diff --git a/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m b/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
index 3a04ea8e1..fd76cd89e 100644
--- a/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
+++ b/matlab/perfect-foresight-models/linear_perfect_foresight_problem.m
@@ -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);
diff --git a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
index 000fff50f..89d9a7b82 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
@@ -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
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/perfect_foresight_problem.m b/matlab/perfect-foresight-models/perfect_foresight_problem.m
index c6c6f77a7..e71c25248 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_problem.m
@@ -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
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver.m b/matlab/perfect-foresight-models/perfect_foresight_solver.m
index 17f607ca0..c7fab2a6f 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver.m
@@ -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;
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
index 1f55a2de4..f0a73f580 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
@@ -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)));
diff --git a/matlab/perfect-foresight-models/private/initialize_stacked_problem.m b/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
index f019805dc..55290fc9f 100644
--- a/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
+++ b/matlab/perfect-foresight-models/private/initialize_stacked_problem.m
@@ -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']);
\ No newline at end of file
diff --git a/matlab/perfect-foresight-models/private/simulation_core.m b/matlab/perfect-foresight-models/private/simulation_core.m
index aa66ef2de..6e7bae6a0 100644
--- a/matlab/perfect-foresight-models/private/simulation_core.m
+++ b/matlab/perfect-foresight-models/private/simulation_core.m
@@ -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)));
diff --git a/matlab/perfect-foresight-models/sim1.m b/matlab/perfect-foresight-models/sim1.m
index f43d6df0d..66e698c7c 100644
--- a/matlab/perfect-foresight-models/sim1.m
+++ b/matlab/perfect-foresight-models/sim1.m
@@ -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
diff --git a/matlab/perfect-foresight-models/sim1_linear.m b/matlab/perfect-foresight-models/sim1_linear.m
index 3fa40bf41..2a68ac0b5 100644
--- a/matlab/perfect-foresight-models/sim1_linear.m
+++ b/matlab/perfect-foresight-models/sim1_linear.m
@@ -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)
diff --git a/matlab/perfect-foresight-models/solve_stacked_linear_problem.m b/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
index 5464c9e06..cd52ac444 100644
--- a/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
@@ -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 .
-[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)
diff --git a/matlab/perfect-foresight-models/solve_stacked_problem.m b/matlab/perfect-foresight-models/solve_stacked_problem.m
index 3a1e2e715..d673a1973 100644
--- a/matlab/perfect-foresight-models/solve_stacked_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_problem.m
@@ -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 .
-[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
\ No newline at end of file