diff --git a/matlab/ols/olseqs.m b/matlab/ols/olseqs.m
deleted file mode 100644
index c91b1368e..000000000
--- a/matlab/ols/olseqs.m
+++ /dev/null
@@ -1,151 +0,0 @@
-function olseqs(ds, varargin)
-
-% Run OLS on chosen model equations
-%
-% INPUTS
-%   ds      [dseries]    data
-%
-% OUTPUTS
-%   none
-%
-% SPECIAL REQUIREMENTS
-%   none
-
-% Copyright (C) 2017-2018 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/>.
-
-global M_ oo_
-
-jsonfile = [M_.fname filesep() 'model' filesep() 'json' filesep() 'modfile-original.json']);
-if exist(jsonfile, 'file') ~= 2
-    error('Could not find %s! Please use the json option (See the Dynare invocation section in the reference manual).', jsonfile);
-end
-
-%% Get Equation(s)
-jsonmodel = loadjson(jsonfile);
-jsonmodel = jsonmodel.model;
-[lhs, rhs, lineno] = getEquationsByTags(jsonmodel, 'name', varargin{:});
-
-for i = 1:length(lhs)
-    %% Construct regression matrices
-    rhs_ = strsplit(rhs{i}, {'+','-','*','/','^','log(','exp(','(',')'});
-    rhs_(cellfun(@(x) all(isstrprop(x, 'digit')), rhs_)) = [];
-    vnames = setdiff(rhs_, M_.param_names);
-    regexprnoleads = cell2mat(strcat('(', vnames, {'\(\d+\))|'}));
-    if ~isempty(regexp(rhs{i}, regexprnoleads(1:end-1), 'match'))
-        error(['olseqs: you cannot have leads in equation on line ' ...
-            lineno{i} ': ' lhs{i} ' = ' rhs{i}]);
-    end
-    regexpr = cell2mat(strcat('(', vnames, {'\(-\d+\))|'}));
-    vwlags = regexp(rhs{i}, regexpr(1:end-1), 'match');
-
-    % Find parameters
-    pnames = cell(1, length(vwlags));
-    for j = 1:length(vwlags)
-        regexmatch = regexp(rhs{i}, ['(\w*\*?)?' strrep(strrep(vwlags{j}, '(', '\('), ')', '\)') '(\*?\w*)?'], 'match');
-        regexmatch = strsplit(regexmatch{:}, '*');
-        assert(length(regexmatch) == 2);
-        if strcmp(vwlags{j}, regexmatch{1})
-            pnames{j} = regexmatch{2};
-        else
-            pnames{j} = regexmatch{1};
-        end
-    end
-
-    Y = ds{lhs{i}};
-    Xt = cellfun(@eval, strcat('ds.', vwlags), 'UniformOutput', false);
-    X = dseries();
-    for j = 1:length(Xt)
-        Xt{j}.rename_(vwlags{j});
-        X = [X Xt{j}];
-    end
-    fp = max(Y.firstobservedperiod, X.firstobservedperiod);
-    lp = min(Y.lastobservedperiod, X.lastobservedperiod);
-
-    Y = Y(fp:lp).data;
-    X = X(fp:lp).data;
-
-    %% Estimation
-    % From LeSage, James P. "Applied Econometrics using MATLAB"
-    if nargin == 2
-        if iscell(varargin{1})
-            tagv = varargin{1}{i};
-        else
-            tagv = varargin{1};
-        end
-    else
-        tagv = ['eqlineno' num2str(lineno{i})];
-    end
-    [nobs, nvars] = size(X);
-    oo_.ols.(tagv).dof = nobs - nvars;
-
-    % Estimated Parameters
-    [q, r] = qr(X, 0);
-    xpxi = (r'*r)\eye(nvars);
-    oo_.ols.(tagv).beta = r\(q'*Y);
-    for j = 1:length(pnames)
-        M_.params(strmatch(pnames{j}, M_.param_names, 'exact')) = oo_.ols.(tagv).beta(j);
-    end
-
-    % Yhat
-    oo_.ols.(tagv).Yhat = X*oo_.ols.(tagv).beta;
-
-    % Residuals
-    oo_.ols.(tagv).resid = Y - oo_.ols.(tagv).Yhat;
-
-    %% Calculate statistics
-    % Estimate for sigma^2
-    SS_res = oo_.ols.(tagv).resid'*oo_.ols.(tagv).resid;
-    oo_.ols.(tagv).s2 = SS_res/oo_.ols.(tagv).dof;
-
-    % R^2
-    ym = Y - mean(Y);
-    SS_tot = ym'*ym;
-    oo_.ols.(tagv).R2 = 1 - SS_res/SS_tot;
-
-    % Adjusted R^2
-    oo_.ols.(tagv).adjR2 = oo_.ols.(tagv).R2 - (1 - oo_.ols.(tagv).R2)*nvars/(oo_.ols.(tagv).dof-1);
-
-    % Durbin-Watson
-    ediff = oo_.ols.(tagv).resid(2:nobs) - oo_.ols.(tagv).resid(1:nobs-1);
-    oo_.ols.(tagv).dw = (ediff'*ediff)/SS_res;
-
-    % Standard Error
-    oo_.ols.(tagv).stderr = sqrt(oo_.ols.(tagv).s2*diag(xpxi));
-
-    % T-Stat
-    oo_.ols.(tagv).tstat = oo_.ols.(tagv).beta./oo_.ols.(tagv).stderr;
-
-    %% Print Output
-    title = sprintf('OLS Estimation of equation  `%s`', tagv);
-    if nargin == 3
-        title = [title sprintf(' [%s = %s]', 'name', tagv)];
-    end
-
-    preamble = {sprintf('Dependent Variable: %s', lhs{i}), ...
-        sprintf('No. Independent Variables: %d', nvars), ...
-        sprintf('Observations: %d', nobs)};
-
-    afterward = {sprintf('R^2: %f', oo_.ols.(tagv).R2), ...
-        sprintf('R^2 Adjusted: %f', oo_.ols.(tagv).adjR2), ...
-        sprintf('s^2: %f', oo_.ols.(tagv).s2), ...
-        sprintf('Durbin-Watson: %f', oo_.ols.(tagv).dw)};
-
-    dyn_table(title, preamble, afterward, vwlags, ...
-        {'Coefficients','t-statistic','Std. Error'}, 4, ...
-        [oo_.ols.(tagv).beta oo_.ols.(tagv).tstat oo_.ols.(tagv).stderr]);
-end
\ No newline at end of file