diff --git a/matlab/correlation_mc_analysis.m b/matlab/correlation_mc_analysis.m
index def6c94a9..372fda45d 100644
--- a/matlab/correlation_mc_analysis.m
+++ b/matlab/correlation_mc_analysis.m
@@ -90,46 +90,26 @@ for file = 1:length(ListOfFiles)
     i1 = i2+1;
 end
 name = [ var1 '.' var2 ];
-if ~isconst(tmp)
-    if options_.estimation.moments_posterior_density.indicator
-        [p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
-            posterior_moments(tmp,1,mh_conf_sig);
-    else
-        [p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
-            posterior_moments(tmp,0,mh_conf_sig);
-    end
-    if isfield(oo_,[ TYPE 'TheoreticalMoments'])
-        temporary_structure = oo_.([TYPE, 'TheoreticalMoments']);
-        if isfield(temporary_structure,'dsge')
-            temporary_structure = oo_.([TYPE, 'TheoreticalMoments']).dsge;
-            if isfield(temporary_structure,'correlation')
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Mean',nar,p_mean);
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Median',nar,p_median);
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Variance',nar,p_var);
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDinf',nar,hpd_interval(1));
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDsup',nar,hpd_interval(2));
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,p_deciles);
-                if options_.estimation.moments_posterior_density.indicator
-                    oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,density);
-                end
-            end
-        end
-    end
+if options_.estimation.moments_posterior_density.indicator
+    [p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
+        posterior_moments(tmp,1,mh_conf_sig);
 else
-    if isfield(oo_,'PosteriorTheoreticalMoments')
-        temporary_structure = oo_.([TYPE, 'TheoreticalMoments']);
-        if isfield(temporary_structure,'dsge')
-            temporary_structure = oo_.([TYPE, 'TheoreticalMoments']).dsge;
-            if isfield(temporary_structure,'correlation')
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Mean',nar,NaN);
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Median',nar,NaN);
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Variance',nar,NaN);
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDinf',nar,NaN);
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDsup',nar,NaN);
-                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,NaN);
-                if options_.estimation.moments_posterior_density.indicator
-                    oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,NaN);
-                end
+    [p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
+        posterior_moments(tmp,0,mh_conf_sig);
+end
+if isfield(oo_,[ TYPE 'TheoreticalMoments'])
+    temporary_structure = oo_.([TYPE, 'TheoreticalMoments']);
+    if isfield(temporary_structure,'dsge')
+        temporary_structure = oo_.([TYPE, 'TheoreticalMoments']).dsge;
+        if isfield(temporary_structure,'correlation')
+            oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Mean',nar,p_mean);
+            oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Median',nar,p_median);
+            oo_ = fill_output_structure(var1,var2,TYPE,oo_,'Variance',nar,p_var);
+            oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDinf',nar,hpd_interval(1));
+            oo_ = fill_output_structure(var1,var2,TYPE,oo_,'HPDsup',nar,hpd_interval(2));
+            oo_ = fill_output_structure(var1,var2,TYPE,oo_,'deciles',nar,p_deciles);
+            if options_.estimation.moments_posterior_density.indicator
+                oo_ = fill_output_structure(var1,var2,TYPE,oo_,'density',nar,density);
             end
         end
     end
diff --git a/matlab/covariance_mc_analysis.m b/matlab/covariance_mc_analysis.m
index a5bda07ba..ccb0a0c30 100644
--- a/matlab/covariance_mc_analysis.m
+++ b/matlab/covariance_mc_analysis.m
@@ -105,32 +105,20 @@ for file = 1:length(ListOfFiles)
     i1 = i2+1;
 end
 
-if ~isconst(tmp)
-    if options_.estimation.moments_posterior_density.indicator
-        [p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
-                posterior_moments(tmp,1,mh_conf_sig);
-        oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.density.(var1).(var2) = density;
-    else
-        [p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
-                        posterior_moments(tmp,0,mh_conf_sig);
-    end
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var1).(var2) = p_mean;
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Median.(var1).(var2) = p_median;
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Variance.(var1).(var2) = p_var;
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDinf.(var1).(var2) = hpd_interval(1);
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDsup.(var1).(var2) = hpd_interval(2);
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.deciles.(var1).(var2) = p_deciles;
+if options_.estimation.moments_posterior_density.indicator
+    [p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
+            posterior_moments(tmp,1,mh_conf_sig);
+    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.density.(var1).(var2) = density;
 else
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var1).(var2) = NaN;
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Median.(var1).(var2) = NaN;
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Variance.(var1).(var2) = NaN;
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDinf.(var1).(var2) = NaN;
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDsup.(var1).(var2) = NaN;
-    oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.deciles.(var1).(var2) = NaN;
-    if options_.estimation.moments_posterior_density.indicator
-        oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.density.(var1).(var2) = NaN;
-    end
+    [p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
+                    posterior_moments(tmp,0,mh_conf_sig);
 end
+oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Mean.(var1).(var2) = p_mean;
+oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Median.(var1).(var2) = p_median;
+oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.Variance.(var1).(var2) = p_var;
+oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDinf.(var1).(var2) = hpd_interval(1);
+oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.HPDsup.(var1).(var2) = hpd_interval(2);
+oo_.([TYPE, 'TheoreticalMoments']).dsge.covariance.deciles.(var1).(var2) = p_deciles;
 
 if options_.contemporaneous_correlation
     if options_.estimation.moments_posterior_density.indicator
diff --git a/matlab/variance_decomposition_mc_analysis.m b/matlab/variance_decomposition_mc_analysis.m
index b9e2a114b..1a04fea48 100644
--- a/matlab/variance_decomposition_mc_analysis.m
+++ b/matlab/variance_decomposition_mc_analysis.m
@@ -86,30 +86,14 @@ for file = 1:length(ListOfFiles)
     i1 = i2+1;
 end
 
-t1 = min(tmp); t2 = max(tmp);
-t3 = t2-t1;% How to normalize ? t1 and t2 may be zero...
-if t3<1.0e-12
-    if t1<1.0e-12
-        t1 = 0;
-    end
-    if abs(t1-1)<1.0e-12
-        t1 = 1;
-    end
-    p_mean = t1;
-    p_median = t1;
-    p_var = 0;
-    hpd_interval = NaN(2,1);
-    p_deciles = NaN(9,1);
-    density = NaN;
+if options_.estimation.moments_posterior_density.indicator
+    [p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
+        posterior_moments(tmp,1,mh_conf_sig);
 else
-    if options_.estimation.moments_posterior_density.indicator
-        [p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
-            posterior_moments(tmp,1,mh_conf_sig);
-    else
-        [p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
-            posterior_moments(tmp,0,mh_conf_sig);        
-    end
+    [p_mean, p_median, p_var, hpd_interval, p_deciles] = ...
+        posterior_moments(tmp,0,mh_conf_sig);        
 end
+
 oo_.([TYPE, 'TheoreticalMoments']).dsge.VarianceDecomposition.Mean.(var).(exo) = p_mean;
 oo_.([TYPE, 'TheoreticalMoments']).dsge.VarianceDecomposition.Median.(var).(exo) = p_median;
 oo_.([TYPE, 'TheoreticalMoments']).dsge.VarianceDecomposition.Variance.(var).(exo) = p_var;