From 74520e77bfe102128de992aebdc0eeeea249418e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Adjemia=20=28Scylla=29?= Date: Mon, 28 Jan 2019 11:25:30 +0100 Subject: [PATCH] Allow for lags on endogenous variable in growth option of pac_model. --- matlab/+pac/+estimate/iterative_ols.m | 2 +- preprocessor | 2 +- tests/Makefile.am | 3 + tests/pac/trend-component-19/clean | 7 ++ tests/pac/trend-component-19/example1.mod | 108 ++++++++++++++++++++++ tests/pac/trend-component-19/example2.mod | 107 +++++++++++++++++++++ tests/pac/trend-component-19/example3.mod | 108 ++++++++++++++++++++++ 7 files changed, 335 insertions(+), 2 deletions(-) create mode 100755 tests/pac/trend-component-19/clean create mode 100644 tests/pac/trend-component-19/example1.mod create mode 100644 tests/pac/trend-component-19/example2.mod create mode 100644 tests/pac/trend-component-19/example3.mod diff --git a/matlab/+pac/+estimate/iterative_ols.m b/matlab/+pac/+estimate/iterative_ols.m index 132e0f9f4..936be1d56 100644 --- a/matlab/+pac/+estimate/iterative_ols.m +++ b/matlab/+pac/+estimate/iterative_ols.m @@ -265,7 +265,7 @@ function [PacExpectations, Model] = UpdatePacExpectationsData(dataPAC0, dataPAC1 GrowthVariable = GrowthVariable(range).data; correction = GrowthVariable*Model.params(Model.pac.(pacmodl).growth_neutrality_param_index); case 'endogenous' - GrowthVariable = data{Model.endo_names{Model.pac.(pacmodl).growth_index}}; + GrowthVariable = data{Model.endo_names{Model.pac.(pacmodl).growth_index}}.lag(abs(Model.pac.(pacmodl).growth_lag)); GrowthVariable = GrowthVariable(range).data; correction = GrowthVariable*Model.params(Model.pac.(pacmodl).growth_neutrality_param_index); otherwise diff --git a/preprocessor b/preprocessor index a3e754785..71be51c46 160000 --- a/preprocessor +++ b/preprocessor @@ -1 +1 @@ -Subproject commit a3e7547857151eb364f50e9daa0dd22ed7c99c68 +Subproject commit 71be51c46437777a363f31608fb389975c9751c5 diff --git a/tests/Makefile.am b/tests/Makefile.am index 5a7404a0a..a9be11592 100644 --- a/tests/Makefile.am +++ b/tests/Makefile.am @@ -415,6 +415,9 @@ MODFILES = \ pac/trend-component-16/example.mod \ pac/trend-component-17/example.mod \ pac/trend-component-18/example.mod \ + pac/trend-component-19/example1.mod \ + pac/trend-component-19/example2.mod \ + pac/trend-component-19/example3.mod \ dynare-command-options/ramst.mod OLS_MODFILES = \ diff --git a/tests/pac/trend-component-19/clean b/tests/pac/trend-component-19/clean new file mode 100755 index 000000000..352d17692 --- /dev/null +++ b/tests/pac/trend-component-19/clean @@ -0,0 +1,7 @@ +#!/bin/sh + +rm -rf example1 example2 example3 +rm -rf +example1 +example2 +example3 + +rm -f *.log +rm -f *.mat H.dat diff --git a/tests/pac/trend-component-19/example1.mod b/tests/pac/trend-component-19/example1.mod new file mode 100644 index 000000000..b1d019ba4 --- /dev/null +++ b/tests/pac/trend-component-19/example1.mod @@ -0,0 +1,108 @@ +// --+ options: json=compute, stochastic +-- + +var x1 x2 x1bar x2bar z y gg; + +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'], targets=['eq:x1bar', 'eq:x2bar']); + +pac_model(auxiliary_model_name=toto, discount=beta, growth=gg(-1), model_name=pacman); + +model; + +[name='eq:gg'] +gg = g; + +[name='eq:y'] +y = rho_1*y(-1) + rho_2*y(-2) + ey; + +[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='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) diff --git a/tests/pac/trend-component-19/example2.mod b/tests/pac/trend-component-19/example2.mod new file mode 100644 index 000000000..8fdb2ce9e --- /dev/null +++ b/tests/pac/trend-component-19/example2.mod @@ -0,0 +1,107 @@ +// --+ options: json=compute, stochastic +-- + +var x1 x2 x1bar x2bar z y gg; + +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'], targets=['eq:x1bar', 'eq:x2bar']); + +pac_model(auxiliary_model_name=toto, discount=beta, growth=gg(-2), model_name=pacman); + +model; + +[name='eq:gg'] +gg = g; + +[name='eq:y'] +y = rho_1*y(-1) + rho_2*y(-2) + ey; + +[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='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) diff --git a/tests/pac/trend-component-19/example3.mod b/tests/pac/trend-component-19/example3.mod new file mode 100644 index 000000000..86ed929da --- /dev/null +++ b/tests/pac/trend-component-19/example3.mod @@ -0,0 +1,108 @@ +// --+ options: json=compute, stochastic +-- + +var x1 x2 x1bar x2bar z y gg; + +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'], targets=['eq:x1bar', 'eq:x2bar']); + +pac_model(auxiliary_model_name=toto, discount=beta, growth=gg(-3), model_name=pacman); + +model; + +[name='eq:gg'] +gg = g; + +[name='eq:y'] +y = rho_1*y(-1) + rho_2*y(-2) + ey; + +[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='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)