Call dynare_solve instead of trust region when simulating backward models.
Gives access to other solvers.time-shift
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@ -0,0 +1,40 @@
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function [r, J] = dynamic_backward_model_for_simulation(z, dynamicmodel, ylag, x, params, steady_state, it_)
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% Copyright (C) 2017 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% Get indices of the variables appearing at time t.
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% NOTE: It is assumed that all variables appear at time t in the model.
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idy = length(ylag)+(1:length(z));
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% Build y vector to be passed to the dynamic model.
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y = zeros(length(ylag)+length(z), 1);
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y(1:length(ylag)) = ylag;
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y(idy) = z;
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if nargout>1
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% Compute residuals and jacobian of the full dynamic model.
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[r, Jacobian] = feval(dynamicmodel, y, x, params, steady_state, it_);
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else
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% Compute residuals and return.
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r = feval(dynamicmodel, y, x, params, steady_state, it_);
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return
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end
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% If the jacobian is computed, remove the columns related to the innovations
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% and the variables appearing at time t-1.
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J = Jacobian(:,idy);
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@ -70,7 +70,6 @@ else
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end
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end
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% Get usefull vector of indices.
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% Get usefull vector of indices.
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ny0 = nnz(DynareModel.lead_lag_incidence(2,:));
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ny1 = nnz(DynareModel.lead_lag_incidence(1,:));
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ny1 = nnz(DynareModel.lead_lag_incidence(1,:));
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iy1 = find(DynareModel.lead_lag_incidence(1,:)>0);
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iy1 = find(DynareModel.lead_lag_incidence(1,:)>0);
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idx = 1:DynareModel.endo_nbr;
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idx = 1:DynareModel.endo_nbr;
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@ -79,6 +78,7 @@ hdx = 1:ny1;
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% Get the name of the dynamic model routine.
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% Get the name of the dynamic model routine.
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model_dynamic = str2func([DynareModel.fname,'_dynamic']);
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model_dynamic = str2func([DynareModel.fname,'_dynamic']);
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model_dynamic_s = str2func('dynamic_backward_model_for_simulation');
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% initialization of vector y.
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% initialization of vector y.
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y = NaN(length(idx)+ny1,1);
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y = NaN(length(idx)+ny1,1);
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@ -99,14 +99,9 @@ Y = DynareOutput.endo_simul;
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% Simulations (call a Newton-like algorithm for each period).
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% Simulations (call a Newton-like algorithm for each period).
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for it = 2:sample_size+1
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for it = 2:sample_size+1
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y(jdx) = Y(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables.
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ylag = Y(iy1,it-1); % Set lagged variables.
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y(hdx) = y(jdx(iy1)); % Set lagged variables.
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y = Y(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables.
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z = trust_region(model_dynamic, y, idx, jdx, 1, DynareOptions.gstep, ...
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Y(:,it) = dynare_solve(model_dynamic_s, y, DynareOptions, model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
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DynareOptions.solve_tolf,DynareOptions.solve_tolx, ...
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DynareOptions.simul.maxit,DynareOptions.debug, ...
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DynareOutput.exo_simul, DynareModel.params, ...
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DynareOutput.steady_state, it);
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Y(:,it) = z(jdx);
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end
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end
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DynareOutput.endo_simul = Y;
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DynareOutput.endo_simul = Y;
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