Allow for lags on endogenous variable in growth option of pac_model.
Stéphane Adjemia (Scylla)
parent
668e641a6a
commit
74520e77bf
GPG Key ID:
A6D44CB9C64CE77B
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@ -265,7 +265,7 @@ function [PacExpectations, Model] = UpdatePacExpectationsData(dataPAC0, dataPAC1
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GrowthVariable = GrowthVariable(range).data;
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correction = GrowthVariable*Model.params(Model.pac.(pacmodl).growth_neutrality_param_index);
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case 'endogenous'
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GrowthVariable = data{Model.endo_names{Model.pac.(pacmodl).growth_index}};
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GrowthVariable = data{Model.endo_names{Model.pac.(pacmodl).growth_index}}.lag(abs(Model.pac.(pacmodl).growth_lag));
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GrowthVariable = GrowthVariable(range).data;
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correction = GrowthVariable*Model.params(Model.pac.(pacmodl).growth_neutrality_param_index);
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otherwise
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@ -1 +1 @@
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Subproject commit a3e7547857151eb364f50e9daa0dd22ed7c99c68
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Subproject commit 71be51c46437777a363f31608fb389975c9751c5
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@ -415,6 +415,9 @@ MODFILES = \
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pac/trend-component-16/example.mod \
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pac/trend-component-17/example.mod \
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pac/trend-component-18/example.mod \
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pac/trend-component-19/example1.mod \
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pac/trend-component-19/example2.mod \
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pac/trend-component-19/example3.mod \
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dynare-command-options/ramst.mod
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OLS_MODFILES = \
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@ -0,0 +1,7 @@
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#!/bin/sh
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rm -rf example1 example2 example3
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rm -rf +example1 +example2 +example3
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rm -f *.log
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rm -f *.mat H.dat
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@ -0,0 +1,108 @@
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// --+ options: json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y gg;
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varexo ex1 ex2 ex1bar ex2bar ez ey g;
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parameters
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rho_1 rho_2
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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 beta ;
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rho_1 = .9;
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rho_2 = -.2;
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a_x1_0 = -.9;
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a_x1_1 = .4;
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a_x1_2 = .3;
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a_x1_x2_1 = .1;
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a_x1_x2_2 = .2;
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a_x2_0 = -.9;
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a_x2_1 = .2;
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a_x2_2 = -.1;
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a_x2_x1_1 = -.1;
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a_x2_x1_2 = .2;
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beta = .2;
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
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pac_model(auxiliary_model_name=toto, discount=beta, growth=gg(-1), model_name=pacman);
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model;
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[name='eq:gg']
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gg = g;
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[name='eq:y']
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y = rho_1*y(-1) + rho_2*y(-2) + ey;
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[name='eq:x1']
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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;
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[name='eq:x2']
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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;
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[name='eq:x1bar']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar']
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x2bar = x2bar(-1) + ex2bar;
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[name='zpac']
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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;
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end;
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shocks;
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var ex1 = 1.0;
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var ex2 = 1.0;
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var ex1bar = 1.0;
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var ex2bar = 1.0;
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var ez = 1.0;
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var ey = 0.1;
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var g = 0.1;
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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. Please use more sensible values if any...
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initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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B = 1;
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X = zeros(3,B);
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set_dynare_seed('default');
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options_.bnlms.set_dynare_seed_to_default = false;
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for i=1:B
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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// Simulate the model for 500 periods
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TrueData = simul_backward_model(initialconditions, 300);
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// Define a structure describing the parameters to be estimated (with initial conditions).
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clear eparams
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eparams.e_c_m = .5;
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eparams.c_z_1 = .2;
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eparams.c_z_2 =-.1;
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// Define the dataset used for estimation
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edata = TrueData;
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edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
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pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
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X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
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X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
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X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
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end
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mean(X, 2)
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@ -0,0 +1,107 @@
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// --+ options: json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y gg;
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varexo ex1 ex2 ex1bar ex2bar ez ey g;
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parameters
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rho_1 rho_2
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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 beta ;
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rho_1 = .9;
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rho_2 = -.2;
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a_x1_0 = -.9;
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a_x1_1 = .4;
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a_x1_2 = .3;
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a_x1_x2_1 = .1;
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a_x1_x2_2 = .2;
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a_x2_0 = -.9;
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a_x2_1 = .2;
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a_x2_2 = -.1;
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a_x2_x1_1 = -.1;
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a_x2_x1_2 = .2;
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beta = .2;
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
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pac_model(auxiliary_model_name=toto, discount=beta, growth=gg(-2), model_name=pacman);
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model;
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[name='eq:gg']
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gg = g;
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[name='eq:y']
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y = rho_1*y(-1) + rho_2*y(-2) + ey;
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[name='eq:x1']
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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;
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[name='eq:x2']
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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;
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[name='eq:x1bar']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar']
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x2bar = x2bar(-1) + ex2bar;
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[name='zpac']
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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;
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end;
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shocks;
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var ex1 = 1.0;
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var ex2 = 1.0;
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var ex1bar = 1.0;
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var ex2bar = 1.0;
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var ez = 1.0;
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var ey = 0.1;
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var g = 0.1;
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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. Please use more sensible values if any...
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initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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B = 1;
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X = zeros(3,B);
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set_dynare_seed('default');
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options_.bnlms.set_dynare_seed_to_default = false;
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for i=1:B
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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// Simulate the model for 500 periods
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TrueData = simul_backward_model(initialconditions, 300);
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// Define a structure describing the parameters to be estimated (with initial conditions).
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clear eparams
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eparams.e_c_m = .5;
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eparams.c_z_1 = .2;
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eparams.c_z_2 =-.1;
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// Define the dataset used for estimation
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edata = TrueData;
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edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
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pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
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X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
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X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
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X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
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end
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mean(X, 2)
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@ -0,0 +1,108 @@
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// --+ options: json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y gg;
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varexo ex1 ex2 ex1bar ex2bar ez ey g;
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parameters
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rho_1 rho_2
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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 beta ;
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rho_1 = .9;
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rho_2 = -.2;
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a_x1_0 = -.9;
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a_x1_1 = .4;
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a_x1_2 = .3;
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a_x1_x2_1 = .1;
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a_x1_x2_2 = .2;
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a_x2_0 = -.9;
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a_x2_1 = .2;
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a_x2_2 = -.1;
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a_x2_x1_1 = -.1;
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a_x2_x1_2 = .2;
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beta = .2;
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
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pac_model(auxiliary_model_name=toto, discount=beta, growth=gg(-3), model_name=pacman);
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model;
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[name='eq:gg']
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gg = g;
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[name='eq:y']
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y = rho_1*y(-1) + rho_2*y(-2) + ey;
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[name='eq:x1']
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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;
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[name='eq:x2']
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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;
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[name='eq:x1bar']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar']
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x2bar = x2bar(-1) + ex2bar;
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[name='zpac']
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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;
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end;
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shocks;
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var ex1 = 1.0;
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var ex2 = 1.0;
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var ex1bar = 1.0;
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var ex2bar = 1.0;
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var ez = 1.0;
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var ey = 0.1;
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var g = 0.1;
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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. Please use more sensible values if any...
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initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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B = 1;
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X = zeros(3,B);
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set_dynare_seed('default');
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options_.bnlms.set_dynare_seed_to_default = false;
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for i=1:B
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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// Simulate the model for 500 periods
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TrueData = simul_backward_model(initialconditions, 300);
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// Define a structure describing the parameters to be estimated (with initial conditions).
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clear eparams
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eparams.e_c_m = .5;
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eparams.c_z_1 = .2;
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eparams.c_z_2 =-.1;
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// Define the dataset used for estimation
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edata = TrueData;
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edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
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pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
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X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
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X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
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X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
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end
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mean(X, 2)
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