diff --git a/matlab/DsgeSmoother.m b/matlab/DsgeSmoother.m
index 51dab6569eb88bf4d801378e3dc6190cfa95bf51..e8e76f97e02015bf064a2959463cbadbe7df9b2b 100644
--- a/matlab/DsgeSmoother.m
+++ b/matlab/DsgeSmoother.m
@@ -74,7 +74,7 @@ bayestopt_.mf = bayestopt_.smoother_mf;
 if options_.noconstant
     constant = zeros(nobs,1);
 else
-    if options_.loglinear == 1
+    if options_.loglinear
         constant = log(SteadyState(bayestopt_.mfys));
     else
         constant = SteadyState(bayestopt_.mfys);
diff --git a/matlab/PosteriorFilterSmootherAndForecast.m b/matlab/PosteriorFilterSmootherAndForecast.m
index d39d67d35d28ebe7b273e89e16a34f1c166f40eb..0b3bd1e35075aa96e2c8dbd3ad62d516dbbe540c 100644
--- a/matlab/PosteriorFilterSmootherAndForecast.m
+++ b/matlab/PosteriorFilterSmootherAndForecast.m
@@ -170,19 +170,19 @@ for b=1:B
                                              horizon+maxlag,1);
         end
         yf(:,IdObs) = yf(:,IdObs)+(gend+[1-maxlag:horizon]')*trend_coeff';
-        if options_.loglinear == 1
+        if options_.loglinear
             yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
         else
             yf = yf+repmat(SteadyState',horizon+maxlag,1);
         end
         yf1 = forcst2(yyyy,horizon,dr,1);
-        if options_.prefilter == 1
+        if options_.prefilter
             yf1(:,IdObs,:) = yf1(:,IdObs,:)+ ...
                 repmat(bayestopt_.mean_varobs',[horizon+maxlag,1,1]);
         end
         yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat((gend+[1-maxlag:horizon]')* ...
                                                trend_coeff',[1,1,1]);
-        if options_.loglinear == 1
+        if options_.loglinear
             yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
         else
             yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);
diff --git a/matlab/dr_block.m b/matlab/dr_block.m
index 6a707f45b8c02e993658b44743d1e87d214f1790..1878d572cea27887fd110f942cd485b6acf8cdce 100644
--- a/matlab/dr_block.m
+++ b/matlab/dr_block.m
@@ -628,7 +628,7 @@ for i = 1:Size;
 
 
             
-            if options_.loglinear == 1
+            if options_.loglinear
                 error('log linear option is for the moment not supported in first order approximation for a block decomposed mode');
 %                 k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
 %                 klag = dr.kstate(k,[1 2]);
diff --git a/matlab/dynare_sensitivity.m b/matlab/dynare_sensitivity.m
index e4df56984ca3fe29568c7119d06437391275dac3..4b106f2efac49ce0b1c42a423e957e0ac3c0b581 100644
--- a/matlab/dynare_sensitivity.m
+++ b/matlab/dynare_sensitivity.m
@@ -388,7 +388,7 @@ if options_gsa.glue,
     gend = options_.nobs;
     rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
     rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
-    if options_.loglinear == 1
+    if options_.loglinear
         rawdata = log(rawdata);
     end
     if options_.prefilter == 1
diff --git a/matlab/k_order_pert.m b/matlab/k_order_pert.m
index 315c4494a8d999fc9c3b4c1d8b1a41706862edfc..db1ef91f48587ba727b132f73f37c53128f49c8d 100644
--- a/matlab/k_order_pert.m
+++ b/matlab/k_order_pert.m
@@ -97,11 +97,10 @@ else
     dr.ghx = dr.g_1(:,1:nspred);
     dr.ghu = dr.g_1(:,nspred+1:end);
 
-    if options.loglinear == 1
+    if options.loglinear
         k = find(dr.kstate(:,2) <= M.maximum_endo_lag+1);
         klag = dr.kstate(k,[1 2]);
         k1 = dr.order_var;
-    
         dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
                  repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
         dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
diff --git a/matlab/prior_posterior_statistics_core.m b/matlab/prior_posterior_statistics_core.m
index f7eaa6f28ee2eb1d1e8cb06ec32d8ec9e6f7e980..41b5b6d33f579d4d8b342999e162083e371b2076 100644
--- a/matlab/prior_posterior_statistics_core.m
+++ b/matlab/prior_posterior_statistics_core.m
@@ -195,12 +195,12 @@ for b=fpar:B
         if horizon
             yyyy = alphahat(iendo,i_last_obs);
             yf = forcst2a(yyyy,dr,zeros(horizon,exo_nbr));
-            if options_.prefilter == 1
+            if options_.prefilter
                 yf(:,IdObs) = yf(:,IdObs)+repmat(bayestopt_.mean_varobs', ...
                                                  horizon+maxlag,1);
             end
             yf(:,IdObs) = yf(:,IdObs)+(gend+[1-maxlag:horizon]')*trend_coeff';
-            if options_.loglinear == 1
+            if options_.loglinear
                 yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
             else
                 yf = yf+repmat(SteadyState',horizon+maxlag,1);
@@ -212,7 +212,7 @@ for b=fpar:B
             end
             yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat((gend+[1-maxlag:horizon]')* ...
                                                    trend_coeff',[1,1,1]);
-            if options_.loglinear == 1
+            if options_.loglinear
                 yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
             else
                 yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);
diff --git a/matlab/stochastic_solvers.m b/matlab/stochastic_solvers.m
index 0d4827c33130ded547258a18b1d2b945644ae3b7..dd835e2ba611eb90ff732f71e1ccafd0e8406723 100644
--- a/matlab/stochastic_solvers.m
+++ b/matlab/stochastic_solvers.m
@@ -297,22 +297,20 @@ if M_.exo_det_nbr > 0
                                       kron(hudi,Eud)+dr.ghxud{i-1}(kf,:)* ...
                                       kron(hudj,Eud)+dr.ghxx(kf,:)*kron(hudj,hudi))-M1*R2;
             end
-            
         end
     end
 end
 
-if options_.loglinear == 1
+if options_.loglinear
     % this needs to be extended for order=2,3
     k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
     klag = dr.kstate(k,[1 2]);
     k1 = dr.order_var;
-    
     dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
              repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
     dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
     if options_.order>1
-       error('Loglinear options currently only works at order 1') 
+       error('Loglinear options currently only works at order 1')
     end
 end