Store MCMC information recorded in record in oo_

Closes issue #315 (https://github.com/DynareTeam/dynare/issues/315)

doc/dynare.texi

@@ -4461,6 +4461,9 @@ Median of the posterior distribution
 @item Std
 Standard deviation of the posterior distribution
 
+@item Variance
+Variance of the posterior distribution
+
 @item deciles
 Deciles of the distribution.
 
@@ -4649,6 +4652,21 @@ oo_.posterior_mean.shocks_std.ex
 oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso
 @end example
 
+@defvr {MATLAB/Octave variable} oo_.MC_record.Seeds
+Variable set by the @code{estimation} command. Stores seeds used in MCMC chains
+@end defvr
+
+@defvr {MATLAB/Octave variable} oo_.MC_record.AcceptationRates
+Variable set by the @code{estimation} command. Stores acceptation rates of the MCMC chains
+@end defvr
+
+@defvr {MATLAB/Octave variable} oo_.MC_record.LastParameters
+Variable set by the @code{estimation} command. Stores parameter vector of final MCMC chain draw
+@end defvr
+
+@defvr {MATLAB/Octave variable} oo_.MC_record.LastLogPost
+Variable set by the @code{estimation} command. Stores log-posterior of final MCMC chain draw
+@end defvr
 
 @deffn Command model_comparison @var{FILENAME}[(@var{DOUBLE})]@dots{};
 @deffnx Command model_comparison (marginal_density = laplace | modifiedharmonicmean) @var{FILENAME}[(@var{DOUBLE})]@dots{};
@@ -5316,7 +5334,7 @@ The discussion of the methodologies and their application is described in
 
 With respect to the previous version of the toolbox, in order to work
 properly, the GSA toolbox no longer requires that the Dynare
-estimation environment is setup.
+estimation environment is set up.
 
 Sensitivity analysis results are saved locally in @code{<mod_file>/GSA},
 where @code{<mod_file>.mod} is the name of the DYNARE model file.

matlab/adaptive_metropolis_hastings.m

-function adaptive_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
+function record=adaptive_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
 %function adaptive_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
 % Random walk Metropolis-Hastings algorithm. 
 % 
@@ -11,7 +11,7 @@ function adaptive_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds
 %   o varargin              list of argument following mh_bounds
 %  
 % OUTPUTS 
-%   None  
+%   o record     [struct]   structure describing the iterations
 %
 % ALGORITHM 
 %   Metropolis-Hastings.       

matlab/dynare_estimation_1.m

@@ -156,7 +156,7 @@ if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.m
         oo_.Smoother.SteadyState = ys;
         oo_.Smoother.TrendCoeffs = trend_coeff;
         if options_.filter_covariance
-            oo_.Smoother.variance = P;
+            oo_.Smoother.Variance = P;
         end
         i_endo = bayestopt_.smoother_saved_var_list;
         if options_.nk ~= 0
@@ -482,12 +482,12 @@ if options_.mode_check == 1 && ~options_.mh_posterior_mode_estimation
 end
 
 oo_.posterior.optimization.mode = xparam1;
-oo_.posterior.optimization.variance = [];
+oo_.posterior.optimization.Variance = [];
 if ~options_.mh_posterior_mode_estimation
     if options_.cova_compute
         invhess = inv(hh);
         stdh = sqrt(diag(invhess));
-        oo_.posterior.optimization.variance = invhess;
+        oo_.posterior.optimization.Variance = invhess;
     end
 else
     variances = bayestopt_.p2.*bayestopt_.p2;
@@ -921,7 +921,7 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
         ana_deriv = options_.analytic_derivation;
         options_.analytic_derivation = 0;
         if options_.cova_compute
-            feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+            oo_.MC_record=feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
         else
             error('I Cannot start the MCMC because the hessian of the posterior kernel at the mode was not computed.')
         end
@@ -943,7 +943,7 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
             if ~options_.nograph
                 oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
             end
-            [oo_.posterior.metropolis.mean,oo_.posterior.metropolis.variance] ...
+            [oo_.posterior.metropolis.mean,oo_.posterior.metropolis.Variance] ...
                 = GetPosteriorMeanVariance(M_,options_.mh_drop);
         else
             load([M_.fname '_results'],'oo_');
@@ -974,7 +974,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
     [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,dataset_.info.ntobs,dataset_.data,dataset_.missing.aindex,dataset_.missing.state);
     oo_.Smoother.SteadyState = ys;
     oo_.Smoother.TrendCoeffs = trend_coeff;
-    oo_.Smoother.variance = P;
+    oo_.Smoother.Variance = P;
     i_endo = bayestopt_.smoother_saved_var_list;
     if options_.nk ~= 0
         oo_.FilteredVariablesKStepAhead = aK(options_.filter_step_ahead, ...

matlab/independent_metropolis_hastings.m

-function independent_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
+function record=independent_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,varargin)
 
 % Independent Metropolis-Hastings algorithm. 
 % 
@@ -11,7 +11,7 @@ function independent_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bou
 %   o varargin              list of argument following mh_bounds
 %  
 % OUTPUTS 
-%   None  
+%   o record     [struct]   structure describing the iterations
 %
 % ALGORITHM 
 %   Metropolis-Hastings.       
@@ -103,7 +103,7 @@ else
     [fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, fblck, nblck,NamFileInput,'independent_metropolis_hastings_core', localVars, globalVars, options_.parallel_info);
     for j=1:totCPU,
         offset = sum(nBlockPerCPU(1:j-1))+fblck-1;
-        record.LastLogLiK(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogLiK(offset+1:sum(nBlockPerCPU(1:j)));
+        record.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)));
         record.LastParameters(offset+1:sum(nBlockPerCPU(1:j)),:)=fout(j).record.LastParameters(offset+1:sum(nBlockPerCPU(1:j)),:);
         record.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)));
         record.Seeds(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.Seeds(offset+1:sum(nBlockPerCPU(1:j)));

matlab/independent_metropolis_hastings_core.m

@@ -229,7 +229,7 @@ for b = fblck:nblck,
             jsux = 0;
             if j == nruns(b) % I record the last draw...
                 record.LastParameters(b,:) = x2(end,:);
-                record.LastLogLiK(b) = logpo2(end);
+                record.LastLogPost(b) = logpo2(end);
             end
             % size of next file in chain b
             InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);

matlab/metropolis_hastings_initialization.m

@@ -194,7 +194,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
     record.InitialParameters = ix2;
     record.InitialLogLiK = ilogpo2;
     record.LastParameters = zeros(nblck,npar);
-    record.LastLogLiK = zeros(nblck,1);
+    record.LastLogPost = zeros(nblck,1);
     record.LastFileNumber = AnticipatedNumberOfFiles ;
     record.LastLineNumber = AnticipatedNumberOfLinesInTheLastFile;
     save([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');  
@@ -253,7 +253,7 @@ elseif options_.load_mh_file && ~options_.mh_recover
     else
         NewFile = ones(nblck,1)*(LastFileNumber+1);
     end
-    ilogpo2 = record.LastLogLiK;
+    ilogpo2 = record.LastLogPost;
     ix2 = record.LastParameters;
     fblck = 1;
     fline = ones(nblck,1)*(LastLineNumber+1);
@@ -292,7 +292,7 @@ elseif options_.mh_recover
     end
     %% Default initialization:
     if OldMh
-        ilogpo2 = record.LastLogLiK;
+        ilogpo2 = record.LastLogPost;
         ix2 = record.LastParameters;
     else
         ilogpo2 = record.InitialLogLiK;

matlab/random_walk_metropolis_hastings.m

@@ -148,7 +148,7 @@ else
     [fout, nBlockPerCPU, totCPU] = masterParallel(options_.parallel, fblck, nblck,NamFileInput,'random_walk_metropolis_hastings_core', localVars, globalVars, options_.parallel_info);
     for j=1:totCPU,
         offset = sum(nBlockPerCPU(1:j-1))+fblck-1;
-        record.LastLogLiK(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogLiK(offset+1:sum(nBlockPerCPU(1:j)));
+        record.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.LastLogPost(offset+1:sum(nBlockPerCPU(1:j)));
         record.LastParameters(offset+1:sum(nBlockPerCPU(1:j)),:)=fout(j).record.LastParameters(offset+1:sum(nBlockPerCPU(1:j)),:);
         record.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.AcceptationRates(offset+1:sum(nBlockPerCPU(1:j)));
         record.Seeds(offset+1:sum(nBlockPerCPU(1:j)))=fout(j).record.Seeds(offset+1:sum(nBlockPerCPU(1:j)));

matlab/random_walk_metropolis_hastings_core.m

@@ -247,7 +247,7 @@ for b = fblck:nblck,
             jsux = 0;
             if j == nruns(b) % I record the last draw...
                 record.LastParameters(b,:) = x2(end,:);
-                record.LastLogLiK(b) = logpo2(end);
+                record.LastLogPost(b) = logpo2(end);
             end
             % size of next file in chain b
             InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);