diff --git a/matlab/perfect-foresight-models/linear_approximation_accuracy.m b/matlab/perfect-foresight-models/linear_approximation_accuracy.m
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+++ b/matlab/perfect-foresight-models/linear_approximation_accuracy.m
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+function err = linear_approximation_accuracy(options_, M_, oo_)
+% Evaluates the accuracy of the linear approximation when solving perfect foresight models, by
+% reporting the max absolute value of the dynamic residuals.
+%
+% INPUTS 
+% - options_ [struct] contains various options.
+% - M_       [struct] contains a description of the model.
+% - oo_      [struct] contains results.
+%
+% OUTPUTS 
+% - err      [double] n*1 vector, evaluation of the accuracy (n is the number of equations).
+
+% Copyright (C) 2015 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+lead_lag_incidence = M_.lead_lag_incidence;
+
+ny = M_.endo_nbr;
+
+maximum_lag = M_.maximum_lag;
+
+periods = options_.periods;
+steady_state = oo_.steady_state;
+params = M_.params;
+endo_simul = oo_.endo_simul;
+exo_simul = oo_.exo_simul;
+
+model_dynamic = str2func([M_.fname,'_dynamic']);
+
+residuals = zeros(ny,periods);
+
+Y = endo_simul(:);
+
+i_cols = find(lead_lag_incidence')+(maximum_lag-1)*ny;
+
+for it = (maximum_lag+1):(maximum_lag+periods)
+    residuals(:,it-1) = model_dynamic(Y(i_cols), exo_simul, params, steady_state,it);
+    i_cols = i_cols + ny;
+end
+
+err = transpose(max(abs(transpose(residuals))));
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