diff --git a/matlab/cli/prior.m b/matlab/cli/prior.m
index c5c25330e435b8443406fd6450eac67301da7fd3..1fb8a01b2e926038c8d601b20cff841e318060c5 100644
--- a/matlab/cli/prior.m
+++ b/matlab/cli/prior.m
@@ -59,7 +59,7 @@ if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
end
% Fill or update bayestopt_ structure
-[xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
+[xparam1, EstimatedParams, BayesOptions, lb, ub, Model] = set_prior(estim_params_, M_, options_);
% Temporarly change qz_criterium option value
@@ -69,24 +69,24 @@ if isempty(options_.qz_criterium)
changed_qz_criterium_flag = 1;
end
-M_.dname = M_.fname;
+Model.dname = Model.fname;
% Temporarly set options_.order equal to one
order = options_.order;
options_.order = 1;
if ismember('plot', varargin)
- plot_priors(bayestopt_,M_,estim_params_,options_)
+ plot_priors(BayesOptions, Model, EstimatedParams, options_)
donesomething = true;
end
if ismember('table', varargin)
- print_table_prior(lb, ub, options_, M_, bayestopt_, estim_params_);
+ print_table_prior(lb, ub, options_, Model, BayesOptions, EstimatedParams);
donesomething = true;
end
if ismember('simulate', varargin) % Prior simulations (BK).
- results = prior_sampler(0,M_,bayestopt_,options_,oo_,estim_params_);
+ results = prior_sampler(0, Model, BayesOptions, options_, oo_, EstimatedParams);
% Display prior mass info
skipline(2)
disp(['Prior mass = ' num2str(results.prior.mass)])
@@ -103,26 +103,27 @@ if ismember('simulate', varargin) % Prior simulations (BK).
end
if ismember('optimize', varargin) % Prior optimization.
- optimize_prior(options_, M_, oo_, bayestopt_, estim_params_);
+ optimize_prior(options_, Model, oo_, BayesOptions, EstimatedParams);
donesomething = true;
end
if ismember('moments', varargin) % Prior simulations (2nd order moments).
% Set estimated parameters to the prior mode...
- xparam1 = bayestopt_.p5;
+ xparam1 = BayesOptions.p5;
% ... Except for uniform priors!
k = find(isnan(xparam1));
- xparam1(k) = bayestopt_.p5(k);
+ xparam1(k) = BayesOptions.p5(k);
% Update vector of parameters and covariance matrices
- M_ = set_all_parameters(xparam1,estim_params_,M_);
+ Model = set_all_parameters(xparam1, EstimatedParams, Model);
% Check model.
- check_model(M_);
+ check_model(Model);
% Compute state space representation of the model.
- oo_.dr=set_state_space(oo_.dr, M_, options_);
+ oo__ = oo_;
+ oo__.dr = set_state_space(oo__.dr, Model, options_);
% Solve model
- [dr,info, M_ ,options_ , oo_] = resol(0, M_ , options_ ,oo_);
+ [dr, info, Model , options__ , oo__] = resol(0, Model , options_ ,oo__);
% Compute and display second order moments
- oo_=disp_th_moments(oo_.dr,[],M_,options_,oo_);
+ oo__ = disp_th_moments(oo__.dr, [], Model, options__, oo__);
skipline(2)
donesomething = true;
end
@@ -137,7 +138,7 @@ if ~donesomething
error('prior: Unexpected arguments!')
end
-function format_string = build_format_string(PriorStandardDeviation,LowerBound,UpperBound)
+function format_string = build_format_string(PriorStandardDeviation, LowerBound, UpperBound)
format_string = ['%s & %s & %6.4f &'];
if ~isnumeric(PriorStandardDeviation)
format_string = [ format_string , ' %s &'];
diff --git a/matlab/write_latex_prior_table.m b/matlab/write_latex_prior_table.m
index 8d841605db0de04a467bec0e731ccc2a5ae2ef99..46a5e3bf5258d6f955c93fbfffb6794369869493 100644
--- a/matlab/write_latex_prior_table.m
+++ b/matlab/write_latex_prior_table.m
@@ -45,16 +45,16 @@ if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
end
% Fill or update bayestopt_ structure
-[xparam1, estim_params_, bayestopt_, lb, ub, M_] = set_prior(estim_params_, M_, options_);
+[xparam1, EstimatedParameters, BayesOptions, lb, ub, Model] = set_prior(estim_params_, M_, options_);
% Get untruncated bounds
-bounds = prior_bounds(bayestopt_, options_.prior_trunc);
+bounds = prior_bounds(BayesOptions, options_.prior_trunc);
lb=bounds.lb;
ub=bounds.ub;
PriorNames = { 'Beta' , 'Gamma' , 'Gaussian' , 'Inv. Gamma' , 'Uniform' , 'Inv. Gamma -- 2', '', 'Weibull' };
-fidTeX = fopen([M_.fname '_priors_table.tex'],'w+');
+fidTeX = fopen([Model.fname '_priors_table.tex'],'w+');
fprintf(fidTeX,'%% TeX-table generated by Dynare write_latex_prior_table.m.\n');
fprintf(fidTeX,'%% Prior Information\n');
fprintf(fidTeX,['%% ' datestr(now,0)]);
@@ -107,17 +107,17 @@ fprintf(fidTeX,'\\endlastfoot\n');
% Column 6: the upper bound of the prior density support.
% Column 7: the lower bound of the interval containing 90% of the prior mass.
% Column 8: the upper bound of the interval containing 90% of the prior mass.
-PriorIntervals = prior_bounds(bayestopt_,(1-options_.prior_interval)/2) ;
-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);
- PriorMode = bayestopt_.p5(i);
- PriorStandardDeviation = bayestopt_.p2(i);
- switch bayestopt_.pshape(i)
+PriorIntervals = prior_bounds(BayesOptions,(1-options_.prior_interval)/2) ;
+for i=1:size(BayesOptions.name,1)
+ [tmp,TexName] = get_the_name(i, 1, Model, EstimatedParameters, options_);
+ PriorShape = PriorNames{ BayesOptions.pshape(i) };
+ PriorMean = BayesOptions.p1(i);
+ PriorMode = BayesOptions.p5(i);
+ PriorStandardDeviation = BayesOptions.p2(i);
+ switch BayesOptions.pshape(i)
case { 1 , 5 }
- LowerBound = bayestopt_.p3(i);
- UpperBound = bayestopt_.p4(i);
+ LowerBound = BayesOptions.p3(i);
+ UpperBound = BayesOptions.p4(i);
if ~isinf(lb(i))
LowerBound=max(LowerBound,lb(i));
end
@@ -125,7 +125,7 @@ for i=1:size(bayestopt_.name,1)
UpperBound=min(UpperBound,ub(i));
end
case { 2 , 4 , 6, 8 }
- LowerBound = bayestopt_.p3(i);
+ LowerBound = BayesOptions.p3(i);
if ~isinf(lb(i))
LowerBound=max(LowerBound,lb(i));
end
@@ -135,18 +135,18 @@ for i=1:size(bayestopt_.name,1)
UpperBound = '$\infty$';
end
case 3
- if isinf(bayestopt_.p3(i)) && isinf(lb(i))
+ if isinf(BayesOptions.p3(i)) && isinf(lb(i))
LowerBound = '$-\infty$';
else
- LowerBound = bayestopt_.p3(i);
+ LowerBound = BayesOptions.p3(i);
if ~isinf(lb(i))
LowerBound=max(LowerBound,lb(i));
end
end
- if isinf(bayestopt_.p4(i)) && isinf(ub(i))
+ if isinf(BayesOptions.p4(i)) && isinf(ub(i))
UpperBound = '$\infty$';
else
- UpperBound = bayestopt_.p4(i);
+ UpperBound = BayesOptions.p4(i);
if ~isinf(ub(i))
UpperBound=min(UpperBound,ub(i));
end