Remove calls to dbstack in dynare_solve.
Adding three input parameters for maxit, tolf and tolx. Closes #1841.fix-nonlinear-solvers
parent
01203f7e39
commit
f1aff2b370
GPG Key ID:
295C1FE89E17EB3C
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@ -1,13 +1,13 @@
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function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,oo_,options_,err_index,filtered_errs_init,my_obs_list,obs,init_val)
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% function [filtered_errs, resids, Emat, stateval] = IVF_core(M_,oo_,options_,err_index,filtered_errs_init,my_obs_list,obs,init_val)
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% Computes thre
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% Computes thre
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%
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% Outputs:
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% - filtered_errs [T by N_obs] filtered shocks
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% - resids [T by N_obs] residuals
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% - Emat [N by N_obs by T] response matrix of endogenous variables to shocks at each point in time
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% - stateval [T by N] vector of endogenous variables
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% - error_code [4 by 1] error code
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% - error_code [4 by 1] error code
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%
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% Inputs
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% - M_ [structure] Matlab's structure describing the model (M_).
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@ -16,7 +16,7 @@ function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,oo_,o
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% - err_index [double] index of shocks with strictly positive variance in M_.exo_names
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% - filtered_errs_init [T by N_obs] initial values for the shocks
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% - my_obs_list [cell] names of observables
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% - obs [T by N_obs] observed data
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% - obs [T by N_obs] observed data
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% - init_val [N by 1] initial value of endogenous variables
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% Original authors: Pablo Cuba-Borda, Luca Guerrieri, Matteo Iacoviello, and Molin Zhong
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@ -28,7 +28,7 @@ function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,oo_,o
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% However the authors would appreciate acknowledgement of the source by
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% citation of any of the following papers:
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%
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% Pablo Cuba-Borda, Luca Guerrieri, Matteo Iacoviello, and Molin Zhong (2019): "Likelihood evaluation of models
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% Pablo Cuba-Borda, Luca Guerrieri, Matteo Iacoviello, and Molin Zhong (2019): "Likelihood evaluation of models
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% with occasionally binding constraints", Journal of Applied Econometrics,
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% 34(7), 1073-1085
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@ -70,14 +70,14 @@ if options_.occbin.likelihood.waitbar
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hh = dyn_waitbar(0,'IVF_core: Filtering the shocks');
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set(hh,'Name','IVF_core: Filtering the shocks.');
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end
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for this_period=1:sample_length
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if options_.occbin.likelihood.waitbar
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dyn_waitbar(this_period/sample_length, hh, sprintf('Period %u of %u', this_period,sample_length));
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end
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current_obs = obs(this_period,:);
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init_val_old = init_val;
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inan = ~isnan(current_obs);
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current_obs = current_obs(inan);
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obs_list = my_obs_list(inan);
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@ -85,23 +85,20 @@ for this_period=1:sample_length
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opts_simul.exo_pos=err_index(inan); %err_index is predefined mapping from observables to shocks
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opts_simul.endo_init = init_val_old;
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opts_simul.SHOCKS = filtered_errs_init(this_period,inan);
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% [ err_vals_out, exitflag ] = csolve(@(err_vals) occbin.match_function(...
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% err_vals, obs_list,current_obs, opts_simul, M_,oo_,options_),...
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% err0',@(err_vals) occbin.match_function(...
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% err_vals, obs_list,current_obs, opts_simul, M_,oo_,options_),options_.solve_tolf,options_.occbin.solver.maxit);
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[err_vals_out, exitflag] = dynare_solve(@occbin.match_function, filtered_errs_init(this_period,inan)', options_, obs_list,current_obs, opts_simul, M_,oo_,options_);
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% TODO We should probably have a specific field for maxit (new option?)
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[err_vals_out, exitflag] = dynare_solve(@occbin.match_function, filtered_errs_init(this_period,inan)', options_.steady.maxit, options_.solve_tolf, options_.solve_tolx, options_, obs_list, current_obs, opts_simul, M_, oo_, options_);
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if exitflag
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filtered_errs=NaN;
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error_code(1) = 304;
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error_code(4) = 1000;
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if options_.occbin.likelihood.waitbar; dyn_waitbar_close(hh); end
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return
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return
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end
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filtered_errs(this_period,inan)=err_vals_out';
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opts_simul.SHOCKS = err_vals_out;
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[ resids(this_period,inan), ~, stateval(this_period,:), Emat(:,inan,this_period), M_] = occbin.match_function(...
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err_vals_out,obs_list,current_obs,opts_simul, M_,oo_,options_);
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init_val = stateval(this_period,:); %update
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@ -123,7 +120,7 @@ for this_period=1:sample_length
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end
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end
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if options_.occbin.likelihood.waitbar
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dyn_waitbar_close(hh);
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dyn_waitbar_close(hh);
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end
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end
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@ -108,7 +108,9 @@ for t = 1:nobs(constraints)
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% values) and the free exogenous variables (initialized with 0).
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z = [Y(freeendogenousvariables_id,ity-1); zeros(nxfree, 1)];
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% Solves for z.
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[z, failed, ~, ~, errorcode] = dynare_solve(model_dtransf, z, DynareOptions, model_dynamic, ylag, ycur, X, DynareModel.params, DynareOutput.steady_state, itx, ModelInversion);
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[z, failed, ~, ~, errorcode] = dynare_solve(model_dtransf, z, ...
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DynareOptions.simul.maxit, DynareOptions.dynatol.f, DynareOptions.dynatol.x, ...
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DynareOptions, model_dynamic, ylag, ycur, X, DynareModel.params, DynareOutput.steady_state, itx, ModelInversion);
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if failed
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error('Nonlinear solver failed with errorcode=%i', errorcode)
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end
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@ -56,13 +56,16 @@ for it = initialconditions.nobs+(1:samplesize)
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y = y_; % A good guess for the initial conditions is the previous values for the endogenous variables.
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try
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if ismember(DynareOptions.solve_algo, [12,14])
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[DynareOutput.endo_simul(:,it), errorflag, ~, ~, errorcode] = ...
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dynare_solve(model_dynamic_s, y, DynareOptions, ...
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DynareModel.isloggedlhs, DynareModel.isauxdiffloggedrhs, DynareModel.endo_names, DynareModel.lhs, ...
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model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
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[DynareOutput.endo_simul(:,it), errorflag, ~, ~, errorcode] = dynare_solve(model_dynamic_s, y, ...
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DynareOptions.simul.maxit, DynareOptions.dynatol.f, DynareOptions.dynatol.x, ...
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DynareOptions, ...
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DynareModel.isloggedlhs, DynareModel.isauxdiffloggedrhs, DynareModel.endo_names, DynareModel.lhs, ...
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model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
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else
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[DynareOutput.endo_simul(:,it), errorflag, ~, ~, errorcode] = ...
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dynare_solve(model_dynamic_s, y, DynareOptions, ...
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dynare_solve(model_dynamic_s, y, ...
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DynareOptions.simul.maxit, DynareOptions.dynatol.f, DynareOptions.dynatol.x, ...
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DynareOptions, ...
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model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
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end
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if errorflag
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@ -1,6 +1,5 @@
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function [steady_state,params,check] = dyn_ramsey_static(ys_init,M,options_,oo)
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function [steady_state, params, check] = dyn_ramsey_static(ys_init, M, options_, oo)
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% function [steady_state,params,check] = dyn_ramsey_static_(ys_init,M,options_,oo)
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% Computes the steady state for optimal policy
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%
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% INPUTS
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@ -18,7 +17,7 @@ function [steady_state,params,check] = dyn_ramsey_static(ys_init,M,options_,oo)
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2020 Dynare Team
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% Copyright © 2003-2022 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -66,9 +65,8 @@ elseif options_.steadystate_flag
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%initial value for instrument
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opt = options_;
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opt.jacobian_flag = false;
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[inst_val,info1] = dynare_solve(nl_func,ys_init(k_inst), ...
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opt);
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if info1~=0
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[inst_val, errorflag] = dynare_solve(nl_func, ys_init(k_inst), options_.ramsey.maxit, options_.solve_tolf, options_.solve_tolx, opt);
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if errorflag
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check=81;
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end
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end
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@ -81,8 +79,8 @@ else
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xx = oo.steady_state(1:n_var);
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opt = options_;
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opt.jacobian_flag = false;
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[xx,info1] = dynare_solve(nl_func,xx,opt);
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if info1~=0
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[xx, errorflag] = dynare_solve(nl_func, xx, options_.ramsey.maxit, options_.solve_tolf, options_.solve_tolx, opt);
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if errorflag
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check=81;
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end
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[~,~,steady_state] = nl_func(xx);
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@ -1,4 +1,4 @@
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function [x, errorflag, fvec, fjac, errorcode] = dynare_solve(f, x, options, varargin)
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function [x, errorflag, fvec, fjac, errorcode] = dynare_solve(f, x, maxit, tolf, tolx, options, varargin)
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% Solves a nonlinear system of equations, f(x) = 0 with n unknowns
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% and n equations.
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@ -41,32 +41,6 @@ function [x, errorflag, fvec, fjac, errorcode] = dynare_solve(f, x, options, var
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jacobian_flag = options.jacobian_flag; % true iff Jacobian is returned by f routine (as a second output argument).
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% Set tolerance parameter depending the the caller function.
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stack = dbstack;
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if isoctave
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[~, name, ext]=fileparts(stack(2).file);
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caller_file_name=[name,ext];
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else
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if size(stack,1)>1
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caller_file_name=stack(2).file;
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else
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caller_file_name=stack(1).file;
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end
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end
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if strcmp(caller_file_name, 'solve_stacked_problem.m') || strcmp(caller_file_name, 'sim1_purely_backward.m')
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tolf = options.dynatol.f;
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tolx = options.dynatol.x;
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else
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tolf = options.solve_tolf;
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tolx = options.solve_tolx;
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end
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if strcmp(caller_file_name,'dyn_ramsey_static.m')
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maxit = options.ramsey.maxit;
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else
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maxit = options.steady.maxit;
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end
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errorflag = false; % Let's be optimistic!
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nn = size(x,1);
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@ -96,6 +70,7 @@ else
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end
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% checking initial values
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% TODO We should have an option to deactivate the randomization.
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if jacobian_flag
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[fvec, fjac] = feval(f, x, arguments{:});
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wrong_initial_guess_flag = false;
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@ -1,5 +1,6 @@
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function [x,info] = dynare_solve_block_or_bytecode(y, exo, params, options, M)
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% Copyright (C) 2010-2022 Dynare Team
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% Copyright © 2010-2022 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -25,12 +26,11 @@ if options.block && ~options.bytecode
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if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
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M.block_structure_stat.block(b).Simulation_Type ~= 2
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if options.solve_algo <= 4 || options.solve_algo >= 9
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[y, check] = dynare_solve('block_mfs_steadystate', ...
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ss(M.block_structure_stat.block(b).variable), ...
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options, b, ss, exo, params, T, M);
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if check ~= 0
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% error(['STEADY: convergence
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% problems in block ' int2str(b)])
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[y, errorflag] = dynare_solve('block_mfs_steadystate', ...
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ss(M.block_structure_stat.block(b).variable), ...
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options.simul.maxit, options.solve_tolf, options.solve_tolx, ...
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options, b, ss, exo, params, T, M);
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if errorflag
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info = 1;
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return
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end
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@ -65,13 +65,11 @@ elseif options.bytecode
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for b = 1:length(M.block_structure_stat.block)
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if M.block_structure_stat.block(b).Simulation_Type ~= 1 && ...
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M.block_structure_stat.block(b).Simulation_Type ~= 2
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[y, check] = dynare_solve('block_bytecode_mfs_steadystate', ...
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x(M.block_structure_stat ...
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.block(b).variable), ...
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options, b, x, exo, params, T, M);
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if check
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% error(['STEADY: convergence problems in block '
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% int2str(b)])
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[y, errorflag] = dynare_solve('block_bytecode_mfs_steadystate', ...
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x(M.block_structure_stat.block(b).variable), ...
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options.simul.maxit, options.solve_tolf, options.solve_tolx, ...
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options, b, x, exo, params, T, M);
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if errorflag
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info = 1;
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return
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end
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@ -89,10 +87,10 @@ elseif options.bytecode
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end
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end
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else
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[x, check] = dynare_solve('bytecode_steadystate', y, ...
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options, exo, params);
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if check
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% error('STEADY: convergence problems')
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[x, errorflag] = dynare_solve('bytecode_steadystate', y, ...
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options.simul.maxit, options.solve_tolf, options.solve_tolx, ...
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options, exo, params);
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if errorflag
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info = 1;
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return
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end
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@ -143,9 +143,5 @@ Options.steady.maxit = 100;
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y = repmat(steady_state,block_nbr,1);
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Options.solve_algo = Options.ep.solve_algo;
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Options.steady.maxit = Options.ep.maxit;
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[y, errorflag, ~, ~, errorcode] = dynare_solve(@ep_problem_2,y,Options,exo_simul,pfm);
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if info
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flag = 1;
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err = info;
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end
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[y, errorflag, ~, ~, errorcode] = dynare_solve(@ep_problem_2, y, Options.simul.maxit, Options.dynatol.f, Options.dynatol.x, Options, exo_simul, pfm);
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endo_simul(:,2) = y(1:ny);
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@ -261,18 +261,14 @@ elseif steadystate_flag
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elseif ~options.bytecode && ~options.block
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if ~options.linear
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% non linear model
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static_model = str2func([M.fname '.static']);
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[ys, check] = dynare_solve(@static_problem, ...
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ys_init, ...
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options, exo_ss, params, ...
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M.endo_nbr, ...
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static_model);
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static_model = str2func(sprintf('%s.static', M.fname));
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[ys, check] = dynare_solve(@static_problem, ys_init, ...
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options.steady.maxit, options.solve_tolf, options.solve_tolx, ...
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options, exo_ss, params, M.endo_nbr, static_model);
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if check && options.debug
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[ys, check, fvec, fjac, errorcode] = dynare_solve(@static_problem, ...
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ys_init, ...
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options, exo_ss, params, ...
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M.endo_nbr, ...
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static_model);
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[ys, check, fvec, fjac, errorcode] = dynare_solve(@static_problem, ys_init, ...
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options.steady.maxit, options.solve_tolf, options.solve_tolx, ...
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options, exo_ss, params, M.endo_nbr, static_model);
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dprintf('Nonlinear solver routine returned errorcode=%i.', errorcode)
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skipline()
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[infrow,infcol]=find(isinf(fjac) | isnan(fjac));
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@ -127,5 +127,5 @@ elseif ~isempty(options.steadystate_partial)
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for i = 1:nov
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indv(i) = strmatch(ssvar(i), M.endo_names, 'exact');
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end
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ys = dynare_solve('restricted_steadystate', ys(indv), options, exo_ss,indv);
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ys = dynare_solve('restricted_steadystate', ys(indv), options.steady.maxit, options.dynatol.f, options.dynatol.x, options, exo_ss, indv);
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end
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@ -91,9 +91,8 @@ else
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oo_.exo_det_steady_state], M_.params))) > options_.solve_tolf
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opt = options_;
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opt.jacobian_flag = false;
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[dr.ys,check1] = dynare_solve(fh,dr.ys,opt,...
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[oo_.exo_steady_state; ...
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oo_.exo_det_steady_state], M_.params);
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[dr.ys, check1] = dynare_solve(fh,dr.ys, options.steady_.maxit, options_.solve_tolf, options_.solve_tolx, ...
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opt, [oo_.exo_steady_state; oo_.exo_det_steady_state], M_.params);
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end
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else
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% linear models
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@ -89,7 +89,9 @@ if options_.ramsey_policy && ~options_.ACES_solver
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% options_.solve_algo = 1;
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opt = options_;
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opt.jacobian_flag = false;
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oo_.steady_state = dynare_solve('ramsey_static',oo_.steady_state,opt,M_,options_,oo_,it_);
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oo_.steady_state = dynare_solve('ramsey_static', oo_.steady_state, ...
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options_.ramsey.maxit, options_.solve_tolf, options_.solve_tolx, ...
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opt, M_, options_, oo_, it_);
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options_.solve_algo = old_solve_algo;
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[~,~,multbar] = ramsey_static(oo_.steady_state,M_,options_,oo_,it_);
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[jacobia_,M_] = ramsey_dynamic(oo_.steady_state,multbar,M_,options_,oo_,it_);
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@ -39,11 +39,14 @@ for it = M.maximum_lag + (1:options.periods)
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y = endogenousvariables(:,it-1); % Values at previous period, also used as guess value for current period
|
||||
ylag = y(iyb);
|
||||
if ismember(options.solve_algo, [12,14])
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, y, options, M.isloggedlhs, M.isauxdiffloggedrhs, M.endo_names, M.lhs, ...
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, y, ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
options, M.isloggedlhs, M.isauxdiffloggedrhs, M.endo_names, M.lhs, ...
|
||||
dynamicmodel, ylag, exogenousvariables, M.params, steadystate, it);
|
||||
else
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, y, options, ...
|
||||
dynamicmodel, ylag, exogenousvariables, M.params, steadystate, it);
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, y, ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
options, dynamicmodel, ylag, exogenousvariables, M.params, steadystate, it);
|
||||
end
|
||||
if check
|
||||
info.status = false;
|
||||
|
|
|
|||
|
|
@ -33,10 +33,14 @@ info.status = true;
|
|||
for it = options.periods:-1:1
|
||||
yf = endogenousvariables(:,it+1); % Values at next period, also used as guess value for current period
|
||||
if ismember(options.solve_algo, [12,14])
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, yf, options, M.isloggedlhs, M.isauxdiffloggedrhs, M.endo_names, M.lhs, ...
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, yf, ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
options, M.isloggedlhs, M.isauxdiffloggedrhs, M.endo_names, M.lhs, ...
|
||||
dynamicmodel, yf(iyf), exogenousvariables, M.params, steadystate, it);
|
||||
else
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, yf, options, ...
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, yf, ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
options, ...
|
||||
dynamicmodel, yf(iyf), exogenousvariables, M.params, steadystate, it);
|
||||
end
|
||||
if check
|
||||
|
|
|
|||
|
|
@ -36,11 +36,14 @@ y = endogenousvariables(:,1);
|
|||
|
||||
for it = 1:options.periods
|
||||
if ismember(options.solve_algo, [12,14])
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, y, options, M.isloggedlhs, M.isauxdiffloggedrhs, M.endo_names, M.lhs, ...
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, y, ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
options, M.isloggedlhs, M.isauxdiffloggedrhs, M.endo_names, M.lhs, ...
|
||||
dynamicmodel, exogenousvariables, M.params, steadystate, it);
|
||||
else
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, y, options, ...
|
||||
dynamicmodel, exogenousvariables, M.params, steadystate, it);
|
||||
[tmp, check, ~, ~, errorcode] = dynare_solve(dynamicmodel_s, y, ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
options, dynamicmodel, exogenousvariables, M.params, steadystate, it);
|
||||
end
|
||||
if check
|
||||
info.status = false;
|
||||
|
|
|
|||
|
|
@ -41,7 +41,9 @@ jendo = transpose(1:nd);
|
|||
z = bsxfun(@minus, z, steadystate_y);
|
||||
x = bsxfun(@minus, exogenousvariables, steadystate_x');
|
||||
|
||||
[y, check, ~, ~, errorcode] = dynare_solve(@linear_perfect_foresight_problem,z(:), options, ...
|
||||
[y, check, ~, ~, errorcode] = dynare_solve(@linear_perfect_foresight_problem, z(:), ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
options, ...
|
||||
jacobian, y0-steadystate_y, yT-steadystate_y, ...
|
||||
x, M.params, steadystate_y, ...
|
||||
M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
|
||||
|
|
|
|||
|
|
@ -46,14 +46,18 @@ if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity
|
|||
options.mcppath.lb = repmat(lb,options.periods,1);
|
||||
options.mcppath.ub = repmat(ub,options.periods,1);
|
||||
end
|
||||
[y, check, ~, ~, errorcode] = dynare_solve(@perfect_foresight_mcp_problem,z(:),options, ...
|
||||
[y, check, ~, ~, errorcode] = dynare_solve(@perfect_foresight_mcp_problem, z(:), ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
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_0, i_cols_J0, ...
|
||||
eq_index);
|
||||
else
|
||||
[y, check, ~, ~, errorcode] = dynare_solve(@perfect_foresight_problem,z(:), options, y0, yT, exogenousvariables, M.params, steadystate, options.periods, M, options);
|
||||
[y, check, ~, ~, errorcode] = dynare_solve(@perfect_foresight_problem, z(:), ...
|
||||
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
|
||||
options, y0, yT, exogenousvariables, M.params, steadystate, options.periods, M, options);
|
||||
end
|
||||
|
||||
if all(imag(y)<.1*options.dynatol.x)
|
||||
|
|
|
|||
|
|
@ -88,7 +88,7 @@ staticmodel = str2fun(sprintf('%s.static', M_.fname));
|
|||
% Simulations (call a Newton-like algorithm for each period).
|
||||
for t=1:samplesize
|
||||
y = zeros(M_.endo_nbr, 1);
|
||||
[oo_.endo_simul(:,t), info, ~, ~, errorcode] = dynare_solve(staticmodel, y, options_, oo_.exo_simul(t,:), M_.params);
|
||||
[oo_.endo_simul(:,t), info, ~, ~, errorcode] = dynare_solve(staticmodel, y, options_.simul.maxit, options_.dynatol.f, options_.dynatol.x, options_, oo_.exo_simul(t,:), M_.params);
|
||||
if info
|
||||
error('Newton failed (with error code %i).', errorcode)
|
||||
end
|
||||
|
|
|
|||
|
|
@ -103,7 +103,8 @@ for t = 1:nobs(constraints)
|
|||
% values) and the free exogenous variables (initialized with 0).
|
||||
z = [Y(freeendogenousvariables_id,ity); zeros(nxfree, 1)];
|
||||
% Solves for z.
|
||||
[z, errorflag, ~, ~, errorcode] = dynare_solve(model_stransf, z, DynareOptions, model_dynamic, ycur, X(itx, :), DynareModel.params, ModelInversion);
|
||||
[z, errorflag, ~, ~, errorcode] = dynare_solve(model_stransf, z, DynareOptions.simul.maxit, DynareOptions.dynatol.f, DynareOptions.dynatol.x, ...
|
||||
DynareOptions, model_dynamic, ycur, X(itx, :), DynareModel.params, ModelInversion);
|
||||
if errorflag
|
||||
error('Enable to solve the system of equations (with error code %i).', errorcode)
|
||||
end
|
||||
|
|
|
|||
|
|
@ -48,7 +48,7 @@ c_z_2 = -.1;
|
|||
c_z_dx2 = .3;
|
||||
c_z_u = .3;
|
||||
c_z_dv = .4;
|
||||
c_z_s = -.2;
|
||||
c_z_s = -.2;
|
||||
cx = 1.0;
|
||||
cy = 1.0;
|
||||
|
||||
|
|
@ -122,6 +122,7 @@ init = rand(10, M_.endo_nbr+M_.exo_nbr);
|
|||
initialconditions = dseries(init, 2000Q1, vertcat(M_.endo_names,M_.exo_names));
|
||||
|
||||
// Simulate the model for 500 periods
|
||||
options_.dynatol.f = 1e-9;
|
||||
TrueData = simul_backward_model(initialconditions, 500);
|
||||
TrueData_ = copy(TrueData);
|
||||
|
||||
|
|
@ -144,4 +145,4 @@ fprintf('Max. abs. error is %s.\n', num2str(max(abs(TrueData.x3.data(12:end)-Tru
|
|||
|
||||
if max(abs(TrueData.x3.data(12:end)-TrueData_.x3.data(12:end)))>1e-5
|
||||
error('equation.evaluate() returned wrong values.')
|
||||
end
|
||||
end
|
||||
|
|
|
|||
|
|
@ -119,7 +119,7 @@ init = ones(10, M_.endo_nbr+M_.exo_nbr);
|
|||
initialconditions = dseries(init, 2000Q1, vertcat(M_.endo_names,M_.exo_names));
|
||||
|
||||
// Simulate the model for 500 periods
|
||||
options_.solve_tolf = 1e-9;
|
||||
options_.dynatol.f = 1e-9;
|
||||
TrueData = simul_backward_model(initialconditions, 5000);
|
||||
TrueData_ = copy(TrueData);
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue