Commit 61a25153 authored by Ferhat Mihoubi's avatar Ferhat Mihoubi Committed by Sébastien Villemot
Browse files

Correction of bugs in check command with block decomposition and

Add the missing dr_block m-file
parent e5c7e848
......@@ -51,7 +51,7 @@ oo_.exo_simul = tempex;
eigenvalues_ = dr.eigval;
if (options_.block)
nyf = dr.nfwrd;
nyf = dr.nfwrd+dr.nboth;
else
nyf = nnz(dr.kstate(:,2)>M_.maximum_endo_lag+1);
end;
......
function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_)
% function [dr,info,M_,options_,oo_] = dr_block(dr,task,M_,options_,oo_)
% computes the reduced form solution of a rational expectation model (first
% approximation of the stochastic model around the deterministic steady state).
%
% INPUTS
% dr [matlab structure] Decision rules for stochastic simulations.
% task [integer] if task = 0 then dr1 computes decision rules.
% if task = 1 then dr1 computes eigenvalues.
% M_ [matlab structure] Definition of the model.
% options_ [matlab structure] Global options.
% oo_ [matlab structure] Results
%
% OUTPUTS
% dr [matlab structure] Decision rules for stochastic simulations.
% info [integer] info=1: the model doesn't define current variables uniquely
% info=2: problem in mjdgges.dll info(2) contains error code.
% info=3: BK order condition not satisfied info(2) contains "distance"
% absence of stable trajectory.
% info=4: BK order condition not satisfied info(2) contains "distance"
% indeterminacy.
% info=5: BK rank condition not satisfied.
% info=6: The jacobian matrix evaluated at the steady state is complex.
% M_ [matlab structure]
% options_ [matlab structure]
% oo_ [matlab structure]
%
% ALGORITHM
% first order block relaxation method applied to the model
% E[A Yt-1 + B Yt + C Yt-1 + ut] = 0
%
% SPECIAL REQUIREMENTS
% none.
%
% Copyright (C) 2010 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
info = 0;
verbose = 0;
%it_ = M_.maximum_lag + 1;
z = repmat(dr.ys,1,M_.maximum_lead + M_.maximum_lag + 1);
if (isfield(M_,'block_structure'))
data = M_.block_structure.block;
Size = length(M_.block_structure.block);
else
data = M_;
Size = 1;
end;
if (options_.bytecode)
[chck, zz, data]= bytecode('dynamic','evaluate',z,[oo_.exo_simul oo_.exo_det_simul], M_.params, 1, data);
else
[r, data] = feval([M_.fname '_dynamic'], z', [oo_.exo_simul oo_.exo_det_simul], M_.params, 2, data);
chck = 0;
end;
mexErrCheck('bytecode', chck);
dr.rank = 0;
dr.eigval = [];
dr.nstatic = 0;
dr.nfwrd = 0;
dr.npred = 0;
dr.nboth = 0;
dr.nd = 0;
dr.state_var = [];
dr.exo_var = [];
dr.ghx = [];
dr.ghu = [];
for i = 1:Size;
ghx = [];
indexi_0 = 0;
if (verbose)
disp(['Block ' int2str(i)]);
disp('-----------');
data(i)
end;
n_pred = data(i).n_backward;
n_fwrd = data(i).n_forward;
n_both = data(i).n_mixed;
n_static = data(i).n_static;
dr.nstatic = dr.nstatic + n_static;
dr.nfwrd = dr.nfwrd + n_fwrd;
dr.npred = dr.npred + n_pred;
dr.nboth = dr.nboth + n_both;
nd = n_pred + n_fwrd + 2*n_both;
dr.nd = dr.nd + nd;
n_dynamic = n_pred + n_fwrd + n_both;
exo_nbr = M_.block_structure.block(i).exo_nbr;
exo_det_nbr = M_.block_structure.block(i).exo_det_nbr;
jacob = full(data(i).g1);
lead_lag_incidence = data(i).lead_lag_incidence;
endo = data(i).variable;
exo = data(i).exogenous;
if (verbose)
disp('jacob');
disp(jacob);
disp('lead_lag_incidence');
disp(lead_lag_incidence);
end;
maximum_lag = data(i).maximum_endo_lag;
maximum_lead = data(i).maximum_endo_lead;
n = n_dynamic + n_static;
switch M_.block_structure.block(i).Simulation_Type
case 1
%Evaluate Forward
if maximum_lag > 0 && n_pred > 0
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
indx_c = M_.block_structure.block(i).lead_lag_incidence(1,indx_r);
data(i).eigval = diag(jacob(indx_r, indx_c));
data(i).rank = sum(abs(data(i).eigval) > 0);
else
data(i).eigval = [];
data(i).rank = 0;
end
dr.eigval = [dr.eigval ; data(i).eigval];
%First order approximation
if task ~= 1
if (maximum_lag > 0)
indexi_0 = min(lead_lag_incidence(2,:));
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
indx_c = M_.block_structure.block(i).lead_lag_incidence(1,indx_r);
ghx = jacob(indx_r, indx_c);
end;
ghu = data(i).g1_x;
end
case 2
%Evaluate Backward
if maximum_lead > 0 && n_fwrd > 0
indx_r = find(M_.block_structure.block(i).lead_lag_incidence(2,:));
indx_c = M_.block_structure.block(i).lead_lag_incidence(2,indx_r);
data(i).eigval = 1./ diag(jacob(indx_r, indx_c));
data(i).rank = sum(abs(data(i).eigval) > 0);
else
data(i).eigval = [];
data(i).rank = 0;
end
dr.rank = dr.rank + data(i).rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case 3
%Solve Forward simple
if maximum_lag > 0 && n_pred > 0
data(i).eigval = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
data(i).rank = sum(abs(data(i).eigval) > 0);
else
data(i).eigval = [];
data(i).rank = 0;
end;
dr.eigval = [dr.eigval ; data(i).eigval];
%First order approximation
if task ~= 1
if (maximum_lag > 0)
indexi_0 = min(lead_lag_incidence(2,:));
ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
end;
ghu = data(i).g1_x;
end
case 4
%Solve Backward simple
if maximum_lead > 0 && n_fwrd > 0
data(i).eigval = - jacob(1 , n_pred + n - n_fwrd + 1 : n_pred + n) / jacob(1 , n_pred + n + 1 : n_pred + n + n_fwrd) ;
data(i).rank = sum(abs(data(i).eigval) > 0);
else
data(i).eigval = [];
data(i).rank = 0;
end;
dr.rank = dr.rank + data(i).rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case 5
%Solve Forward complete
if maximum_lag > 0 && n_pred > 0
data(i).eigval = eig(- jacob(: , 1 : n_pred) / ...
jacob(: , (n_pred + n_static + 1 : n_pred + n_static + n_pred )));
data(i).rank = sum(abs(data(i).eigval) > 0);
else
data(i).eigval = [];
data(i).rank = 0;
end;
dr.eigval = [dr.eigval ; data(i).eigval];
case 6
%Solve Backward complete
if maximum_lead > 0 && n_fwrd > 0
data(i).eigval = eig(- jacob(: , n_pred + n - n_fwrd + 1: n_pred + n))/ ...
jacob(: , n_pred + n + 1 : n_pred + n + n_fwrd);
data(i).rank = sum(abs(data(i).eigval) > 0);
else
data(i).eigval = [];
data(i).rank = 0;
end;
dr.rank = dr.rank + data(i).rank;
dr.eigval = [dr.eigval ; data(i).eigval];
case 8
%The lead_lag_incidence contains columns in the following order :
% static variables, backward variable, mixed variables and forward variables
%
%Procedes to a QR decomposition on the jacobian matrix to reduce the problem size
index_c = lead_lag_incidence(2,:); % Index of all endogenous variables present at time=t
index_s = lead_lag_incidence(2,1:n_static); % Index of all static endogenous variables present at time=t
if n_static > 0
[Q, R] = qr(jacob(:,index_s));
aa = Q'*jacob;
else
aa = jacob;
end;
indexi_0 = min(lead_lag_incidence(2,:));
index_0m = (n_static+1:n_static+n_pred) + indexi_0 - 1;
index_0p = (n_static+n_pred+1:n) + indexi_0 - 1;
index_m = 1:(n_pred+n_both);
indexi_p = max(lead_lag_incidence(2,:))+1;
index_p = indexi_p:size(jacob, 2);
nyf = n_fwrd + n_both;
A = aa(:,index_m); % Jacobain matrix for lagged endogeneous variables
B = aa(:,index_c); % Jacobian matrix for contemporaneous endogeneous variables
C = aa(:,index_p); % Jacobain matrix for led endogeneous variables
row_indx = n_static+1:n;
D = [[aa(row_indx,index_0m) zeros(n_dynamic,n_both) aa(row_indx,index_p)] ; [zeros(n_both, n_pred) eye(n_both) zeros(n_both, n_both + n_fwrd)]];
E = [-aa(row_indx,[index_m index_0p]) ; [zeros(n_both, n_both + n_pred) eye(n_both, n_both + n_fwrd) ] ];
[err, ss, tt, w, sdim, data(i).eigval, info1] = mjdgges(E,D,options_.qz_criterium);
if (verbose)
disp('eigval');
disp(data(i).eigval);
end;
if info1
info(1) = 2;
info(2) = info1;
return
end
%sdim
nba = nd-sdim;
if task == 1
data(i).rank = rank(w(nd-nyf+1:end,nd-nyf+1:end));
dr.rank = dr.rank + data(i).rank;
if ~exist('OCTAVE_VERSION','builtin')
data(i).eigval = eig(E,D);
end
dr.eigval = [dr.eigval ; data(i).eigval];
end
if (verbose)
disp(['sum eigval > 1 = ' int2str(sum(abs(data(i).eigval) > 1.)) ' nyf=' int2str(nyf) ' and dr.rank=' int2str(data(i).rank)]);
disp(['data(' int2str(i) ').eigval']);
disp(data(i).eigval);
end;
%First order approximation
if task ~= 1
if nba ~= nyf
sorted_roots = sort(abs(dr.eigval));
if isfield(options_,'indeterminacy_continuity')
if options_.indeterminacy_msv == 1
[ss,tt,w,q] = qz(e',d');
[ss,tt,w,q] = reorder(ss,tt,w,q);
ss = ss';
tt = tt';
w = w';
nba = nyf;
end
else
if nba > nyf
temp = sorted_roots(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
info(1) = 3;
elseif nba < nyf;
temp = sorted_roots(nd-nyf+1:nd-nba)-1-options_.qz_criterium;
info(1) = 4;
end
info(2) = temp'*temp;
return
end
end
indx_stable_root = 1: (nd - nyf); %=> index of stable roots
indx_explosive_root = n_pred + 1:nd; %=> index of explosive roots
% derivatives with respect to dynamic state variables
% forward variables
Z = w';
Z11t = Z(indx_stable_root, indx_stable_root)';
Z21 = Z(indx_explosive_root, indx_stable_root);
Z22 = Z(indx_explosive_root, indx_explosive_root);
if ~isfloat(Z21) && (condest(Z21) > 1e9)
% condest() fails on a scalar under Octave
info(1) = 5;
info(2) = condest(Z21);
return;
else
gx = -inv(Z22) * Z21;
end
% predetermined variables
hx = Z11t * inv(tt(indx_stable_root, indx_stable_root)) * ss(indx_stable_root, indx_stable_root) * inv(Z11t);
k1 = 1:(n_pred+n_both);
k2 = 1:(n_fwrd+n_both);
ghx = [hx(k1,:); gx(k2(n_both+1:end),:)];
if (verbose)
disp('ghx');
disp(ghx);
end;
%lead variables actually present in the model
j4 = n_static+n_pred+1:n_static+n_pred+n_both+n_fwrd;
j3 = nonzeros(lead_lag_incidence(2,j4)) - n_static - 2 * n_pred - n_both;
j4 = find(lead_lag_incidence(2,j4));
if (verbose)
disp('j3');
disp(j3);
disp('j4');
disp(j4);
end;
% derivatives with respect to exogenous variables
if exo_nbr
if n_static > 0
fu = Q' * data(i).g1_x;
else
fu = data(i).g1_x;
end;
B_static = [];
if n_static > 0
B_static = B(:,1:n_static); % submatrix containing the derivatives w.r. to static variables
end
B_pred = B(:,n_static+1:n_static+n_pred);
B_fyd = B(:,n_static+n_pred+1:end);
ghu = -[B_static C(:,j3)*gx(j4,1:n_pred)+B_pred B_fyd]\fu;
if (verbose)
disp('ghu');
disp(ghu);
end;
else
ghu = [];
end
% static variables
if n_static > 0
temp = - C(1:n_static,j3)*gx(j4,:)*hx;
if (verbose)
disp('temp');
disp(temp);
end;
j5 = index_m;
if (verbose)
disp('j5');
disp(j5);
end;
b = aa(:,index_c);
b10 = b(1:n_static, 1:n_static);
b11 = b(1:n_static, n_static+1:n);
if (verbose)
disp('b10');
disp(b10);
disp('b11');
disp(b11);
end;
temp(:,j5) = temp(:,j5)-A(1:n_static,:);
if (verbose)
disp('temp');
disp(temp);
end;
disp(temp-b11*ghx);
temp = b10\(temp-b11*ghx);
if (verbose)
disp('temp');
disp(temp);
end;
ghx = [temp; ghx];
temp = [];
if (verbose)
disp('ghx');
disp(ghx);
end;
end
if options_.loglinear == 1
k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
klag = dr.kstate(k,[1 2]);
k1 = dr.order_var;
ghx = repmat(1./dr.ys(k1),1,size(ghx,2)).*ghx.* ...
repmat(dr.ys(k1(klag(:,1)))',size(ghx,1),1);
ghu = repmat(1./dr.ys(k1),1,size(ghu,2)).*ghu;
end
if options_.aim_solver ~= 1 && options_.use_qzdiv
% Necessary when using Sims' routines for QZ
gx = real(gx);
hx = real(hx);
ghx = real(ghx);
ghu = real(ghu);
end
ghx
%exogenous deterministic variables
if exo_det_nbr > 0
f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
fudet = data(i).g1_xd;
M1 = inv(f0+[zeros(n,n_static) f1*gx zeros(n,nyf-n_both)]);
M2 = M1*f1;
dr.ghud = cell(M_.exo_det_length,1);
dr.ghud{1} = -M1*fudet;
for i = 2:M_.exo_det_length
dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
end
end
%Endogeneous variables of the previous blocks
end
end;
if task ~=1
if (maximum_lag > 0)
lead_lag_incidence(maximum_lag+1, n_static+1:n_static + n_pred + n_both) - indexi_0 + 1
state_var = endo(lead_lag_incidence(maximum_lag+1, n_static+1:n_static + n_pred + n_both) - indexi_0 + 1);
[common_state_var, indx_common_dr_state_var, indx_common_state_var] = intersect(dr.state_var, state_var);
[diff_state_var, indx_diff_dr_state_var, indx_diff_state_var] = setxor(dr.state_var, state_var);
[union_state_var, indx_union_dr_state_var, indx_union_state_var] = union(dr.state_var, state_var);
[row_dr_ghx, col_dr_ghx] = size(dr.ghx);
ghx_new = zeros(row_dr_ghx + n, length(union_state_var));
ghx_new(1:row_dr_ghx, 1:col_dr_ghx) = dr.ghx;
ghx_new(row_dr_ghx + 1: row_dr_ghx + n, indx_common_dr_state_var) = ghx(:, indx_common_state_var);
ghx_new(row_dr_ghx + 1: row_dr_ghx + n, length(dr.state_var)+1:length(dr.state_var)+length(indx_diff_state_var)) = ghx(:, indx_diff_state_var);
dr.ghx = ghx_new;
dr.state_var = [dr.state_var state_var(indx_diff_state_var)];
end;
exo_var = exo;
[common_exo_var, indx_common_dr_exo_var, indx_common_exo_var] = intersect(dr.exo_var, exo_var);
[diff_exo_var, indx_diff_dr_exo_var, indx_diff_exo_var] = setxor(dr.exo_var, exo_var);
[union_exo_var, indx_union_dr_exo_var, indx_union_exo_var] = union(dr.exo_var, exo_var);
[row_dr_ghu, col_dr_ghu] = size(dr.ghu);
ghu_new = zeros(row_dr_ghu + exo_nbr, length(union_exo_var));
ghu_new(1:row_dr_ghu, 1:col_dr_ghu) = dr.ghu;
ghu_new(row_dr_ghu + 1: row_dr_ghu + n, indx_common_dr_exo_var) = ghu(:, indx_common_exo_var);
ghu_new(row_dr_ghu + 1: row_dr_ghu + n, length(dr.exo_var)+1:length(dr.exo_var)+length(indx_diff_exo_var)) = ghu(:, indx_diff_exo_var);
dr.ghu = ghu_new;
dr.exo_var = [dr.exo_var exo_var(indx_diff_exo_var)];
end
end;
if (verbose)
dr.ghx
dr.ghu
end;
if (task == 1)
return;
end;
\ No newline at end of file
......@@ -44,7 +44,7 @@ if options_.block && ~options_.bytecode
end
elseif options_.bytecode
if options_.solve_algo > 4
[check, x] = bytecode('static', y, exo, params);
[check, x] = bytecode('static', x, exo, params);
mexErrCheck('bytecode', check);
info = check;
elseif options_.block
......
......@@ -633,22 +633,22 @@ ModelTree::computeBlockDecompositionAndFeedbackVariablesForEachBlock(const jacob
for (vector<int>::iterator its = Reordered_Vertice.begin(); its != Reordered_Vertice.end(); its++)
{
bool something_done = false;
if (j == 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].first != 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].second != 0)
if (j == 2 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].first != 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].second != 0)
{
n_mixed[prologue+i]++;
something_done = true;
}
else if (j == 1 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].first == 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].second != 0)
else if (j == 3 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].first == 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].second != 0)
{
n_forward[prologue+i]++;
something_done = true;
}
else if (j == 2 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].first != 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].second == 0)
else if (j == 1 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].first != 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].second == 0)
{
n_backward[prologue+i]++;
something_done = true;
}
else if (j == 3 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].first == 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].second == 0)
else if (j == 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].first == 0 && variable_lag_lead[tmp_variable_reordered[*its +prologue]].second == 0)
{
n_static[prologue+i]++;
something_done = true;
......@@ -668,22 +668,22 @@ ModelTree::computeBlockDecompositionAndFeedbackVariablesForEachBlock(const jacob
for (set<int>::iterator its = feed_back_vertices.begin(); its != feed_back_vertices.end(); its++)
{
bool something_done = false;
if (j == 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].first != 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].second != 0)
if (j == 2 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].first != 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].second != 0)
{
n_mixed[prologue+i]++;
something_done = true;
}
else if (j == 1 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].first == 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].second != 0)
else if (j == 3 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].first == 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].second != 0)
{
n_forward[prologue+i]++;
something_done = true;
}
else if (j == 2 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].first != 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].second == 0)
else if (j == 1 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].first != 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].second == 0)
{
n_backward[prologue+i]++;
something_done = true;
}
else if (j == 3 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].first == 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].second == 0)
else if (j == 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].first == 0 && variable_lag_lead[tmp_variable_reordered[v_index[vertex(*its, G)]+prologue]].second == 0)
{
n_static[prologue+i]++;
something_done = true;
......
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