From c6bac390d45bb8f3efa94123c977766c940700b0 Mon Sep 17 00:00:00 2001
From: Houtan Bastani <houtan@dynare.org>
Date: Fri, 21 Apr 2017 18:15:32 +0200
Subject: [PATCH] add exogenous steady state to dynamic function call. #825
---
matlab/discretionary_policy_1.m | 5 +++--
matlab/dyn_risky_steadystate_solver.m | 17 +++++++++++------
matlab/evaluate_max_dynamic_residual.m | 6 +++---
matlab/evaluate_steady_state.m | 7 ++++---
matlab/getH.m | 10 +++++-----
matlab/identification_analysis.m | 4 ++--
matlab/model_diagnostics.m | 6 +++---
matlab/partial_information/dr1_PI.m | 5 +++--
.../det_cond_forecast.m | 8 +++++---
.../perfect_foresight_mcp_problem.m | 11 ++++++-----
.../perfect_foresight_problem.m | 11 ++++++-----
.../perfect_foresight_solver_core.m | 18 +++++++++---------
.../private/simulation_core.m | 16 ++++++++--------
matlab/perfect-foresight-models/sim1.m | 6 +++---
matlab/perfect-foresight-models/sim1_lbj.m | 8 ++++----
matlab/perfect-foresight-models/sim1_linear.m | 6 +++---
.../sim1_purely_backward.m | 6 +++---
.../sim1_purely_forward.m | 6 +++---
.../solve_stacked_linear_problem.m | 4 ++--
.../solve_stacked_problem.m | 6 +++---
matlab/simul_backward_linear_model.m | 4 ++--
matlab/simul_backward_nonlinear_model.m | 4 ++--
matlab/stochastic_solvers.m | 7 ++++---
matlab/thet2tau.m | 4 ++--
tests/lmmcp/sw_newton.mod | 4 ++--
25 files changed, 101 insertions(+), 88 deletions(-)
diff --git a/matlab/discretionary_policy_1.m b/matlab/discretionary_policy_1.m
index 931d3b596f..7a987f7b8d 100644
--- a/matlab/discretionary_policy_1.m
+++ b/matlab/discretionary_policy_1.m
@@ -1,6 +1,6 @@
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'])
diff --git a/matlab/dyn_risky_steadystate_solver.m b/matlab/dyn_risky_steadystate_solver.m
index a7e09b1bd3..7864b86f97 100644
--- a/matlab/dyn_risky_steadystate_solver.m
+++ b/matlab/dyn_risky_steadystate_solver.m
@@ -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));
diff --git a/matlab/evaluate_max_dynamic_residual.m b/matlab/evaluate_max_dynamic_residual.m
index 41aa4ec249..aea6167bba 100644
--- a/matlab/evaluate_max_dynamic_residual.m
+++ b/matlab/evaluate_max_dynamic_residual.m
@@ -1,6 +1,6 @@
-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));
diff --git a/matlab/evaluate_steady_state.m b/matlab/evaluate_steady_state.m
index 053d62255e..c2ef05920d 100644
--- a/matlab/evaluate_steady_state.m
+++ b/matlab/evaluate_steady_state.m
@@ -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
diff --git a/matlab/getH.m b/matlab/getH.m
index 0db11d8e33..d114f152ec 100644
--- a/matlab/getH.m
+++ b/matlab/getH.m
@@ -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);
diff --git a/matlab/identification_analysis.m b/matlab/identification_analysis.m
index 88b60ca4d4..6c2c085433 100644
--- a/matlab/identification_analysis.m
+++ b/matlab/identification_analysis.m
@@ -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);
diff --git a/matlab/model_diagnostics.m b/matlab/model_diagnostics.m
index 32a01725d5..b96ec7d48d 100644
--- a/matlab/model_diagnostics.m
+++ b/matlab/model_diagnostics.m
@@ -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
diff --git a/matlab/partial_information/dr1_PI.m b/matlab/partial_information/dr1_PI.m
index 0475d4befc..76c9405921 100644
--- a/matlab/partial_information/dr1_PI.m
+++ b/matlab/partial_information/dr1_PI.m
@@ -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
diff --git a/matlab/perfect-foresight-models/det_cond_forecast.m b/matlab/perfect-foresight-models/det_cond_forecast.m
index e261de89c3..b21d262665 100644
--- a/matlab/perfect-foresight-models/det_cond_forecast.m
+++ b/matlab/perfect-foresight-models/det_cond_forecast.m
@@ -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;
diff --git a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
index d4b4193086..fbf0fa6494 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_mcp_problem.m
@@ -1,10 +1,10 @@
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));
diff --git a/matlab/perfect-foresight-models/perfect_foresight_problem.m b/matlab/perfect-foresight-models/perfect_foresight_problem.m
index 9561190945..311eac3616 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_problem.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_problem.m
@@ -1,10 +1,10 @@
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];
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
index da267b7dae..d3bd13a1de 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
@@ -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));
diff --git a/matlab/perfect-foresight-models/private/simulation_core.m b/matlab/perfect-foresight-models/private/simulation_core.m
index b52ecad172..0afdba517e 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 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
diff --git a/matlab/perfect-foresight-models/sim1.m b/matlab/perfect-foresight-models/sim1.m
index 1a173b2115..e1a6d42812 100644
--- a/matlab/perfect-foresight-models/sim1.m
+++ b/matlab/perfect-foresight-models/sim1.m
@@ -1,4 +1,4 @@
-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(:)];
diff --git a/matlab/perfect-foresight-models/sim1_lbj.m b/matlab/perfect-foresight-models/sim1_lbj.m
index d4d8c24f2b..d5d3bd931d 100644
--- a/matlab/perfect-foresight-models/sim1_lbj.m
+++ b/matlab/perfect-foresight-models/sim1_lbj.m
@@ -1,4 +1,4 @@
-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;
diff --git a/matlab/perfect-foresight-models/sim1_linear.m b/matlab/perfect-foresight-models/sim1_linear.m
index 6820c92bc2..dc057bfcf9 100644
--- a/matlab/perfect-foresight-models/sim1_linear.m
+++ b/matlab/perfect-foresight-models/sim1_linear.m
@@ -1,4 +1,4 @@
-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
diff --git a/matlab/perfect-foresight-models/sim1_purely_backward.m b/matlab/perfect-foresight-models/sim1_purely_backward.m
index abc871a685..86121b54de 100644
--- a/matlab/perfect-foresight-models/sim1_purely_backward.m
+++ b/matlab/perfect-foresight-models/sim1_purely_backward.m
@@ -1,8 +1,8 @@
-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
diff --git a/matlab/perfect-foresight-models/sim1_purely_forward.m b/matlab/perfect-foresight-models/sim1_purely_forward.m
index ca2b2b5b27..81e7cb53ee 100644
--- a/matlab/perfect-foresight-models/sim1_purely_forward.m
+++ b/matlab/perfect-foresight-models/sim1_purely_forward.m
@@ -1,7 +1,7 @@
-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
diff --git a/matlab/perfect-foresight-models/solve_stacked_linear_problem.m b/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
index 52a2efe48c..7f635e9444 100644
--- a/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_linear_problem.m
@@ -1,6 +1,6 @@
function [endogenousvariables, info] = solve_stacked_linear_problem(endogenousvariables, exogenousvariables, steadystate_y, steadystate_x, M, options)
-% Copyright (C) 2015 Dynare Team
+% Copyright (C) 2015-2017 Dynare Team
%
% This file is part of Dynare.
%
@@ -25,7 +25,7 @@ ic = find(M.lead_lag_incidence(2,:)');
in = find(M.lead_lag_incidence(3,:)');
% Evaluate the Jacobian of the dynamic model at the deterministic steady state.
-[d1,jacobian] = dynamicmodel(steadystate_y([ip; ic; in]), transpose(steadystate_x), M.params, steadystate_y, 1);
+[d1,jacobian] = dynamicmodel(steadystate_y([ip; ic; in]), transpose(steadystate_x), M.params, steadystate_y, steadystate_x, 1);
% Check that the dynamic model was evaluated at the steady state.
if max(abs(d1))>1e-12
diff --git a/matlab/perfect-foresight-models/solve_stacked_problem.m b/matlab/perfect-foresight-models/solve_stacked_problem.m
index bff500b0d2..7545434ff9 100644
--- a/matlab/perfect-foresight-models/solve_stacked_problem.m
+++ b/matlab/perfect-foresight-models/solve_stacked_problem.m
@@ -1,4 +1,4 @@
-function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options)
+function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options, oo)
% [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options);
% Solves the perfect foresight model using dynare_solve
%
@@ -48,14 +48,14 @@ if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity
end
[y, check] = dynare_solve(@perfect_foresight_mcp_problem,z(:),options, ...
dynamicmodel, y0, yT, ...
- exogenousvariables, M.params, steadystate, ...
+ exogenousvariables, M.params, steadystate, 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),eq_index);
else
[y, check] = dynare_solve(@perfect_foresight_problem,z(:),options, ...
dynamicmodel, y0, yT, ...
- exogenousvariables, M.params, steadystate, ...
+ exogenousvariables, M.params, steadystate, 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));
diff --git a/matlab/simul_backward_linear_model.m b/matlab/simul_backward_linear_model.m
index c4ed4fe264..5a3249ada0 100644
--- a/matlab/simul_backward_linear_model.m
+++ b/matlab/simul_backward_linear_model.m
@@ -34,7 +34,7 @@ function DynareOutput = simul_backward_linear_model(initial_conditions, sample_s
%! @end deftypefn
%@eod:
-% Copyright (C) 2012-2016 Dynare Team
+% Copyright (C) 2012-2017 Dynare Team
%
% This file is part of Dynare.
%
@@ -79,7 +79,7 @@ Y = DynareOutput.endo_simul;
[cst,jacob] = model_dynamic(zeros(DynareModel.endo_nbr+ny1,1), ...
zeros(2,size(DynareOutput.exo_simul, 2)), ...
DynareModel.params, ...
- DynareOutput.steadystate,2);
+ DynareOutput.steadystate, DynareOutput.exo_steady_state, 2);
A0inv = inv(jacob(:,jdx));
A1 = jacob(:,nonzeros(DynareModel.lead_lag_incidence(1,:)));
B = jacob(:,end-number_of_shocks+1:end);
diff --git a/matlab/simul_backward_nonlinear_model.m b/matlab/simul_backward_nonlinear_model.m
index 99dd3932be..cb38a2af1c 100644
--- a/matlab/simul_backward_nonlinear_model.m
+++ b/matlab/simul_backward_nonlinear_model.m
@@ -34,7 +34,7 @@ function DynareOutput = simul_backward_nonlinear_model(initial_conditions, sampl
%! @end deftypefn
%@eod:
-% Copyright (C) 2012-2016 Dynare Team
+% Copyright (C) 2012-2017 Dynare Team
%
% This file is part of Dynare.
%
@@ -118,7 +118,7 @@ for it = 2:sample_size+1
DynareOptions.solve_tolf,DynareOptions.solve_tolx, ...
DynareOptions.simul.maxit,DynareOptions.debug, ...
DynareOutput.exo_simul, DynareModel.params, ...
- DynareOutput.steady_state, it);
+ DynareOutput.steady_state, DynareOutput.exo_steady_state, it);
Y(:,it) = z(jdx);
end
diff --git a/matlab/stochastic_solvers.m b/matlab/stochastic_solvers.m
index 2772001431..3c3126d1ed 100644
--- a/matlab/stochastic_solvers.m
+++ b/matlab/stochastic_solvers.m
@@ -29,7 +29,7 @@ function [dr,info] = stochastic_solvers(dr,task,M_,options_,oo_)
% none.
%
-% Copyright (C) 1996-2016 Dynare Team
+% Copyright (C) 1996-2017 Dynare Team
%
% This file is part of Dynare.
%
@@ -110,7 +110,7 @@ if options_.order == 1
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)
@@ -120,7 +120,8 @@ elseif options_.order == 2
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
diff --git a/matlab/thet2tau.m b/matlab/thet2tau.m
index 929193ca44..c34fd78e9d 100644
--- a/matlab/thet2tau.m
+++ b/matlab/thet2tau.m
@@ -1,7 +1,7 @@
function tau = thet2tau(params, estim_params_, M_, oo_, indx, indexo, flagmoments,mf,nlags,useautocorr,iv)
%
-% Copyright (C) 2011-2012 Dynare Team
+% Copyright (C) 2011-2017 Dynare Team
%
% This file is part of Dynare.
%
@@ -51,7 +51,7 @@ elseif flagmoments==-1
[I,J]=find(M_.lead_lag_incidence');
yy0=oo_.dr.ys(I);
[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);
tau=[oo_.dr.ys(oo_.dr.order_var); g1(:)];
else
diff --git a/tests/lmmcp/sw_newton.mod b/tests/lmmcp/sw_newton.mod
index 074fd82586..7fb5fc079e 100644
--- a/tests/lmmcp/sw_newton.mod
+++ b/tests/lmmcp/sw_newton.mod
@@ -69,11 +69,11 @@ end;
simul(periods=1000);
-newton_solution_is_wrong = abs(evaluate_max_dynamic_residual(str2func('sw_newton_dynamic'), oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1000, size(oo_.endo_simul, 1), 1, M_.lead_lag_incidence))>options_.dynatol.f;
+newton_solution_is_wrong = abs(evaluate_max_dynamic_residual(str2func('sw_newton_dynamic'), oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, oo_.exo_steady_state, 1000, size(oo_.endo_simul, 1), 1, M_.lead_lag_incidence))>options_.dynatol.f;
lmmcp = load('sw_lmmcp_results');
-lmmcp_solution_is_wrong = abs(evaluate_max_dynamic_residual(str2func('sw_newton_dynamic'), lmmcp.oo_.endo_simul, lmmcp.oo_.exo_simul, M_.params, oo_.steady_state, 1000, size(oo_.endo_simul, 1), 1, M_.lead_lag_incidence))>options_.dynatol.f;
+lmmcp_solution_is_wrong = abs(evaluate_max_dynamic_residual(str2func('sw_newton_dynamic'), lmmcp.oo_.endo_simul, lmmcp.oo_.exo_simul, M_.params, oo_.steady_state, oo_.exo_steady_state, 1000, size(oo_.endo_simul, 1), 1, M_.lead_lag_incidence))>options_.dynatol.f;
solutions_are_different = max(max(abs(lmmcp.oo_.endo_simul-oo_.endo_simul)))>options_.dynatol.x;
--
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