252 lines
9.5 KiB
Matlab
252 lines
9.5 KiB
Matlab
function results = get_prior_info(info,plt_flag)
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% Computes various prior statistics.
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%
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% INPUTS
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% info [integer] scalar specifying what has to be done.
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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 (C) 2009-2012 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 <http://www.gnu.org/licenses/>.
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global options_ M_ estim_params_ oo_ objective_function_penalty_base
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if ~nargin
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info = 0;
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plt_flag = 0;
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end
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if nargin==1
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plt_flag = 1;
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end
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% Initialize returned variable.
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results = [];
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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_.dname = M_.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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[xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
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if plt_flag
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plot_priors(bayestopt_,M_,estim_params_,options_)
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end
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PriorNames = { 'Beta' , 'Gamma' , 'Gaussian' , 'Inverted Gamma' , 'Uniform' , 'Inverted Gamma -- 2' };
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if size(M_.param_names,1)==size(M_.param_names_tex,1)% All the parameters have a TeX name.
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fidTeX = fopen('priors_data.tex','w+');
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fprintf(fidTeX,'%% TeX-table generated by get_prior_info (Dynare).\n');
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fprintf(fidTeX,'%% Prior Information\n');
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fprintf(fidTeX,['%% ' datestr(now,0)]);
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fprintf(fidTeX,' \n');
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fprintf(fidTeX,' \n');
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fprintf(fidTeX,'\\begin{center}\n');
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fprintf(fidTeX,'\\begin{longtable}{l|ccccccc} \n');
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fprintf(fidTeX,'\\caption{Prior information (parameters)}\\\\\n ');
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fprintf(fidTeX,'\\label{Table:Prior}\\\\\n');
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fprintf(fidTeX,'\\hline\\hline \\\\ \n');
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fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Lower Bound & Upper Bound & LB Untrunc. 80\\%% HPDI & UB Untrunc. 80\\%% HPDI \\\\ \n');
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fprintf(fidTeX,'\\hline \\endfirsthead \n');
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fprintf(fidTeX,'\\caption{(continued)}\\\\\n ');
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fprintf(fidTeX,'\\hline\\hline \\\\ \n');
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fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Lower Bound & Upper Bound & LB Untrunc. 80\\%% HPDI & UB Untrunc. 80\\%% HPDI \\\\ \n');
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fprintf(fidTeX,'\\hline \\endhead \n');
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fprintf(fidTeX,'\\hline \\multicolumn{8}{r}{(Continued on next page)} \\\\ \\hline \\endfoot \n');
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fprintf(fidTeX,'\\hline \\hline \\endlastfoot \n');
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% Column 1: a string for the name of the prior distribution.
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% Column 2: the prior mean.
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% Column 3: the prior standard deviation.
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% Column 4: the lower bound of the prior density support.
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% Column 5: the upper bound of the prior density support.
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% Column 6: the lower bound of the interval containing 80% of the prior mass.
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% Column 7: the upper bound of the interval containing 80% of the prior mass.
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prior_trunc_backup = options_.prior_trunc ;
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options_.prior_trunc = (1-options_.prior_interval)/2 ;
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PriorIntervals = prior_bounds(bayestopt_,options_) ;
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options_.prior_trunc = prior_trunc_backup ;
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for i=1:size(bayestopt_.name,1)
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[tmp,TexName] = get_the_name(i,1,M_,estim_params_,options_);
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PriorShape = PriorNames{ bayestopt_.pshape(i) };
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PriorMean = bayestopt_.p1(i);
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PriorStandardDeviation = bayestopt_.p2(i);
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switch bayestopt_.pshape(i)
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case { 1 , 5 }
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LowerBound = bayestopt_.p3(i);
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UpperBound = bayestopt_.p4(i);
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if ~isinf(bayestopt_.lb(i))
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LowerBound=max(LowerBound,bayestopt_.lb(i));
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end
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if ~isinf(bayestopt_.ub(i))
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UpperBound=min(UpperBound,bayestopt_.ub(i));
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end
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case { 2 , 4 , 6 }
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LowerBound = bayestopt_.p3(i);
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if ~isinf(bayestopt_.lb(i))
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LowerBound=max(LowerBound,bayestopt_.lb(i));
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end
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if ~isinf(bayestopt_.ub(i))
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UpperBound=bayestopt_.ub(i);
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else
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UpperBound = '$\infty$';
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end
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case 3
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if isinf(bayestopt_.p3(i)) && isinf(bayestopt_.lb(i))
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LowerBound = '$-\infty$';
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else
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LowerBound = bayestopt_.p3(i);
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if ~isinf(bayestopt_.lb(i))
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LowerBound=max(LowerBound,bayestopt_.lb(i));
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end
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end
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if isinf(bayestopt_.p4(i)) && isinf(bayestopt_.ub(i))
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UpperBound = '$\infty$';
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else
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UpperBound = bayestopt_.p4(i);
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if ~isinf(bayestopt_.ub(i))
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UpperBound=min(UpperBound,bayestopt_.ub(i));
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end
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end
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otherwise
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error('get_prior_info:: Dynare bug!')
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end
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format_string = build_format_string(PriorStandardDeviation,LowerBound,UpperBound);
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fprintf(fidTeX,format_string, ...
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TexName, ...
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PriorShape, ...
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PriorMean, ...
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PriorStandardDeviation, ...
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LowerBound, ...
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UpperBound, ...
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PriorIntervals(i,1), ...
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PriorIntervals(i,2) );
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end
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fprintf(fidTeX,'\\end{longtable}\n ');
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fprintf(fidTeX,'\\end{center}\n');
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fprintf(fidTeX,'%% End of TeX file.\n');
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fclose(fidTeX);
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end
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M_.dname = M_.fname;
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if info==1% Prior simulations (BK).
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results = prior_sampler(0,M_,bayestopt_,options_,oo_,estim_params_);
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% Display prior mass info
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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(['Analytical steady state problem share = ' num2str(results.ass.problem_share)])
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end
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if info==2% Prior optimization.
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% Initialize to the prior mode if possible
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oo_.dr=set_state_space(oo_.dr,M_,options_);
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k = find(~isnan(bayestopt_.p5));
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xparam1(k) = bayestopt_.p5(k);
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% Pertubation of the initial condition.
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look_for_admissible_initial_condition = 1;
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scale = 1.0;
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iter = 0;
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while look_for_admissible_initial_condition
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xinit = xparam1+scale*randn(size(xparam1));
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if all(xinit(:)>bayestopt_.p3) && all(xinit(:)<bayestopt_.p4)
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M_ = set_all_parameters(xinit,estim_params_,M_);
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[dr,INFO,M_,options_,oo_] = resol(0,M_,options_,oo_);
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if ~INFO(1)
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look_for_admissible_initial_condition = 0;
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end
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else
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if iter == 2000;
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scale = scale/1.1;
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iter = 0;
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else
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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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objective_function_penalty_base = minus_logged_prior_density(xinit, bayestopt_.pshape, ...
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bayestopt_.p6, ...
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bayestopt_.p7, ...
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bayestopt_.p3, ...
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bayestopt_.p4,options_,M_,estim_params_,oo_);
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% Maximization
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[xparams,lpd,hessian] = ...
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maximize_prior_density(xinit, bayestopt_.pshape, ...
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bayestopt_.p6, ...
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bayestopt_.p7, ...
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bayestopt_.p3, ...
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bayestopt_.p4,options_,M_,estim_params_,oo_);
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% Display the results.
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skipline(2)
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disp('------------------')
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disp('PRIOR OPTIMIZATION')
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disp('------------------')
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skipline()
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for i = 1:length(xparams)
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disp(['deep parameter ' int2str(i) ': ' get_the_name(i,0,M_,estim_params_,options_) '.'])
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disp([' Initial condition ....... ' num2str(xinit(i)) '.'])
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disp([' Prior mode .............. ' num2str(bayestopt_.p5(i)) '.'])
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disp([' Optimized prior mode .... ' num2str(xparams(i)) '.'])
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skipline()
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end
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end
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if info==3% Prior simulations (2nd order moments).
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oo_ = compute_moments_varendo('prior',options_,M_,oo_);
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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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function format_string = build_format_string(PriorStandardDeviation,LowerBound,UpperBound)
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format_string = ['%s & %s & %6.4f &'];
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if ~isnumeric(PriorStandardDeviation)
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format_string = [ format_string , ' %s &'];
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else
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format_string = [ format_string , ' %6.4f &'];
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end
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if ~isnumeric(LowerBound)
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format_string = [ format_string , ' %s &'];
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else
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format_string = [ format_string , ' %6.4f &'];
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end
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if ~isnumeric(UpperBound)
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format_string = [ format_string , ' %s &'];
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else
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format_string = [ format_string , ' %6.4f &'];
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end
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format_string = [ format_string , ' %6.4f & %6.4f \\\\ \n']; |