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Ensure that all perfect foresight solvers work with periods=1. 

See #1205 and #1176.
time-shift
Stéphane Adjemian (Charybdis) 2019-04-26 15:36:33 +02:00
parent c5c1307725
commit 24cc67e585
11 changed files with 142 additions and 110 deletions
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29
matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
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@ -1,25 +1,22 @@
function [residuals,JJacobian] = linear_perfect_foresight_problem(y, dynamicjacobian, Y0, YT, ...
exo_simul, params, steady_state, ...
maximum_lag, T, ny, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, ...
i_cols_j,nnzJ,jendo,jexog)
% function [residuals,JJacobian] = perfect_foresight_problem(x, model_dynamic, Y0, YT,exo_simul,
% params, steady_state, maximum_lag, periods, ny, i_cols,i_cols_J1, i_cols_1,
% i_cols_T, i_cols_j, nnzA)
% computes the residuals and th Jacobian matrix
% for a perfect foresight problem over T periods.
exo_simul, params, steady_state, maximum_lag, T, ny, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, nnzJ, jendo, jexog)
% Computes the residuals and the Jacobian matrix for a linear perfect foresight problem over T periods.
%
% INPUTS
% ...
% ...
%
% OUTPUTS
% ...
% ...
%
% ALGORITHM
% ...
% ...
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2015-2017 Dynare Team
% Copyright (C) 2015-2019 Dynare Team
%
% This file is part of Dynare.
%
@ -44,7 +41,7 @@ residuals = zeros(T*ny,1);
z = zeros(columns(dynamicjacobian), 1);
if nargout == 2
JJacobian = sparse([],[],[],T*ny,T*ny,T*nnzJ);
JJacobian = spalloc(T*ny, T*ny, T*nnzJ);
end
i_rows = 1:ny;
@ -55,7 +52,9 @@ for it = maximum_lag+(1:T)
z(jexog) = transpose(exo_simul(it,:));
residuals(i_rows) = dynamicjacobian*z;
if nargout == 2
if it == maximum_lag+1
if T==1 && it==maximum_lag+1
JJacobian(i_rows, i_cols_J0) = dynamicjacobian(:,i_cols_0);
elseif it == maximum_lag+1
JJacobian(i_rows,i_cols_J1) = dynamicjacobian(:,i_cols_1);
elseif it == maximum_lag+T
JJacobian(i_rows,i_cols_J(i_cols_T)) = dynamicjacobian(:,i_cols_T);

13
matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
View File

@ -2,7 +2,7 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
exo_simul, params, steady_state, ...
maximum_lag, T, ny, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, ...
i_cols_j,nnzJ,eq_index)
i_cols_j, i_cols_0,i_cols_J0, nnzJ,eq_index)
% function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_function, Y0, YT, ...
% exo_simul, params, steady_state, ...
% maximum_lag, T, ny, i_cols, ...
@ -80,10 +80,12 @@ for it = maximum_lag+(1:T)
steady_state,it);
residuals(i_rows) = res(eq_index);
elseif nargout == 2
[res,jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
[res,jacobian] = dynamic_function(YY(i_cols),exo_simul, params, steady_state,it);
residuals(i_rows) = res(eq_index);
if it == maximum_lag+1
if T==1 && it==maximum_lag+1
[rows, cols, vals] = find(jacobian(:,i_cols_0));
iJacobian{1} = [rows, i_cols_J0(cols), vals];
elseif it == maximum_lag+1
[rows,cols,vals] = find(jacobian(eq_index,i_cols_1));
iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
elseif it == maximum_lag+T
@ -103,6 +105,5 @@ end
if nargout == 2
iJacobian = cat(1,iJacobian{:});
JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
ny,T*ny);
JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T*ny,T*ny);
end

72
matlab/perfect-foresight-models/perfect_foresight_problem.m
View File

@ -2,47 +2,48 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
exo_simul, params, steady_state, ...
maximum_lag, T, ny, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, ...
i_cols_j,nnzJ)
% function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function, Y0, YT, ...
% exo_simul, params, steady_state, ...
% maximum_lag, T, ny, i_cols, ...
% i_cols_J1, i_cols_1, i_cols_T, ...
% i_cols_j,nnzJ)
% computes the residuals and the Jacobian matrix for a perfect foresight problem over T periods.
i_cols_j, i_cols_0, i_cols_J0, nnzJ)
% Computes the residuals and the Jacobian matrix for a perfect foresight problem over T periods.
%
% INPUTS
% y [double] N*1 array, terminal conditions for the endogenous variables
% dynamic_function [handle] function handle to _dynamic-file
% Y0 [double] N*1 array, initial conditions for the endogenous variables
% YT [double] N*1 array, terminal conditions for the endogenous variables
% exo_simul [double] nperiods*M_.exo_nbr matrix of exogenous variables (in declaration order)
% - y [double] N*1 array, terminal conditions for the endogenous variables
% - dynamic_function [handle] function handle to _dynamic-file
% - Y0 [double] N*1 array, initial conditions for the endogenous variables
% - YT [double] N*1 array, terminal conditions for the endogenous variables
% - exo_simul [double] nperiods*M_.exo_nbr matrix of exogenous variables (in declaration order)
% for all simulation periods
% params [double] nparams*1 array, parameter values
% steady_state [double] endo_nbr*1 vector of steady state values
% maximum_lag [scalar] maximum lag present in the model
% T [scalar] number of simulation periods
% ny [scalar] number of endogenous variables
% i_cols [double] indices of variables appearing in M.lead_lag_incidence
% - params [double] nparams*1 array, parameter values
% - steady_state [double] endo_nbr*1 vector of steady state values
% - maximum_lag [scalar] maximum lag present in the model
% - T [scalar] number of simulation periods
% - ny [scalar] number of endogenous variables
% - i_cols [double] indices of variables appearing in M.lead_lag_incidence
% and that need to be passed to _dynamic-file
% i_cols_J1 [double] indices of contemporaneous and forward looking variables
% - i_cols_J1 [double] indices of contemporaneous and forward looking variables
% appearing in M.lead_lag_incidence
% i_cols_1 [double] indices of contemporaneous and forward looking variables in
% - i_cols_1 [double] indices of contemporaneous and forward looking variables in
% M.lead_lag_incidence in dynamic Jacobian (relevant in first period)
% i_cols_T [double] columns of dynamic Jacobian related to contemporaneous and backward-looking
% - i_cols_T [double] columns of dynamic Jacobian related to contemporaneous and backward-looking
% variables (relevant in last period)
% i_cols_j [double] indices of variables in M.lead_lag_incidence
% - i_cols_j [double] indices of contemporaneous variables in M.lead_lag_incidence
% in dynamic Jacobian (relevant in intermediate periods)
% nnzJ [scalar] number of non-zero elements in Jacobian
% - i_cols_0 [double] indices of contemporaneous variables in M.lead_lag_incidence in dynamic
% Jacobian (relevant in problems with periods=1)
% - i_cols_J0 [double] indices of contemporaneous variables appearing in M.lead_lag_incidence (relevant in problems with periods=1)
% - nnzJ [scalar] number of non-zero elements in Jacobian
%
% OUTPUTS
% residuals [double] (N*T)*1 array, residuals of the stacked problem
% JJacobian [double] (N*T)*(N*T) array, Jacobian of the stacked problem
% - residuals [double] (N*T)*1 array, residuals of the stacked problem
% - JJacobian [double] (N*T)*(N*T) array, Jacobian of the stacked problem
%
% ALGORITHM
% None
% None
%
% SPECIAL REQUIREMENTS
% None.
% None.
% Copyright (C) 1996-2017 Dynare Team
% Copyright (C) 1996-2019 Dynare Team
%
% This file is part of Dynare.
%
@ -73,12 +74,13 @@ offset = 0;
for it = maximum_lag+(1:T)
if nargout == 1
residuals(i_rows) = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
residuals(i_rows) = dynamic_function(YY(i_cols), exo_simul, params, steady_state, it);
elseif nargout == 2
[residuals(i_rows),jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
if it == maximum_lag+1
[residuals(i_rows),jacobian] = dynamic_function(YY(i_cols), exo_simul, params, steady_state, it);
if T==1 && it==maximum_lag+1
[rows, cols, vals] = find(jacobian(:,i_cols_0));
iJacobian{1} = [rows, i_cols_J0(cols), vals];
elseif it == maximum_lag+1
[rows,cols,vals] = find(jacobian(:,i_cols_1));
iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
elseif it == maximum_lag+T
@ -91,13 +93,11 @@ for it = maximum_lag+(1:T)
end
offset = offset + ny;
end
i_rows = i_rows + ny;
i_cols = i_cols + ny;
end
if nargout == 2
iJacobian = cat(1,iJacobian{:});
JJacobian = sparse(iJacobian(:,1),iJacobian(:,2),iJacobian(:,3),T* ...
ny,T*ny);
JJacobian = sparse(iJacobian(:,1), iJacobian(:,2), iJacobian(:,3), T*ny, T*ny);
end

27
matlab/perfect-foresight-models/perfect_foresight_solver.m
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@ -37,6 +37,8 @@ if isempty(options_.scalv) || options_.scalv == 0
options_.scalv = oo_.steady_state;
end
periods = options_.periods;
options_.scalv= 1;
if options_.debug
@ -56,7 +58,7 @@ if options_.debug
end
initperiods = 1:M_.maximum_lag;
lastperiods = (M_.maximum_lag+options_.periods+1):(M_.maximum_lag+options_.periods+M_.maximum_lead);
lastperiods = (M_.maximum_lag+periods+1):(M_.maximum_lag+periods+M_.maximum_lead);
oo_ = perfect_foresight_solver_core(M_,options_,oo_);
@ -91,8 +93,8 @@ if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
options_.verbosity = 0;
% Set initial paths for the endogenous and exogenous variables.
endoinit = repmat(oo_.steady_state, 1,M_.maximum_lag+options_.periods+M_.maximum_lead);
exoinit = repmat(oo_.exo_steady_state',M_.maximum_lag+options_.periods+M_.maximum_lead,1);
endoinit = repmat(oo_.steady_state, 1,M_.maximum_lag+periods+M_.maximum_lead);
exoinit = repmat(oo_.exo_steady_state',M_.maximum_lag+periods+M_.maximum_lead,1);
% Copy the current paths for the exogenous and endogenous variables.
exosim = oo_.exo_simul;
@ -131,7 +133,7 @@ if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
if isequal(iteration, 1)
% First iteration, same initial guess as in the first call to perfect_foresight_solver_core routine.
oo_.endo_simul(:,M_.maximum_lag+1:end-M_.maximum_lead) = endoinit(:,1:options_.periods);
oo_.endo_simul(:,M_.maximum_lag+1:end-M_.maximum_lead) = endoinit(:,1:periods);
elseif path_with_nans || path_with_cplx
% If solver failed with NaNs or complex number, use previous solution as an initial guess.
oo_.endo_simul(:,M_.maximum_lag+1:end-M_.maximum_lead) = saved_endo_simul(:,1+M_.maximum_lag:end-M_.maximum_lead);
@ -174,19 +176,26 @@ if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
end
if ~isreal(oo_.endo_simul(:)) %can only happen without bytecode
if ~isreal(oo_.endo_simul(:)) % can only happen without bytecode
y0 = real(oo_.endo_simul(:,1));
yT = real(oo_.endo_simul(:,options_.periods+2));
yy = real(oo_.endo_simul(:,2:options_.periods+1));
yT = real(oo_.endo_simul(:,periods+2));
yy = real(oo_.endo_simul(:,2:periods+1));
illi = M_.lead_lag_incidence';
[i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3);
[i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
if periods==1
i_cols_0 = nonzeros(M_.lead_lag_incidence(2,:)');
i_cols_J0 = find(M_.lead_lag_incidence(2,:)');
else
i_cols_0 = [];
i_cols_J0 = [];
end
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ...
oo_.exo_simul,M_.params,oo_.steady_state, ...
M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
M_.maximum_lag, periods, M_.endo_nbr, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
M_.NNZDerivatives(1));
if max(abs(residuals))< options_.dynatol.f
oo_.deterministic_simulation.status = 1;

41
matlab/perfect-foresight-models/perfect_foresight_solver_core.m
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@ -1,5 +1,5 @@
function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
%function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
% Core function calling solvers for perfect foresight model
%
% INPUTS
@ -11,7 +11,7 @@ function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
% - oo_ [struct] contains results
% - maxerror [double] contains the maximum absolute error
% Copyright (C) 2015-2017 Dynare Team
% Copyright (C) 2015-2019 Dynare Team
%
% This file is part of Dynare.
%
@ -33,18 +33,20 @@ if options_.lmmcp.status
options_.solve_algo = 10;
end
periods = options_.periods;
if options_.linear_approximation && ~(isequal(options_.stack_solve_algo,0) || isequal(options_.stack_solve_algo,7))
error('perfect_foresight_solver: Option linear_approximation is only available with option stack_solve_algo equal to 0.')
error('perfect_foresight_solver: Option linear_approximation is only available with option stack_solve_algo equal to 0 or 7.')
end
if options_.linear && isequal(options_.stack_solve_algo,0)
options_.linear_approximation = 1;
if options_.linear && (isequal(options_.stack_solve_algo, 0) || isequal(options_.stack_solve_algo, 7))
options_.linear_approximation = true;
end
if options_.block
if options_.bytecode
try
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods);
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1, periods+2), periods);
catch
info = 1;
end
@ -63,7 +65,7 @@ if options_.block
else
if options_.bytecode
try
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods);
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state, 1, periods+2), periods);
catch
info = 1;
end
@ -119,14 +121,19 @@ end
if nargout>1
y0 = oo_.endo_simul(:,1);
yT = oo_.endo_simul(:,options_.periods+2);
yy = oo_.endo_simul(:,2:options_.periods+1);
if ~exist('illi')
illi = M_.lead_lag_incidence';
[i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3);
[i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
yT = oo_.endo_simul(:,periods+2);
yy = oo_.endo_simul(:,2:periods+1);
illi = M_.lead_lag_incidence';
[i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3);
[i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
if periods==1
i_cols_0 = nonzeros(M_.lead_lag_incidence(2,:)');
i_cols_J0 = find(M_.lead_lag_incidence(2,:)');
else
i_cols_0 = [];
i_cols_J0 = [];
end
if options_.block && ~options_.bytecode
maxerror = oo_.deterministic_simulation.error;
@ -136,8 +143,8 @@ if nargout>1
else
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ...
oo_.exo_simul,M_.params,oo_.steady_state, ...
M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
M_.maximum_lag, periods,M_.endo_nbr,i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
M_.NNZDerivatives(1));
end
maxerror = max(max(abs(residuals)));

20
matlab/perfect-foresight-models/private/initialize_stacked_problem.m
View File

@ -1,7 +1,6 @@
function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, ...
dynamicmodel] = initialize_stacked_problem(endogenousvariables, options, M, steadystate_y)
% function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, ...
% dynamicmodel] = initialize_stacked_problem(endogenousvariables, options, M, steadystate_y)
function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, i_cols_0, i_cols_J0, dynamicmodel] = ...
initialize_stacked_problem(endogenousvariables, options, M, steadystate_y)
% Sets up the stacked perfect foresight problem for use with dynare_solve.m
%
% INPUTS
@ -9,6 +8,7 @@ function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, .
% - options [struct] contains various options.
% - M [struct] contains a description of the model.
% - steadystate_y [double] N*1 array, steady state for the endogenous variables.
%
% OUTPUTS
% - options [struct] contains various options.
% - y0 [double] N*1 array, initial conditions for the endogenous variables
@ -25,9 +25,12 @@ function [options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, .
% in dynamic Jacobian (relevant in intermediate periods)
% - i_cols_1 [double] indices of contemporaneous and forward looking variables in
% M.lead_lag_incidence in dynamic Jacobian (relevant in first period)
% - i_cols_0 [double] indices of contemporaneous variables in M.lead_lag_incidence in dynamic
% Jacobian (relevant in problems with periods=1)
% - i_cols_J0 [double] indices of contemporaneous variables appearing in M.lead_lag_incidence (relevant in problems with periods=1)
% - dynamicmodel [handle] function handle to _dynamic-file
% Copyright (C) 2015-2017 Dynare Team
% Copyright (C) 2015-2019 Dynare Team
%
% This file is part of Dynare.
%
@ -75,4 +78,11 @@ illi = M.lead_lag_incidence';
illi = illi(:,2:3);
[i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M.lead_lag_incidence(1:2,:)');
if periods==1
i_cols_0 = nonzeros(M.lead_lag_incidence(2,:)');
i_cols_J0 = find(M.lead_lag_incidence(2,:)');
else
i_cols_0 = [];
i_cols_J0 = [];
end
dynamicmodel = str2func([M.fname,'.dynamic']);

31
matlab/perfect-foresight-models/private/simulation_core.m
View File

@ -1,7 +1,7 @@
function [oo_, maxerror] = simulation_core(options_, M_, oo_)
%function [oo_, maxerror] = simulation_core(options_, M_, oo_)
% Copyright (C) 2015-2017 Dynare Team
% Copyright (C) 2015-2019 Dynare Team
%
% This file is part of Dynare.
%
@ -26,10 +26,12 @@ if options_.linear && isequal(options_.stack_solve_algo,0)
options_.linear_approximation = 1;
end
periods = options_.periods;
if options_.block
if options_.bytecode
try
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods);
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state, 1, periods+2), periods);
catch
info = 0;
end
@ -48,7 +50,7 @@ if options_.block
else
if options_.bytecode
try
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods);
[info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state, 1, periods+2), periods);
catch
info = 0;
end
@ -95,14 +97,19 @@ end
if nargout>1
y0 = oo_.endo_simul(:,1);
yT = oo_.endo_simul(:,options_.periods+2);
yy = oo_.endo_simul(:,2:options_.periods+1);
if ~exist('illi')
illi = M_.lead_lag_incidence';
[i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3);
[i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
yT = oo_.endo_simul(:,periods+2);
yy = oo_.endo_simul(:,2:periods+1);
illi = M_.lead_lag_incidence';
[i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3);
[i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
if periods==1
i_cols_0 = nonzeros(M_.lead_lag_incidence(2,:)');
i_cols_J0 = find(M_.lead_lag_incidence(2,:)');
else
i_cols_0 = [];
i_cols_J0 = [];
end
if options_.block && ~options_.bytecode
maxerror = oo_.deterministic_simulation.error;
@ -113,7 +120,7 @@ if nargout>1
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ...
oo_.exo_simul,M_.params,oo_.steady_state, ...
M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
M_.NNZDerivatives(1));
end
maxerror = max(max(abs(residuals)));

2
matlab/perfect-foresight-models/sim1.m
View File

@ -101,7 +101,7 @@ for iter = 1:options.simul.maxit
m = 0;
for it = (maximum_lag+1):(maximum_lag+periods)
[d1,jacobian] = model_dynamic(Y(i_cols), exogenousvariables, params, steadystate,it);
if it == maximum_lag+periods && it == maximum_lag+1
if periods==1 && it==maximum_lag+1
[r,c,v] = find(jacobian(:,i_cols_0));
iA((1:length(v))+m,:) = [i_rows(r(:)),i_cols_A0(c(:)),v(:)];
elseif it == maximum_lag+periods

2
matlab/perfect-foresight-models/sim1_linear.m
View File

@ -142,7 +142,7 @@ i_cols_A = ipcn;
i_cols = ipcn+(maximum_lag-1)*ny;
m = 0;
for it = (maximum_lag+1):(maximum_lag+periods)
if isequal(it, maximum_lag+periods) && isequal(it, maximum_lag+1)
if periods==1 && isequal(it, maximum_lag+1)
nv = length(v0);
iA(iv0+m,:) = [i_rows(r0),ic(c0),v0];
elseif isequal(it, maximum_lag+periods)

4
matlab/perfect-foresight-models/solve_stacked_linear_problem.m
View File

@ -17,7 +17,7 @@ function [endogenousvariables, info] = solve_stacked_linear_problem(endogenousva
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, dynamicmodel] = ...
[options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, i_cols_0, i_cols_J0, dynamicmodel] = ...
initialize_stacked_problem(endogenousvariables, options, M, steadystate_y);
ip = find(M.lead_lag_incidence(1,:)');
@ -45,7 +45,7 @@ x = bsxfun(@minus, exogenousvariables, steadystate_x');
jacobian, y0-steadystate_y, yT-steadystate_y, ...
x, M.params, steadystate_y, ...
M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
M.NNZDerivatives(1), jendo, jexog);
if all(imag(y)<.1*options.dynatol.x)

11
matlab/perfect-foresight-models/solve_stacked_problem.m
View File

@ -1,5 +1,5 @@
function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options)
% [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options)
% Solves the perfect foresight model using dynare_solve
%
% INPUTS
@ -13,7 +13,7 @@ function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables
% - endogenousvariables [double] N*T array, paths for the endogenous variables (solution of the perfect foresight model).
% - info [struct] contains informations about the results.
% Copyright (C) 2015-2017 Dynare Team
% Copyright (C) 2015-2019 Dynare Team
%
% This file is part of Dynare.
%
@ -30,7 +30,7 @@ function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, dynamicmodel] = ...
[options, y0, yT, z, i_cols, i_cols_J1, i_cols_T, i_cols_j, i_cols_1, i_cols_0, i_cols_J0, dynamicmodel] = ...
initialize_stacked_problem(endogenousvariables, options, M, steadystate);
if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity problem
@ -50,14 +50,14 @@ if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity
dynamicmodel, y0, yT, ...
exogenousvariables, M.params, steadystate, ...
M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
M.NNZDerivatives(1),eq_index);
else
[y, check] = dynare_solve(@perfect_foresight_problem,z(:),options, ...
dynamicmodel, y0, yT, ...
exogenousvariables, M.params, steadystate, ...
M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
M.NNZDerivatives(1));
end
@ -74,6 +74,5 @@ endogenousvariables(:, M.maximum_lag+(1:options.periods)) = reshape(y, M.endo_nb
if check
info.status = false;
else
info.status = true;
end