Added test where the PAC equation doesn't have lags on the endogenous variable.

This example is currently failing.

tests/pac/trend-component-31/clean

+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example*.mat
+rm -f example.log
\ No newline at end of file

tests/pac/trend-component-31/example.mod

+// --+ 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'], targets=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:x1']
+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']
+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']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='eq:pac']
+diff(z) = gamma*(e_c_m*(x1(-1)-z(-1)) + 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);