142 lines
8.9 KiB
Matlab
142 lines
8.9 KiB
Matlab
function [xparam1, weighting_info, mom_verbose] = mode_compute_gmm_smm(xparam0, objective_function, m_data, 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, weighting_info, mom_verbose] = mode_compute_gmm_smm(xparam0, objective_function, m_data, 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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% Iterated method of moments for GMM and SMM, computes the minimum of the
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% objective function (distance between data moments and model moments)
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% for a sequence of optimizers and GMM/SMM iterations with different
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% weighting matrices.
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% -------------------------------------------------------------------------
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% INPUTS
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% xparam0: [vector] vector of initialized parameters
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% objective_function: [func handle] name of the objective function
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% m_data: [matrix] selected data moments at each point in time
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% data_moments: [vector] vector of 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] reduced form model
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% endo_steady_state: [vector] steady state for endogenous variables (initval)
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% exo_steady_state: [vector] steady state for exogenous variables (initval)
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% exo_det_steady_state: [vector] steady state for exogenous deterministic 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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% weighting_info: [structure] information on weighting matrix
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% mom_verbose: [structure] information on intermediate estimation results
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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 mom.optimal_weighting_matrix
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% o mom.display_estimation_results_table
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% o dynare_minimize_objective
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% o mom.objective_function
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% o prior_dist_names
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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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if size(options_mom_.mom.weighting_matrix,1)>1 && ~(any(strcmpi('diagonal',options_mom_.mom.weighting_matrix)) || any(strcmpi('optimal',options_mom_.mom.weighting_matrix)))
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fprintf('\nYou did not specify the use of an optimal or diagonal weighting matrix. There is no point in running an iterated method of moments.\n');
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end
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for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
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fprintf('Estimation stage %u\n',stage_iter);
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weighting_info.Woptflag = false;
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switch lower(options_mom_.mom.weighting_matrix{stage_iter})
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case 'identity_matrix'
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fprintf(' - identity weighting matrix\n');
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weighting_matrix = eye(options_mom_.mom.mom_nbr);
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case 'diagonal'
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fprintf(' - diagonal of optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
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if stage_iter == 1
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fprintf(' and using data-moments as initial estimate of model-moments\n');
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weighting_matrix = diag(diag( mom.optimal_weighting_matrix(m_data, data_moments, options_mom_.mom.bartlett_kernel_lag) ));
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else
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fprintf(' and using previous stage estimate of model-moments\n');
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weighting_matrix = diag(diag( mom.optimal_weighting_matrix(m_data, model_moments, options_mom_.mom.bartlett_kernel_lag) ));
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end
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case 'optimal'
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fprintf(' - optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
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if stage_iter == 1
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fprintf(' and using data-moments as initial estimate of model-moments\n');
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weighting_matrix = mom.optimal_weighting_matrix(m_data, data_moments, options_mom_.mom.bartlett_kernel_lag);
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else
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fprintf(' and using previous stage estimate of model-moments\n');
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weighting_matrix = mom.optimal_weighting_matrix(m_data, model_moments, options_mom_.mom.bartlett_kernel_lag);
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weighting_info.Woptflag = true;
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end
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otherwise % user specified matrix in file
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fprintf(' - user-specified weighting matrix\n');
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try
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load(options_mom_.mom.weighting_matrix{stage_iter},'weighting_matrix')
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catch
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error(['method_of_moments: No matrix named ''weighting_matrix'' could be found in ',options_mom_.mom.weighting_matrix{stage_iter},'.mat !']);
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end
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[nrow, ncol] = size(weighting_matrix);
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if ~isequal(nrow,ncol) || ~isequal(nrow,length(data_moments)) %check if square and right size
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error(['method_of_moments: ''weighting_matrix'' must be square and have ',num2str(length(data_moments)),' rows and columns!']);
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end
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end
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try % check for positive definiteness of weighting_matrix
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weighting_info.Sw = chol(weighting_matrix);
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catch
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error('method_of_moments: Specified ''weighting_matrix'' is not positive definite. Check whether your model implies stochastic singularity!');
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end
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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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xparam1 = xparam0; % no minimization, evaluate objective at current values
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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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if options_mom_.optimizer_vec{optim_iter} == 13
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options_mom_.mom.vector_output = true;
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else
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options_mom_.mom.vector_output = false;
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end
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if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(options_mom_.optimizer_vec{optim_iter},options_mom_.mom.analytic_jacobian_optimizers) %do this only for gradient-based optimizers
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options_mom_.mom.compute_derivs = true;
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else
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options_mom_.mom.compute_derivs = false;
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end
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[xparam1, fval] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [BoundsInfo.lb BoundsInfo.ub], bayestopt_.name, bayestopt_, [],...
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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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if options_mom_.mom.vector_output
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fval = fval'*fval;
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end
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end
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fprintf('\nStage %d Iteration %d: Value of minimized moment distance objective function: %12.10f.\n',stage_iter,optim_iter,fval);
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if options_mom_.mom.verbose
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fprintf('\n''verbose'' option: ');
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std_via_invhessian_xparam1_iter = NaN(size(xparam1));
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tbl_title_iter = sprintf('FREQUENTIST %s (STAGE %d ITERATION %d) VERBOSE',options_mom_.mom.mom_method,stage_iter,optim_iter);
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field_name_iter = sprintf('%s_stage_%d_iter_%d',lower(options_mom_.mom.mom_method),stage_iter,optim_iter);
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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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end
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options_mom_.vector_output = false;
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[~, ~, ~, ~, ~, ~, model_moments] = 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); % get model_moments for iterated GMM/SMM to compute optimal weighting matrix
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end
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