diff --git a/matlab/DsgeLikelihood.m b/matlab/DsgeLikelihood.m
index 11a98fa58..45f1b11a8 100644
--- a/matlab/DsgeLikelihood.m
+++ b/matlab/DsgeLikelihood.m
@@ -193,15 +193,22 @@ elseif options_.lik_init == 3   % Diffuse Kalman filter
     [Z,ST,R1,QT,Pstar,Pinf] = schur_statespace_transformation(mf,T,R,Q,options_.qz_criterium);
 elseif options_.lik_init==4
     % Start from the solution of the Riccati equation.
-    [err, Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(mf,np,length(mf))),H);
-    mexErrCheck('kalman_steady_state',err);
-    Pinf  = [];
-    if kalman_algo~=2
+    if kalman_algo ~= 2
         kalman_algo = 1;
     end
+    if isequal(H,0)
+        [err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(mf,np,length(mf))));
+    else
+        [err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(mf,np,length(mf))),H);
+    end
+    if err
+        disp(['I am not able to solve the Riccati equation so I switch to lik_init=1!']);
+        options_.lik_init = 1;
+        Pstar = lyapunov_symm(T,R*Q*R',options_.qz_criterium,options_.lyapunov_complex_threshold);
+    end
+    Pinf  = [];
 end
-if kalman_algo == 2
-end
+
 kalman_tol = options_.kalman_tol;
 riccati_tol = options_.riccati_tol;
 mf = bayestopt_.mf1;