Account for exogenous variables in PAC's RoT part.
Fixes the iterative_ols estimation of PAC equation when the Rule of Thumbs (non optimizing) part of the equations contains endogenous and/or exogenous variables.time-shift
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6500099f4e
commit
f07b1e8028
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@ -129,7 +129,11 @@ end
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if is_non_optimizing_agents
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dataForNonOptimizingBehaviour = dseries();
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for i=1:length(non_optimizing_behaviour.vars)
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variable = M_.endo_names{non_optimizing_behaviour.vars(i)};
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if non_optimizing_behaviour.isendo(i)
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variable = M_.endo_names{non_optimizing_behaviour.vars(i)};
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else
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variable = M_.exo_names{non_optimizing_behaviour.vars(i)};
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end
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if non_optimizing_behaviour.lags(i)
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dataForNonOptimizingBehaviour = [dataForNonOptimizingBehaviour, data{variable}.lag(non_optimizing_behaviour.lags(i))];
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else
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@ -1 +1 @@
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Subproject commit effa40543e816507187fe90da340fd76be2dc237
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Subproject commit 2312ce13dc58d3645e0f37f1a757fbe50b6932fd
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@ -425,6 +425,7 @@ MODFILES = \
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pac/trend-component-20-1/example.mod \
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pac/trend-component-20-2/example.mod \
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pac/trend-component-20-3/example.mod \
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pac/trend-component-20-4/example.mod \
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ecb/backward-models/irf/solow_1.mod \
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ecb/backward-models/irf/solow_2.mod \
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dynare-command-options/ramst.mod
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@ -0,0 +1,8 @@
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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.log
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rm -f *.mat
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rm -f *.m
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rm -f *.dat
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@ -0,0 +1,72 @@
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// --+ options: json=compute, stochastic +--
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// Check that the content of pac.[pacmodel].equations.[eqtag].non_optimizing_behaviour.vars is correct.
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var x1 x2 x1bar x2bar z y x;
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varexo ex1 tt ex2 ex1bar ex2bar ez ey ex;
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parameters
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rho_1 rho_2 rho_3 rho_4
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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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lambda;
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rho_1 = .9;
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rho_2 = -.2;
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rho_3 = .4;
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rho_4 = -.3;
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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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lambda = 0.5; // Share of optimizing agents.
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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, model_name=pacman);
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model;
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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:x']
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x = rho_3*x(-1) + rho_4*x(-2) + ex;
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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) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-lambda)*(y + x + tt) + ez;
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end;
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if ~isequal([M_.endo_names(M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars(M_.pac.pacman.equations.eq0.non_optimizing_behaviour.isendo)); M_.exo_names(M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars(~M_.pac.pacman.equations.eq0.non_optimizing_behaviour.isendo))], {'y'; 'x'; 'tt'})
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error('PAC non_optimizing_behaviour.vars and/or non_optimizing_behaviour.isendo fields are wrong.')
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end
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