Save log_density at the mode and document oo_.posterior-fields

doc/dynare.texi

@@ -5240,6 +5240,7 @@ Instructs Dynare to run mode computing/optimization silently without displaying
 saving files in between. Useful when running loops.
 
 @item mcmc_jumping_covariance = hessian|prior_variance|identity_matrix|@var{FILENAME}
+@anchor{MCMC_jumping_covariance}
 Tells Dynare which covariance to use for the proposal density of the MCMC sampler. @code{mcmc_jumping_covariance} can be one of the following:
 
 @table @code
@@ -6160,6 +6161,52 @@ Variable set by the @code{estimation} command, if it is used with
 based on @cite{Geweke (1999)} Modified Harmonic Mean estimator.
 @end defvr
 
+@defvr {MATLAB/Octave variable} oo_.posterior.optimization
+Variable set by the @code{estimation} command if mode-finding is used. Stores the results at the mode. 
+Fields are of the form 
+@example
+@code{oo_.posterior.optimization.@var{OBJECT}}
+@end example
+
+where @var{OBJECT} is one of the following:
+
+@table @code
+
+@item mode
+Parameter vector at the mode
+
+@item Variance
+Inverse Hessian matrix at the mode or MCMC jumping covariance matrix when used with the 
+@ref{MCMC_jumping_covariance} option
+
+@item log_density
+Log likelihood (ML)/log posterior density (Bayesian) at the mode when used with @code{mode_compute>0}
+
+@end table
+
+@end defvr
+
+@defvr {MATLAB/Octave variable} oo_.posterior.metropolis
+Variable set by the @code{estimation} command if @code{mh_replic>0} is used. 
+Fields are of the form 
+@example
+@code{oo_.posterior.metropolis.@var{OBJECT}}
+@end example
+
+where @var{OBJECT} is one of the following:
+
+@table @code
+
+@item mean
+Mean parameter vector from the MCMC
+
+@item Variance
+Covariance matrix of the parameter draws in the MCMC 
+
+@end table
+
+@end defvr
+
 @defvr {MATLAB/Octave variable} oo_.FilteredVariables
 Variable set by the @code{estimation} command, if it is used with the
 @code{filtered_vars} option.

matlab/dynare_estimation_1.m

@@ -355,9 +355,14 @@ end
 
 oo_.posterior.optimization.mode = [];
 oo_.posterior.optimization.Variance = [];
+oo_.posterior.optimization.log_density=[];
+
 invhess=[];
 if ~options_.mh_posterior_mode_estimation
     oo_.posterior.optimization.mode = xparam1;
+    if exist('fval','var')
+        oo_.posterior.optimization.log_density=-fval;
+    end
     if options_.cova_compute
         invhess = inv(hh);
         stdh = sqrt(diag(invhess));