Added new test.

run_all_tests.m

@@ -33,6 +33,7 @@ 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')];
 
 print_results(r);
 
@@ -55,7 +56,7 @@ end
 function print_results(r)
 message = sprintf('Testsuite results (PAC model):\n');
 for i = 1:size(r, 1)
-    if r{i, 2}
+    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});

trend-component-11/clean

@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example*.mat
+rm -f example.log

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*(z(-1)-log(x1(-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);