diff --git a/doc/dynare.texi b/doc/dynare.texi
index 9a7f5c88ef2c443357814637eb31ceb0ecb96e89..2a13b153f789d4e330313eb0be9094e3bd148329 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -5038,7 +5038,7 @@ 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 b47f5448184f54057159b4c3d662550ab1f09dbe..8192e412d57d47ab2d50b345d3db59304607d15b 100644
--- a/matlab/dsge_var_likelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -118,6 +118,8 @@ if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/NumberOfObse
     fval = objective_function_penalty_base+abs(NumberOfObservations*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 34017e513f67f1fbb1e23a5824b40b420c03dde1..2aded2e6b98398ef3ac5142450a864d3a1c074c1 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!');