122 lines
7.5 KiB
Matlab
122 lines
7.5 KiB
Matlab
function [xparam1, hessian_xparam1, fval, mom_verbose] = mode_compute_irf_matching(xparam0, hessian_xparam0, objective_function, doBayesianEstimation, weighting_info, data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
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% [xparam1, hessian_xparam1, fval, mom_verbose] = mode_compute_irf_matching(xparam0, hessian_xparam0, objective_function, doBayesianEstimation, weighting_info, data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
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% -------------------------------------------------------------------------
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% Computes the minimum of the objective function (distance between data IRFs
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% and model IRFs) for a sequence of optimizers.
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% Note that we call a "mode" the minimum of the objective function, i.e.
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% the parameter vector that minimizes the distance between the IRFs
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% computed from the model and the IRFs computed from the data.
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% -------------------------------------------------------------------------
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% INPUTS
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% xparam0: [vector] initialized parameters
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% hessian_xparam0: [matrix] initialized hessian at xparam0
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% objective_function: [func handle] name of the objective function
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% doBayesianEstimation: [logical] true if Bayesian estimation
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% weighting_info: [structure] information on weighting matrix
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% data_moments: [vector] data moments
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% options_mom_: [structure] options
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% M_: [structure] model information
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% estim_params_: [structure] information on estimated parameters
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% bayestopt_: [structure] information on priors
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% BoundsInfo: [structure] bounds for optimization
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% dr: [structure] information reduced-form model
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% endo_steady_state: [vector] steady state of endogenous variables (initval)
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% exo_steady_state: [vector] steady state of exogenous variables (initval)
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% exo_det_steady_state: [vector] steady state of deterministic exogenous variables (initval)
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% -------------------------------------------------------------------------
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% OUTPUT
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% xparam1: [vector] mode of objective function
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% hessian_xparam1: [matrix] hessian at xparam1
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% fval: [double] function value at mode
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% mom_verbose: [structure] information on intermediate estimation results
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% Also saves the computed mode and hessian to a file.
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% -------------------------------------------------------------------------
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% This function is called by
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% o mom.run
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% -------------------------------------------------------------------------
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% This function calls
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% o display_estimation_results_table
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% o dynare_minimize_objective
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% o hessian
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% o mom.objective_function
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% -------------------------------------------------------------------------
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% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
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mom_verbose = [];
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for optim_iter = 1:length(options_mom_.optimizer_vec)
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options_mom_.current_optimizer = options_mom_.optimizer_vec{optim_iter};
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if options_mom_.optimizer_vec{optim_iter}==0
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% no minimization, evaluate objective at current values
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xparam1 = xparam0;
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hessian_xparam1 = hessian_xparam0;
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fval = feval(objective_function, xparam1, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
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else
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[xparam1, fval, exitflag, hessian_xparam1, options_mom_, Scale, new_rat_hess_info] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [BoundsInfo.lb BoundsInfo.ub], bayestopt_.name, bayestopt_, hessian_xparam0,...
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data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
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end
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fprintf('\nMode Compute Iteration %d: Value of minimized moment distance objective function: %12.10f.\n',optim_iter,fval);
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if options_mom_.mom.verbose
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fprintf('\n''verbose'' option: ');
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if options_mom_.cova_compute
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if options_mom_.optimizer_vec{optim_iter}==0
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hessian_xparam1_iter = hessian_xparam1;
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else
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fprintf('computing Hessian');
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hessian_xparam1_iter = hessian(objective_function, xparam1, options_mom_.gstep,...
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data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
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hessian_xparam1_iter = reshape(hessian_xparam1_iter, length(xparam1), length(xparam1));
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end
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hsd_iter = sqrt(diag(hessian_xparam1_iter));
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invhessian_xparam1_iter = inv(hessian_xparam1_iter./(hsd_iter*hsd_iter'))./(hsd_iter*hsd_iter');
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std_via_invhessian_xparam1_iter = sqrt(diag(invhessian_xparam1_iter));
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else
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std_via_invhessian_xparam1_iter = NaN(size(xparam1));
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end
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fprintf(' and displaying intermediate results.');
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if doBayesianEstimation
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tbl_title_iter = sprintf('BAYESIAN %s (OPTIM ITERATION %d) VERBOSE',strrep(options_mom_.mom.mom_method,'_',' '),optim_iter);
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field_name_iter = sprintf('posterior_iter_%d',optim_iter);
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else
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tbl_title_iter = sprintf('FREQUENTIST %s (OPTIM ITERATION %d) VERBOSE',strrep(options_mom_.mom.mom_method,'_',' '),optim_iter);
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field_name_iter = sprintf('iter_%d',optim_iter);
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end
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mom_verbose.(field_name_iter) = display_estimation_results_table(xparam1,std_via_invhessian_xparam1_iter,M_,options_mom_,estim_params_,bayestopt_,[],prior_dist_names,tbl_title_iter,field_name_iter);
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end
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xparam0 = xparam1;
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hessian_xparam0 = hessian_xparam1;
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end
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if options_mom_.cova_compute
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if options_mom_.mom.verbose
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hessian_xparam1 = hessian_xparam1_iter;
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else
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fprintf('\nComputing Hessian at the mode.\n');
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hessian_xparam1 = hessian(objective_function, xparam1, options_mom_.gstep,...
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data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
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hessian_xparam1 = reshape(hessian_xparam1, length(xparam1), length(xparam1));
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end
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end
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parameter_names = bayestopt_.name;
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if options_mom_.cova_compute || options_mom_.mode_compute==5 || options_mom_.mode_compute==6
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hh = hessian_xparam1;
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save([M_.dname filesep 'method_of_moments' filesep M_.fname '_mode.mat'],'xparam1','hh','parameter_names','fval');
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else
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save([M_.dname filesep 'method_of_moments' filesep M_.fname '_mode.mat'],'xparam1','parameter_names','fval');
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end |