Added routine to evaluate the RHS of an equation.
Stéphane Adjemian (Charybdis)
parent
15bd0e3397
commit
567ca19000
GPG Key ID:
A6D44CB9C64CE77B
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function nds = evaluate(ds, eqtag)
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% Copyright (C) 2019 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global M_
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% Get equation
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[LHS, RHS] = get_lhs_and_rhs(eqtag, M_, true);
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% Parse equation and return list of parameters, endogenous and exogenous variables.
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[pnames, enames, xnames] = get_variables_and_parameters_in_equation(LHS, RHS, M_);
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% Load parameter values.
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commands = sprintf('%s = %s;', pnames{1}, num2str(M_.params(strcmp(pnames{1},M_.param_names)), 16));
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for i=2:length(pnames)
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commands = sprintf('%s %s = %s;', commands, pnames{i}, ...
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num2str(M_.params(strcmp(pnames{i},M_.param_names)), 16));
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end
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eval(commands)
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% Substitute endogenous variable x with ds.x
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enames = unique(enames);
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for i=1:length(enames)
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if ismember(enames{i}, ds.name)
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RHS = regexprep(RHS, sprintf('\\<(%s)\\>', enames{i}), sprintf('ds.%s', enames{i}));
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else
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error('Endogenous variable %s is unknown in dseries objet.', enames{i})
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end
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end
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% Substitute exogenous variable x with ds.x, except if
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if ~isfield(M_, 'simulation_exo_names')
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M_.simulation_exo_names = M_.exo_names;
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end
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xnames = unique(xnames);
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for i=1:length(xnames)
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if ismember(xnames{i}, M_.simulation_exo_names)
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if ismember(xnames{i}, ds.name)
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RHS = regexprep(RHS, sprintf('\\<(%s)\\>', xnames{i}), sprintf('ds.%s', xnames{i}));
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else
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RHS = regexprep(RHS, sprintf('\\<(%s)\\>', xnames{i}), '0');
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warning('Exogenous variable %s is unknown in dseries objet. Assign zero value.', xnames{i})
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end
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else
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RHS = regexprep(RHS, sprintf('(\\ *)(+)(\\ *)%s', xnames{i}), '');
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end
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end
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nds = eval(RHS);
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@ -437,6 +437,9 @@ ECB_MODFILES = \
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pac/trend-component-24/example.mod \
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pac/trend-component-25/example.mod \
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pac/trend-component-26/example.mod \
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pac/trend-component-27/example.mod \
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pac/trend-component-28/example1.mod \
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pac/trend-component-28/example2.mod \
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write/example1.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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@ -0,0 +1 @@
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simulation-files/*
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@ -0,0 +1,15 @@
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#!/bin/sh
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rm -rf example1
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rm -rf +example1
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rm -f example1.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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rm -rf example2
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rm -rf +example2
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rm -f example2.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 +1,123 @@
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// --+ options: json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y x u v s;
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varexo ex1
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ex2
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ex1bar (used='estimationonly')
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ex2bar (used='estimationonly')
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ez
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ey
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ex
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eu
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ev
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es;
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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 c_z_dx2 c_z_u c_z_dv c_z_s cx cy 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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c_z_dx2 = .3;
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c_z_u = .3;
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c_z_dv = .4;
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c_z_s = -.2;
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cx = 1.0;
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cy = 1.0;
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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:u']
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s = .3*s(-1) - .1*s(-2) + es;
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[name='eq:diff(v)']
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diff(v) = .5*diff(v(-1)) + ev;
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[name='eq:u']
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u = .5*u(-1) - .2*u(-2) + eu;
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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) + c_z_s*s + c_z_dv*diff(v) ) + (1-lambda)*( cy*y + cx*x) + c_z_u*u + c_z_dx2*diff(x2) + 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 ex = 0.1;
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var eu = 0.05;
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var ev = 0.05;
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var es = 0.07;
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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 = zeros(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, 500);
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TrueData.x1bis = equation.evaluate(TrueData, 'eq:x1');
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if max(abs(TrueData.x1bis.data(5:end)-diff(TrueData.x1.data(4:end))))>1e-8
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error('equation.evaluate() returned wrong values.')
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end
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@ -0,0 +1,125 @@
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// --+ options: json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y x u v s;
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varexo ex1
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ex2 (used='estimationonly')
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ex1bar (used='estimationonly')
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ex2bar (used='estimationonly')
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ez
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ey
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ex
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eu
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ev
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es;
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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 c_z_dx2 c_z_u c_z_dv c_z_s cx cy 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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c_z_dx2 = .3;
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c_z_u = .3;
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c_z_dv = .4;
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c_z_s = -.2;
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cx = 1.0;
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cy = 1.0;
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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:u']
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s = .3*s(-1) - .1*s(-2) + es;
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[name='eq:diff(v)']
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diff(v) = .5*diff(v(-1)) + ev;
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[name='eq:u']
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u = .5*u(-1) - .2*u(-2) + eu;
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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) + c_z_s*s + c_z_dv*diff(v) ) + (1-lambda)*( cy*y + cx*x) + c_z_u*u + c_z_dx2*diff(x2) + 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 ex = 0.1;
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var eu = 0.05;
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var ev = 0.05;
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var es = 0.07;
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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 = zeros(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, 500);
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TrueData.diff_x2_fit = equation.evaluate(TrueData, 'eq:x2');
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dx2 = diff(TrueData.x2);
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if max(abs((TrueData.diff_x2_fit.data+TrueData.ex2.data-dx2.data)))>1e-8
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error('equation.evaluate() returned wrong values.')
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end
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