diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 2834c8b80..d305828e1 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -37,7 +37,7 @@ else
     objective_function = str2func('DsgeVarLikelihood');
 end
 
-[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
+[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
 
 data = dataset_.data;
 rawdata = dataset_.rawdata;
diff --git a/matlab/dynare_estimation_init.m b/matlab/dynare_estimation_init.m
index d14f4d6be..9dc2ae568 100644
--- a/matlab/dynare_estimation_init.m
+++ b/matlab/dynare_estimation_init.m
@@ -308,81 +308,3 @@ end
 dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
 
 options_.nobs = dataset_.info.ntobs;
-
-% $$$ rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
-% $$$ % Set the number of observations (nobs) and build a subsample between first_obs and nobs.
-% $$$ options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
-% $$$ gend = options_.nobs;
-% $$$ rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
-% $$$ % Take the log of the variables if needed
-% $$$ if options_.loglinear      % If the model is log-linearized...
-% $$$     if ~options_.logdata   % and if the data are not in logs, then...
-% $$$         rawdata = log(rawdata);
-% $$$     end
-% $$$ end
-% $$$ % Test if the observations are real numbers.
-% $$$ if ~isreal(rawdata)
-% $$$     error('There are complex values in the data! Probably  a wrong transformation')
-% $$$ end
-% $$$ % Test for missing observations.
-% $$$ options_.missing_data = any(any(isnan(rawdata)));
-% $$$ % Prefilter the data if needed.
-% $$$ if options_.prefilter == 1
-% $$$     if options_.missing_data
-% $$$         bayestopt_.mean_varobs = zeros(n_varobs,1);
-% $$$         for variable=1:n_varobs
-% $$$             rdx = find(~isnan(rawdata(:,variable)));
-% $$$             m = mean(rawdata(rdx,variable));
-% $$$             rawdata(rdx,variable) = rawdata(rdx,variable)-m;
-% $$$             bayestopt_.mean_varobs(variable) = m;
-% $$$         end
-% $$$     else
-% $$$         bayestopt_.mean_varobs = mean(rawdata,1)';
-% $$$         rawdata = rawdata-repmat(bayestopt_.mean_varobs',gend,1);
-% $$$     end
-% $$$ end
-% $$$ % Transpose the dataset array.
-% $$$ data = transpose(rawdata);
-
-if nargout>7
-    % Compute the steady state:
-    if options_.steadystate_flag% if the *_steadystate.m file is provided.
-        [ys,tchek] = feval([M_.fname '_steadystate'],...
-                           [zeros(M_.exo_nbr,1);...
-                            oo_.exo_det_steady_state]);
-        if size(ys,1) < M_.endo_nbr
-            if length(M_.aux_vars) > 0
-                ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
-                                                            M_.fname,...
-                                                            zeros(M_.exo_nbr,1),...
-                                                            oo_.exo_det_steady_state,...
-                                                            M_.params,...
-                                                            options_.bytecode);
-            else
-                error([M_.fname '_steadystate.m doesn''t match the model']);
-            end
-        end
-        oo_.steady_state = ys;
-    else% if the steady state file is not provided.
-        [dd,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
-        oo_.steady_state = dd.ys; clear('dd');
-    end
-    if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
-        options_.noconstant = 1;
-    else
-        options_.noconstant = 0;
-    end
-
-    fake = [];
-% $$$     [data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data,gend,n_varobs);
-% $$$     missing_value = ~(number_of_observations == gend*n_varobs);
-% $$$ 
-% $$$ %     initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
-% $$$ 
-% $$$     data_info.gend = gend;
-% $$$     data_info.data = data;
-% $$$     data_info.data_index = data_index;
-% $$$     data_info.number_of_observations = number_of_observations;
-% $$$     data_info.no_more_missing_observations = no_more_missing_observations;
-% $$$     data_info.missing_value = missing_value;
-end