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function varargout = prior(varargin)
% Computes various prior statistics and display them in the command window.
%
% INPUTS
% 'table', 'moments', 'optimize', 'simulate', 'plot', 'moments(distribution)'
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2015-2019 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if isempty(varargin) || ( isequal(length(varargin), 1) && isequal(varargin{1},'help'))
skipline()
disp('Possible options are:')
disp(' + table Prints a table describing the priors.')
disp(' + moments Computes and displays moments of the endogenous variables at the prior mode.')
disp(' + optimize Optimizes the prior density (starting from a random initial guess).')
disp(' + simulate Computes the effective prior mass (using a Monte-Carlo).')
disp(' + plot Plots the marginal prior densities.')
disp(' + moments(distribution) Print tables describing the implied prior for the first and second order unconditional')
disp(' moments of all the endogenous variables.')
skipline()
return
end
global options_ M_ estim_params_ bayestopt_ oo_
donesomething = false;
if ~isbayes(estim_params_)
warning('No prior detected!')
return
end
if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
% Prior over measurement errors are defined...
if ((isfield(options_,'varobs') && isempty(options_.varobs)) || ~isfield(options_,'varobs'))
% ... But the list of observed variabled is not yet defined.
warning('Prior detected on measurement erros, but no list of observed variables (varobs is missing)!')
return
end
end
% Fill or update bayestopt_ structure
[xparam1, estim_params_, BayesOptions, lb, ub, Model] = set_prior(estim_params_, M_, options_);
% Set restricted state space
options_plot_priors_old=options_.plot_priors;
options_.plot_priors=0;
[~,~,~,~, M_, options_, oo_, estim_params_, BayesOptions] = ...
dynare_estimation_init(M_.endo_names, M_.fname, 1, M_, options_, oo_, estim_params_, bayestopt_);
options_.plot_priors=options_plot_priors_old;
% Temporarly change qz_criterium option value
changed_qz_criterium_flag = 0;
if isempty(options_.qz_criterium)
options_.qz_criterium = 1+1e-9;
changed_qz_criterium_flag = 1;
end
Model.dname = Model.fname;
% Temporarly set options_.order equal to one
order = options_.order;
options_.order = 1;
if ismember('plot', varargin)
plot_priors(BayesOptions, Model, estim_params_, options_)
donesomething = true;
end
if ismember('table', varargin)
print_table_prior(lb, ub, options_, Model, BayesOptions, estim_params_);
donesomething = true;
end
if ismember('simulate', varargin) % Prior simulations (BK).
if ismember('moments(distribution)', varargin)
results = prior_sampler(1, Model, BayesOptions, options_, oo_, estim_params_);
else
results = prior_sampler(0, Model, BayesOptions, options_, oo_, estim_params_);
end
% Display prior mass info
skipline(2)
disp(['Prior mass = ' num2str(results.prior.mass)])
disp(['BK indeterminacy share = ' num2str(results.bk.indeterminacy_share)])
disp(['BK unstability share = ' num2str(results.bk.unstability_share)])
disp(['BK singularity share = ' num2str(results.bk.singularity_share)])
disp(['Complex jacobian share = ' num2str(results.jacobian.problem_share)])
disp(['mjdgges crash share = ' num2str(results.dll.problem_share)])
disp(['Steady state problem share = ' num2str(results.ss.problem_share)])
disp(['Complex steady state share = ' num2str(results.ss.complex_share)])
disp(['Endogenous prior violation share = ' num2str(results.endogenous_prior_violation_share)])
if options_.loglinear
disp(['Nonpositive steady state share = ' num2str(results.ss.nonpositive_share)])
end
disp(['Analytical steady state problem share = ' num2str(results.ass.problem_share)])
skipline(2)
donesomething = true;
end
if ismember('optimize', varargin) % Prior optimization.
optimize_prior(options_, Model, oo_, BayesOptions, estim_params_);
donesomething = true;
end
if ismember('moments', varargin) % Prior simulations (2nd order moments).
% Set estimated parameters to the prior mode...
xparam1 = BayesOptions.p5;
% ... Except for uniform priors (use the prior mean)!
k = find(isnan(xparam1));
xparam1(k) = BayesOptions.p1(k);
% Update vector of parameters and covariance matrices
Model = set_all_parameters(xparam1, estim_params_, Model);
% Check model.
check_model(Model);
% Compute state space representation of the model.
oo__ = oo_;
oo__.dr = set_state_space(oo__.dr, Model, options_);
% Solve model
[T,R,~,info,Model , options__ , oo__] = dynare_resolve(Model , options_ ,oo__,'restrict');
if ~info(1)
info=endogenous_prior_restrictions(T,R,Model , options__ , oo__);
end
if info
skipline()
message = get_error_message(info,options_);
fprintf('Cannot solve the model on the prior mode (info = %d, %s)\n', info(1), message);
skipline()
return
end
% Compute and display second order moments
oo__ = disp_th_moments(oo__.dr, [], Model, options__, oo__);
skipline(2)
donesomething = true;
end
if ismember('moments(distribution)', varargin) % Prior simulations (BK).
if ~ismember('simulate', varargin)
results = prior_sampler(1, Model, BayesOptions, options_, oo_, estim_params_);
end
priorpath = [Model.dname filesep() 'prior' filesep() 'draws' filesep()];
list_of_files = dir([priorpath 'prior_draws*']);
FirstOrderMoments = NaN(Model.orig_endo_nbr, options_.prior_mc);
SecondOrderMoments = NaN(Model.orig_endo_nbr, Model.orig_endo_nbr, options_.prior_mc);
iter = 1;
noprint = options_.noprint;
options_.noprint = 1;
for i=1:length(list_of_files)
tmp = load([priorpath list_of_files(i).name]);
for j = 1:size(tmp.pdraws, 1)
if ~tmp.pdraws{j,2}
dr = tmp.pdraws{j,3};
oo__ = oo_;
oo__.dr = dr;
Model=set_parameters_locally(Model,tmp.pdraws{j,1});% Needed to update the covariance matrix of the state innovations.
oo__ = disp_th_moments(oo__.dr, [], Model, options_, oo__);
FirstOrderMoments(:,iter) = oo__.mean;
SecondOrderMoments(:,:,iter) = oo__.var;
iter = iter+1;
end
end
end
save([M_.dname filesep() 'prior' filesep() M_.fname '_endogenous_variables_prior_draws.mat'], 'FirstOrderMoments', 'SecondOrderMoments')
skipline(2)
options_.noprint = noprint;
% First order moments
FirstOrderMoments = FirstOrderMoments(:,1:iter-1);
SecondOrderMoments = SecondOrderMoments(:,:,1:iter-1);
PriorExpectationOfFirstOrderMoments = mean(FirstOrderMoments, 2);
PriorVarianceOfFirstOrderMoments = ...
mean(bsxfun(@minus, FirstOrderMoments, PriorExpectationOfFirstOrderMoments).^2, 2);
% Second order moments
PriorExpectationOfSecondOrderMoments = mean(SecondOrderMoments, 3);
PriorVarianceOfSecondOrderMoments = ...
mean(bsxfun(@minus, SecondOrderMoments, PriorExpectationOfSecondOrderMoments).^2, 3);
% Display first and second order moments implied priors (expectation and variance)
print_moments_implied_prior(M_, PriorExpectationOfFirstOrderMoments, ...
PriorVarianceOfFirstOrderMoments, ...
PriorExpectationOfSecondOrderMoments, ...
PriorVarianceOfSecondOrderMoments);
donesomething = true;
end
if changed_qz_criterium_flag
options_.qz_criterium = [];
end
options_.order = order;
if ~donesomething
error('prior: Unexpected arguments!')
end
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