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dynare
Commit 0f3b68ee authored by Johannes Pfeifer 's avatar Johannes Pfeifer
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Fix get_posterior_parameters 

Get rid of setting variables from the base workspace in the function, making the function read-only (as the name suggests)
parent 0f84dadb
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Showing with 66 additions and 58 deletions
matlab/GetOneDraw.m
View file @ 0f3b68ee
function [xparams, logpost] = GetOneDraw(type)
% function [xparams, logpost] = GetOneDraw(type)
function [xparams, logpost] = GetOneDraw(type,M_,estim_params_,oo_,options_,bayestopt_)
% function [xparams, logpost] = GetOneDraw(type,M_,estim_params_,oo_,options_,bayestopt_)
% draws one parameter vector and its posterior from MCMC or the prior
%
% INPUTS
% type: [string] 'posterior': draw from MCMC draws
% 'prior': draw from prior
% M_ [structure] Definition of the model
% estim_params_ [structure] characterizing parameters to be estimated
% oo_ [structure] Storage of results
% options_ [structure] Options
% bayestopt_ [structure] describing the priors
%
% OUTPUTS
% xparams: vector of estimated parameters (drawn from posterior or prior distribution)
......@@ -36,6 +41,6 @@ switch type
case 'prior'
xparams = prior_draw();
if nargout>1
logpost = evaluate_posterior_kernel(xparams');
logpost = evaluate_posterior_kernel(xparams',M_,estim_params_,oo_,options_,bayestopt_);
end
end
\ No newline at end of file
matlab/PosteriorIRF.m
View file @ 0f3b68ee
......@@ -174,7 +174,7 @@ localVars.type=type;
if strcmpi(type,'posterior')
while b<B
b = b + 1;
x(b,:) = GetOneDraw(type);
x(b,:) = GetOneDraw(type,M_,estim_params_,oo_,options_,bayestopt_);
end
end
......
matlab/PosteriorIRF_core1.m
View file @ 0f3b68ee
......@@ -141,7 +141,7 @@ while fpar<B
irun = irun+1;
irun2 = irun2+1;
if strcmpi(type,'prior')
deep = GetOneDraw(type);
deep = GetOneDraw(type,M_,estim_params_,oo_,options_,bayestopt_);
else
deep = x(fpar,:);
end
......
matlab/dynare_estimation_1.m
View file @ 0f3b68ee
......@@ -519,7 +519,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
end
prior_posterior_statistics('posterior',dataset_,dataset_info);
end
xparam1 = get_posterior_parameters('mean');
xparam1 = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
M_ = set_all_parameters(xparam1,estim_params_,M_);
end
end
......
matlab/dynare_identification.m
View file @ 0f3b68ee
......@@ -271,15 +271,15 @@ if iload <=0
case 'posterior_mode'
parameters_TeX = 'Posterior mode';
disp('Testing posterior mode')
params(1,:) = get_posterior_parameters('mode');
params(1,:) = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
case 'posterior_mean'
parameters_TeX = 'Posterior mean';
disp('Testing posterior mean')
params(1,:) = get_posterior_parameters('mean');
params(1,:) = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
case 'posterior_median'
parameters_TeX = 'Posterior median';
disp('Testing posterior median')
params(1,:) = get_posterior_parameters('median');
params(1,:) = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
case 'prior_mode'
parameters_TeX = 'Prior mode';
disp('Testing prior mode')
......
matlab/evaluate_likelihood.m
View file @ 0f3b68ee
function [llik,parameters] = evaluate_likelihood(parameters)
function [llik,parameters] = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_)
% Evaluate the logged likelihood at parameters.
%
% INPUTS
% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
% the (estimated) parameters of the model.
%
% o M_ [structure] Definition of the model
% o estim_params_ [structure] characterizing parameters to be estimated
% o oo_ [structure] Storage of results
% o options_ [structure] Options
% o bayestopt_ [structure] describing the priors
%
% OUTPUTS
% o ldens [double] value of the sample logged density at parameters.
......@@ -35,8 +39,6 @@ function [llik,parameters] = evaluate_likelihood(parameters)
% 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_ bayestopt_ oo_ estim_params_
persistent dataset dataset_info
if nargin==0
......@@ -46,11 +48,11 @@ end
if ischar(parameters)
switch parameters
case 'posterior mode'
parameters = get_posterior_parameters('mode');
parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
case 'posterior mean'
parameters = get_posterior_parameters('mean');
parameters = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
case 'posterior median'
parameters = get_posterior_parameters('median');
parameters = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
case 'prior mode'
parameters = bayestopt_.p5(:);
case 'prior mean'
......@@ -72,5 +74,5 @@ end
options_=select_qz_criterium_value(options_);
llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,prior_bounds(bayestopt_,options_.prior_trunc),oo_);
ldens = evaluate_prior(parameters);
ldens = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_);
llik = llik - ldens;
\ No newline at end of file
matlab/evaluate_posterior_kernel.m
View file @ 0f3b68ee
function lpkern = evaluate_posterior_kernel(parameters,llik)
% Evaluate the prior density at parameters.
function lpkern = evaluate_posterior_kernel(parameters,M_,estim_params_,oo_,options_,bayestopt_,llik)
% Evaluate the evaluate_posterior_kernel at parameters.
%
% INPUTS
% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
% the (estimated) parameters of the model.
%
% o M_ [structure] Definition of the model
% o estim_params_ [structure] characterizing parameters to be estimated
% o oo_ [structure] Storage of results
% o options_ [structure] Options
% o bayestopt_ [structure] describing the priors
% o llik [double] value of the logged likelihood if it
% should not be computed
%
% OUTPUTS
% o lpkern [double] value of the logged posterior kernel.
......@@ -34,8 +40,8 @@ function lpkern = evaluate_posterior_kernel(parameters,llik)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
[ldens,parameters] = evaluate_prior(parameters);
if nargin==1
llik = evaluate_likelihood(parameters);
[ldens,parameters] = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_);
if nargin==6 %llik provided as an input
llik = evaluate_likelihood(parameters,M_,estim_params_,oo_,options_,bayestopt_);
end
lpkern = ldens+llik;
\ No newline at end of file
matlab/evaluate_prior.m
View file @ 0f3b68ee
function [ldens,parameters] = evaluate_prior(parameters)
function [ldens,parameters] = evaluate_prior(parameters,M_,estim_params_,oo_,options_,bayestopt_)
% Evaluate the prior density at parameters.
%
% INPUTS
% o parameters a string ('posterior mode','posterior mean','posterior median','prior mode','prior mean') or a vector of values for
% the (estimated) parameters of the model.
% o M_ [structure] Definition of the model
% o oo_ [structure] Storage of results
% o options_ [structure] Options
% o bayestopt_ [structure] describing the priors
% o estim_params_ [structure] characterizing parameters to be estimated
%
%
% OUTPUTS
......@@ -33,8 +38,6 @@ function [ldens,parameters] = evaluate_prior(parameters)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global bayestopt_
if nargin==0
parameters = 'posterior mode';
end
......@@ -42,11 +45,11 @@ end
if ischar(parameters)
switch parameters
case 'posterior mode'
parameters = get_posterior_parameters('mode');
parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
case 'posterior mean'
parameters = get_posterior_parameters('mean');
parameters = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
case 'posterior median'
parameters = get_posterior_parameters('median');
parameters = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
case 'prior mode'
parameters = bayestopt_.p5(:);
case 'prior mean'
......
matlab/evaluate_smoother.m
View file @ 0f3b68ee
......@@ -73,13 +73,13 @@ end
if ischar(parameters)
switch parameters
case 'posterior_mode'
parameters = get_posterior_parameters('mode');
parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
case 'posterior_mean'
parameters = get_posterior_parameters('mean');
parameters = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
case 'posterior_median'
parameters = get_posterior_parameters('median');
parameters = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
case 'mle_mode'
parameters = get_posterior_parameters('mode','mle_');
parameters = get_posterior_parameters('mode',M_,estim_params_,oo_,options_,'mle_');
case 'prior_mode'
parameters = bayestopt_.p5(:);
case 'prior_mean'
......
matlab/execute_prior_posterior_function.m
View file @ 0f3b68ee
......@@ -68,11 +68,11 @@ else
end
%get draws for later use
first_draw=GetOneDraw(type);
first_draw=GetOneDraw(type,M_,estim_params_,oo_,options_,bayestopt_);
parameter_mat=NaN(n_draws,length(first_draw));
parameter_mat(1,:)=first_draw;
for draw_iter=2:n_draws
parameter_mat(draw_iter,:) = GetOneDraw(type);
parameter_mat(draw_iter,:) = GetOneDraw(type,M_,estim_params_,oo_,options_,bayestopt_);
end
% get output size
......
matlab/get_all_parameters.m
View file @ 0f3b68ee
......@@ -6,7 +6,7 @@ function xparam1=get_all_parameters(estim_params_,M_)
% parameter values
%
% INPUTS
% estim_params_: Dynare structure describing the estimated parameters.
% estim_params_: Dynare structure describing the estimated parameters.
% M_: Dynare structure describing the model.
%
% OUTPUTS
......
matlab/get_posterior_parameters.m
View file @ 0f3b68ee
function xparam = get_posterior_parameters(type,field1)
function xparam = get_posterior_parameters(type,M_,estim_params_,oo_,options_,field1)
% function xparam = get_posterior_parameters(type)
% function xparam = get_posterior_parameters(type,M_,estim_params_,oo_,options_,field1)
% Selects (estimated) parameters (posterior mode or posterior mean).
%
% INPUTS
% o type [char] = 'mode' or 'mean'.
% o field_1 [char] optional field like 'mle_'.
% o type [char] = 'mode' or 'mean'.
% o M_: [structure] Dynare structure describing the model.
% o estim_params_: [structure] Dynare structure describing the estimated parameters.
% o field_1 [char] optional field like 'mle_'.
%
% OUTPUTS
% o xparam vector of estimated parameters
......@@ -30,9 +32,7 @@ function xparam = get_posterior_parameters(type,field1)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global estim_params_ oo_ options_ M_
if nargin<2
if nargin<6
field1='posterior_';
end
nvx = estim_params_.nvx;
......@@ -48,7 +48,6 @@ for i=1:nvx
k1 = estim_params_.var_exo(i,1);
name1 = deblank(M_.exo_names(k1,:));
xparam(m) = oo_.([field1 type]).shocks_std.(name1);
M_.Sigma_e(k1,k1) = xparam(m)^2;
m = m+1;
end
......@@ -65,8 +64,6 @@ for i=1:ncx
name1 = deblank(M_.exo_names(k1,:));
name2 = deblank(M_.exo_names(k2,:));
xparam(m) = oo_.([field1 type]).shocks_corr.([name1 '_' name2]);
M_.Sigma_e(k1,k2) = xparam(m);
M_.Sigma_e(k2,k1) = xparam(m);
m = m+1;
end
......@@ -84,10 +81,5 @@ FirstDeep = m;
for i=1:np
name1 = deblank(M_.param_names(estim_params_.param_vals(i,1),:));
xparam(m) = oo_.([field1 type]).parameters.(name1);
%assignin('base',name1,xparam(m));% Useless with version 4 (except maybe for users)
m = m+1;
end
if np
M_.params(estim_params_.param_vals(:,1)) = xparam(FirstDeep:end);
end
\ No newline at end of file
matlab/imcforecast.m
View file @ 0f3b68ee
......@@ -80,16 +80,16 @@ if estimated_model
if ischar(options_cond_fcst.parameter_set)
switch options_cond_fcst.parameter_set
case 'posterior_mode'
xparam = get_posterior_parameters('mode');
xparam = get_posterior_parameters('mode',M_,estim_params_,oo_,options_);
graph_title='Posterior Mode';
case 'posterior_mean'
xparam = get_posterior_parameters('mean');
xparam = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
graph_title='Posterior Mean';
case 'posterior_median'
xparam = get_posterior_parameters('median');
xparam = get_posterior_parameters('median',M_,estim_params_,oo_,options_);
graph_title='Posterior Median';
case 'mle_mode'
xparam = get_posterior_parameters('mode','mle_');
xparam = get_posterior_parameters('mode',M_,estim_params_,oo_,options_,'mle_');
graph_title='ML Mode';
case 'prior_mode'
xparam = bayestopt_.p5(:);
......
matlab/prior_posterior_statistics.m
View file @ 0f3b68ee
......@@ -226,7 +226,7 @@ if strcmpi(type,'posterior')
else
logpost=NaN(B,1);
for b=1:B
[x(b,:), logpost(b)] = GetOneDraw(type);
[x(b,:), logpost(b)] = GetOneDraw(type,M_,estim_params_,oo_,options_,bayestopt_);
end
end
localVars.logpost=logpost;
......
matlab/prior_posterior_statistics_core.m
View file @ 0f3b68ee
......@@ -190,14 +190,14 @@ end
for b=fpar:B
if strcmpi(type,'prior')
[deep, logpo] = GetOneDraw(type);
[deep, logpo] = GetOneDraw(type,M_,estim_params_,oo_,options_,bayestopt_);
else
deep = x(b,:);
if strcmpi(type,'posterior')
logpo = logpost(b);
else
logpo = evaluate_posterior_kernel(deep');
logpo = evaluate_posterior_kernel(deep',M_,estim_params_,oo_,options_,bayestopt_);
end
end
M_ = set_all_parameters(deep,estim_params_,M_);
......
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