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Commit c6bac390 authored by Houtan Bastani's avatar Houtan Bastani
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add exogenous steady state to dynamic function call. #825

parent 8c469213
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matlab/discretionary_policy_1.m
View file @ c6bac390
function [dr,ys,info]=discretionary_policy_1(oo_,Instruments)
% Copyright (C) 2007-2016 Dynare Team
% Copyright (C) 2007-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -86,7 +86,8 @@ if exo_nbr == 0
end
[junk,jacobia_] = feval([M_.fname '_dynamic'],z, [zeros(size(oo_.exo_simul)) ...
oo_.exo_det_simul], M_.params, zeros(endo_nbr,1), it_);
oo_.exo_det_simul], M_.params, zeros(endo_nbr,1), ...
zeros(exo_nbr,1), it_);
if any(junk~=0)
error(['discretionary_policy: the model must be written in deviation ' ...
'form and not have constant terms'])
......
matlab/dyn_risky_steadystate_solver.m
View file @ c6bac390
......@@ -62,7 +62,7 @@ function [dr,info] = dyn_risky_steadystate_solver(ys0,M, ...
%! @end deftypefn
%@eod:
% Copyright (C) 2001-2012 Dynare Team
% Copyright (C) 2001-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -162,7 +162,8 @@ function [resid,dr] = risky_residuals(ys,pm,M,dr,options,oo)
z = z(iyr0) ;
[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
[oo.exo_simul ...
oo.exo_det_simul], M.params, dr.ys, 2);
oo.exo_det_simul], M.params, dr.ys, oo.exo_steady_state, ...
2);
if ~isreal(d1) || ~isreal(d2)
pause
end
......@@ -222,7 +223,8 @@ function [resid,dr] = risky_residuals_ds(x,pm,M,dr,options,oo)
z = z(iyr0) ;
[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
[oo.exo_simul ...
oo.exo_det_simul], M.params, dr.ys, 2);
oo.exo_det_simul], M.params, dr.ys, oo.exo_steady_state, ...
2);
if ~isreal(d1) || ~isreal(d2)
pause
end
......@@ -255,7 +257,8 @@ function dr_np = first_step_ds(x,pm,M,dr,options,oo)
z = z(iyr0) ;
[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
[oo.exo_simul ...
oo.exo_det_simul], M.params, dr.ys, 2);
oo.exo_det_simul], M.params, dr.ys, oo.exo_steady_state, ...
2);
if ~isreal(d1) || ~isreal(d2)
pause
end
......@@ -296,7 +299,8 @@ function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
z = z(iyr0) ;
[resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
[oo.exo_simul ...
oo.exo_det_simul], M.params, dr.ys, 2);
oo.exo_det_simul], M.params, dr.ys, oo.exo_steady_state, ...
2);
if isfield(options,'portfolio') && options.portfolio == 1
eq_np = pm.eq_np;
......@@ -331,7 +335,8 @@ function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
if nargout > 1
[resid1,d1,d2,d3] = feval([M.fname '_dynamic'],z,...
[oo.exo_simul ...
oo.exo_det_simul], M.params, dr.ys, 2);
oo.exo_det_simul], M.params, dr.ys, oo.exo_steady_state, ...
2);
[a,b,c] = find(d2(eq_np,pm.i_d2_np));
......
matlab/evaluate_max_dynamic_residual.m
View file @ c6bac390
function err = evaluate_max_dynamic_residual(model_dynamic, Y, exogenous_variables, params, steady_state, periods, ny, max_lag, lead_lag_incidence)
function err = evaluate_max_dynamic_residual(model_dynamic, Y, exogenous_variables, params, steady_state, steady_state_x, periods, ny, max_lag, lead_lag_incidence)
% Copyright (C) 2013 Dynare Team
% Copyright (C) 2013-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -23,7 +23,7 @@ i_cols = find(lead_lag_incidence');
err = 0;
for it = (max_lag+1):(max_lag+periods)
d = model_dynamic(Y(i_cols), exogenous_variables, params, steady_state, it);
d = model_dynamic(Y(i_cols), exogenous_variables, params, steady_state, steady_state_x, it);
i_rows = i_rows + ny;
i_cols = i_cols + ny;
r = max(abs(d));
......
matlab/evaluate_steady_state.m
View file @ c6bac390
......@@ -22,7 +22,7 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2001-2016 Dynare Team
% Copyright (C) 2001-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -313,12 +313,13 @@ function [ys,params,info] = evaluate_steady_state(ys_init,M,options,oo,steadysta
[chck, r, junk]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1);
mexErrCheck('bytecode', chck);
elseif options.block
[r, oo.dr] = feval([M.fname '_dynamic'], z', zx, M.params, ys, M.maximum_lag+1, oo.dr);
[r, oo.dr] = feval([M.fname '_dynamic'], z', zx, M.params, ys, ...
M.maximum_lag+1, oo.dr);
else
iyv = M.lead_lag_incidence';
iyr0 = find(iyv(:));
xys = z(iyr0);
r = feval([M.fname '_dynamic'], z(iyr0), zx, M.params, ys, M.maximum_lag + 1);
r = feval([M.fname '_dynamic'], z(iyr0), zx, M.params, ys, oo.exo_steady_state, M.maximum_lag + 1);
end
% Fail if residual greater than tolerance
......
matlab/getH.m
View file @ c6bac390
......@@ -32,7 +32,7 @@ function [H, dA, dOm, Hss, gp, d2A, d2Om, H2ss] = getH(A, B, estim_params_,M_,oo
% H2s: Hessian of steady state with respect to estimated
% structural parameters only (indx)
% Copyright (C) 2010-2016 Dynare Team
% Copyright (C) 2010-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -125,7 +125,7 @@ end
if kronflag==-2,
if nargout>5,
[residual, g1, g2 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
M_.params, oo_.dr.ys, 1);
M_.params, oo_.dr.ys, oo_.exo_steady_state, 1);
g22 = hessian_sparse('thet2tau',[M_.params(indx)],options_.gstep,estim_params_,M_, oo_, indx,[],-1);
H2ss=full(g22(1:M_.endo_nbr,:));
H2ss = reshape(H2ss,[M_.endo_nbr param_nbr param_nbr]);
......@@ -145,7 +145,7 @@ if kronflag==-2,
clear gx22;
else
[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
M_.params, oo_.dr.ys, 1);
M_.params, oo_.dr.ys, oo_.exo_steady_state, 1);
end
gp = fjaco('thet2tau',[M_.params(indx)],estim_params_,M_, oo_, indx,[],-1);
Hss=gp(1:M_.endo_nbr,:);
......@@ -166,7 +166,7 @@ dyssdtheta = -gg1\df;
if nargout>5,
[residual, gg1, gg2] = feval([M_.fname,'_static'],oo_.dr.ys, oo_.exo_steady_state', M_.params);
[residual, g1, g2, g3] = feval([M_.fname,'_dynamic'],yy0, oo_.exo_steady_state', ...
M_.params, oo_.dr.ys, 1);
M_.params, oo_.dr.ys, oo_.exo_steady_state, 1);
[nr, nc]=size(gg2);
[df, gpx, d2f] = feval([M_.fname,'_static_params_derivs'],oo_.dr.ys, oo_.exo_steady_state', ...
......@@ -218,7 +218,7 @@ else
[df, gp] = feval([M_.fname,'_params_derivs'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
M_.params, oo_.dr.ys, 1, dyssdtheta,d2yssdtheta);
[residual, g1, g2 ] = feval([M_.fname,'_dynamic'],yy0, repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1,1]), ...
M_.params, oo_.dr.ys, 1);
M_.params, oo_.dr.ys, oo_.exo_steady_state, 1);
end
[nr, nc]=size(g2);
......
matlab/identification_analysis.m
View file @ c6bac390
......@@ -28,7 +28,7 @@ function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info, opt
% SPECIAL REQUIREMENTS
% None
% Copyright (C) 2008-2016 Dynare Team
% Copyright (C) 2008-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -79,7 +79,7 @@ if info(1)==0,
yy0=oo_.dr.ys(I);
[residual, g1 ] = feval([M_.fname,'_dynamic'],yy0, ...
repmat(oo_.exo_steady_state',[M_.maximum_exo_lag+M_.maximum_exo_lead+1]), M_.params, ...
oo_.dr.ys, 1);
oo_.dr.ys, oo_.exo_steady_state, 1);
vg1 = [oo_.dr.ys(oo_.dr.order_var); vec(g1)];
[JJ, H, gam, gp, dA, dOm, dYss] = getJJ(A, B, estim_params_, M_,oo0,options_,kron_flag,indx,indexo,bayestopt_.mf2,nlags,useautocorr);
......
matlab/model_diagnostics.m
View file @ c6bac390
......@@ -16,7 +16,7 @@ function model_diagnostics(M,options,oo)
% none.
%
% Copyright (C) 1996-2013 Dynare Team
% Copyright (C) 1996-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -209,7 +209,7 @@ if ~options.block
jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
else
[junk,jacobia_] = feval([M.fname '_dynamic'],z(iyr0),exo_simul, ...
M.params, dr.ys, it_);
M.params, dr.ys, oo.exo_steady_state, it_);
end;
elseif options.order >= 2
if (options.bytecode)
......@@ -219,7 +219,7 @@ if ~options.block
else
[junk,jacobia_,hessian1] = feval([M.fname '_dynamic'],z(iyr0),...
exo_simul, ...
M.params, dr.ys, it_);
M.params, dr.ys, oo.exo_steady_state, it_);
end;
if options.use_dll
% In USE_DLL mode, the hessian is in the 3-column sparse representation
......
matlab/partial_information/dr1_PI.m
View file @ c6bac390
......@@ -40,7 +40,7 @@ function [dr,info,M_,options_,oo_] = dr1_PI(dr,task,M_,options_,oo_)
% none.
%
% Copyright (C) 1996-2012 Dynare Team
% Copyright (C) 1996-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -131,7 +131,8 @@ else
end
z = z(iyr0) ;
[junk,jacobia_] = feval([M_.fname '_dynamic'],z,[oo_.exo_simul ...
oo_.exo_det_simul], M_.params, dr.ys, it_);
oo_.exo_det_simul], M_.params, dr.ys, oo_.exo_steady_state, ...
it_);
if options_.ACES_solver==1 && (length(sim_ruleids)>0 || length(tct_ruleids)>0 )
if length(sim_ruleids)>0
......
matlab/perfect-foresight-models/det_cond_forecast.m
View file @ c6bac390
......@@ -12,7 +12,7 @@ function data_set = det_cond_forecast(varargin)
% dataset [dseries] Returns a dseries containing the forecasted endgenous variables and shocks
%
%
% Copyright (C) 2013-2016 Dynare Team
% Copyright (C) 2013-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -475,7 +475,8 @@ if pf && ~surprise
if (options_.bytecode)
[chck, zz, data1]= bytecode('dynamic','evaluate', z, zx, M_.params, oo_.steady_state, k, data1);
else
[zz, g1b] = feval([M_.fname '_dynamic'], z', zx, M_.params, oo_.steady_state, k);
[zz, g1b] = feval([M_.fname '_dynamic'], z', zx, M_.params, ...
oo_.steady_state, oo_.exo_steady_state, k);
data1.g1_x = g1b(:,end - M_.exo_nbr + 1:end);
data1.g1 = g1b(:,1 : end - M_.exo_nbr);
chck = 0;
......@@ -747,7 +748,8 @@ else
if (options_.bytecode)
[chck, zz, data1]= bytecode('dynamic','evaluate', z, zx, M_.params, oo_.steady_state, k, data1);
else
[zz, g1b] = feval([M_.fname '_dynamic'], z', zx, M_.params, oo_.steady_state, k);
[zz, g1b] = feval([M_.fname '_dynamic'], z', zx, ...
M_.params, oo_.steady_state, oo_.exo_steady_state, k);
data1.g1_x = g1b(:,end - M_.exo_nbr + 1:end);
data1.g1 = g1b(:,1 : end - M_.exo_nbr);
chck = 0;
......
matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
View file @ c6bac390
function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_function, Y0, YT, ...
exo_simul, params, steady_state, ...
exo_simul, params, steady_state, exo_steady_state, ...
maximum_lag, T, ny, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, ...
i_cols_j,nnzJ,eq_index)
% function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_function, Y0, YT, ...
% exo_simul, params, steady_state, ...
% exo_simul, params, steady_state, exo_steady_state, ...
% maximum_lag, T, ny, i_cols, ...
% i_cols_J1, i_cols_1, i_cols_T, ...
% i_cols_j,nnzJ,eq_index)
......@@ -20,6 +20,7 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
% for all simulation periods
% params [double] nparams*1 array, parameter values
% steady_state [double] endo_nbr*1 vector of steady state values
% exo_steady_state [double] exo_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
......@@ -45,7 +46,7 @@ function [residuals,JJacobian] = perfect_foresight_mcp_problem(y, dynamic_functi
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 1996-2016 Dynare Team
% Copyright (C) 1996-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -77,11 +78,11 @@ i_cols_J = i_cols;
for it = 2:(T+1)
if nargout == 1
res = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
steady_state, exo_steady_state, it);
residuals(i_rows) = res(eq_index);
elseif nargout == 2
[res,jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
steady_state, exo_steady_state, it);
residuals(i_rows) = res(eq_index);
if it == 2
[rows,cols,vals] = find(jacobian(eq_index,i_cols_1));
......
matlab/perfect-foresight-models/perfect_foresight_problem.m
View file @ c6bac390
function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function, Y0, YT, ...
exo_simul, params, steady_state, ...
exo_simul, params, steady_state, exo_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, ...
% exo_simul, params, steady_state, exo_steady_state, ...
% maximum_lag, T, ny, i_cols, ...
% i_cols_J1, i_cols_1, i_cols_T, ...
% i_cols_j,nnzJ)
......@@ -19,6 +19,7 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
% for all simulation periods
% params [double] nparams*1 array, parameter values
% steady_state [double] endo_nbr*1 vector of steady state values
% exo_steady_state [double] exo_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
......@@ -42,7 +43,7 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 1996-2016 Dynare Team
% Copyright (C) 1996-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -74,10 +75,10 @@ function [residuals,JJacobian] = perfect_foresight_problem(y, dynamic_function,
for it = 2:(T+1)
if nargout == 1
residuals(i_rows) = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
steady_state, exo_steady_state, it);
elseif nargout == 2
[residuals(i_rows),jacobian] = dynamic_function(YY(i_cols),exo_simul, params, ...
steady_state,it);
steady_state, exo_steady_state, it);
if it == 2
[rows,cols,vals] = find(jacobian(:,i_cols_1));
iJacobian{1} = [offset+rows, i_cols_J1(cols), vals];
......
matlab/perfect-foresight-models/perfect_foresight_solver_core.m
View file @ c6bac390
......@@ -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) 2015-2016 Dynare Team
% Copyright (C) 2015-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -79,36 +79,36 @@ else
else
if M_.maximum_endo_lead == 0 % Purely backward model
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
elseif M_.maximum_endo_lag == 0 % Purely forward model
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
else % General case
switch options_.stack_solve_algo
case 0
if options_.linear_approximation
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_linear(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
sim1_linear(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_, oo_);
else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
end
case 6
if options_.linear_approximation
error('Invalid value of stack_solve_algo option!')
end
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
case 7
if options_.linear_approximation
if isequal(options_.solve_algo, 10)
warning('It would be more efficient to set option solve_algo equal to 0!')
end
[oo_.endo_simul, oo_.deterministic_simulation] = ...
solve_stacked_linear_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
solve_stacked_linear_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_, oo_);
else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
solve_stacked_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
solve_stacked_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
end
otherwise
error('Invalid value of stack_solve_algo option!')
......@@ -135,7 +135,7 @@ if nargout>1
[chck, residuals, junk]= bytecode('dynamic','evaluate', oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1);
else
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '_dynamic']), y0, yT, ...
oo_.exo_simul,M_.params,oo_.steady_state, ...
oo_.exo_simul,M_.params,oo_.steady_state,oo_.exo_steady_state, ...
M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
M_.NNZDerivatives(1));
......
matlab/perfect-foresight-models/private/simulation_core.m
View file @ c6bac390
function [oo_, maxerror] = simulation_core(options_, M_, oo_)
%function [oo_, maxerror] = simulation_core(options_, M_, oo_)
% Copyright (C) 2015 Dynare Team
% Copyright (C) 2015-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -64,29 +64,29 @@ else
else
if M_.maximum_endo_lead == 0 % Purely backward model
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
sim1_purely_backward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
elseif M_.maximum_endo_lag == 0 % Purely forward model
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
sim1_purely_forward(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
else % General case
if options_.stack_solve_algo == 0
if options_.linear_approximation
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_linear(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
sim1_linear(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_, oo_);
else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
sim1(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
end
elseif options_.stack_solve_algo == 6
[oo_.endo_simul, oo_.deterministic_simulation] = ...
sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
sim1_lbj(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
elseif options_.stack_solve_algo == 7
if options_.linear_approximation
[oo_.endo_simul, oo_.deterministic_simulation] = ...
solve_stacked_linear_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
solve_stacked_linear_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_, oo_);
else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
solve_stacked_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
solve_stacked_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_, oo_);
end
end
end
......
matlab/perfect-foresight-models/sim1.m
View file @ c6bac390
function [endogenousvariables, info] = sim1(endogenousvariables, exogenousvariables, steadystate, M, options)
function [endogenousvariables, info] = sim1(endogenousvariables, exogenousvariables, steadystate, M, options, oo)
% Performs deterministic simulations with lead or lag on one period. Uses sparse matrices.
%
......@@ -79,7 +79,7 @@ end
model_dynamic = str2func([M.fname,'_dynamic']);
z = Y(find(lead_lag_incidence'));
[d1,jacobian] = model_dynamic(z, exogenousvariables, params, steadystate,maximum_lag+1);
[d1,jacobian] = model_dynamic(z, exogenousvariables, params, steadystate, oo.exo_steady_state, maximum_lag+1);
res = zeros(periods*ny,1);
......@@ -100,7 +100,7 @@ for iter = 1:options.simul.maxit
i_cols = i_cols_A+(maximum_lag-1)*ny;
m = 0;
for it = (maximum_lag+1):(maximum_lag+periods)
[d1,jacobian] = model_dynamic(Y(i_cols), exogenousvariables, params, steadystate,it);
[d1,jacobian] = model_dynamic(Y(i_cols), exogenousvariables, params, steadystate, oo.exo_steady_state, it);
if it == maximum_lag+periods && 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(:)];
......
matlab/perfect-foresight-models/sim1_lbj.m
View file @ c6bac390
function [endogenousvariables, info] = sim1_lbj(endogenousvariables, exogenousvariables, steadystate, M, options)
function [endogenousvariables, info] = sim1_lbj(endogenousvariables, exogenousvariables, steadystate, M, options, oo)
% Performs deterministic simulations with lead or lag on one period using the historical LBJ algorithm
%
......@@ -15,7 +15,7 @@ function [endogenousvariables, info] = sim1_lbj(endogenousvariables, exogenousva
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 1996-2015 Dynare Team
% Copyright (C) 1996-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -70,14 +70,14 @@ for iter = 1:options.simul.maxit
end
it_ = it_init;
z = [endogenousvariables(iyp,it_-1) ; endogenousvariables(:,it_) ; endogenousvariables(iyf,it_+1)];
[d1, jacobian] = feval(dynamicmodel, z, exogenousvariables, M.params, steadystate, it_);
[d1, jacobian] = feval(dynamicmodel, z, exogenousvariables, M.params, steadystate, oo.exo_steady_state, it_);
jacobian = [jacobian(:,iz), -d1];
ic = [1:ny];
icp = iyp;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1);
for it_ = it_init+(1:options.periods-1)
z = [endogenousvariables(iyp,it_-1) ; endogenousvariables(:,it_) ; endogenousvariables(iyf,it_+1)];
[d1, jacobian] = feval(dynamicmodel, z, exogenousvariables, M.params, steadystate, it_);
[d1, jacobian] = feval(dynamicmodel, z, exogenousvariables, M.params, steadystate, oo.exo_steady_state, it_);
jacobian = [jacobian(:,iz), -d1];
jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:);
ic = ic + ny;
......
matlab/perfect-foresight-models/sim1_linear.m
View file @ c6bac390
function [endogenousvariables, info] = sim1_linear(endogenousvariables, exogenousvariables, steadystate_y, steadystate_x, M, options)
function [endogenousvariables, info] = sim1_linear(endogenousvariables, exogenousvariables, steadystate_y, steadystate_x, M, options, oo)
% Solves a linear approximation of a perfect foresight model using sparse matrix.
%
......@@ -38,7 +38,7 @@ function [endogenousvariables, info] = sim1_linear(endogenousvariables, exogenou
% to center the variables around the deterministic steady state to solve the
% perfect foresight model.
% Copyright (C) 2015 Dynare Team
% Copyright (C) 2015-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -111,7 +111,7 @@ dynamicmodel = str2func([M.fname,'_dynamic']);
z = steadystate_y([ip; ic; in]);
% Evaluate the Jacobian of the dynamic model at the deterministic steady state.
[d1,jacobian] = dynamicmodel(z, transpose(steadystate_x), params, steadystate_y, 1);
[d1,jacobian] = dynamicmodel(z, transpose(steadystate_x), params, steadystate_y, oo.exo_steady_state, 1);
% Check that the dynamic model was evaluated at the steady state.
if max(abs(d1))>1e-12
......
matlab/perfect-foresight-models/sim1_purely_backward.m
View file @ c6bac390
function [endogenousvariables, info] = sim1_purely_backward(endogenousvariables, exogenousvariables, steadystate, M, options)
function [endogenousvariables, info] = sim1_purely_backward(endogenousvariables, exogenousvariables, steadystate, M, options, oo)
% Performs deterministic simulation of a purely backward model
% Copyright (C) 2012-2015 Dynare Team
% Copyright (C) 2012-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -44,7 +44,7 @@ for it = 2:options.periods+1
[tmp, check] = solve1(dynamicmodel, [yb1; yb], 1:M.endo_nbr, nyb+1:nyb+M.endo_nbr, ...
1, options.gstep, options.solve_tolf, options.solve_tolx, ...
options.simul.maxit, options.debug, exogenousvariables, ...
M.params, steadystate, it+M.maximum_lag-1);
M.params, steadystate, oo.exo_steady_state, it+M.maximum_lag-1);
if check
info.status = 0;
end
......
matlab/perfect-foresight-models/sim1_purely_forward.m
View file @ c6bac390
function [endogenousvariables, info] = sim1_purely_forward(endogenousvariables, exogenousvariables, steadystate, M, options)
function [endogenousvariables, info] = sim1_purely_forward(endogenousvariables, exogenousvariables, steadystate, M, options, oo)
% Performs deterministic simulation of a purely forward model
% Copyright (C) 2012-2015 Dynare Team
% Copyright (C) 2012-2017 Dynare Team
%
% This file is part of Dynare.
%
......@@ -36,7 +36,7 @@ for it = options.periods:-1:1
1, options.gstep, options.solve_tolf, ...
options.solve_tolx, options.simul.maxit, ...
options.debug, exogenousvariables, M.params, steadystate, ...
it+M.maximum_lag);
oo.exo_steady_state, it+M.maximum_lag);
if check
info.status = 0;
end
......
matlab/perfect-foresight-models/solve_stacked_linear_problem.m
View file @ c6bac390
function [endogenousvariables, info] = solve_stacked_linear_problem(endogenousvariables,