempty(datafile)
+ DynareDataset = dseries(datafile);
+else
+ DynareDataset = dseriesobjectforuserdataset;
+ clear('dseriesobjectforuserdataset');
+end
+
+% Select a subset of the variables.
+DynareDataset = DynareDataset{DynareOptions.varobs{:}};
+
+% Apply log function if needed.
+if DynareOptions.loglinear && ~DynareOptions.logdata
+ DynareDataset = DynareDataset.log();
+end
+
+% Test if an initial period (different from its default value) is explicitely defined in the datafile.
+if isequal(DynareDataset.init, dates(1,1))
+ dataset_default_initial_period = 1;
+else
+ dataset_default_initial_period = 0;
+end
+
+% Test if an initial period (different from its default value) is explicitely defined in the mod file with the set_time command.
+if isequal(DynareOptions.initial_period, dates(1,1))
+ set_time_default_initial_period = 1;
+else
+ set_time_default_initial_period = 0;
+end
+
+if ~set_time_default_initial_period && dataset_default_initial_period
+ % Overwrite the initial period in dataset (it was set to default).
+ % Note that the updates of freq and time members are auto-magically
+ % done by dseries::subsasgn overloaded method.
+ DynareDataset.init = DynareOptions.initial_period;
+end
+
+if set_time_default_initial_period && ~dataset_default_initial_period
+ % Overwrite the global initial period defined by set_time (it was set to default).
+ DynareOptions.initial_period = DynareDataset.init;
+end
+
+if ~set_time_default_initial_period && ~dataset_default_initial_period
+ % Check if dataset.init and options_.initial_period are identical.
+ if DynareOptions.initial_period>DynareDataset.init
+ %if ~isequal(DynareOptions.initial_period, DynareDataset.init)
+ error('The date as defined by the set_time command is not consistent with the initial period in the database!')
+ end
+end
+
+% Set firstobs, lastobs and nobs
+if newdatainterface
+ if isempty(DynareOptions.dataset.firstobs)
+ % first_obs option was not used in the data command.
+ firstobs = DynareDataset.init;
+ else
+ firstobs = DynareOptions.dataset.firstobs;
+ end
+ if isnan(DynareOptions.dataset.nobs)
+ % nobs option was not used in the data command.
+ if isempty(DynareOptions.dataset.lastobs)
+ % last_obs option was not used in the data command.
+ nobs = DynareDataset.nobs;
+ lastobs = DynareDataset.dates(end);
+ else
+ lastobs = DynareOptions.dataset.lastobs;
+ nobs = lastobs-firstobs+1;
+ end
+ else
+ nobs = DynareOptions.dataset.nobs;
+ if isempty(DynareOptions.dataset.lastobs)
+ % last_obs option was not used in the data command.
+ lastobs = firstobs+(nobs-1);
+ else
+ % last_obs and nobs were used in the data command. Check that they are consistent (with firstobs).
+ if ~isequal(lastobs,firstobs+(nobs-1))
+ error(sprintf('Options last_obs (%s), first_obs (%s) and nobs (%s) are not consistent!',char(lastobs),char(firstobs),num2str(nobs)));
+ end
+ end
+ end
+else
+ if isnan(DynareOptions.first_obs)
+ firstobs = DynareDataset.init;
+ else
+ firstobs = DynareDataset.dates(DynareOptions.first_obs);
+ end
+ if isnan(DynareOptions.nobs)
+ lastobs = DynareDataset.dates(end);
+ nobs = lastobs-firstobs+1;
+ else
+ nobs = DynareOptions.nobs;
+ lastobs = firstobs+(nobs-1);
+ end
+end
+
+% Add initial conditions if needed
+FIRSTOBS = firstobs-initialconditions;
+
+% Check that firstobs belongs to DynareDataset.dates
+if firstobs<DynareDataset.init
+ error(sprintf('first_obs (%s) cannot be less than the first date in the dataset (%s)!',char(firstobs),char(DynareDataset.init)))
+end
+
+% Check that FIRSTOBS belongs to DynareDataset.dates
+if initialconditions && FIRSTOBS<DynareDataset.init
+ error(sprintf('first_obs (%s) - %i cannot be less than the first date in the dataset (%s)!\nReduce the number of lags in the VAR model or increase the value of first_obs.', char(firstobs), initialconditions, char(DynareDataset.init)));
+end
+
+% Check that lastobs belongs to DynareDataset.dates...
+if newdatainterface
+ if lastobs>DynareDataset.dates(end)
+ error(sprintf('last_obs (%s) cannot be greater than the last date in the dataset (%s)!',char(lastobs),char(DynareDataset.dates(end))))
+ end
+else
+ % ... or check that nobs is smaller than the number of observations in DynareDataset.
+ if nobs>DynareDataset.nobs
+ error(sprintf('nobs (%s) cannot be greater than the last date in the dataset (%s)!', num2str(nobs), num2str(nobs)))
+ end
+end
+
+% Select a subsample.
+DynareDataset = DynareDataset(FIRSTOBS:lastobs);
+
+% Initialize DatasetInfo structure.
+DatasetInfo = struct('missing', struct('state', NaN, 'aindex', [], 'vindex', [], 'number_of_observations', NaN, 'no_more_missing_observations', NaN), ...
+ 'descriptive', struct('mean', [], 'covariance', [], 'correlation', [], 'autocovariance', []));
+
+% Fill DatasetInfo.missing if some observations are missing
+DatasetInfo.missing.state = isanynan(DynareDataset.data);
+if DatasetInfo.missing.state
+ [DatasetInfo.missing.aindex, DatasetInfo.missing.number_of_observations, DatasetInfo.missing.no_more_missing_observations, DatasetInfo.missing.vindex] = ...
+ describe_missing_data(DynareDataset.data);
+else
+ DatasetInfo.missing.aindex = num2cell(transpose(repmat(1:DynareDataset.vobs,DynareDataset.nobs,1)),1);
+ DatasetInfo.missing.no_more_missing_observations = 1;
+end
+
+% Compute the empirical mean of the observed variables.
+DatasetInfo.descriptive.mean = nanmean(DynareDataset.data);
+
+% Compute the empirical covariance matrix of the observed variables.
+DatasetInfo.descriptive.covariance = nancovariance(DynareDataset.data);
+
+% Compute the empirical correlation matrix of the observed variables.
+normalization_matrix = diag(1./sqrt(diag(DatasetInfo.descriptive.covariance)));
+DatasetInfo.descriptive.correlation = normalization_matrix*DatasetInfo.descriptive.covariance*normalization_matrix;
+
+% Compute autocorrelation function.
+DatasetInfo.descriptive.autocovariance = nanautocovariance(DynareDataset.data, DynareOptions.ar);
\ No newline at end of file
diff --git a/matlab/utilities/dataset/compute_acov.m b/matlab/utilities/dataset/nanautocovariance.m
similarity index 57%
rename from matlab/utilities/dataset/compute_acov.m
rename to matlab/utilities/dataset/nanautocovariance.m
index b1834e854237602778c3182f7224fff1dcbc822f..715ae957f389f66d387053b014a6cd1bda43003b 100644
--- a/matlab/utilities/dataset/compute_acov.m
+++ b/matlab/utilities/dataset/nanautocovariance.m
@@ -1,4 +1,4 @@
-function dataset_ = compute_acov(dataset_)
+function autocov = nanautocovariance(data,order)
% Computes the (multivariate) auto-covariance function of the sample (possibly with missing observations).
%@info:
@@ -8,36 +8,36 @@ function dataset_ = compute_acov(dataset_)
%!
%! @strong{Inputs}
%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @item nlag
-%! Integer scalar. The maximum number of lags of the autocovariance function.
+%! @item data
+%! T*N array of real numbers.
+%! @item order
+%! Integer scalar. The maximum number of lags of the autocovariance function.
%! @end table
%!
%! @strong{Outputs}
%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
+%! @item autocov
+%! A N*N*order array of real numbers.
%! @end table
-%!
-%! @strong{This function is called by:}
+%!
+%! @strong{This function is called by:}
%! @ref{descriptive_statistics}.
-%!
+%!
%! @strong{This function calls:}
-%! @ref{ndim}, @ref{compute_cova}, @ref{demean}, @ref{nandemean}.
-%!
-%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.acov} is a
-%! @tex{n\times n\times p} array (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs},
+%! @ref{ndim}, @ref{nancovariance}, @ref{demean}, @ref{nandemean}.
+%!
+%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.acov} is a
+%! @tex{n\times n\times p} array (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs},
%! and @tex{n} is the maximum number of lags given by the second input @code{nlag}).
-%!
-%! @strong{Remark 2.} If @code{dataset_.descriptive.cova} does not exist, the covariance matrix is computed prior to the
+%!
+%! @strong{Remark 2.} If @code{dataset_.descriptive.cova} does not exist, the covariance matrix is computed prior to the
%! computation of the auto-covariance function.
%!
%! @end deftypefn
%@eod:
-% Copyright (C) 2011-2012 Dynare Team
-%
+% Copyright (C) 2011-2014 Dynare Team
+%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
@@ -53,22 +53,23 @@ function dataset_ = compute_acov(dataset_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
-if ~isfield(dataset_.descriptive,'cova')
- dataset_ = compute_cova(dataset_);
-end
-dataset_.descriptive.acov = zeros(dataset_.nvobs,dataset_.nvobs,nlag);
+n = size(data,2);
+missing = isanynan(data);
-data = transpose(dataset_.data);
+autocov = zeros(n, n, order);
-for lag=1:nlag
- for i=1:dataset_.info.nvobs
- for j=1:dataset_.info.nvobs
- if dataset_.missing.state
- dataset_.descriptive.acov(i,j,lag) = nanmean(nandemean(data(lag+1:end,i)).*nandemean(data(1:end-lag,j)));
+for lag=1:order
+ if missing
+ data = nandemean(data);
+ else
+ data = demean(data);
+ end
+ for i=1:n
+ for j=1:n
+ if missing
+ autocov(i,j,lag) = nanmean(data((lag+1):end,i).*data(1:end-lag,j));
else
- dataset_.descriptive.acov(i,j,lag) = mean(demean(data(lag+1:end,i)).*demean(data(1:end-lag,j)));
+ autocov(i,j,lag) = mean(data((lag+1):end,i).*data(1:end-lag,j));
end
end
end
diff --git a/matlab/utilities/dataset/nancovariance.m b/matlab/utilities/dataset/nancovariance.m
new file mode 100644
index 0000000000000000000000000000000000000000..8e2e2b227522b4bc05802bbbb1e7571547504436
--- /dev/null
+++ b/matlab/utilities/dataset/nancovariance.m
@@ -0,0 +1,101 @@
+function CovarianceMatrix = nancovariance(data)
+% Computes the covariance matrix of a sample (possibly with missing observations).
+
+%@info:
+%! @deftypefn {Function File} {@var{CovarianceMatrix} =} compute_corr(@var{data})
+%! @anchor{compute_cova}
+%! This function computes covariance matrix of a sample defined by a dseries object (possibly with missing observations).
+%!
+%! @strong{Inputs}
+%! @table @var
+%! @item data
+%! a T*N array of real numbers.
+%! @end table
+%!
+%! @strong{Outputs}
+%! @table @var
+%! @item CovarianceMatrix
+%! Array of real numbers.
+%! @end table
+%!
+%! @strong{This function is called by:}
+%! @ref{descriptive_statistics}.
+%!
+%! @strong{This function calls:}
+%! @ref{ndim}, @ref{demean}, @ref{nandemean}.
+%!
+%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.cova} is a
+%! @tex{n\times n} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
+%!
+%! @end deftypefn
+%@eod:
+
+% Copyright (C) 2011-2014 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/>.
+
+% Initialize the output.
+CovarianceMatrix = zeros(size(data,2));
+
+if isanynan(data)
+ data = bsxfun(@minus,data,nanmean(data));
+ for i=1:size(data,2)
+ for j=i:size(data,2)
+ CovarianceMatrix(i,j) = nanmean(data(:,i).*data(:,j));
+ if j>i
+ CovarianceMatrix(j,i) = CovarianceMatrix(i,j);
+ end
+ end
+ end
+else
+ data = bsxfun(@minus,data,mean(data));
+ CovarianceMatrix = (transpose(data)*data)/size(data,1);
+end
+
+%@test:1
+%$
+%$ % Define a dataset.
+%$ data1 = randn(10000000,2);
+%$
+%$ % Same dataset with missing observations.
+%$ data2 = data1;
+%$ data2(45,1) = NaN;
+%$ data2(57,2) = NaN;
+%$ data2(367,:) = NaN(1,2);
+%$
+%$ t = zeros(2,1);
+%$
+%$ % Call the tested routine.
+%$ try
+%$ c1 = nancovariance(data1);
+%$ t(1) = 1;
+%$ catch
+%$ t(1) = 0;
+%$ end
+%$ try
+%$ c2 = nancovariance(data2);
+%$ t(2) = 1;
+%$ catch
+%$ t(2) = 0;
+%$ end
+%$
+%$ if t(1) && t(2)
+%$ t(3) = max(max(abs(c1-c2)))<1e-4;
+%$ end
+%$
+%$ % Check the results.
+%$ T = all(t);
+%@eof:1
\ No newline at end of file
diff --git a/matlab/utilities/dataset/nanmoments.m b/matlab/utilities/dataset/nanmoments.m
new file mode 100644
index 0000000000000000000000000000000000000000..78e95e9b09f242cfcc91addcad094db965cfc420
--- /dev/null
+++ b/matlab/utilities/dataset/nanmoments.m
@@ -0,0 +1,25 @@
+function m = nanmoments(data, n)
+% Compute centered marginal moments of order n (possibly with missing observations).
+
+% Copyright (C) 2014 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 isanynan(data)
+ m = transpose(nanmean(bsxfun(@power,nandemean(data),n)));
+else
+ m = transpose(mean(bsxfun(@power,demean(data),n)));
+end
\ No newline at end of file
diff --git a/matlab/utilities/dataset/compute_stdv.m b/matlab/utilities/dataset/nanvariance.m
similarity index 62%
rename from matlab/utilities/dataset/compute_stdv.m
rename to matlab/utilities/dataset/nanvariance.m
index 4384f83ddaafd3e5805cfbd9a02e8b12b6b2d641..d4894e8e9212d1a37759c464bfd8524d944c1c30 100644
--- a/matlab/utilities/dataset/compute_stdv.m
+++ b/matlab/utilities/dataset/nanvariance.m
@@ -1,21 +1,21 @@
-function dataset_ = compute_stdv(dataset_)
+function variances = nanvariance(data)
% Compute the standard deviation for each observed variable (possibly with missing observations).
%@info:
-%! @deftypefn {Function File} {@var{dataset_} =} compute_stdv(@var{dataset_})
-%! @anchor{compute_stdv}
-%! This function computes the standard deviation of the observed variables (possibly with missing observations).
+%! @deftypefn {Function File} {@var{variances} =} nanvariance(@var{data})
+%! @anchor{nanvariance}
+%! This function computes the variances of the observed variables (possibly with missing observations).
%!
%! @strong{Inputs}
%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
+%! @item datas
+%! A T*N array of real numbers.
%! @end table
%!
%! @strong{Outputs}
%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
+%! @item variances
+%! A N*1 vector of real numbers
%! @end table
%!
%! @strong{This function is called by:}
@@ -30,7 +30,7 @@ function dataset_ = compute_stdv(dataset_)
%! @end deftypefn
%@eod:
-% Copyright (C) 2011-2012 Dynare Team
+% Copyright (C) 2011-2014 Dynare Team
%
% This file is part of Dynare.
%
@@ -47,10 +47,8 @@ function dataset_ = compute_stdv(dataset_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
-if dataset_.missing.state
- dataset_.descriptive.stdv = sqrt(nanmean(bsxfun(@power,nandemean(transpose(dataset_.data)),2)));
+if isanynan(data)
+ variances = transpose(nanmean(bsxfun(@power,nandemean(data),2)));
else
- dataset_.descriptive.stdv = sqrt(mean(bsxfun(@power,demean(transpose(dataset_.data)),2)));
+ variances = transpose(mean(bsxfun(@power,demean(data),2)));
end
\ No newline at end of file
diff --git a/matlab/utilities/dates/isdate.m b/matlab/utilities/dates/isdate.m
index 2fb6038030229279d07b4575aaee57475b63d4dc..726e5e47980429e27458391a5f2c1aedb5fa2e19 100644
--- a/matlab/utilities/dates/isdate.m
+++ b/matlab/utilities/dates/isdate.m
@@ -25,15 +25,16 @@ function b = isdate(str) % --*-- Unitary tests --*--
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-%if length(str)>1
- b = isquaterly(str) || isyearly(str) || ismonthly(str) || isweekly(str);
- %else
- %b = 0;
- %end
+if isnumeric(str) && isscalar(str)
+ b = 1;
+ return
+end
+
+b = isstringdate(str);
%@test:1
%$
-%$ date_1 = '1950M2';
+%$ date_1 = 1950;
%$ date_2 = '1950m2';
%$ date_3 = '-1950m2';
%$ date_4 = '1950m52';
diff --git a/matlab/utilities/dates/isfreq.m b/matlab/utilities/dates/isfreq.m
index 38a779f209ab73aa3959482638e03119c90226a2..79ba42c5395ab9484bff5e18f10a69ffb37b79d0 100644
--- a/matlab/utilities/dates/isfreq.m
+++ b/matlab/utilities/dates/isfreq.m
@@ -25,4 +25,15 @@ function B = isfreq(A)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-B = (ischar(A) && ismember(upper(A),{'Y','A','Q','M','W'})) || ismember(A,[1 4 12 52]);
\ No newline at end of file
+B = 0;
+
+if ischar(A)
+ if isequal(length(A),1) && ismember(upper(A),{'Y','A','Q','M','W'})
+ B = 1;
+ return
+ end
+end
+
+if isnumeric(A) && isequal(length(A),1) && ismember(A,[1 4 12 52])
+ B = 1;
+end
\ No newline at end of file
diff --git a/matlab/utilities/dates/isstringdate.m b/matlab/utilities/dates/isstringdate.m
new file mode 100644
index 0000000000000000000000000000000000000000..fbac8b0072e54c596fc5549445664f689f8c9ee2
--- /dev/null
+++ b/matlab/utilities/dates/isstringdate.m
@@ -0,0 +1,54 @@
+function b = isstringdate(str) % --*-- Unitary tests --*--
+
+% Tests if the input string can be interpreted as a date.
+%
+% INPUTS
+% o str string.
+%
+% OUTPUTS
+% o b integer scalar, equal to 1 if str can be interpreted as a date or 0 otherwise.
+
+% 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/>.
+
+if ischar(str)
+ b = isquaterly(str) || isyearly(str) || ismonthly(str) || isweekly(str);
+else
+ b = 0;
+end
+
+%@test:1
+%$
+%$ date_1 = '1950M2';
+%$ date_2 = '1950m2';
+%$ date_3 = '-1950m2';
+%$ date_4 = '1950m52';
+%$ date_5 = ' 1950';
+%$ date_6 = '1950Y';
+%$ date_7 = '-1950a';
+%$ date_8 = '1950m ';
+%$
+%$ t(1) = dyn_assert(isstringdate(date_1),1);
+%$ t(2) = dyn_assert(isstringdate(date_2),1);
+%$ t(3) = dyn_assert(isstringdate(date_3),1);
+%$ t(4) = dyn_assert(isstringdate(date_4),0);
+%$ t(5) = dyn_assert(isstringdate(date_5),0);
+%$ t(6) = dyn_assert(isstringdate(date_6),1);
+%$ t(7) = dyn_assert(isstringdate(date_7),1);
+%$ t(8) = dyn_assert(isstringdate(date_8),0);
+%$ T = all(t);
+%@eof:1
\ No newline at end of file
diff --git a/matlab/utilities/dseries/from.m b/matlab/utilities/dseries/from.m
index a8ae4bef3854ae23279766c38980f46f2a878c71..02be0136794bcd537a4138332b6dd5e8001832e6 100644
--- a/matlab/utilities/dseries/from.m
+++ b/matlab/utilities/dseries/from.m
@@ -1,4 +1,4 @@
-function from(varargin)
+function from(varargin) % --*-- Unitary tests --*--
% Copyright (C) 2014 Dynare Team
%
@@ -219,10 +219,10 @@ for i=1:number_of_variables
end
if i>1
if ismember(var.name,variable_names)
- error('dseries::from: All the dseries objects should contain variables with different names!')
- else
- variable_names(i) = {var.name{1}};
+ % Locally change variable name.
+ var = var.rename(var.name{1},get_random_string(20));
end
+ variable_names(i) = {var.name{1}};
else
variable_names(i) = {var.name{1}};
end
@@ -421,4 +421,18 @@ function i = isassignedvariable(var,expr)
return
end
end
- i = 0;
\ No newline at end of file
+ i = 0;
+
+%@test:1
+%$ try
+%$ y = dseries(zeros(400,1),dates('1950Q1')) ;
+%$ v = dseries(randn(400,1),dates('1950Q1')) ;
+%$ u = dseries(randn(400,1),dates('1950Q1')) ;
+%$ from 1950Q2 to 2049Q4 do y(t) = (1+.01*u(t))*y(t-1) + v(t)
+%$ t(1) = 1;
+%$ catch
+%$ t(1) = 0;
+%$ end
+%$
+%$ T = all(t);
+%@eof:1
diff --git a/matlab/utilities/general/isanynan.m b/matlab/utilities/general/isanynan.m
new file mode 100644
index 0000000000000000000000000000000000000000..7a16419d007fb84332e1eaf0aa368fe5e1af7234
--- /dev/null
+++ b/matlab/utilities/general/isanynan.m
@@ -0,0 +1,21 @@
+function yes = isanynan(array)
+% Return one if the array contains at least one NaN, 0 otherwise.
+
+% Copyright (C) 2011-2014 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/>.
+
+yes = any(isnan(array(:)));
\ No newline at end of file
diff --git a/matlab/var_sample_moments.m b/matlab/var_sample_moments.m
index ab7b4b74b6575b2be7d6d04ba19593ae560b3495..f9d4f8b022f429aaf57866916a03811f96c5ac10 100644
--- a/matlab/var_sample_moments.m
+++ b/matlab/var_sample_moments.m
@@ -1,29 +1,28 @@
-function [YtY,XtY,YtX,XtX,Y,X] = ...
- var_sample_moments(FirstObservation,LastObservation,qlag,var_trend_order,datafile,varobs,xls_sheet,xls_range)
+function var_sample_moments(nlag, var_trend_order, dataset_)%datafile,varobs,xls_sheet,xls_range)
% Computes the sample moments of a VAR model.
%
% The VAR(p) model is defined by:
%
-% y_t = \sum_{k=1}^p y_{t-k} A_k + z_t C + e_t for t = 1,...,T
+% y_t = \sum_{k=1}^p y_{t-k} A_k + z_t C + e_t for t = 1,...,T
%
% where y_t is a 1*m vector of observed endogenous variables, p is the
% number of lags, A_k is an m*m real matrix, z_t is a 1*q vector of
% exogenous (deterministic) variables, C is a q*m real matrix and
% e_t is a vector of exogenous stochastic shocks. T is the number
-% of observations. The deterministic exogenous variables are assumed to
-% be a polynomial trend of order q = "var_trend_order".
+% of observations. The deterministic exogenous variables are assumed to
+% be a polynomial trend of order q = "var_trend_order".
%
-% We define:
+% We define:
%
% <> Y = (y_1',y_2',...,y_T')' a T*m matrix,
%
-% <> x_t = (y_{t-1},y_{t-2},...,y_{t-p},z_t) a 1*(mp+q) row vector,
+% <> x_t = (y_{t-1},y_{t-2},...,y_{t-p},z_t) a 1*(mp+q) row vector,
%
-% <> X = (x_1',x_2',...,x_T')' a T*(mp+q) matrix,
+% <> X = (x_1',x_2',...,x_T')' a T*(mp+q) matrix,
%
% <> E = (e_1',e_2',...,e_T')' a T*m matrix and
%
-% <> A = (A_1',A_2',...,A_p',C')' an (mp+q)*m matrix of coefficients.
+% <> A = (A_1',A_2',...,A_p',C')' an (mp+q)*m matrix of coefficients.
%
% So that we can equivalently write the VAR(p) model using the following
% matrix representation:
@@ -31,18 +30,17 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
% Y = X * A +E
%
%
-% INPUTS
-% o FirstObservation [integer] First observation.
-% o LastObservation [integer] Last observation.
-% o qlag [integer] Number of lags in the VAR model.
-% o var_trend_order [integer] Order of the polynomial exogenous trend:
+% INPUTS
+% o nlag [integer] Number of lags in the VAR model.
+% o var_trend_order [integer] Order of the polynomial exogenous trend:
% = -1 no constant and no linear trend,
% = 0 constant and no linear trend,
% = 1 constant and linear trend.
+% o dataset_ [dseries] The sample.
%
-% OUTPUTS
+% OUTPUTS
% o YtY [double] Y'*Y an m*m matrix.
-% o XtY [double] X'*Y an (mp+q)*m matrix.
+% o XtY [double] X'*Y an (mp+q)*m matrix.
% o YtX [double] Y'*X an m*(mp+q) matrix.
% o XtX [double] X'*X an (mp+q)*(mp+q) matrix.
% o Y [double] Y a T*m matrix.
@@ -50,8 +48,11 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
%
% SPECIAL REQUIREMENTS
% None.
+%
+% REMARKS
+% Outputs are saved in the base workspace (not returned by the function).
-% Copyright (C) 2007-2009 Dynare Team
+% Copyright (C) 2007-2014 Dynare Team
%
% This file is part of Dynare.
%
@@ -68,42 +69,35 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-X = [];
-Y = [];
-YtY = [];
-YtX = [];
-XtY = [];
-XtX = [];
-
-data = read_variables(datafile,varobs,[],xls_sheet,xls_range);
+LastObservation = dataset_.dates(end);
+FirstObservation = dataset_.dates(1)+nlag;
-if qlag > FirstObservation
- error('VarSampleMoments :: not enough data to initialize! Try to increase FirstObservation.')
- return
-end
+NumberOfObservations = LastObservation-FirstObservation+1;
+NumberOfVariables = dataset_.vobs;
-NumberOfObservations = LastObservation-FirstObservation+1;% This is T.
-NumberOfVariables = size(varobs,1);% This is m.
-if var_trend_order == -1% No constant no linear trend case.
- X = zeros(NumberOfObservations,NumberOfVariables*qlag);
-elseif var_trend_order == 0% Constant and no linear trend case.
-X = ones(NumberOfObservations,NumberOfVariables*qlag+1);
-indx = NumberOfVariables*qlag+1;
-elseif var_trend_order == 1;% Constant and linear trend case.
-X = ones(NumberOfObservations,NumberOfVariables*qlag+2);
-indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+2;
+if isequal(var_trend_order,-1)
+ % No constant no linear trend case.
+ X = zeros(NumberOfObservations,NumberOfVariables*nlag);
+elseif isequal(var_trend_order,0)
+ % Constant and no linear trend case.
+ X = ones(NumberOfObservations,NumberOfVariables*nlag+1);
+ indx = NumberOfVariables*nlag+1;
+elseif isequal(var_trend_order,1)
+ % Constant and linear trend case.
+ X = ones(NumberOfObservations,NumberOfVariables*nlag+2);
+ indx = NumberOfVariables*nlag+1:NumberOfVariables*nlag+2;
else
- error('var_sample_moments :: trend must be equal to -1,0 or 1!')
+ error('Estimation::var_sample_moments: trend must be equal to -1,0 or 1!')
return
end
-% I build matrices Y and X
-Y = data(FirstObservation:LastObservation,:);
+% I build matrices Y and X
+Y = dataset_(FirstObservation:LastObservation).data;
for t=1:NumberOfObservations
- line = t + FirstObservation-1;
- for lag = 1:qlag
- X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = data(line-lag,:);
+ currentdate = FirstObservation+(t-1);
+ for lag = 1:nlag
+ X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = dataset_(currentdate-lag).data;
end
end
@@ -111,7 +105,9 @@ if (var_trend_order == 1)
X(:,end) = transpose(1:NumberOfObservations)
end
-YtY = Y'*Y;
-YtX = Y'*X;
-XtY = X'*Y;
-XtX = X'*X;
\ No newline at end of file
+assignin('base', 'mYY', Y'*Y);
+assignin('base', 'mYX', Y'*X);
+assignin('base', 'mXY', X'*Y);
+assignin('base', 'mXX', X'*X);
+assignin('base', 'Ydata', Y);
+assignin('base', 'Xdata', X);
\ No newline at end of file
diff --git a/preprocessor/ComputingTasks.cc b/preprocessor/ComputingTasks.cc
index e9292f90174e86d63734304cbba8bcf0b2749d41..c23908a0d519eea2aecf9f6d09326eb62d529637 100644
--- a/preprocessor/ComputingTasks.cc
+++ b/preprocessor/ComputingTasks.cc
@@ -1876,9 +1876,17 @@ EstimationDataStatement::checkPass(ModFileStructure &mod_file_struct, WarningCon
exit(EXIT_FAILURE);
}
- if (options_list.string_options.find("file") == options_list.string_options.end())
+ if ((options_list.string_options.find("file") == options_list.string_options.end()) &&
+ (options_list.string_options.find("series") == options_list.string_options.end()))
{
- cerr << "ERROR: The file option must be passed to the data statement." << endl;
+ cerr << "ERROR: The file or series option must be passed to the data statement." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+ if ((options_list.string_options.find("file") != options_list.string_options.end()) &&
+ (options_list.string_options.find("series") != options_list.string_options.end()))
+ {
+ cerr << "ERROR: The file and series options cannot be used simultaneously in the data statement." << endl;
exit(EXIT_FAILURE);
}
}
@@ -1887,8 +1895,8 @@ void
EstimationDataStatement::writeOutput(ostream &output, const string &basename) const
{
options_list.writeOutput(output, "options_.dataset");
- if (options_list.date_options.find("first_obs") == options_list.date_options.end())
- output << "options_.dataset.firstobs = options_.initial_period;" << endl;
+ //if (options_list.date_options.find("first_obs") == options_list.date_options.end())
+ // output << "options_.dataset.first_obs = options_.initial_period;" << endl;
}
SubsamplesStatement::SubsamplesStatement(const string &name1_arg,
diff --git a/preprocessor/DynareBison.yy b/preprocessor/DynareBison.yy
index c2cb723e718060e13898aea04f5a0472d5e7fa3e..0c754a4bc06edef53640eedf800aec4df12c14d7 100644
--- a/preprocessor/DynareBison.yy
+++ b/preprocessor/DynareBison.yy
@@ -90,7 +90,7 @@ class ParsingDriver;
%token BVAR_REPLIC BYTECODE ALL_VALUES_REQUIRED
%token CALIB_SMOOTHER CHANGE_TYPE CHECK CONDITIONAL_FORECAST CONDITIONAL_FORECAST_PATHS CONF_SIG CONSTANT CONTROLLED_VAREXO CORR COVAR CUTOFF CYCLE_REDUCTION LOGARITHMIC_REDUCTION
%token CONSIDER_ALL_ENDOGENOUS CONSIDER_ONLY_OBSERVED
-%token DATAFILE FILE DOUBLING DR_CYCLE_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_MAXITER DR_ALGO DROP DSAMPLE DYNASAVE DYNATYPE CALIBRATION DIFFERENTIATE_FORWARD_VARS
+%token DATAFILE FILE SERIES DOUBLING DR_CYCLE_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_MAXITER DR_ALGO DROP DSAMPLE DYNASAVE DYNATYPE CALIBRATION DIFFERENTIATE_FORWARD_VARS
%token END ENDVAL EQUAL ESTIMATION ESTIMATED_PARAMS ESTIMATED_PARAMS_BOUNDS ESTIMATED_PARAMS_INIT EXTENDED_PATH ENDOGENOUS_PRIOR
%token FILENAME FILTER_STEP_AHEAD FILTERED_VARS FIRST_OBS LAST_OBS SET_TIME
%token <string_val> FLOAT_NUMBER DATES
@@ -833,7 +833,7 @@ restriction_expression : expression {driver.check_restriction_expression_constan
restriction_expression_1 : restriction_elem_expression
| restriction_expression_1 restriction_elem_expression
- ;
+ ;
restriction_elem_expression : COEFF '(' symbol COMMA INT_NUMBER ')'
{ driver.add_positive_restriction_element($3,$5);}
@@ -1366,9 +1366,10 @@ data_options_list : data_options_list COMMA data_options
;
data_options : o_file
- | o_new_estimation_data_first_obs
- | o_last_obs
- | o_new_estimation_data_nobs
+ | o_series
+ | o_data_first_obs
+ | o_data_last_obs
+ | o_data_nobs
| o_xls_sheet
| o_xls_range
;
@@ -2493,6 +2494,7 @@ o_simul_seed : SIMUL_SEED EQUAL INT_NUMBER { driver.error("'simul_seed' option i
o_qz_criterium : QZ_CRITERIUM EQUAL non_negative_number { driver.option_num("qz_criterium", $3); };
o_qz_zero_threshold : QZ_ZERO_THRESHOLD EQUAL non_negative_number { driver.option_num("qz_zero_threshold", $3); };
o_file : FILE EQUAL filename { driver.option_str("file", $3); };
+o_series : SERIES EQUAL symbol { driver.option_str("series", $3); };
o_datafile : DATAFILE EQUAL filename { driver.option_str("datafile", $3); };
o_nobs : NOBS EQUAL vec_int
{ driver.option_vec_int("nobs", $3); }
@@ -2505,12 +2507,9 @@ o_conditional_variance_decomposition : CONDITIONAL_VARIANCE_DECOMPOSITION EQUAL
{ driver.option_vec_int("conditional_variance_decomposition", $3); }
;
o_first_obs : FIRST_OBS EQUAL INT_NUMBER { driver.option_num("first_obs", $3); };
-o_new_estimation_data_first_obs : FIRST_OBS EQUAL date_expr
- { driver.option_date("first_obs", $3); }
- ;
-o_last_obs : LAST_OBS EQUAL date_expr
- { driver.option_date("last_obs", $3); }
- ;
+o_data_first_obs : FIRST_OBS EQUAL date_expr { driver.option_date("firstobs", $3); } ;
+o_data_last_obs : LAST_OBS EQUAL date_expr { driver.option_date("lastobs", $3); } ;
+o_data_nobs : NOBS EQUAL INT_NUMBER { driver.option_num("nobs", $3); };
o_shift : SHIFT EQUAL signed_number { driver.option_num("shift", $3); };
o_shape : SHAPE EQUAL prior_distribution { driver.prior_shape = $3; };
o_mode : MODE EQUAL signed_number { driver.option_num("mode", $3); };
@@ -2523,7 +2522,6 @@ o_bounds : BOUNDS EQUAL vec_value_w_inf { driver.option_num("bounds", $3); };
o_domain : DOMAINN EQUAL vec_value { driver.option_num("domain", $3); };
o_interval : INTERVAL EQUAL vec_value { driver.option_num("interval", $3); };
o_variance : VARIANCE EQUAL expression { driver.set_prior_variance($3); }
-o_new_estimation_data_nobs : NOBS EQUAL INT_NUMBER { driver.option_num("nobs", $3); };
o_prefilter : PREFILTER EQUAL INT_NUMBER { driver.option_num("prefilter", $3); };
o_presample : PRESAMPLE EQUAL INT_NUMBER { driver.option_num("presample", $3); };
o_lik_algo : LIK_ALGO EQUAL INT_NUMBER { driver.option_num("lik_algo", $3); };
diff --git a/preprocessor/DynareFlex.ll b/preprocessor/DynareFlex.ll
index 6ab6faab0ce5ed866986d466fc4a9141ceb415ae..a552f19973237dd2298e4a5c11e466c30c2b1f17 100644
--- a/preprocessor/DynareFlex.ll
+++ b/preprocessor/DynareFlex.ll
@@ -604,6 +604,7 @@ DATE -?[0-9]+([YyAa]|[Mm]([1-9]|1[0-2])|[Qq][1-4]|[Ww]([1-9]{1}|[1-4][0-9]|5[0-2
<DYNARE_STATEMENT>simul_replic {return token::SIMUL_REPLIC;}
<DYNARE_STATEMENT>xls_sheet {return token::XLS_SHEET;}
<DYNARE_STATEMENT>xls_range {return token::XLS_RANGE;}
+<DYNARE_STATEMENT>series {return token::SERIES;}
<DYNARE_STATEMENT>mh_recover {return token::MH_RECOVER;}
<DYNARE_STATEMENT>planner_discount {return token::PLANNER_DISCOUNT;}
<DYNARE_STATEMENT>calibration {return token::CALIBRATION;}
diff --git a/preprocessor/Statement.cc b/preprocessor/Statement.cc
index 70889c6b2e53b1bb7c001f085e333fe9e49ba5d6..db02db9392fe89d54d592b7b37ddf01c32c9b3b4 100644
--- a/preprocessor/Statement.cc
+++ b/preprocessor/Statement.cc
@@ -135,7 +135,20 @@ OptionsList::writeOutput(ostream &output) const
void
OptionsList::writeOutput(ostream &output, const string &option_group) const
{
- output << option_group << " = struct();" << endl;
+ // Initialize option_group as an empty struct iff the field does not exist!
+ unsigned idx = option_group.find_last_of(".");
+ if (idx<UINT_MAX)
+ {
+ output << option_group << endl;
+ output << idx << endl;
+ output << "if ~isfield(" << option_group.substr(0,idx) << ",'" << option_group.substr(idx+1) << "')" << endl;
+ output << " " << option_group << " = struct();" << endl;
+ output << "end" << endl;
+ }
+ else
+ {
+ output << option_group << " = struct();" << endl;
+ }
for (num_options_t::const_iterator it = num_options.begin();
it != num_options.end(); it++)
diff --git a/preprocessor/SymbolTable.cc b/preprocessor/SymbolTable.cc
index 4e207cfe7e60eca5cdb02990653c6fbecc158726..aa636b97f852362dda5db4802e678131706e6eb8 100644
--- a/preprocessor/SymbolTable.cc
+++ b/preprocessor/SymbolTable.cc
@@ -270,14 +270,14 @@ SymbolTable::writeOutput(ostream &output) const throw (NotYetFrozenException)
if (observedVariablesNbr() > 0)
{
- output << "options_.varobs = [];" << endl;
+ int ic = 1;
+ output << "options_.varobs = cell(1);" << endl;
for (vector<int>::const_iterator it = varobs.begin();
it != varobs.end(); it++)
- if (it == varobs.begin())
- output << "options_.varobs = '" << getName(*it) << "';" << endl;
- else
- output << "options_.varobs = char(options_.varobs, '" << getName(*it) << "');" << endl;
-
+ {
+ output << "options_.varobs(" << ic << ") = {'" << getName(*it) << "'};" << endl;
+ ic++;
+ }
output << "options_.varobs_id = [ ";
for (vector<int>::const_iterator it = varobs.begin();
it != varobs.end(); it++)
diff --git a/tests/Makefile.am b/tests/Makefile.am
index 2e8b02fa903c13d99de7ecfc5974adb6922eef51..60ac51c550e8d9a3120a01517cec76612441da81 100644
--- a/tests/Makefile.am
+++ b/tests/Makefile.am
@@ -80,6 +80,7 @@ MODFILES = \
fs2000/fs2000e.mod \
fs2000/fs2000_cmaes.mod \
fs2000/fs2000_calib.mod \
+ fs2000/fs2000_calib_dseries.mod \
fs2000/fs2000_analytic_derivation.mod \
fs2000/fs2000_missing_data.mod \
fs2000/fs2000_sd.mod \
diff --git a/tests/fs2000/fs2000_calib_dseries.mod b/tests/fs2000/fs2000_calib_dseries.mod
new file mode 100644
index 0000000000000000000000000000000000000000..4354acf7ea96e428658f5e665ea33a0f6bab2b7b
--- /dev/null
+++ b/tests/fs2000/fs2000_calib_dseries.mod
@@ -0,0 +1,73 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+set_time(1950Q1);
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+initval;
+k = 6;
+m = mst;
+P = 2.25;
+c = 0.45;
+e = 1;
+W = 4;
+R = 1.02;
+d = 0.85;
+n = 0.19;
+l = 0.86;
+y = 0.6;
+gy_obs = exp(gam);
+gp_obs = exp(-gam);
+dA = exp(gam);
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+
+varobs gp_obs gy_obs;
+
+fsdata = dseries('fsdat_simul.m',1950Q1);
+fsdata = fsdata{'g@p,y@_obs'}(1960Q1:1980Q4);
+
+fsdata
+
+data(series=fsdata);//, first_obs=1960Q1, last_obs=1980Q4);
+
+
+calib_smoother(filtered_vars, filter_step_ahead = [3:4]) m P c e W R k d n l y dA;
+
diff --git a/tests/fs2000/fs2000_data.mod b/tests/fs2000/fs2000_data.mod
new file mode 100644
index 0000000000000000000000000000000000000000..23018eeddb19d02c1bb078aa615e2f379d1c328d
--- /dev/null
+++ b/tests/fs2000/fs2000_data.mod
@@ -0,0 +1,79 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+initval;
+k = 6;
+m = mst;
+P = 2.25;
+c = 0.45;
+e = 1;
+W = 4;
+R = 1.02;
+d = 0.85;
+n = 0.19;
+l = 0.86;
+y = 0.6;
+gy_obs = exp(gam);
+gp_obs = exp(-gam);
+dA = exp(gam);
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+set_time(1970Q3); // Interpreted as the first date available in the sample loaded below.
+
+data(file='fsdat_simul.m',first_obs=1971Q1, nobs=40);
+
+estimation(order=1,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);
diff --git a/tests/fs2000/fs2000_dseries_a.mod b/tests/fs2000/fs2000_dseries_a.mod
new file mode 100644
index 0000000000000000000000000000000000000000..1210e7425ccde79a2a5b5be3854651584def469c
--- /dev/null
+++ b/tests/fs2000/fs2000_dseries_a.mod
@@ -0,0 +1,77 @@
+set_time(1950Q3);
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+initval;
+k = 6;
+m = mst;
+P = 2.25;
+c = 0.45;
+e = 1;
+W = 4;
+R = 1.02;
+d = 0.85;
+n = 0.19;
+l = 0.86;
+y = 0.6;
+gy_obs = exp(gam);
+gp_obs = exp(-gam);
+dA = exp(gam);
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+data(file=fsdat_simul_dseries,first_obs=1950Q3, nobs=192);
+
+estimation(order=1,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);
diff --git a/tests/fs2000/fs2000_dseries_b.mod b/tests/fs2000/fs2000_dseries_b.mod
new file mode 100644
index 0000000000000000000000000000000000000000..c2e31087ca56e2486a14351a5b85fb85535e40d3
--- /dev/null
+++ b/tests/fs2000/fs2000_dseries_b.mod
@@ -0,0 +1,80 @@
+set_time(1950Q3);
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+initval;
+k = 6;
+m = mst;
+P = 2.25;
+c = 0.45;
+e = 1;
+W = 4;
+R = 1.02;
+d = 0.85;
+n = 0.19;
+l = 0.86;
+y = 0.6;
+gy_obs = exp(gam);
+gp_obs = exp(-gam);
+dA = exp(gam);
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+fsdataset = dseries('fsdat_simul_dseries.m');
+fsdataset = fsdataset(1950Q3:1950Q3+191);
+
+data(series=fsdataset);
+
+estimation(order=1,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);
diff --git a/tests/gsa/ls2003.mod b/tests/gsa/ls2003.mod
index 883eab9efe47aa9014a159fd12137682d807be21..6cf6410b0785049cf619798026e4ce4ef3bd271b 100644
--- a/tests/gsa/ls2003.mod
+++ b/tests/gsa/ls2003.mod
@@ -108,7 +108,7 @@ disp(' ');
disp('MC FILTERING(rmse=1), TO MAP THE FIT FROM PRIORS');
disp('Press ENTER to continue'); pause(5);
-dynare_sensitivity(nodisplay, datafile=data_ca1,first_obs=8,nobs=79,prefilter=1, // also presample=2,loglinear, are admissible
+dynare_sensitivity(nodisplay, datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1, // also presample=2,loglinear, are admissible
load_stab=1, // load prior sample
istart_rmse=2, //start computing rmse from second observation (i.e. rmse does not inlude initial big error)
stab=0, // don't plot again stability analysis results
@@ -121,7 +121,7 @@ disp('BY USING THE COMBINED CALL');
disp(' ');
disp('dynare_sensitivity(redform=1,')
disp('logtrans_redform=1, namendo=(pie,R), namexo=(e_R), namlagendo=(R),')
-disp('datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,')
+disp('datafile=data_ca1.m,first_obs=8,nobs=79,prefilter=1,')
disp('istart_rmse=2, rmse=1);')
disp(' ');
disp('Press ENTER to continue'); pause(5);
@@ -131,7 +131,7 @@ disp('Press ENTER to continue'); pause(5);
//namendo=(pie,R), // evaluate relationships for pie and R (namendo=(:) for all variables)
//namexo=(e_R), // evaluate relationships with exogenous e_R (use namexo=(:) for all shocks)
//namlagendo=(R), // evaluate relationships with lagged R (use namlagendo=(:) for all lagged endogenous)
-//datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+//datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
//istart_rmse=2, //start computing rmse from second observation (i.e. rmse does not inlude initial big error)
//rmse=1, // do rmse analysis
//);
@@ -144,13 +144,13 @@ disp(' ');
disp('Press ENTER to continue'); pause(5);
// run this to generate posterior mode and Metropolis files if not yet done
-estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2,
+estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=2,
prefilter=1,mh_jscale=0.5,mh_replic=5000, mode_compute=4, mh_drop=0.6, nodisplay,
bayesian_irf, filtered_vars, smoother) y_obs R_obs pie_obs dq de;
// run this to produce posterior samples of filtered, smoothed and irf variables, if not yet done
-//estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2,prefilter=1,mh_jscale=0.3,
+//estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=2,prefilter=1,mh_jscale=0.3,
// mh_replic=0, mode_file=ls2003_mode, mode_compute=0, load_mh_file, bayesian_irf,
// filtered_vars, smoother, mh_drop=0.6);
@@ -163,7 +163,7 @@ disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(nodisplay, pprior=0,Nsam=2048,neighborhood_width=0.2,
mode_file=ls2003_mode, // specifies the mode file where the mode and Hessian are stored
-datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
rmse=1);
disp(' ');
@@ -182,7 +182,7 @@ disp('RMSE ANALYSIS FOR MULTIVARIATE SAMPLE AT THE POSTERIOR MODE');
disp(' ');
disp('Press ENTER to continue'); pause(5);
dynare_sensitivity(nodisplay, mode_file=ls2003_mode,
-datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
pprior=0,
stab=0,
rmse=1,
@@ -195,12 +195,12 @@ disp('THE LAST TWO CALLS COULD BE DONE TOGETHER');
disp('BY USING THE COMBINED CALL');
disp(' ');
disp('dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,mode_file=ls2003_mode,')
-disp('datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,')
+disp('datafile=data_ca1.m,first_obs=8,nobs=79,prefilter=1,')
disp('rmse=1, alpha2_rmse=0, alpha_rmse=0);')
disp(' ');
disp('Press ENTER to continue'); pause(5);
//dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,mode_file=ls2003_mode,
-//datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+//datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
//rmse=1,
//alpha2_rmse=0, // no correlation analysis
//alpha_rmse=0 // no Smirnov sensitivity analysis
@@ -210,10 +210,10 @@ disp(' ');
disp('RMSE ANALYSIS FOR POSTERIOR MCMC sample (ppost=1)');
disp('Needs a call to dynare_estimation to load all MH environment');
disp('Press ENTER to continue'); pause(5);
-//estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2, mode_file=ls2003_mode, load_mh_file,
+//estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=2, mode_file=ls2003_mode, load_mh_file,
// prefilter=1,mh_jscale=0.5,mh_replic=0, mode_compute=0, mh_drop=0.6);
dynare_sensitivity(nodisplay, stab=0, // no need for stability analysis since the posterior sample is surely OK
-datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
rmse=1,ppost=1);