Use economy-size decomposition option of qr2 routine.

matlab/particle/fit_gaussian_mixture.m

@@ -22,7 +22,7 @@ for n=1:niters
     diffs = bsxfun(@minus,X,StateMu(:,j));
     tpost = (1/sqrt(new_pr(j)))*sqrt(posterior(j,:));
     diffs = bsxfun(@times,diffs,tpost);
-    [foo,tcov] = qr2(diffs') ;
+    [foo,tcov] = qr2(diffs',0);
     StateSqrtP(:,:,j) = tcov';
     if check
       if min(abs(diag(StateSqrtP(:,:,j)))) < MIN_COVAR_SQRT

matlab/particle/gaussian_filter_bank.m

@@ -100,7 +100,7 @@ if strcmpi(DynareOptions.particle.IS_approximation_method,'cubature') || strcmpi
     dObserved = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean)'.*sqrt(weights);
     PredictedStateVarianceSquareRoot = chol(dState'*dState)';
     big_mat = [dObserved  dState ; [H_lower_triangular_cholesky zeros(number_of_observed_variables,number_of_state_variables)] ] ;
-    [mat1,mat] = qr2(big_mat) ;
+    [mat1,mat] = qr2(big_mat,0) ;
     mat = mat' ; 
     clear('mat1');
     PredictedObservedVarianceSquareRoot = mat(1:number_of_observed_variables,1:number_of_observed_variables) ;

matlab/particle/gaussian_mixture_filter_bank.m

@@ -107,7 +107,7 @@ if strcmpi(DynareOptions.particle.IS_approximation_method,'cubature')
     dObserved = (bsxfun(@minus,tmp(mf1,:),PredictedObservedMean)').*sqrt(weights3);
     PredictedStateVariance = dState'*dState;
     big_mat = [dObserved  dState ; [H_lower_triangular_cholesky zeros(number_of_observed_variables,number_of_state_variables)] ] ;
-    [mat1,mat] = qr2(big_mat) ;
+    [mat1,mat] = qr2(big_mat,0) ;
     mat = mat' ; 
     clear('mat1');
     PredictedObservedVarianceSquareRoot = mat(1:number_of_observed_variables,1:number_of_observed_variables) ;