stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Call dynare_solve instead of trust region when simulating backward models. 

Gives access to other solvers.
time-shift
Stéphane Adjemian (Charybdis) 2017-05-06 16:32:48 +02:00
parent 36fb15f34c
commit d471bede09
2 changed files with 44 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

40
matlab/dynamic_backward_model_for_simulation.m Normal file
View File

@ -0,0 +1,40 @@
function [r, J] = dynamic_backward_model_for_simulation(z, dynamicmodel, ylag, x, params, steady_state, it_)
% Copyright (C) 2017 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Get indices of the variables appearing at time t.
% NOTE: It is assumed that all variables appear at time t in the model.
idy = length(ylag)+(1:length(z));
% Build y vector to be passed to the dynamic model.
y = zeros(length(ylag)+length(z), 1);
y(1:length(ylag)) = ylag;
y(idy) = z;
if nargout>1
% Compute residuals and jacobian of the full dynamic model.
[r, Jacobian] = feval(dynamicmodel, y, x, params, steady_state, it_);
else
% Compute residuals and return.
r = feval(dynamicmodel, y, x, params, steady_state, it_);
return
end
% If the jacobian is computed, remove the columns related to the innovations
% and the variables appearing at time t-1.
J = Jacobian(:,idy);

13
matlab/simul_backward_nonlinear_model.m
View File

@ -70,7 +70,6 @@ else
end
% Get usefull vector of indices.
ny0 = nnz(DynareModel.lead_lag_incidence(2,:));
ny1 = nnz(DynareModel.lead_lag_incidence(1,:));
iy1 = find(DynareModel.lead_lag_incidence(1,:)>0);
idx = 1:DynareModel.endo_nbr;
@ -79,6 +78,7 @@ hdx = 1:ny1;
% Get the name of the dynamic model routine.
model_dynamic = str2func([DynareModel.fname,'_dynamic']);
model_dynamic_s = str2func('dynamic_backward_model_for_simulation');
% initialization of vector y.
y = NaN(length(idx)+ny1,1);
@ -99,14 +99,9 @@ Y = DynareOutput.endo_simul;
% Simulations (call a Newton-like algorithm for each period).
for it = 2:sample_size+1
y(jdx) = Y(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables.
y(hdx) = y(jdx(iy1)); % Set lagged variables.
z = trust_region(model_dynamic, y, idx, jdx, 1, DynareOptions.gstep, ...
DynareOptions.solve_tolf,DynareOptions.solve_tolx, ...
DynareOptions.simul.maxit,DynareOptions.debug, ...
DynareOutput.exo_simul, DynareModel.params, ...
DynareOutput.steady_state, it);
Y(:,it) = z(jdx);
ylag = Y(iy1,it-1); % Set lagged variables.
y = Y(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables.
Y(:,it) = dynare_solve(model_dynamic_s, y, DynareOptions, model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
end
DynareOutput.endo_simul = Y;