diff --git a/matlab/DsgeLikelihood.m b/matlab/DsgeLikelihood.m
index 6118abcb1..1f273db77 100644
--- a/matlab/DsgeLikelihood.m
+++ b/matlab/DsgeLikelihood.m
@@ -89,7 +89,7 @@ function [fval,cost_flag,ys,trend_coeff] = DsgeLikelihood(xparam1,gend,data)
     offset = offset+estim_params_.ncn;
   end
   for i=1:estim_params_.np
-    M_.params(i) = xparam1(i+offset);
+    M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);
   end
   M_.Sigma_e = Q;
   %------------------------------------------------------------------------------
diff --git a/matlab/DsgeSmoother.m b/matlab/DsgeSmoother.m
index a91a42766..6d654cfb0 100644
--- a/matlab/DsgeSmoother.m
+++ b/matlab/DsgeSmoother.m
@@ -2,113 +2,112 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff] = DsgeSmoothe
 % stephane.adjemian@cepremap.cnrs.fr [09-07-2004]
 %
 % Adapted from mj_optmumlik.m
-global bayestopt_ M_ oo_ estim_params_ options_
+  global bayestopt_ M_ oo_ estim_params_ options_
 
-alphahat 	= [];
-epsilonhat	= [];
-etahat		= [];
-nobs 		= size(options_.varobs,1);
-smpl        = size(Y,2);
+  alphahat 	= [];
+  epsilonhat	= [];
+  etahat		= [];
+  nobs 		= size(options_.varobs,1);
+  smpl        = size(Y,2);
 
-Q = M_.Sigma_e;
-for i=1:estim_params_.nvx
-	k =estim_params_.var_exo(i,1);
-	Q(k,k) = xparam1(i)*xparam1(i);
-end
-offset = estim_params_.nvx;
-if estim_params_.nvn
-	H = zeros(nobs,nobs);
-	for i=1:estim_params_.nvn
-		k = estim_params_.var_endo(i,1);
-		H(k,k) = xparam1(i+offset)*xparam1(i+offset);
-	end
-end	
-offset = offset+estim_params_.nvn;
-for i=1:estim_params_.ncx
-	k1 =estim_params_.corrx(i,1);
-	k2 =estim_params_.corrx(i,2);
-	Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
-	Q(k2,k1) = Q(k1,k2);
-end
-offset = offset+estim_params_.ncx;
+  Q = M_.Sigma_e;
+  for i=1:estim_params_.nvx
+    k =estim_params_.var_exo(i,1);
+    Q(k,k) = xparam1(i)*xparam1(i);
+  end
+  offset = estim_params_.nvx;
+  if estim_params_.nvn
+    H = zeros(nobs,nobs);
+    for i=1:estim_params_.nvn
+      k = estim_params_.var_endo(i,1);
+      H(k,k) = xparam1(i+offset)*xparam1(i+offset);
+    end
+  end	
+  offset = offset+estim_params_.nvn;
+  for i=1:estim_params_.ncx
+    k1 =estim_params_.corrx(i,1);
+    k2 =estim_params_.corrx(i,2);
+    Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
+    Q(k2,k1) = Q(k1,k2);
+  end
+  offset = offset+estim_params_.ncx;
 
-if estim_params_.nvn & estim_params_.ncn 
-	for i=1:estim_params_.ncn
-		k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
-		k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
-		H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
-		H(k2,k1) = H(k1,k2);
-	end
-	offset = offset+estim_params_.ncn;
-end	
-for i=1:estim_params_.np
-	% assignin('base',deblank(estim_params_.param_names(i,:)),xparam1(i+offset));
-	M_.params(i) = xparam1(i+offset);
-end
-M_.Sigma_e = Q;
-%------------------------------------------------------------------------------
-% 2. call model setup & reduction program
-%------------------------------------------------------------------------------
-[T,R,SteadyState] = dynare_resolve;
-if options_.loglinear == 1
-	constant = log(SteadyState(bayestopt_.mfys));
-else
-	constant = SteadyState(bayestopt_.mfys);
-end
-trend_coeff = zeros(nobs,1);
-if bayestopt_.with_trend == 1
-	trend_coeff = zeros(nobs,1);
-	nx1 = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn;
-	for i=1:nobs
-    	trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i});
-	end
-	trend = constant*ones(1,gend)+trend_coeff*(1:gend);
-else
-	trend = constant*ones(1,gend);
-end
-start = options_.presample+1;
-np    = size(T,1);
-mf    = bayestopt_.mf;
-% ------------------------------------------------------------------------------
-%  3. Initial condition of the Kalman filter
-% ------------------------------------------------------------------------------
-% 
-%  C'est ici qu'il faut déterminer Pinf et Pstar. Si le modèle est stationnaire,
-%  alors il suffit de poser Pstar comme la solution de l'éuation de Lyapounov et
-%  Pinf=[].
-%
-if options_.lik_init == 1		% Kalman filter
-  Pstar = lyapunov_symm(T,R*Q*transpose(R));
-  Pinf	= [];
-elseif options_.lik_init == 2 % Old Diffuse Kalman filter
-  Pstar = 10*eye(np);
-  Pinf	= [];
-elseif options_.lik_init == 3 % Diffuse Kalman filter
-  Pstar = zeros(np,np);
-  ivs = bayestopt_.i_T_var_stable;
-  Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R(ivs,:)*Q* ...
-			transpose(R(ivs,:)));
-  Pinf  = bayestopt_.Pinf;
-end
-% -----------------------------------------------------------------------------
-%  4. Kalman smoother
-% -----------------------------------------------------------------------------
-if estim_params_.nvn
-	if options_.kalman_algo == 1
-		[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
-		if all(alphahat(:)==0)
-			[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
-		end
-	elseif options_.kalman_algo == 3
-		[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
-	end
-else
-	if options_.kalman_algo == 1
-		[alphahat,etahat,ahat] = DiffuseKalmanSmoother1(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
-		if all(alphahat(:)==0)
-			[alphahat,etahat,ahat] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
-		end
-	elseif options_.kalman_algo == 3
-		[alphahat,etahat,ahat] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
-	end
-end
\ No newline at end of file
+  if estim_params_.nvn & estim_params_.ncn 
+    for i=1:estim_params_.ncn
+      k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
+      k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
+      H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
+      H(k2,k1) = H(k1,k2);
+    end
+    offset = offset+estim_params_.ncn;
+  end	
+  for i=1:estim_params_.np
+    M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);
+  end
+  M_.Sigma_e = Q;
+  %------------------------------------------------------------------------------
+  % 2. call model setup & reduction program
+  %------------------------------------------------------------------------------
+  [T,R,SteadyState] = dynare_resolve;
+  if options_.loglinear == 1
+    constant = log(SteadyState(bayestopt_.mfys));
+  else
+    constant = SteadyState(bayestopt_.mfys);
+  end
+  trend_coeff = zeros(nobs,1);
+  if bayestopt_.with_trend == 1
+    trend_coeff = zeros(nobs,1);
+    nx1 = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn;
+    for i=1:nobs
+      trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i});
+    end
+    trend = constant*ones(1,gend)+trend_coeff*(1:gend);
+  else
+    trend = constant*ones(1,gend);
+  end
+  start = options_.presample+1;
+  np    = size(T,1);
+  mf    = bayestopt_.mf;
+  % ------------------------------------------------------------------------------
+  %  3. Initial condition of the Kalman filter
+  % ------------------------------------------------------------------------------
+  % 
+  %  C'est ici qu'il faut déterminer Pinf et Pstar. Si le modèle est stationnaire,
+  %  alors il suffit de poser Pstar comme la solution de l'éuation de Lyapounov et
+  %  Pinf=[].
+  %
+  if options_.lik_init == 1		% Kalman filter
+    Pstar = lyapunov_symm(T,R*Q*transpose(R));
+    Pinf	= [];
+  elseif options_.lik_init == 2 % Old Diffuse Kalman filter
+    Pstar = 10*eye(np);
+    Pinf	= [];
+  elseif options_.lik_init == 3 % Diffuse Kalman filter
+    Pstar = zeros(np,np);
+    ivs = bayestopt_.i_T_var_stable;
+    Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R(ivs,:)*Q* ...
+				   transpose(R(ivs,:)));
+    Pinf  = bayestopt_.Pinf;
+  end
+  % -----------------------------------------------------------------------------
+  %  4. Kalman smoother
+  % -----------------------------------------------------------------------------
+  if estim_params_.nvn
+    if options_.kalman_algo == 1
+      [alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
+      if all(alphahat(:)==0)
+	[alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
+      end
+    elseif options_.kalman_algo == 3
+      [alphahat,epsilonhat,etahat,ahat] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
+    end
+  else
+    if options_.kalman_algo == 1
+      [alphahat,etahat,ahat] = DiffuseKalmanSmoother1(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
+      if all(alphahat(:)==0)
+	[alphahat,etahat,ahat] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
+      end
+    elseif options_.kalman_algo == 3
+      [alphahat,etahat,ahat] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
+    end
+  end