diff --git a/tests/pac/.gitignore b/tests/pac/.gitignore
new file mode 100644
index 000000000..7f69f328c
--- /dev/null
+++ b/tests/pac/.gitignore
@@ -0,0 +1,14 @@
+*~
+*.mat
+*.eps
+*.fig
+*.pdf
+*.log
+*.tex
+*_dynamic.m
+*_static.m
+
+*.m
+*.json
+
+checksum
diff --git a/tests/pac/LICENSE b/tests/pac/LICENSE
new file mode 100644
index 000000000..706edf868
--- /dev/null
+++ b/tests/pac/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    pac-dynare
+    Copyright (C) 2018  Stéphane Adjemian
+
+    This program 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.
+
+    This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    pac-dynare  Copyright (C) 2018  Stéphane Adjemian
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/tests/pac/README.md b/tests/pac/README.md
new file mode 100644
index 000000000..a4a302bbd
--- /dev/null
+++ b/tests/pac/README.md
@@ -0,0 +1,29 @@
+Collection of `*.mod` files for testing the PAC routines in Dynare. To run all the tests sequentially and check that all the `*.mod` files pass the tests, just use the matlab function in the base directory:
+
+```matlab
+>> run_all_tests()
+```
+
+If all goes well, the output should terminate with something like:
+
+```example
+Testsuite results (PAC model):
+
+var-1		             PASS (2.0479s)
+var-2		             PASS (1.9601s)
+var-3		             PASS (1.9826s)
+var-4		             PASS (2.0079s)
+trend-component-1		 PASS (2.2214s)
+trend-component-2		 PASS (2.2195s)
+trend-component-3		 PASS (2.3003s)
+trend-component-4		 PASS (10.5143s)
+trend-component-5		 PASS (2.1538s)
+trend-component-6		 PASS (2.4203s)
+trend-component-7		 PASS (2.7112s)
+trend-component-9		 PASS (2.2164s)
+trend-component-10		 PASS (2.2593s)
+trend-component-11		 PASS (0.69409s)
+trend-component-12		 PASS (2.2663s)
+trend-component-13a		 PASS (0.4119s)
+trend-component-13b		 PASS (0.39554s)
+```
diff --git a/tests/pac/run_all_tests.m b/tests/pac/run_all_tests.m
new file mode 100644
index 000000000..27cf54e49
--- /dev/null
+++ b/tests/pac/run_all_tests.m
@@ -0,0 +1,68 @@
+function run_all_tests()
+
+% Copyright (C) 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/>.
+
+r = [];
+
+r = [r; run_this_test('var-1')];
+r = [r; run_this_test('var-2')];
+r = [r; run_this_test('var-3')];
+r = [r; run_this_test('var-4')];
+
+r = [r; run_this_test('trend-component-1')];
+r = [r; run_this_test('trend-component-2')];
+r = [r; run_this_test('trend-component-3')];
+r = [r; run_this_test('trend-component-4')];
+r = [r; run_this_test('trend-component-5')];
+r = [r; run_this_test('trend-component-6')];
+r = [r; run_this_test('trend-component-7')];
+r = [r; run_this_test('trend-component-9')];
+r = [r; run_this_test('trend-component-10')];
+r = [r; run_this_test('trend-component-11')];
+r = [r; run_this_test('trend-component-12')];
+r = [r; run_this_test('trend-component-13a')];
+r = [r; run_this_test('trend-component-13b')];
+
+print_results(r);
+
+function o = run_this_test(folder)
+try
+    cd(folder)
+    tstart = tic;
+    clear M_ oo_ options_
+    dynare example
+    elapsed = toc(tstart);
+    o = {folder, true, elapsed};
+    system('./clean')
+    cd ..
+catch
+    o = {folder, false, NaN};
+    system('./clean')
+    cd ..
+end
+
+function print_results(r)
+message = sprintf('Testsuite results (PAC model):\n');
+for i = 1:size(r, 1)
+    if r{i,2}
+        message = sprintf('%s\n%s\t\t PASS (%ss)', message, r{i,1}, num2str(r{i,3}));
+    else
+        message = sprintf('%s\n%s\t\t FAILED', message, r{i,1});
+    end
+end
+disp(message)
diff --git a/tests/pac/trend-component-1/clean b/tests/pac/trend-component-1/clean
new file mode 100755
index 000000000..be0d5e00c
--- /dev/null
+++ b/tests/pac/trend-component-1/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/trend-component-1/example.mod b/tests/pac/trend-component-1/example.mod
new file mode 100644
index 000000000..dc4d84233
--- /dev/null
+++ b/tests/pac/trend-component-1/example.mod
@@ -0,0 +1,72 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z ;
+
+varexo ex1 ex2 ex1bar ex2bar ez ;
+
+parameters a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+gamma =  .7;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='eq:pac']
+diff(z) = gamma*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-gamma)*ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
\ No newline at end of file
diff --git a/tests/pac/trend-component-10/clean b/tests/pac/trend-component-10/clean
new file mode 100755
index 000000000..bdc7da19a
--- /dev/null
+++ b/tests/pac/trend-component-10/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
\ No newline at end of file
diff --git a/tests/pac/trend-component-10/example.mod b/tests/pac/trend-component-10/example.mod
new file mode 100644
index 000000000..bf99be3b2
--- /dev/null
+++ b/tests/pac/trend-component-10/example.mod
@@ -0,0 +1,95 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 beta;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(log(x1)) = a_x1_0*(log(x1(-1))-x1bar(-1)) + a_x1_1*diff(log(x1(-1))) + a_x1_2*diff(log(x1(-2))) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(log(x1(-1))) + a_x2_2*diff(log(x1(-2))) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(log(x1(-1))-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = .1;
+    var ex2bar = .1;
+    var ez = 1.0;
+    var ey = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+INITIALCONDITIONS = zeros(10, M_.endo_nbr+M_.exo_nbr);
+INITIALCONDITIONS(:,1) = ones(10, 1);
+initialconditions = dseries(INITIALCONDITIONS, 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 300);
+
+// Define a structure describing the parameters to be estimated (with initial conditions). 
+clear eparams
+eparams.e_c_m = .9;
+eparams.c_z_1 = .5;
+eparams.c_z_2 = .2;
+
+// Define the dataset used for estimation
+edata = TrueData;
+edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
+pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
+
+disp(sprintf('Estimate of e_c_m: %f', M_.params(strmatch('e_c_m', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of c_z_1: %f', M_.params(strmatch('c_z_1', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of c_z_2: %f', M_.params(strmatch('c_z_2', M_.param_names, 'exact'))))
diff --git a/tests/pac/trend-component-11/clean b/tests/pac/trend-component-11/clean
new file mode 100755
index 000000000..2935a38ad
--- /dev/null
+++ b/tests/pac/trend-component-11/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/trend-component-11/example.mod b/tests/pac/trend-component-11/example.mod
new file mode 100644
index 000000000..09ebeb275
--- /dev/null
+++ b/tests/pac/trend-component-11/example.mod
@@ -0,0 +1,74 @@
+// --+ options: json=compute, transform_unary_ops, stochastic +--
+
+var x1 x2 x1bar x2bar z ;
+
+varexo ex1 ex2 ex1bar ex2bar ez ;
+
+parameters a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+a_x1_0 =  -.9999;
+a_x1_1 =  .4;
+a_x1_2 =  0;//.3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = 0;//.2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  0;//-.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = 0;//.2;
+
+beta  =  .1;
+e_c_m =  .1;
+c_z_1 =  .07;
+c_z_2 = -.3;
+
+gamma =  .7;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar',  'eq:x2bar'], trends=['eq:x1bar',  'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:x1', data_type='nonstationary']
+diff(diff(log(x1))) = a_x1_0*(diff(log(x1(-1)))-x1bar(-1)) + a_x1_1*diff(diff(log(x1(-1)))) + a_x1_2*diff(diff(x1(-2))) + a_x1_x2_1*diff(log(x2(-1))) + a_x1_x2_2*diff(log(x2(-2))) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(log(x2)) = a_x2_0*(log(x2(-1))-x2bar(-1)) + a_x2_1*diff(diff(log(x1(-1)))) + a_x2_2*diff(diff(log(x1(-2)))) + a_x2_x1_1*diff(log(x2(-1))) + a_x2_x1_2*diff(log(x2(-2))) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='eq:pac']
+diff(z) = gamma*(e_c_m*(log(x1(-1))-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-gamma)*ez;
+
+
+end;
+
+shocks;
+    var ex1 = 1;
+    var ex2 = 1;
+    var ex1bar = .1;
+    var ex2bar = .11;
+    var ez = 1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero for non logged variables, and one for logged variables
+init = .1*ones(10,M_.endo_nbr+M_.exo_nbr);
+initialconditions = dseries(init, 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 10);
\ No newline at end of file
diff --git a/tests/pac/trend-component-12/clean b/tests/pac/trend-component-12/clean
new file mode 100755
index 000000000..bdc7da19a
--- /dev/null
+++ b/tests/pac/trend-component-12/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
\ No newline at end of file
diff --git a/tests/pac/trend-component-12/example.mod b/tests/pac/trend-component-12/example.mod
new file mode 100644
index 000000000..ef916581a
--- /dev/null
+++ b/tests/pac/trend-component-12/example.mod
@@ -0,0 +1,105 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey g;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 beta ;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, growth=g, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+    var g = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+B = 1;
+X = zeros(3,B);
+
+set_dynare_seed('default');
+options_.bnlms.set_dynare_seed_to_default = false;
+
+for i=1:B
+    e_c_m =  .5;
+    c_z_1 =  .2;
+    c_z_2 = -.1;
+    // Simulate the model for 500 periods
+    TrueData = simul_backward_model(initialconditions, 300);
+    // Define a structure describing the parameters to be estimated (with initial conditions). 
+    clear eparams
+    eparams.e_c_m = .5;
+    eparams.c_z_1 = .2;
+    eparams.c_z_2 =-.1;
+    // Define the dataset used for estimation
+    edata = TrueData;
+    edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
+    pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
+    X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
+    X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
+    X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
+end
+
+mean(X, 2)
\ No newline at end of file
diff --git a/tests/pac/trend-component-13a/clean b/tests/pac/trend-component-13a/clean
new file mode 100755
index 000000000..bdc7da19a
--- /dev/null
+++ b/tests/pac/trend-component-13a/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
\ No newline at end of file
diff --git a/tests/pac/trend-component-13a/example.mod b/tests/pac/trend-component-13a/example.mod
new file mode 100644
index 000000000..f7355a201
--- /dev/null
+++ b/tests/pac/trend-component-13a/example.mod
@@ -0,0 +1,74 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey g;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 beta ;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, growth=g, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+    var g = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+if ~isequal(M_.pac.pacman.ec.isendo, [false, true])
+   error('ec.isendo vector is wrong.')
+end
diff --git a/tests/pac/trend-component-13b/clean b/tests/pac/trend-component-13b/clean
new file mode 100755
index 000000000..bdc7da19a
--- /dev/null
+++ b/tests/pac/trend-component-13b/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
\ No newline at end of file
diff --git a/tests/pac/trend-component-13b/example.mod b/tests/pac/trend-component-13b/example.mod
new file mode 100644
index 000000000..a2f920dc2
--- /dev/null
+++ b/tests/pac/trend-component-13b/example.mod
@@ -0,0 +1,74 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey g;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 beta ;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, growth=g, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(z(-1)-x1(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+    var g = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+if ~isequal(M_.pac.pacman.ec.isendo, [true, false])
+   error('ec.isendo vector is wrong.')
+end
diff --git a/tests/pac/trend-component-2/clean b/tests/pac/trend-component-2/clean
new file mode 100755
index 000000000..be0d5e00c
--- /dev/null
+++ b/tests/pac/trend-component-2/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/trend-component-2/example.mod b/tests/pac/trend-component-2/example.mod
new file mode 100644
index 000000000..757f4b610
--- /dev/null
+++ b/tests/pac/trend-component-2/example.mod
@@ -0,0 +1,73 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z ;
+
+varexo ex1 ex2 ex1bar ex2bar ez ;
+
+parameters a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .1;
+e_c_m =  .1;
+c_z_1 =  .07;
+c_z_2 = -.3;
+
+gamma =  .7;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar',  'eq:x2bar'], trends=['eq:x1bar',  'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:x1', data_type='nonstationary']
+diff(diff(x1)) = a_x1_0*(diff(x1(-1))-diff(x1bar(-1))) + a_x1_1*diff(diff(x1(-1))) + a_x1_2*diff(diff(x1(-2))) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(diff(x1(-1))) + a_x2_2*diff(diff(x1(-2))) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+diff(x1bar) = diff(x1bar(-1)) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='eq:pac']
+diff(z) = gamma*( e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-gamma)*ez;
+
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
\ No newline at end of file
diff --git a/tests/pac/trend-component-3/clean b/tests/pac/trend-component-3/clean
new file mode 100755
index 000000000..2935a38ad
--- /dev/null
+++ b/tests/pac/trend-component-3/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/trend-component-3/example.mod b/tests/pac/trend-component-3/example.mod
new file mode 100644
index 000000000..f3a59a803
--- /dev/null
+++ b/tests/pac/trend-component-3/example.mod
@@ -0,0 +1,75 @@
+// --+ options: json=compute, transform_unary_ops, stochastic +--
+
+var x1 x2 x1bar x2bar z ;
+
+varexo ex1 ex2 ex1bar ex2bar ez ;
+
+parameters a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .1;
+e_c_m =  .1;
+c_z_1 =  .07;
+c_z_2 = -.3;
+
+gamma =  .7;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar',  'eq:x2bar'], trends=['eq:x1bar',  'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:x1', data_type='nonstationary']
+diff(diff(x1)) = a_x1_0*(diff(x1(-1))-diff(x1bar(-1))) + a_x1_1*diff(diff(x1(-1))) + a_x1_2*diff(diff(x1(-2))) + a_x1_x2_1*diff(log(x2(-1))) + a_x1_x2_2*diff(log(x2(-2))) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(log(x2)) = a_x2_0*(log(x2(-1))-log(x2bar(-1))) + a_x2_1*diff(diff(x1(-1))) + a_x2_2*diff(diff(x1(-2))) + a_x2_x1_1*diff(log(x2(-1))) + a_x2_x1_2*diff(log(x2(-2))) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+diff(x1bar) = diff(x1bar(-1)) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+log(x2bar) = log(x2bar(-1)) + ex2bar;
+
+[name='eq:pac']
+diff(z) = gamma*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-gamma)*ez;
+
+
+end;
+
+shocks;
+    var ex1 = 1;
+    var ex2 = 1;
+    var ex1bar = 1;
+    var ex2bar = 1;
+    var ez = 1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero for non logged variables, and one for logged variables
+init = zeros(10, M_.endo_nbr+M_.exo_nbr);
+init(:,[2,4]) = ones(10,2);
+initialconditions = dseries(init, 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
\ No newline at end of file
diff --git a/tests/pac/trend-component-4/clean b/tests/pac/trend-component-4/clean
new file mode 100755
index 000000000..2935a38ad
--- /dev/null
+++ b/tests/pac/trend-component-4/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/trend-component-4/example.mod b/tests/pac/trend-component-4/example.mod
new file mode 100644
index 000000000..ec435abf0
--- /dev/null
+++ b/tests/pac/trend-component-4/example.mod
@@ -0,0 +1,73 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z ;
+
+varexo ex1 ex2 ex1bar ex2bar ez ;
+
+parameters a_x1_0 a_x1_0_ a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+a_x1_0 =  -.9;
+a_x1_0_ =  -.8;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+gamma =  .9;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1))+a_x1_0_*(x2(-1)-x2bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='eq:pac']
+diff(z) = gamma*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-gamma)*ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 5000);
\ No newline at end of file
diff --git a/tests/pac/trend-component-5/clean b/tests/pac/trend-component-5/clean
new file mode 100755
index 000000000..2935a38ad
--- /dev/null
+++ b/tests/pac/trend-component-5/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/trend-component-5/example.mod b/tests/pac/trend-component-5/example.mod
new file mode 100644
index 000000000..43eb56628
--- /dev/null
+++ b/tests/pac/trend-component-5/example.mod
@@ -0,0 +1,82 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+gamma =  .7;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='eq:pac']
+diff(z) = gamma*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-gamma)*y + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
+
diff --git a/tests/pac/trend-component-6/clean b/tests/pac/trend-component-6/clean
new file mode 100755
index 000000000..0d64aefde
--- /dev/null
+++ b/tests/pac/trend-component-6/clean
@@ -0,0 +1,7 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
+rm *.m
diff --git a/tests/pac/trend-component-6/example.mod b/tests/pac/trend-component-6/example.mod
new file mode 100644
index 000000000..3e35606bc
--- /dev/null
+++ b/tests/pac/trend-component-6/example.mod
@@ -0,0 +1,101 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+gamma =  .7;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = gamma*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-gamma)*y + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
+
+//[pnames, enames, xnames, pid, eid, xid] = get_variables_and_parameters_in_equation('zpac', M_)
+
+// Define a structure describing the parameters to be estimated (with initial conditions). 
+clear eparams
+eparams.e_c_m = .9;
+eparams.c_z_1 = .5;
+eparams.c_z_2 = .2;
+eparams.gamma = .1;
+
+// Define the dataset used for estimation
+edata = TrueData;
+edata.ez = dseries(NaN(TrueData.nobs, 1), 200Q1, 'ez');
+
+pac.estimate.nls('zpac', eparams, edata, 2005Q1:2120Q1);
+
+disp(sprintf('Estimate of e_c_m: %f', M_.params(strmatch('e_c_m', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of c_z_1: %f', M_.params(strmatch('c_z_1', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of c_z_2: %f', M_.params(strmatch('c_z_2', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of gamma: %f', M_.params(strmatch('gamma', M_.param_names, 'exact'))))
diff --git a/tests/pac/trend-component-7/clean b/tests/pac/trend-component-7/clean
new file mode 100755
index 000000000..6f2442c33
--- /dev/null
+++ b/tests/pac/trend-component-7/clean
@@ -0,0 +1,7 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example*.mat
+rm -f example.log
+rm -f *.m
\ No newline at end of file
diff --git a/tests/pac/trend-component-7/example.mod b/tests/pac/trend-component-7/example.mod
new file mode 100644
index 000000000..100c87e7f
--- /dev/null
+++ b/tests/pac/trend-component-7/example.mod
@@ -0,0 +1,97 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
+
+//[pnames, enames, xnames, pid, eid, xid] = get_variables_and_parameters_in_equation('zpac', M_)
+
+// Define a structure describing the parameters to be estimated (with initial conditions). 
+clear eparams
+eparams.e_c_m = .9;
+eparams.c_z_1 = .5;
+eparams.c_z_2 = .2;
+
+// Define the dataset used for estimation
+edata = TrueData;
+edata.ez = dseries(NaN(TrueData.nobs, 1), 200Q1, 'ez');
+
+pac.estimate.nls('zpac', eparams, edata, 2005Q1:2120Q1);
+
+disp(sprintf('Estimate of e_c_m: %f', M_.params(strmatch('e_c_m', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of c_z_1: %f', M_.params(strmatch('c_z_1', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of c_z_2: %f', M_.params(strmatch('c_z_2', M_.param_names, 'exact'))))
diff --git a/tests/pac/trend-component-8-mc-iterative-ols/clean b/tests/pac/trend-component-8-mc-iterative-ols/clean
new file mode 100755
index 000000000..bdc7da19a
--- /dev/null
+++ b/tests/pac/trend-component-8-mc-iterative-ols/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
\ No newline at end of file
diff --git a/tests/pac/trend-component-8-mc-iterative-ols/example.mod b/tests/pac/trend-component-8-mc-iterative-ols/example.mod
new file mode 100644
index 000000000..26438fcb3
--- /dev/null
+++ b/tests/pac/trend-component-8-mc-iterative-ols/example.mod
@@ -0,0 +1,106 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 beta g;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+g=.1;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, growth=g, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+B = 1;
+X = zeros(3,B);
+
+set_dynare_seed('default');
+options_.bnlms.set_dynare_seed_to_default = false;
+
+for i=1:B
+    e_c_m =  .5;
+    c_z_1 =  .2;
+    c_z_2 = -.1;
+    // Simulate the model for 500 periods
+    TrueData = simul_backward_model(initialconditions, 300);
+    // Define a structure describing the parameters to be estimated (with initial conditions). 
+    clear eparams
+    eparams.e_c_m = -.5;
+    eparams.c_z_1 = .2;
+    eparams.c_z_2 =-.1;
+    // Define the dataset used for estimation
+    edata = TrueData;
+    edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
+    pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
+    X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
+    X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
+    X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
+end
+
+mean(X, 2)
\ No newline at end of file
diff --git a/tests/pac/trend-component-8-mc-nls/clean b/tests/pac/trend-component-8-mc-nls/clean
new file mode 100755
index 000000000..0d64aefde
--- /dev/null
+++ b/tests/pac/trend-component-8-mc-nls/clean
@@ -0,0 +1,7 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
+rm *.m
diff --git a/tests/pac/trend-component-8-mc-nls/example.mod b/tests/pac/trend-component-8-mc-nls/example.mod
new file mode 100644
index 000000000..bf6549072
--- /dev/null
+++ b/tests/pac/trend-component-8-mc-nls/example.mod
@@ -0,0 +1,102 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 beta g;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+g = .1;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, growth=g, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+B = 1;
+X = zeros(3,B);
+
+set_dynare_seed('default');
+options_.bnlms.set_dynare_seed_to_default = false;
+
+for i=1:B
+    // Simulate the model for 500 periods
+    TrueData = simul_backward_model(initialconditions, 300);
+    // Define a structure describing the parameters to be estimated (with initial conditions). 
+    clear eparams
+    eparas.e_c_m =  .5;
+    eparams.c_z_1 = .2;
+    eparams.c_z_2 =-.1;
+    // Define the dataset used for estimation
+    edata = TrueData;
+    edata.ez = dseries(NaN(TrueData.nobs, 1), 200Q1, 'ez');
+    pac.estimate.nls('zpac', eparams, edata, 2005Q1:2000Q1+200);
+    X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
+    X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
+    X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
+end
+
+mean(X, 2)
\ No newline at end of file
diff --git a/tests/pac/trend-component-9/clean b/tests/pac/trend-component-9/clean
new file mode 100755
index 000000000..bdc7da19a
--- /dev/null
+++ b/tests/pac/trend-component-9/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
\ No newline at end of file
diff --git a/tests/pac/trend-component-9/example.mod b/tests/pac/trend-component-9/example.mod
new file mode 100644
index 000000000..455e86f6c
--- /dev/null
+++ b/tests/pac/trend-component-9/example.mod
@@ -0,0 +1,93 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+       rho_1 rho_2
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 beta;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2', data_type='nonstationary']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 300);
+
+// Define a structure describing the parameters to be estimated (with initial conditions). 
+clear eparams
+eparams.e_c_m =  .9;
+eparams.c_z_1 =  .5;
+eparams.c_z_2 =  .2;
+
+// Define the dataset used for estimation
+edata = TrueData;
+edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
+pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
+
+disp(sprintf('Estimate of e_c_m: %f', M_.params(strmatch('e_c_m', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of c_z_1: %f', M_.params(strmatch('c_z_1', M_.param_names, 'exact'))))
+disp(sprintf('Estimate of c_z_2: %f', M_.params(strmatch('c_z_2', M_.param_names, 'exact'))))
diff --git a/tests/pac/var-0/clean b/tests/pac/var-0/clean
new file mode 100755
index 000000000..e0d1cd114
--- /dev/null
+++ b/tests/pac/var-0/clean
@@ -0,0 +1,7 @@
+#!/bin/sh
+
+rm -rf m1 m2 m3 m4
+rm -f m1_dynamic.m  m1.log  m1.m  m1_results.mat  m1_set_auxiliary_variables.m  m1_static.m
+rm -f m2_dynamic.m  m2.log  m2.m  m2_results.mat  m2_set_auxiliary_variables.m  m2_static.m
+rm -f m3_dynamic.m  m3.log  m3.m  m3_results.mat  m3_set_auxiliary_variables.m  m3_static.m
+rm -f m4_dynamic.m  m4.log  m4.m  m4_results.mat  m4_set_auxiliary_variables.m  m4_static.m
diff --git a/tests/pac/var-0/m1.mod b/tests/pac/var-0/m1.mod
new file mode 100644
index 000000000..7ebe59034
--- /dev/null
+++ b/tests/pac/var-0/m1.mod
@@ -0,0 +1,30 @@
+var y x z;
+
+varexo ex ey ez;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 c_z_1 d;
+
+a_y_1 =  .2;
+a_y_2 =  .3;
+b_y_1 =  .1;
+b_y_2 =  .4;
+b_x_1 = -.1;
+b_x_2 = -.2; 
+c_z_1 =  .9;
+d     =  .1;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
+
+model;
+
+[name='eq:y']
+y = a_y_1*y(-1) + a_y_2*x(-1) + b_y_1*y(-2) + b_y_2*x(-2) + ey ;
+
+z = c_z_1*z(-3) + d*y + ez;
+
+[name='eq:x']
+x = b_x_1*y(-2) + b_x_2*x(-2) + ez ;
+
+end;
+
+get_companion_matrix('toto');
diff --git a/tests/pac/var-0/m2.mod b/tests/pac/var-0/m2.mod
new file mode 100644
index 000000000..475a52039
--- /dev/null
+++ b/tests/pac/var-0/m2.mod
@@ -0,0 +1,30 @@
+var y x z;
+
+varexo ex ey ez;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 c_z_1 d;
+
+a_y_1 =  .2;
+a_y_2 =  .3;
+b_y_1 =  .1;
+b_y_2 =  .4;
+b_x_1 = -.1;
+b_x_2 = -.2; 
+c_z_1 =  .9;
+d     =  .1;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
+
+model;
+
+[name='eq:y']
+y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
+
+z = c_z_1*z(-3) + d*y + ez;
+
+[name='eq:x']
+diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ez ;
+
+end;
+
+get_companion_matrix('toto');
\ No newline at end of file
diff --git a/tests/pac/var-0/m3.mod b/tests/pac/var-0/m3.mod
new file mode 100644
index 000000000..eaf79b782
--- /dev/null
+++ b/tests/pac/var-0/m3.mod
@@ -0,0 +1,32 @@
+var y x z;
+
+varexo ex ey ez;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 c_z_1 c_z_2 c_z_3;
+
+a_y_1 =  .2;
+a_y_2 =  .3;
+b_y_1 =  .1;
+b_y_2 =  .4;
+b_x_1 = -.1;
+b_x_2 = -.2; 
+c_z_1 =  .9;
+c_z_2 =  .1;
+c_z_3 = -.8;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:y', 'eq:z']);
+
+model;
+
+[name='eq:y']
+y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
+
+[name='eq:x']
+diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ex ;
+
+[name='eq:z']
+diff(z) = c_z_1*(x(-1)-z(-1)) + c_z_2*diff(z(-1)) + c_z_3*diff(z(-2)) + ez ;
+
+end;
+
+get_ar_matrices('toto');
\ No newline at end of file
diff --git a/tests/pac/var-0/m4.mod b/tests/pac/var-0/m4.mod
new file mode 100644
index 000000000..f92fc75ed
--- /dev/null
+++ b/tests/pac/var-0/m4.mod
@@ -0,0 +1,29 @@
+var y x z;
+
+varexo ex ey ez;
+
+parameters a_x_0 a_x_1 a_x_2 b_z_0 b_z_1 b_z_2;
+
+a_x_0 =  .2;
+a_x_1 =  .9;
+a_x_2 = -.2;
+b_z_0 =  .3;
+b_z_1 =  .7;
+b_z_2 = -.4;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:z', 'eq:y']);
+
+model;
+
+[name='eq:y']
+y = y(-1) + ey ;
+
+[name='eq:x']
+diff(x) = -a_x_0*(x(-1)-y(-1)) + a_x_1*diff(x(-1)) + a_x_2*diff(x(-2)) + ex ;
+
+[name='eq:z']
+diff(z) = -b_z_0*(z(-1)-x(-1)) + b_z_1*diff(z(-1)) + b_z_2*diff(z(-2)) + ez ;
+
+end;
+
+get_ar_matrices('toto');
\ No newline at end of file
diff --git a/tests/pac/var-1/clean b/tests/pac/var-1/clean
new file mode 100755
index 000000000..be0d5e00c
--- /dev/null
+++ b/tests/pac/var-1/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/var-1/example.mod b/tests/pac/var-1/example.mod
new file mode 100644
index 000000000..bb5d6e0ed
--- /dev/null
+++ b/tests/pac/var-1/example.mod
@@ -0,0 +1,56 @@
+// --+ options: json=compute, stochastic +--
+
+var y x z;
+
+varexo ex ey ez;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 ; // VAR parameters
+
+parameters beta e_c_m c_z_1 c_z_2;               // PAC equation parameters
+
+a_y_1 =  .2;
+a_y_2 =  .3;
+b_y_1 =  .1;
+b_y_2 =  .4;
+b_x_1 = -.1;
+b_x_2 = -.2;
+
+beta  =  .9;
+e_c_m =  .1;
+c_z_1 =  .7;
+c_z_2 = -.3;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
+
+[name='eq:x', data_type='nonstationary']
+diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ex ;
+
+[name='eq:pac']
+diff(z) = e_c_m*(x(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex = 1.0;
+    var ey = 1.0;
+    var ez = 1.0;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
\ No newline at end of file
diff --git a/tests/pac/var-2/clean b/tests/pac/var-2/clean
new file mode 100755
index 000000000..be0d5e00c
--- /dev/null
+++ b/tests/pac/var-2/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/var-2/example.mod b/tests/pac/var-2/example.mod
new file mode 100644
index 000000000..9f3e7068a
--- /dev/null
+++ b/tests/pac/var-2/example.mod
@@ -0,0 +1,58 @@
+// --+ options: json=compute, stochastic +--
+
+var y x z ;
+
+varexo ex ey ez;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 gamma; // VAR parameters
+
+parameters beta e_c_m c_z_1 c_z_2;               // PAC equation parameters
+
+a_y_1 =  .2;
+a_y_2 =  .3;
+b_y_1 =  .1;
+b_y_2 =  .4;
+b_x_1 = -.1;
+b_x_2 = -.2;
+
+beta  =  .9;
+e_c_m =  .1;
+c_z_1 =  .7;
+c_z_2 = -.3;
+
+gamma =  .7;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
+
+[name='eq:x', data_type='nonstationary']
+diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ex ;
+
+[name='eq:pac']
+diff(z) = gamma*(e_c_m*(x(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-gamma)*ez;
+
+end;
+
+shocks;
+    var ey = 1.0;
+    var ex = 1.0;
+    var ez = 1.0;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
\ No newline at end of file
diff --git a/tests/pac/var-3/clean b/tests/pac/var-3/clean
new file mode 100755
index 000000000..be0d5e00c
--- /dev/null
+++ b/tests/pac/var-3/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/var-3/example.mod b/tests/pac/var-3/example.mod
new file mode 100644
index 000000000..21a18a4c5
--- /dev/null
+++ b/tests/pac/var-3/example.mod
@@ -0,0 +1,58 @@
+// --+ options: json=compute, stochastic +--
+
+var y x z;
+
+varexo ex ey ez ;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 g; // VAR parameters
+
+parameters beta e_c_m c_z_1 c_z_2;               // PAC equation parameters
+
+a_y_1 =  .2;
+a_y_2 =  .3;
+b_y_1 =  .1;
+b_y_2 =  .4;
+b_x_1 = -.1;
+b_x_2 = -.2;
+
+beta  =  .9;
+e_c_m =  .1;
+c_z_1 =  .7;
+c_z_2 = -.3;
+
+g = .02;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
+
+[name='eq:x', data_type='nonstationary']
+diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ex ;
+
+[name='eq:pac']
+diff(z) = e_c_m*(x(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ey = 1.0;
+    var ex = 1.0;
+    var ez = 1.0;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
\ No newline at end of file
diff --git a/tests/pac/var-4/clean b/tests/pac/var-4/clean
new file mode 100755
index 000000000..be0d5e00c
--- /dev/null
+++ b/tests/pac/var-4/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file
diff --git a/tests/pac/var-4/example.mod b/tests/pac/var-4/example.mod
new file mode 100644
index 000000000..f5a8f62f0
--- /dev/null
+++ b/tests/pac/var-4/example.mod
@@ -0,0 +1,58 @@
+// --+ options: json=compute, stochastic +--
+
+var y x z;
+
+varexo ex ey ez;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 ; // VAR parameters
+
+parameters g beta e_c_m c_z_1 c_z_2;               // PAC equation parameters
+
+a_y_1 =  .2;
+a_y_2 =  .3;
+b_y_1 =  .1;
+b_y_2 =  .4;
+b_x_1 = -.1;
+b_x_2 = -.2;
+
+beta  =  .9;
+e_c_m =  .1;
+c_z_1 =  .7;
+c_z_2 = -.3;
+
+g = .1;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman, growth=g);
+
+model;
+
+[name='eq:y']
+y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
+
+[name='eq:x', data_type='nonstationary']
+diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + g*(1-b_x_2)  + ex ;
+
+[name='eq:pac']
+diff(z) = e_c_m*(x(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex = 1.0;
+    var ey = 1.0;
+    var ez = 1.0;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 500);
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