diff --git a/doc/dynare.texi b/doc/dynare.texi
index f8a4908f4fe68b6d20db61ce0cdba4109de7f2e3..a1282180bb1d67a9abf72cbf9070f46db31afb5d 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -4928,7 +4928,7 @@ distribution of IRFs. The length of the IRFs are controlled by the
 @item dsge_var = @var{DOUBLE}
 @anchor{dsge_var} Triggers the estimation of a DSGE-VAR model, where the
 weight of  the DSGE prior  of the VAR model  is calibrated to  the value
-passed (see @cite{Del  Negro and Schorfheide (2004)}). NB:  The previous method
+passed (see @cite{Del  Negro and Schorfheide (2004)}). It represents ratio of dummy over actual observations. To assure that the prior is proper, the value must be bigger than @math{(k+n)/T}, where @math{k} is the number of estimated parameters, @math{n} is the number of observables, and @math{T} is the number of observations. NB:  The previous method
 of   declaring  @code{dsge_prior_weight}   as  a   parameter  and   then
 calibrating it is now deprecated and will be removed in a future release
 of Dynare.
diff --git a/matlab/dsge_var_likelihood.m b/matlab/dsge_var_likelihood.m
index 1177b47510980d13d703c16a0440426c1279bad4..f2ee0b2e12143aefc4b9573b56a5463ee78d57fb 100644
--- a/matlab/dsge_var_likelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -114,6 +114,8 @@ if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/DynareDatase
     fval = objective_function_penalty_base+abs(DynareDataset.info.ntobs*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
     exit_flag = 0;
     info = 51;
+    info(2)=dsge_prior_weight;
+    info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.info.ntobs;
     return
 end
 
diff --git a/matlab/print_info.m b/matlab/print_info.m
index 7914043904defb569d193d3bfc1115af17275a40..38158dacda8771a557258f02d03de0fad613ab0a 100644
--- a/matlab/print_info.m
+++ b/matlab/print_info.m
@@ -105,6 +105,7 @@ if ~noprint
       case 49
         error('The model violates one (many) endogenous prior restriction(s)')
       case 51
+        fprintf('\n The dsge_prior_weight is dsge_var=%5.4f, but must be at least %5.4f for the prior to be proper.\n',info(2),info(3));
         error('You are estimating a DSGE-VAR model, but the value of the dsge prior weight is too low!')
       case 52 %dsge_var_likelihood
         error('You are estimating a DSGE-VAR model, but the implied covariance matrix of the VAR''s innovations is not positive definite!');