diff --git a/matlab/DsgeSmoother.m b/matlab/DsgeSmoother.m
index f3718efe4..a0a68acc5 100644
--- a/matlab/DsgeSmoother.m
+++ b/matlab/DsgeSmoother.m
@@ -1,4 +1,4 @@
-function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value)
+function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value)
 % Estimation of the smoothed variables and innovations. 
 % 
 % INPUTS 
@@ -21,8 +21,6 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,d,
 %                   matrices
 %    PK:            4D array of k-step ahead forecast error variance
 %                   matrices (meaningless for periods 1:d)
-%    d:             number of periods where filter remains in diffuse part
-%                  (should be equal to the order of integration of the model)
 %    
 % ALGORITHM 
 %   Diffuse Kalman filter (Durbin and Koopman)       
@@ -60,7 +58,6 @@ T             = [];
 R             = [];
 P             = [];
 PK            = [];
-d             = [];
 decomp        = [];
 nobs            = size(options_.varobs,1);
 smpl          = size(Y,2);
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 55833b74f..f35c0ae2d 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -356,10 +356,9 @@ initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no
 
 if options_.mode_compute == 0 && length(options_.mode_file) == 0
     if options_.smoother == 1
-        [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
+        [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
         oo_.Smoother.SteadyState = ys;
         oo_.Smoother.TrendCoeffs = trend_coeff;
-        oo_.Smoother.integration_order = d;
         if options_.filter_covariance
             oo_.Smoother.variance = P;
         end
@@ -1112,10 +1111,9 @@ if (~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape
                                                       > 0) & options_.load_mh_file)) ...
     | ~options_.smoother ) & M_.endo_nbr^2*gend < 1e7 & options_.partial_information == 0  % to be fixed   
     %% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
-    [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,d,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
+    [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
     oo_.Smoother.SteadyState = ys;
     oo_.Smoother.TrendCoeffs = trend_coeff;
-    oo_.Smoother.integration_order = d;
     oo_.Smoother.variance = P;
     i_endo = bayestopt_.smoother_saved_var_list;
     if options_.nk ~= 0