212 lines
8.7 KiB
Matlab
212 lines
8.7 KiB
Matlab
function varargout = prior(varargin)
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% varargout = prior(varargin)
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% Computes various prior statistics and display them in the command window.
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%
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% INPUTS
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% 'table', 'moments', 'optimize', 'simulate', 'plot', 'moments(distribution)'
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%
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% OUTPUTS
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% none
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2015-2023 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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if isempty(varargin) || ( isequal(length(varargin), 1) && isequal(varargin{1},'help'))
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skipline()
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disp('Possible options are:')
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disp(' + table Prints a table describing the priors.')
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disp(' + moments Computes and displays moments of the endogenous variables at the prior mode.')
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disp(' + optimize Optimizes the prior density (starting from a random initial guess).')
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disp(' + simulate Computes the effective prior mass (using a Monte-Carlo).')
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disp(' + plot Plots the marginal prior densities.')
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disp(' + moments(distribution) Print tables describing the implied prior for the first and second order unconditional')
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disp(' moments of all the endogenous variables.')
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skipline()
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return
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end
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global options_ M_ estim_params_ bayestopt_ oo_
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donesomething = false;
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if ~isbayes(estim_params_)
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warning('No prior detected!')
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return
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end
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if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
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% Prior over measurement errors are defined...
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if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
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% ... But the list of observed variabled is not yet defined.
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warning('Prior detected on measurement erros, but no list of observed variables (varobs is missing)!')
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return
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end
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end
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% Fill or update bayestopt_ structure
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[xparam1, estim_params_, BayesOptions, lb, ub, M_local] = set_prior(estim_params_, M_, options_);
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% Set restricted state space
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options_plot_priors_old=options_.plot_priors;
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options_.plot_priors=0;
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[~,~,~,~, M_, options_, oo_, estim_params_, BayesOptions] = ...
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dynare_estimation_init(M_.endo_names, M_.fname, 1, M_, options_, oo_, estim_params_, bayestopt_);
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options_.plot_priors=options_plot_priors_old;
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% Temporarly change qz_criterium option value
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changed_qz_criterium_flag = 0;
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if isempty(options_.qz_criterium)
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options_.qz_criterium = 1+1e-9;
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changed_qz_criterium_flag = 1;
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end
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M_local.dname = M_local.fname;
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% Temporarly set options_.order equal to one
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order = options_.order;
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options_.order = 1;
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if ismember('plot', varargin)
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plot_priors(BayesOptions, M_local, estim_params_, options_)
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donesomething = true;
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end
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if ismember('table', varargin)
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print_table_prior(lb, ub, options_, M_local, BayesOptions, estim_params_);
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donesomething = true;
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end
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if ismember('simulate', varargin) % Prior simulations (BK).
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if ismember('moments(distribution)', varargin)
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results = prior_sampler(1, M_local, BayesOptions, options_, oo_, estim_params_);
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else
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results = prior_sampler(0, M_local, BayesOptions, options_, oo_, estim_params_);
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end
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% Display prior mass info
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skipline(2)
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disp(['Prior mass = ' num2str(results.prior.mass)])
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disp(['BK indeterminacy share = ' num2str(results.bk.indeterminacy_share)])
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disp(['BK unstability share = ' num2str(results.bk.unstability_share)])
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disp(['BK singularity share = ' num2str(results.bk.singularity_share)])
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disp(['Complex jacobian share = ' num2str(results.jacobian.problem_share)])
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disp(['mjdgges crash share = ' num2str(results.dll.problem_share)])
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disp(['Steady state problem share = ' num2str(results.ss.problem_share)])
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disp(['Complex steady state share = ' num2str(results.ss.complex_share)])
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disp(['Endogenous prior violation share = ' num2str(results.endogenous_prior_violation_share)])
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if options_.loglinear
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disp(['Nonpositive steady state share = ' num2str(results.ss.nonpositive_share)])
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end
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disp(['Analytical steady state problem share = ' num2str(results.ass.problem_share)])
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skipline(2)
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donesomething = true;
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end
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if ismember('optimize', varargin) % Prior optimization.
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optimize_prior(options_, M_local, oo_, BayesOptions, estim_params_);
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donesomething = true;
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end
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if ismember('moments', varargin) % Prior simulations (2nd order moments).
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% Set estimated parameters to the prior mode...
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xparam1 = BayesOptions.p5;
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% ... Except for uniform priors (use the prior mean)!
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k = find(isnan(xparam1));
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xparam1(k) = BayesOptions.p1(k);
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% Update vector of parameters and covariance matrices
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M_local = set_all_parameters(xparam1, estim_params_, M_local);
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% Check model.
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check_model(M_local);
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% Compute state space representation of the model.
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oo__ = oo_;
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oo__.dr = set_state_space(oo__.dr, M_local);
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% Solve model
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[T,R,~,info,oo__.dr, M_local.params] = dynare_resolve(M_local , options_ , oo__.dr, oo__.steady_state, oo__.exo_steady_state, oo__.exo_det_steady_state,'restrict');
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if ~info(1)
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info=endogenous_prior_restrictions(T,R,M_local , options__ , oo__.dr,oo__.steady_state,oo__.exo_steady_state,oo__.exo_det_steady_state);
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end
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if info
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skipline()
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message = get_error_message(info,options_);
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fprintf('Cannot solve the model on the prior mode (info = %d, %s)\n', info(1), message);
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skipline()
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return
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end
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% Compute and display second order moments
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oo__ = disp_th_moments(oo__.dr, [], M_local, options__, oo__);
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skipline(2)
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donesomething = true;
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end
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if ismember('moments(distribution)', varargin) % Prior simulations (BK).
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if ~ismember('simulate', varargin)
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results = prior_sampler(1, M_local, BayesOptions, options_, oo_, estim_params_);
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end
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priorpath = [M_local.dname filesep() 'prior' filesep() 'draws' filesep()];
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list_of_files = dir([priorpath 'prior_draws*']);
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FirstOrderMoments = NaN(M_local.orig_endo_nbr, options_.prior_mc);
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SecondOrderMoments = NaN(M_local.orig_endo_nbr, M_local.orig_endo_nbr, options_.prior_mc);
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iter = 1;
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noprint = options_.noprint;
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options_.noprint = 1;
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for i=1:length(list_of_files)
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tmp = load([priorpath list_of_files(i).name]);
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for j = 1:size(tmp.pdraws, 1)
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if ~tmp.pdraws{j,2}
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dr = tmp.pdraws{j,3};
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oo__ = oo_;
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oo__.dr = dr;
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M_local=set_parameters_locally(M_local,tmp.pdraws{j,1});% Needed to update the covariance matrix of the state innovations.
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oo__ = disp_th_moments(oo__.dr, [], M_local, options_, oo__);
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FirstOrderMoments(:,iter) = oo__.mean;
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SecondOrderMoments(:,:,iter) = oo__.var;
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iter = iter+1;
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end
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end
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end
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save([M_.dname filesep() 'prior' filesep() M_.fname '_endogenous_variables_prior_draws.mat'], 'FirstOrderMoments', 'SecondOrderMoments')
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skipline(2)
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options_.noprint = noprint;
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% First order moments
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FirstOrderMoments = FirstOrderMoments(:,1:iter-1);
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SecondOrderMoments = SecondOrderMoments(:,:,1:iter-1);
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PriorExpectationOfFirstOrderMoments = mean(FirstOrderMoments, 2);
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PriorVarianceOfFirstOrderMoments = ...
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mean(bsxfun(@minus, FirstOrderMoments, PriorExpectationOfFirstOrderMoments).^2, 2);
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% Second order moments
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PriorExpectationOfSecondOrderMoments = mean(SecondOrderMoments, 3);
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PriorVarianceOfSecondOrderMoments = ...
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mean(bsxfun(@minus, SecondOrderMoments, PriorExpectationOfSecondOrderMoments).^2, 3);
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% Display first and second order moments implied priors (expectation and variance)
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print_moments_implied_prior(M_, PriorExpectationOfFirstOrderMoments, ...
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PriorVarianceOfFirstOrderMoments, ...
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PriorExpectationOfSecondOrderMoments, ...
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PriorVarianceOfSecondOrderMoments);
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donesomething = true;
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end
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if changed_qz_criterium_flag
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options_.qz_criterium = [];
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end
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options_.order = order;
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if ~donesomething
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error('prior: Unexpected arguments!')
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end |