diff --git a/matlab/ols/sur.m b/matlab/ols/sur.m
new file mode 100644
index 000000000..ce4b6fdab
--- /dev/null
+++ b/matlab/ols/sur.m
@@ -0,0 +1,228 @@
+function sur(ds)
+% function sur(ds)
+% Seemingly Unrelated Regressions
+%
+% INPUTS
+% ds [dseries] data to use in estimation
+%
+% OUTPUTS
+% none
+%
+% SPECIAL REQUIREMENTS
+% dynare must be run with the option: json=parse
+
+% Copyright (C) 2017 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 .
+
+global M_ oo_ options_
+
+%% Check input argument
+assert(~isempty(ds) && isdseries(ds), 'The first argument must be a dseries');
+
+%% Read JSON
+jsonfile = [M_.fname '_original.json'];
+if exist(jsonfile, 'file') ~= 2
+ error('Could not find %s! Please use the json=parse option (See the Dynare invocation section in the reference manual).', jsonfile);
+end
+
+jsonmodel = loadjson(jsonfile);
+jsonmodel = jsonmodel.model;
+[lhs, rhs, lineno] = getEquationsByTags(jsonmodel);
+
+%% Find parameters and variable names in equations and setup estimation matrices
+M_exo_names_trim = cellstr(M_.exo_names);
+M_endo_exo_names_trim = [cellstr(M_.endo_names); M_exo_names_trim];
+M_param_names_trim = cellstr(M_.param_names);
+regex = strjoin(M_endo_exo_names_trim(:,1), '|');
+mathops = '[\+\*\^\-\/]';
+params = cell(length(rhs),1);
+vars = cell(length(rhs),1);
+Y = [];
+X = [];
+startidxs = zeros(length(lhs), 1);
+startdates = cell(length(lhs), 1);
+enddates = cell(length(lhs), 1);
+residnames = cell(length(lhs), 1);
+pidxs = zeros(M_.param_nbr, 1);
+pidx = 0;
+vnamesall = {};
+for i = 1:length(lhs)
+ rhs_ = strsplit(rhs{i}, {'+','-','*','/','^','log(','ln(','log10(','exp(','(',')','diff('});
+ rhs_(cellfun(@(x) all(isstrprop(x, 'digit')), rhs_)) = [];
+ vnames = setdiff(rhs_, M_param_names_trim);
+ if ~isempty(regexp(rhs{i}, ...
+ ['(' strjoin(vnames, '\\(\\d+\\)|') '\\(\\d+\\))'], ...
+ 'once'))
+ error(['sur1: you cannot have leads in equation on line ' ...
+ lineno{i} ': ' lhs{i} ' = ' rhs{i}]);
+ end
+
+ % Find parameters and associated variables
+ pnames = intersect(rhs_, M_param_names_trim);
+ vnames = cell(1, length(pnames));
+ xjdata = dseries;
+ for j = 1:length(pnames)
+ pidx = pidx + 1;
+ pidxs(pidx, 1) = find(strcmp(pnames{j}, M_param_names_trim));
+ createdvar = false;
+ pregex = [...
+ mathops pnames{j} mathops ...
+ '|^' pnames{j} mathops ...
+ '|' mathops pnames{j} '$' ...
+ ];
+ [startidx, endidx] = regexp(rhs{i}, pregex, 'start', 'end');
+ assert(length(startidx) == 1);
+ if rhs{i}(startidx) == '*'
+ vnames{j} = getStrMoveLeft(rhs{i}(1:startidx-1));
+ elseif rhs{i}(endidx) == '*'
+ vnames{j} = getStrMoveRight(rhs{i}(endidx+1:end));
+ elseif rhs{i}(startidx) == '+' ...
+ || rhs{i}(startidx) == '-' ...
+ || rhs{i}(endidx) == '+' ...
+ || rhs{i}(endidx) == '-'
+ % intercept
+ createdvar = true;
+ if any(strcmp(M_endo_exo_names_trim, 'intercept'))
+ [~, vnames{j}] = fileparts(tempname);
+ vnames{j} = ['intercept_' vnames{j}];
+ assert(~any(strcmp(M_endo_exo_names_trim, vnames{j})));
+ else
+ vnames{j} = 'intercept';
+ end
+ else
+ error('sur1: Shouldn''t arrive here');
+ end
+ if createdvar
+ xjdatatmp = dseries(ones(ds.nobs, 1), ds.firstdate, vnames{j});
+ else
+ xjdatatmp = eval(regexprep(vnames{j}, regex, 'ds.$&'));
+ xjdatatmp.rename_(vnames{j});
+ end
+ xjdatatmp.rename_(num2str(j));
+ xjdata = [xjdata xjdatatmp];
+ end
+
+ residuals = intersect(rhs_, cellstr(M_.exo_names));
+ for j = 1:length(residuals)
+ if any(strcmp(residuals{j}, vnames))
+ residuals{j} = [];
+ end
+ end
+ idx = ~cellfun(@isempty, residuals);
+ assert(sum(idx) == 1, ['More than one residual in equation ' num2str(i)]);
+ residnames{i} = residuals{idx};
+
+ params{i} = pnames;
+ vars{i} = vnames;
+
+ ydata = eval(regexprep(lhs{i}, regex, 'ds.$&'));
+
+ fp = max(ydata.firstobservedperiod, xjdata.firstobservedperiod);
+ lp = min(ydata.lastobservedperiod, xjdata.lastobservedperiod);
+
+ startidxs(i) = length(Y) + 1;
+ startdates{i} = fp;
+ enddates{i} = lp;
+ Y(startidxs(i):startidxs(i)+lp-fp, 1) = ydata(fp:lp).data;
+ X(startidxs(i):startidxs(i)+lp-fp, end+1:end+size(xjdata(fp:lp).data,2)) = xjdata(fp:lp).data;
+end
+
+assert(size(X, 2) == M_.param_nbr, 'Not all parameters were used in model');
+
+%% Force equations to have the same sample range
+maxfp = max([startdates{:}]);
+minlp = min([enddates{:}]);
+nobs = minlp - maxfp;
+newY = zeros(nobs*length(lhs), 1);
+newX = zeros(nobs*length(lhs), columns(X));
+lastidx = 1;
+for i = 1:length(lhs)
+ if i == length(lhs)
+ yds = dseries(Y(startidxs(i):end), startdates{i});
+ xds = dseries(X(startidxs(i):end, :), startdates{i});
+ else
+ yds = dseries(Y(startidxs(i):startidxs(i+1)-1), startdates{i});
+ xds = dseries(X(startidxs(i):startidxs(i+1)-1, :), startdates{i});
+ end
+ newY(lastidx:lastidx + nobs, 1) = yds(maxfp:minlp).data;
+ newX(lastidx:lastidx + nobs, :) = xds(maxfp:minlp, :).data;
+ if i ~= length(lhs)
+ lastidx = lastidx + nobs + 1;
+ end
+end
+Y = newY;
+X = newX;
+
+%% Estimation
+% Estimated Parameters
+oo_.sur.dof = length(maxfp:minlp);
+[q, r] = qr(X, 0);
+xpxi = (r'*r)\eye(M_.param_nbr);
+resid = Y - X * (r\(q'*Y));
+resid = reshape(resid, oo_.sur.dof, length(lhs));
+
+M_.Sigma_e = resid'*resid/oo_.sur.dof;
+kLeye = kron(inv(M_.Sigma_e), eye(oo_.sur.dof));
+[q, r] = qr(kLeye*X, 0);
+oo_.sur.beta = r\(q'*kLeye*Y);
+M_.params(pidxs, 1) = oo_.sur.beta;
+
+% Yhat
+oo_.sur.Yhat = X * oo_.sur.beta;
+
+% Residuals
+oo_.sur.resid = Y - oo_.sur.Yhat;
+
+%% Calculate statistics
+% Estimate for sigma^2
+SS_res = oo_.sur.resid'*oo_.sur.resid;
+oo_.sur.s2 = SS_res/oo_.sur.dof;
+
+% R^2
+ym = Y - mean(Y);
+SS_tot = ym'*ym;
+oo_.sur.R2 = 1 - SS_res/SS_tot;
+
+% Adjusted R^2
+oo_.sur.adjR2 = oo_.sur.R2 - (1 - oo_.sur.R2)*M_.param_nbr/(oo_.sur.dof - 1);
+
+% Durbin-Watson
+ediff = oo_.sur.resid(2:oo_.sur.dof) - oo_.sur.resid(1:oo_.sur.dof - 1);
+oo_.sur.dw = (ediff'*ediff)/SS_res;
+
+% Standard Error
+oo_.sur.stderr = sqrt(oo_.sur.s2*diag(xpxi));
+
+% T-Stat
+oo_.sur.tstat = oo_.sur.beta./oo_.sur.stderr;
+
+%% Print Output
+if ~options_.noprint
+ preamble = {sprintf('Dependent Variable: %s', lhs{i}), ...
+ sprintf('No. Independent Variables: %d', M_.param_nbr), ...
+ sprintf('Observations: %d', oo_.sur.dof)};
+
+ afterward = {sprintf('R^2: %f', oo_.sur.R2), ...
+ sprintf('R^2 Adjusted: %f', oo_.sur.adjR2), ...
+ sprintf('s^2: %f', oo_.sur.s2), ...
+ sprintf('Durbin-Watson: %f', oo_.sur.dw)};
+
+ dyn_table('SUR Estimation', preamble, afterward, [vars{:}], ...
+ {'Coefficients','t-statistic','Std. Error'}, 4, ...
+ [oo_.sur.beta oo_.sur.tstat oo_.sur.stderr]);
+end
+end
diff --git a/matlab/sur.m b/matlab/sur.m
deleted file mode 100644
index 470399c29..000000000
--- a/matlab/sur.m
+++ /dev/null
@@ -1,196 +0,0 @@
-function varargout = sur(ds, varargin)
-%function varargout = sur(ds, varargin)
-% Run a Seemingly Unrelated Regression on the provided equations
-%
-% INPUTS
-% ds [dseries] data
-%
-% OUTPUTS
-% varargout [cell array] contains the common work between sur and
-% surgibbs
-%
-% SPECIAL REQUIREMENTS
-% none
-
-% Copyright (C) 2017 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 .
-
-global M_ oo_
-
-%% Check input
-assert(nargin == 1 || nargin == 3, 'Incorrect number of arguments passed to sur');
-
-jsonfile = [M_.fname '_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 Equations
-jsonmodel = loadjson(jsonfile);
-jsonmodel = jsonmodel.model;
-[lhs, rhs, lineno] = getEquationsByTags(jsonmodel, varargin{:});
-
-m = length(lhs);
-if m <= 1
- error('SUR estimation requires the selection of at least two equations')
-end
-
-%% Construct regression matrices
-Y = dseries();
-Xi = cell(m, 1);
-pnamesall = [];
-vwlagsall = [];
-for i = 1:m
- Y = [Y ds{lhs{i}}];
-
- rhs_ = strsplit(rhs{i}, {'+','-','*','/','^','log(','exp(','(',')'});
- rhs_(cellfun(@(x) all(isstrprop(x, 'digit')), rhs_)) = [];
- vnames = setdiff(rhs_, cellstr(M_.param_names));
- regexprnoleads = cell2mat(strcat('(', vnames, {'\(\d+\))|'}));
- if ~isempty(regexp(rhs{i}, regexprnoleads(1:end-1), 'match'))
- error(['sur: 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
- pnamesall = [pnamesall pnames];
- vwlagsall = [vwlagsall vwlags];
- Xi{i} = cellfun(@eval, strcat('ds.', vwlags), 'UniformOutput', false);
-end
-
-fp = Y.firstobservedperiod;
-lp = Y.lastobservedperiod;
-for i = 1:m
- X = dseries();
- for j = 1:length(Xi{i})
- X = [X dseries(Xi{i}{j}.data, Xi{i}{j}.dates, ['V' num2str(i) num2str(j)])];
- end
- Xi{i} = X;
- fp = max(fp, X.firstobservedperiod);
- lp = min(lp, X.lastobservedperiod);
-end
-Y = Y(fp:lp).data(:);
-X = [];
-for i = 1:m
- Xi{i} = Xi{i}(fp:lp).data;
- ind = size(X);
- X(ind(1)+1:ind(1)+size(Xi{i}, 1), ind(2)+1:ind(2)+size(Xi{i},2)) = Xi{i};
-end
-
-%% Estimation
-nobs = length(fp:lp);
-nvars = size(X, 2);
-[q, r] = qr(X, 0);
-xpxi = (r'*r)\eye(nvars);
-resid = Y - X * (r\(q'*Y));
-resid = reshape(resid, nobs, m);
-s2 = resid'*resid/nobs;
-tmp = kron(inv(s2), eye(nobs));
-beta = (X'*tmp*X)\X'*tmp*Y;
-
-% if called from surgibbs, return common work
-st = dbstack(1);
-if strcmp(st(1).name, 'surgibbs')
- varargout{1} = nobs;
- varargout{2} = nvars;
- varargout{3} = pnamesall;
- varargout{4} = beta;
- varargout{5} = X;
- varargout{6} = Y;
- varargout{7} = m;
- return
-end
-
-oo_.sur.s2 = s2;
-oo_.sur.beta = beta;
-
-for j = 1:length(pnamesall)
- M_.params(strmatch(pnamesall{j}, M_.param_names, 'exact')) = oo_.sur.beta(j);
-end
-
-% Yhat
-oo_.sur.Yhat = X * oo_.sur.beta;
-
-% Residuals
-oo_.sur.resid = Y - oo_.sur.Yhat;
-
-%% Calculate statistics
-oo_.sur.dof = nobs;
-
-% Estimate for sigma^2
-SS_res = oo_.sur.resid'*oo_.sur.resid;
-oo_.sur.s2 = SS_res/oo_.sur.dof;
-
-% R^2
-ym = Y - mean(Y);
-SS_tot = ym'*ym;
-oo_.sur.R2 = 1 - SS_res/SS_tot;
-
-% Adjusted R^2
-oo_.sur.adjR2 = oo_.sur.R2 - (1 - oo_.sur.R2)*nvars/(oo_.sur.dof-1);
-
-% Durbin-Watson
-ediff = oo_.sur.resid(2:nobs) - oo_.sur.resid(1:nobs-1);
-oo_.sur.dw = (ediff'*ediff)/SS_res;
-
-% Standard Error
-oo_.sur.stderr = sqrt(oo_.sur.s2*diag(xpxi));
-
-% T-Stat
-oo_.sur.tstat = oo_.sur.beta./oo_.sur.stderr;
-
-%% Print Output
-title = sprintf('SUR Estimation');
-if nargin == 1
- title = [title sprintf(' of all equations')];
-else
- title = [title s(' [%s = {', varargin{1})];
- for i = 1:length(varargin{2})
- if i ~= 1
- title = [title sprintf(', ')];
- end
- title = [title sprintf('%s', varargin{2}{i})];
- end
- title = [title sprintf('}]')];
-end
-
-preamble = {sprintf('Dependent Variable: %s', lhs{i}), ...
- sprintf('No. Independent Variables: %d', nvars), ...
- sprintf('Observations: %d', nobs)};
-
-afterward = {sprintf('R^2: %f', oo_.sur.R2), ...
- sprintf('R^2 Adjusted: %f', oo_.sur.adjR2), ...
- sprintf('s^2: %f', oo_.sur.s2), ...
- sprintf('Durbin-Watson: %f', oo_.sur.dw)};
-
-dyn_table(title, preamble, afterward, vwlagsall, ...
- {'Coefficients','t-statistic','Std. Error'}, 4, ...
- [oo_.sur.beta oo_.sur.tstat oo_.sur.stderr]);
-end
diff --git a/tests/ECB/SUR/panel_var_diff_NB_simulation_test.mod b/tests/ECB/SUR/panel_var_diff_NB_simulation_test.mod
new file mode 100644
index 000000000..db41db854
--- /dev/null
+++ b/tests/ECB/SUR/panel_var_diff_NB_simulation_test.mod
@@ -0,0 +1,151 @@
+// --+ options: json=compute +--
+
+/* REMARK
+** ------
+**
+** You need to have the first line on top of the mod file. The options defined on this line are passed
+** to the dynare command (you can add other options, separated by spaces or commas). The option defined
+** here is mandatory for the decomposition. It forces Dynare to output another representation of the
+** model in JSON file (additionaly to the matlab files) which is used here to manipulate the equations.
+*/
+
+var
+U2_Q_YED
+U2_G_YER
+U2_STN
+U2_ESTN
+U2_EHIC
+DE_Q_YED
+DE_G_YER
+DE_EHIC
+
+;
+
+varexo
+res_U2_Q_YED
+res_U2_G_YER
+res_U2_STN
+res_U2_ESTN
+res_U2_EHIC
+res_DE_Q_YED
+res_DE_G_YER
+res_DE_EHIC
+;
+
+parameters
+u2_q_yed_ecm_u2_q_yed_L1
+u2_q_yed_ecm_u2_stn_L1
+u2_q_yed_u2_g_yer_L1
+u2_q_yed_u2_stn_L1
+u2_g_yer_ecm_u2_q_yed_L1
+u2_g_yer_ecm_u2_stn_L1
+u2_g_yer_u2_q_yed_L1
+u2_g_yer_u2_g_yer_L1
+u2_g_yer_u2_stn_L1
+u2_stn_ecm_u2_q_yed_L1
+u2_stn_ecm_u2_stn_L1
+u2_stn_u2_q_yed_L1
+u2_stn_u2_g_yer_L1
+u2_estn_u2_estn_L1
+u2_ehic_u2_ehic_L1
+
+de_q_yed_ecm_de_q_yed_L1
+de_q_yed_ecm_u2_stn_L1
+de_q_yed_de_g_yer_L1
+de_q_yed_u2_stn_L1
+de_g_yer_ecm_de_q_yed_L1
+de_g_yer_ecm_u2_stn_L1
+de_g_yer_de_q_yed_L1
+de_g_yer_de_g_yer_L1
+de_g_yer_u2_stn_L1
+de_ehic_de_ehic_L1
+
+
+;
+
+u2_q_yed_ecm_u2_q_yed_L1 = -0.82237516589315 ;
+u2_q_yed_ecm_u2_stn_L1 = -0.323715338568976 ;
+u2_q_yed_u2_g_yer_L1 = 0.0401361895021084 ;
+u2_q_yed_u2_stn_L1 = 0.058397703958446 ;
+u2_g_yer_ecm_u2_q_yed_L1 = 0.0189896046977421 ;
+u2_g_yer_ecm_u2_stn_L1 = -0.109597659887432 ;
+u2_g_yer_u2_q_yed_L1 = 0.0037667967632025 ;
+u2_g_yer_u2_g_yer_L1 = 0.480506381923644 ;
+u2_g_yer_u2_stn_L1 = -0.0722359286123494 ;
+u2_stn_ecm_u2_q_yed_L1 = -0.0438500662608356 ;
+u2_stn_ecm_u2_stn_L1 = -0.153283917138772 ;
+u2_stn_u2_q_yed_L1 = 0.0328744983772825 ;
+u2_stn_u2_g_yer_L1 = 0.292121949736756 ;
+u2_estn_u2_estn_L1 = 1 ;
+u2_ehic_u2_ehic_L1 = 1 ;
+
+de_q_yed_ecm_de_q_yed_L1 = -0.822375165893149 ;
+de_q_yed_ecm_u2_stn_L1 = -0.323715338568977 ;
+de_q_yed_de_g_yer_L1 = 0.0401361895021082 ;
+de_q_yed_u2_stn_L1 = 0.0583977039584461 ;
+de_g_yer_ecm_de_q_yed_L1 = 0.0189896046977422 ;
+de_g_yer_ecm_u2_stn_L1 = -0.109597659887433 ;
+de_g_yer_de_q_yed_L1 = 0.00376679676320256;
+de_g_yer_de_g_yer_L1 = 0.480506381923643 ;
+de_g_yer_u2_stn_L1 = -0.0722359286123494 ;
+de_ehic_de_ehic_L1 = 1 ;
+
+
+model(linear);
+
+diff(U2_Q_YED) = u2_q_yed_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
+ + u2_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ + u2_q_yed_u2_g_yer_L1 * diff(U2_G_YER(-1))
+ + u2_q_yed_u2_stn_L1 * diff(U2_STN(-1))
+ + res_U2_Q_YED ;
+
+diff(U2_G_YER) = u2_g_yer_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
+ + u2_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ + u2_g_yer_u2_q_yed_L1 * diff(U2_Q_YED(-1))
+ + u2_g_yer_u2_g_yer_L1 * diff(U2_G_YER(-1))
+ + u2_g_yer_u2_stn_L1 * diff(U2_STN(-1))
+ + res_U2_G_YER ;
+
+diff(U2_STN) = u2_stn_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
+ + u2_stn_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ + u2_stn_u2_q_yed_L1 * diff(U2_Q_YED(-1))
+ + u2_stn_u2_g_yer_L1 * diff(U2_G_YER(-1))
+ + res_U2_STN ;
+
+U2_ESTN = u2_estn_u2_estn_L1 * U2_ESTN(-1)
+ + res_U2_ESTN ;
+
+U2_EHIC = u2_ehic_u2_ehic_L1 * U2_EHIC(-1)
+ + res_U2_EHIC ;
+
+diff(DE_Q_YED) = de_q_yed_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
+ + de_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ + de_q_yed_de_g_yer_L1 * diff(DE_G_YER(-1))
+ + de_q_yed_u2_stn_L1 * diff(U2_STN(-1))
+ + res_DE_Q_YED ;
+
+diff(DE_G_YER) = de_g_yer_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
+ + de_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ + de_g_yer_de_q_yed_L1 * diff(DE_Q_YED(-1))
+ + de_g_yer_de_g_yer_L1 * diff(DE_G_YER(-1))
+ + de_g_yer_u2_stn_L1 * diff(U2_STN(-1))
+ + res_DE_G_YER ;
+
+DE_EHIC = de_ehic_de_ehic_L1 * DE_EHIC(-1)
+ + res_DE_EHIC ;
+
+
+
+end;
+
+shocks;
+var res_U2_Q_YED = 0.005;
+var res_U2_G_YER = 0.005;
+var res_U2_STN = 0.005;
+var res_U2_ESTN = 0.005;
+var res_U2_EHIC = 0.005;
+var res_DE_Q_YED = 0.005;
+var res_DE_G_YER = 0.005;
+var res_DE_EHIC = 0.005;
+end;
+
diff --git a/tests/ECB/SUR/run_simulation_test.m b/tests/ECB/SUR/run_simulation_test.m
new file mode 100644
index 000000000..f514fceb1
--- /dev/null
+++ b/tests/ECB/SUR/run_simulation_test.m
@@ -0,0 +1,36 @@
+close all
+
+dynare panel_var_diff_NB_simulation_test.mod;
+
+NSIMS = 1000;
+
+options_.noprint = 1;
+calibrated_values = M_.params;
+Sigma_e = M_.Sigma_e;
+
+options_.bnlms.set_dynare_seed_to_default = false;
+
+M_endo_names_trim = cellstr(M_.endo_names);
+nparampool = length(M_.params);
+BETA = zeros(NSIMS, nparampool);
+for i=1:NSIMS
+ i
+ firstobs = rand(3, length(M_endo_names_trim));
+ M_.params = calibrated_values;
+ M_.Sigma_e = Sigma_e;
+ simdata = simul_backward_model(dseries(firstobs, dates('1995Q1'), M_endo_names_trim), 10000);
+ simdata = simdata(simdata.dates(5001:6000));
+ sur(simdata);
+ BETA(i, :) = M_.params';
+end
+
+mean(BETA)' - calibrated_values
+
+for i=1:nparampool
+ figure
+ hold on
+ title(strrep(M_.param_names(i,:), '_', '\_'));
+ histogram(BETA(:,i),50);
+ line([calibrated_values(i) calibrated_values(i)], [0 NSIMS/10], 'LineWidth', 2, 'Color', 'r');
+ hold off
+end
\ No newline at end of file