Added new test.
parent
27f2f198e4
commit
48c1370071
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@ -33,6 +33,7 @@ r = [r; run_this_test('trend-component-6')];
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r = [r; run_this_test('trend-component-7')];
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r = [r; run_this_test('trend-component-9')];
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r = [r; run_this_test('trend-component-10')];
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r = [r; run_this_test('trend-component-11')];
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print_results(r);
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@ -55,7 +56,7 @@ end
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function print_results(r)
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message = sprintf('Testsuite results (PAC model):\n');
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for i = 1:size(r, 1)
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if r{i, 2}
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if r{i,2}
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message = sprintf('%s\n%s\t\t PASS (%ss)', message, r{i,1}, num2str(r{i,3}));
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else
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message = sprintf('%s\n%s\t\t FAILED', message, r{i,1});
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@ -0,0 +1,6 @@
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#!/bin/sh
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rm -rf example
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rm -rf +example
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rm -f example*.mat
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rm -f example.log
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@ -0,0 +1,74 @@
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// --+ options: json=compute, transform_unary_ops, stochastic +--
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var x1 x2 x1bar x2bar z ;
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varexo ex1 ex2 ex1bar ex2bar ez ;
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parameters a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
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a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
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e_c_m c_z_1 c_z_2 gamma beta ;
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a_x1_0 = -.9999;
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a_x1_1 = .4;
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a_x1_2 = 0;//.3;
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a_x1_x2_1 = .1;
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a_x1_x2_2 = 0;//.2;
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a_x2_0 = -.9;
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a_x2_1 = .2;
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a_x2_2 = 0;//-.1;
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a_x2_x1_1 = -.1;
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a_x2_x1_2 = 0;//.2;
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beta = .1;
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e_c_m = -.1;
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c_z_1 = .07;
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c_z_2 = -.3;
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gamma = .7;
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trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
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pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
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model;
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[name='eq:x1', data_type='nonstationary']
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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;
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[name='eq:x2', data_type='nonstationary']
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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;
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[name='eq:x1bar', data_type='nonstationary']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar', data_type='nonstationary']
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x2bar = x2bar(-1) + ex2bar;
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[name='eq:pac']
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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;
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end;
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shocks;
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var ex1 = 1;
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var ex2 = 1;
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var ex1bar = .1;
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var ex2bar = .11;
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var ez = 1;
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end;
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// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
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pac.initialize('pacman');
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// Update the parameters of the PAC expectation model (h0 and h1 vectors).
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pac.update.expectation('pacman');
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// Set initial conditions to zero for non logged variables, and one for logged variables
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init = .1*ones(10,M_.endo_nbr+M_.exo_nbr);
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initialconditions = dseries(init, 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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// Simulate the model for 500 periods
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TrueData = simul_backward_model(initialconditions, 10);
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