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function get_prior_info(info,plt_flag)
% Computes various prior statistics.
%
% INPUTS
% info [integer] scalar specifying what has to be done.
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2009-2012 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/>.
global options_ M_ estim_params_ oo_
if ~nargin
info = 0;
plt_flag = 0;
end
if nargin==1
plt_flag = 1;
end
changed_qz_criterium_flag = 0;
if isempty(options_.qz_criterium)
options_.qz_criterium = 1+1e-9;
changed_qz_criterium_flag = 1;
end
M_.dname = M_.fname;
[xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
if plt_flag
plot_priors(bayestopt_,M_,options_);
end
PriorNames = { 'Beta' , 'Gamma' , 'Gaussian' , 'Inverted Gamma' , 'Uniform' , 'Inverted Gamma -- 2' };
if size(M_.param_names,1)==size(M_.param_names_tex,1)% All the parameters have a TeX name.
fidTeX = fopen('priors_data.tex','w+');
% Column 1: a string for the name of the prior distribution.
% Column 2: the prior mean.
% Column 3: the prior standard deviation.
% Column 4: the lower bound of the prior density support.
% Column 5: the upper bound of the prior density support.
% Column 6: the lower bound of the interval containing 80% of the prior mass.
% Column 7: the upper bound of the interval containing 80% of the prior mass.
prior_trunc_backup = options_.prior_trunc ;
options_.prior_trunc = (1-options_.prior_interval)/2 ;
PriorIntervals = prior_bounds(bayestopt_,options_) ;
options_.prior_trunc = prior_trunc_backup ;
for i=1:size(bayestopt_.name,1)
[tmp,TexName] = get_the_name(i,1,M_,estim_params_,options_);
PriorShape = PriorNames{ bayestopt_.pshape(i) };
PriorMean = bayestopt_.p1(i);
PriorStandardDeviation = bayestopt_.p2(i);
switch bayestopt_.pshape(i)
case { 1 , 5 }
LowerBound = bayestopt_.p3(i);
UpperBound = bayestopt_.p4(i);
case { 2 , 4 , 6 }
LowerBound = bayestopt_.p3(i);
UpperBound = '$\infty$';
case 3
if isinf(bayestopt_.p3(i))
LowerBound = '$-\infty$';
else
LowerBound = bayestopt_.p3(i);
end
if isinf(bayestopt_.p4(i))
UpperBound = '$\infty$';
else
UpperBound = bayestopt_.p4(i);
end
otherwise
error('get_prior_info:: Dynare bug!')
end
format_string = build_format_string(bayestopt_,i);
fprintf(fidTeX,format_string, ...
TexName, ...
PriorShape, ...
PriorMean, ...
PriorStandardDeviation, ...
LowerBound, ...
UpperBound, ...
PriorIntervals(i,1), ...
PriorIntervals(i,2) );
end
fclose(fidTeX);
end
M_.dname = M_.fname;
if info==1% Prior simulations (BK).
results = prior_sampler(0,M_,bayestopt_,options_,oo_,estim_params_);
% Display prior mass info
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(['Analytical steady state problem share = ' num2str(results.ass.problem_share)])
end
if info==2% Prior optimization.
% Initialize to the prior mode if possible
k = find(~isnan(bayestopt_.p5));
xparam1(k) = bayestopt_.p5(k);
% Pertubation of the initial condition.
look_for_admissible_initial_condition = 1;
scale = 1.0;
iter = 0;
while look_for_admissible_initial_condition
xinit = xparam1+scale*randn(size(xparam1));
if all(xinit>bayestopt_.p3) && all(xinit<bayestopt_.p4)
look_for_admissible_initial_condition = 0;
else
if iter == 2000;
scale = scale/1.1;
iter = 0;
else
iter = iter+1;
end
end
end
% Maximization
[xparams,lpd,hessian] = ...
maximize_prior_density(xinit, bayestopt_.pshape, ...
bayestopt_.p6, ...
bayestopt_.p7, ...
bayestopt_.p3, ...
bayestopt_.p4);
% Display the results.
disp(' ')
disp(' ')
disp('------------------')
disp('PRIOR OPTIMIZATION')
disp('------------------')
disp(' ')
for i = 1:length(xparams)
disp(['deep parameter ' int2str(i) ': ' get_the_name(i,0,M_,estim_params_,options_) '.'])
disp([' Initial condition ....... ' num2str(xinit(i)) '.'])
disp([' Prior mode .............. ' num2str(bayestopt_.p5(i)) '.'])
disp([' Optimized prior mode .... ' num2str(xparams(i)) '.'])
disp(' ')
end
end
if info==3% Prior simulations (2nd order moments).
oo_ = compute_moments_varendo('prior',options_,M_,oo_);
end
if changed_qz_criterium_flag
options_.qz_criterium = [];
end
function format_string = build_format_string(bayestopt,i)
format_string = ['%s & %s & %6.4f &'];
if isinf(bayestopt.p2(i))
format_string = [ format_string , ' %s &'];
else
format_string = [ format_string , ' %6.4f &'];
end
if isinf(bayestopt.p3(i))
format_string = [ format_string , ' %s &'];
else
format_string = [ format_string , ' %6.4f &'];
end
if isinf(bayestopt.p4(i))
format_string = [ format_string , ' %s &'];
else
format_string = [ format_string , ' %6.4f &'];
end
format_string = [ format_string , ' %6.4f & %6.4f \\\\ \n'];
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