diff --git a/matlab/dsge_likelihood.m b/matlab/dsge_likelihood.m
index 40a7203c43bccbadad3245141b5f648dd480bd08..7ba2347852bd4b6c0c5471f03786aefa4e37f990 100644
--- a/matlab/dsge_likelihood.m
+++ b/matlab/dsge_likelihood.m
@@ -861,7 +861,7 @@ if DynareOptions.endogenous_prior==1
     if DynareOptions.lik_init==2 || DynareOptions.lik_init==3
         error('Endogenous prior not supported with non-stationary models')
     else
-        [lnpriormom]  = endogenous_prior(Y,Pstar,BayesInfo,H);
+        [lnpriormom]  = endogenous_prior(Y,DatasetInfo,Pstar,BayesInfo,H);
         fval    = (likelihood-lnprior-lnpriormom);
     end
 else
diff --git a/matlab/dynare_estimation_init.m b/matlab/dynare_estimation_init.m
index c893be9c63ded305bbcf704a5a657a659ff91d21..bc8454ee2e04031e994734a651cda7d9d9c54bb4 100644
--- a/matlab/dynare_estimation_init.m
+++ b/matlab/dynare_estimation_init.m
@@ -555,6 +555,18 @@ end
 %set options for old interface from the ones for new interface
 if ~isempty(dataset_)
     options_.nobs = dataset_.nobs;
+    if options_.endogenous_prior 
+        if dataset_info.missing.no_more_missing_observations<dataset_.nobs-10
+            fprintf('\ndynare_estimation_init: There are missing observations in the data.\n')
+            fprintf('dynare_estimation_init: I am computing the moments for the endogenous prior only\n')
+            fprintf('dynare_estimation_init: on the observations after the last missing one, i.e. %u.\n',dataset_info.missing.no_more_missing_observations)
+        else
+            fprintf('\ndynare_estimation_init: There are too many missing observations in the data.\n')        
+            fprintf('dynare_estimation_init: The endogenous_prior-option needs a consistent sample of \n')
+            fprintf('dynare_estimation_init: at least 10 full observations at the end.\n')
+            error('The endogenous_prior-option does not support your missing data.')
+        end
+    end
 end
 
 % setting steadystate_check_flag option
diff --git a/matlab/endogenous_prior.m b/matlab/endogenous_prior.m
index 26ea7ce5f5b9028f0ab68f28a0ca546e67f84278..5a192ad190eee360da4ed61061b6d50af8f5ffb9 100644
--- a/matlab/endogenous_prior.m
+++ b/matlab/endogenous_prior.m
@@ -1,8 +1,9 @@
-function [lnpriormom] = endogenous_prior(data,Pstar,BayesInfo,H)
+function [lnpriormom] = endogenous_prior(data,dataset_info, Pstar,BayesInfo,H)
 % Computes the endogenous log prior addition to the initial prior
 %
 % INPUTS
 %    data           [double]     n*T vector of data observations
+%    dataset_info   [structure]  various information about the dataset
 %    Pstar          [double]     k*k matrix of
 %    BayesInfo      [structure]
 %
@@ -11,7 +12,7 @@ function [lnpriormom] = endogenous_prior(data,Pstar,BayesInfo,H)
 
 % Code to implement notes on endogenous priors by Lawrence Christiano,
 % specified in the appendix of:
-% ’Introducing Financial Frictions and Unemployment into a Small Open Economy Model’
+% Introducing Financial Frictions and Unemployment into a Small Open Economy Model
 % by Lawrence J. Christiano, Mathias Trabandt and Karl Walentin (2011), Journal of Economic Dynamics and Control
 % this is the 'mother' of the priors on the model parameters.
 % the priors include a metric across some choosen moments of the (supposedly
@@ -41,7 +42,8 @@ function [lnpriormom] = endogenous_prior(data,Pstar,BayesInfo,H)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-Y=data';
+Y=data(:,dataset_info.missing.no_more_missing_observations:end)';
+
 [Tsamp,n]=size(Y);    % sample length and number of matched moments (here set equal to nr of observables)
 
 hmat=zeros(n,Tsamp);
@@ -85,4 +87,4 @@ Z=II(mf,:);
 Ftheta=diag(Z*Pstar(:,mf)+H);
 % below commented out line is for Del Negro Schorfheide style priors:
 %     lnpriormom=-.5*n*TT*log(2*pi)-.5*TT*log(det(sigma))-.5*TT*trace(inv(sigma)*(gamyy-2*phi'*gamxy+phi'*gamxx*phi));
-lnpriormom=.5*n*log(Tsamp/(2*pi))-.5*log(det(Shat))-.5*Tsamp*(Fhat-Ftheta)'/Shat*(Fhat-Ftheta);
+lnpriormom=.5*n*log(Tsamp/(2*pi))-.5*log(det(Shat))-.5*Tsamp*(Fhat-Ftheta)'/Shat*(Fhat-Ftheta);
\ No newline at end of file