Adapted matlab code to new TREND_COMPONENT auxiliary model.
Stéphane Adjemia (Scylla)
parent
38b2593e31
commit
4a0b3ffe7e
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@ -44,21 +44,21 @@ end
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pacmodel = DynareModel.pac.(pacname);
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% Get the name of the associated VAR model and test its existence.
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if ~isfield(DynareModel.var, pacmodel.var_model_name)
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error('Unknown VAR (%s) in PAC model (%s)!', pacmodel.var_model_name, pacname)
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if ~isfield(DynareModel.(pacmodel.auxiliary_model_type), pacmodel.auxiliary_model_name)
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error('Unknown auxiliary model (%s) in PAC model (%s)!', pacmodel.auxiliary_model_name, pacname)
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end
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varmodel = DynareModel.var.(pacmodel.var_model_name);
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varmodel = DynareModel.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
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% Check that we have the values of the VAR matrices.
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if ~isfield(DynareOutput.var, pacmodel.var_model_name)
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error('VAR model %s has to be estimated first!', pacmodel.var_model_name)
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if ~isfield(DynareOutput.(pacmodel.auxiliary_model_type), pacmodel.auxiliary_model_name)
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error('Auxiliary model %s has to be estimated first!', pacmodel.auxiliary_model_name)
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end
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varcalib = DynareOutput.var.(pacmodel.var_model_name);
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varcalib = DynareOutput.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
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if ~isfield(varcalib, 'CompanionMatrix') || any(isnan(varcalib.CompanionMatrix(:)))
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error('VAR model %s has to be estimated first.', pacmodel.var_model_name)
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error('Auxiliary model %s has to be estimated first.', pacmodel.auxiliary_model_name)
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end
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% Build the vector of PAC parameters (ECM parameter + autoregressive parameters).
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@ -86,12 +86,18 @@ if isempty(id)
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end
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end
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if varmodel.nonstationary(id)
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idns = id;
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ids = [];
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if isequal(pacmodel.auxiliary_model_type, 'var')
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if varmodel.nonstationary(id)
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idns = id;
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ids = [];
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else
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idns = [];
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ids = id;
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end
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else
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idns = [];
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ids = id;
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% Trend component model is assumed.
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ids = [];
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idns = id;
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end
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% Get the value of the discount factor.
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@ -0,0 +1,43 @@
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function initialize(pacmodel)
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% Initialization of a PAC model.
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%
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% INPUTS
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% - pacmodel [string] Name of the pac model.
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%
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% OUTPUTS
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% None
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% Copyright (C) 2018 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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auxiliary_model_name = M_.pac.(pacmodel).auxiliary_model_name;
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if isfield(M_, 'var') && isfield(M_.var, auxiliary_model_name)
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auxiliary_model_type = 'var';
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elseif isfield(M_, 'trend_component') && isfield(M_.trend_component, auxiliary_model_name)
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auxiliary_model_type = 'trend_component';
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else
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error('Unknown type of auxiliary model.')
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end
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M_.pac.(pacmodel).auxiliary_model_type = auxiliary_model_type;
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get_companion_matrix(auxiliary_model_name, auxiliary_model_type);
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@ -1,11 +1,11 @@
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function get_companion_matrix(var_model_name, pac_model_name)
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%function get_companion_matrix(var_model_name)
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function get_companion_matrix(auxiliary_model_name, auxiliary_model_type)
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% Gets the companion matrix associated with the var specified by
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% var_model_name. Output stored in cellarray oo_.var.(var_model_name).H.
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% Gets the companion VAR representation of a PAC auxiliary model.
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% Depending on the nature of this auxiliary model the output is
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% saved in oo_.{var,trend_component}.(auxiliary_model_name).H
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%
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% INPUTS
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% - var_model_name [string] the name of the VAR model
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% - auxiliary_model_name [string] the name of the auxiliary model
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%
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% OUTPUTS
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% - None
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@ -29,185 +29,146 @@ function get_companion_matrix(var_model_name, pac_model_name)
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global oo_ M_
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get_ar_ec_matrices(var_model_name);
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% Get the number of lags
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p = size(oo_.var.(var_model_name).ar, 3);
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% Get the number of variables
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n = length(oo_.var.(var_model_name).ar(:,:,1));
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% If not used in a PAC equation
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if nargin<2
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oo_.var.(var_model_name).CompanionMatrix = zeros(n*p);
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oo_.var.(var_model_name).CompanionMatrix(1:n,1:n) = oo_.var.(var_model_name).ar(:,:,1);
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if p>1
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for i=2:p
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oo_.var.(var_model_name).CompanionMatrix(1:n,(i-1)*n+(1:n)) = oo_.var.(var_model_name).ar(:,:,i);
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oo_.var.(var_model_name).CompanionMatrix((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
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end
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if isfield(M_, 'var') && isfield(M_.var, auxiliary_model_name)
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auxiliary_model_type = 'var';
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elseif isfield(M_, 'trend_component') && isfield(M_.trend_component, auxiliary_model_name)
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auxiliary_model_type = 'trend_component';
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else
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error('Unknown type of auxiliary model.')
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end
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M_.var.(var_model_name).list_of_variables_in_companion_var = M_.endo_names(M_.var.(var_model_name).lhs);
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return
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end
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get_ar_ec_matrices(auxiliary_model_name, auxiliary_model_type);
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if all(~oo_.var.(var_model_name).ec(:))
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% The auxiliary model is a VAR model.
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M_.pac.(pac_model_name).auxmodel = 'var';
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if isempty(M_.pac.(pac_model_name).undiff_eqtags)
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% Build the companion matrix (standard VAR)
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oo_.var.(var_model_name).CompanionMatrix = zeros(n*p);
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oo_.var.(var_model_name).CompanionMatrix(1:n,1:n) = oo_.var.(var_model_name).ar(:,:,1);
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if p>1
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for i=2:p
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oo_.var.(var_model_name).CompanionMatrix(1:n,(i-1)*n+(1:n)) = oo_.var.(var_model_name).ar(:,:,i);
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oo_.var.(var_model_name).CompanionMatrix((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
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end
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end
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M_.var.(var_model_name).list_of_variables_in_companion_var = M_.endo_names(M_.var.(var_model_name).lhs);
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else
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error('You should not use undiff option in this model!')
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end
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else
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% The auxiliary model is a VECM model.
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M_.pac.(pac_model_name).auxmodel = 'vecm';
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if ~isempty(M_.pac.(pac_model_name).undiff_eqtags)
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% REMARK It is assumed that the equations with undiff option are the
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% ECM equations. By complementarity, the other equations are
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% the trends appearing in the error correction terms. We
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% assume that the model can be cast in the following form:
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%
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% Δ Xₜ = A₀ (Xₜ₋₁ - Zₜ₋₁) + Σᵢ₌₁ᵖ Aᵢ Δ Xₜ₋ᵢ + ϵₜ
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%
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% Zₜ = Zₜ₋₁ + ηₜ
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%
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% We first recast the equation into this representation, and
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% we rewrite the model in levels (we integrate the first set
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% of equations) to rewrite the model as a VAR(1) model. Let
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% Yₜ = [Xₜ; Zₜ] be the vertical concatenation of vectors
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% Xₜ (variables with EC) and Zₜ (trends). We have
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%
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% Yₜ = Σᵢ₌₁ᵖ⁺¹ Bᵢ Yₜ₋ᵢ + [εₜ; ηₜ]
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%
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% with
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%
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% B₁ = [I+Λ+A₁, -Λ; 0, I]
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%
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% Bᵢ = [Aᵢ-Aᵢ₋₁, 0; 0, 0] for i = 2,…, p
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% and
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% Bₚ₊₁ = -[Aₚ, 0; 0, 0]
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%
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% where the dimensions of I and 0 matrices can easily be
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% deduced from the number of EC and trend equations.
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%
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% Get the indices of the equations with error correction terms.
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m = length(M_.pac.(pac_model_name).undiff_eqtags);
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q = length(M_.var.(var_model_name).eqn)-m;
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ecm_eqnums = zeros(m, 1);
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ecm_eqnums_in_auxiliary_model = zeros(m, 1);
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trend_eqnums = zeros(q, 1);
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trend_eqnums_in_auxiliary_model = zeros(q, 1);
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% EC equations in the order of M_.pac.(pac_model_name).undiff_eqtags{i}
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ecm = zeros(m, 1);
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for i = 1:m
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ecm(i) = get_equation_number_by_tag(M_.pac.(pac_model_name).undiff_eqtags{i});
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end
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% Trend equations
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trends = setdiff(M_.var.(var_model_name).eqn(:), ecm);
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i1 = 1;
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i2 = 1;
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for i=1:m+q
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% Only EC or trend equations are allowed in this model.
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if ismember(M_.var.(var_model_name).eqn(i), ecm)
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ecm_eqnums(i1) = M_.var.(var_model_name).eqn(i);
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ecm_eqnums_in_auxiliary_model(i1) = i;
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i1 = i1 + 1;
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else
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trend_eqnums(i2) = M_.var.(var_model_name).eqn(i);
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trend_eqnums_in_auxiliary_model(i2) = i;
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i2 = i2 + 1;
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end
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end
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% Check that the lhs of candidate ecm equations are at least first differences.
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difference_orders_in_error_correction_eq = zeros(m, 1);
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for i=1:m
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difference_orders_in_error_correction_eq(i) = get_difference_order(M_.var.(var_model_name).lhs(ecm_eqnums_in_auxiliary_model(i)));
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end
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if any(~difference_orders_in_error_correction_eq)
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error('Model %s is not a VECM model! LHS variables should be in difference', var_model_name)
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end
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% Get the trend variables indices (lhs variables in trend equations).
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[~, id_trend_in_var, ~] = intersect(M_.var.(var_model_name).eqn, trend_eqnums);
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trend_variables = reshape(M_.var.(var_model_name).lhs(id_trend_in_var), q, 1);
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% Get the rhs variables in trend equations.
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trend_autoregressive_variables = zeros(q, 1);
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for i=1:q
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% Check that there is only one variable on the rhs and update trend_autoregressive_variables.
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v = M_.var.(var_model_name).rhs.vars_at_eq{id_trend_in_var(i)}.var;
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if ~(length(v)==1)
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error('A trend equation (%s) must have only one variable on the RHS!', M_.var.(var_model_name).eqtags{trend_eqnums(i)})
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end
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trend_autoregressive_variables(i) = v;
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% Check that the variables on lhs and rhs have the same difference orders.
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if get_difference_order(trend_variables(i))~=get_difference_order(trend_autoregressive_variables(i))
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error('In a trend equation (%s) LHS and RHS variables must have the same difference orders!', M_.var.(var_model_name).eqtags{trend_eqnums(i)})
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end
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% Check that the trend equation is autoregressive.
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if isdiff(v)
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if ~M_.aux_vars(get_aux_variable_id(v)).type==9
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error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.var.(var_model_name).eqtags{trend_eqnums(i)})
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else
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if M_.aux_vars(get_aux_variable_id(v)).orig_index~=trend_variables(i)
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error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.var.(var_model_name).eqtags{trend_eqnums(i)})
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end
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end
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else
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if get_aux_variable_id(v) && M_.aux_vars(get_aux_variable_id(v)).endo_index~=trend_variables(i)
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error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.var.(var_model_name).eqtags{trend_eqnums(i)})
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end
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end
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end
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% Get the EC matrix (the EC term is assumend to be in t-1).
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%
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% TODO: Check that the EC term is the difference between the
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% endogenous variable and the trend variable.
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%
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A0 = oo_.var.(var_model_name).ec(ecm_eqnums_in_auxiliary_model,:,1);
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% Get the AR matrices.
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AR = oo_.var.(var_model_name).ar(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,:);
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% Build B matrices (VAR in levels)
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B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,1) = eye(m)+A0+AR(:,:,1);
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B(ecm_eqnums_in_auxiliary_model,trend_eqnums_in_auxiliary_model) = -A0;
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B(trend_eqnums_in_auxiliary_model,trend_eqnums_in_auxiliary_model) = eye(q);
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% Get the number of lags
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p = size(oo_.(auxiliary_model_type).(auxiliary_model_name).ar, 3);
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% Get the number of variables
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n = length(oo_.(auxiliary_model_type).(auxiliary_model_name).ar(:,:,1));
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switch auxiliary_model_type
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case 'var'
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oo_.var.(auxiliary_model_name).CompanionMatrix = zeros(n*p);
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oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,1:n) = oo_.var.(auxiliary_model_name).ar(:,:,1);
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if p>1
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for i=2:p
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B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,i) = AR(:,:,i)-AR(:,:,i-1);
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oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,(i-1)*n+(1:n)) = oo_.var.(auxiliary_model_name).ar(:,:,i);
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oo_.var.(auxiliary_model_name).CompanionMatrix((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
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end
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B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,p+1) = -AR(:,:,p);
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% Write Companion matrix
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oo_.var.(var_model_name).CompanionMatrix = zeros(size(B, 1)*size(B, 3));
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for i=1:p
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oo_.var.(var_model_name).CompanionMatrix(1:n, (i-1)*n+(1:n)) = B(:,:,i);
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oo_.var.(var_model_name).CompanionMatrix(i*n+(1:n),(i-1)*n+(1:n)) = eye(n);
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end
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M_.var.(auxiliary_model_name).list_of_variables_in_companion_var = M_.endo_names(M_.var.(auxiliary_model_name).lhs);
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case 'trend_component'
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% Get number of trends.
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q = sum(M_.trend_component.(auxiliary_model_name).trends);
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% Get the number of equations with error correction.
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m = n-q;
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% Get the indices of trend and EC equations in the auxiliary model.
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trend_eqnums_in_auxiliary_model = find(M_.trend_component.(auxiliary_model_name).trends);
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ecm_eqnums_in_auxiliary_model = find(~M_.trend_component.(auxiliary_model_name).trends);
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% Get the indices of trend equations in model.
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trend_eqnums = M_.trend_component.(auxiliary_model_name).trend_eqn;
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% REMARK It is assumed that the non trend equations are the error correction
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% equations. We assume that the model can be cast in the following form:
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%
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% Δ Xₜ₋₁ = A₀ (Xₜ₋₁ - Zₜ₋₁) + Σᵢ₌₁ᵖ Aᵢ Δ Xₜ₋ᵢ + ϵₜ
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%
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% Zₜ = Zₜ₋₁ + ηₜ
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%
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% We first recast the equation into this representation, and
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% we rewrite the model in levels (we integrate the first set
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% of equations) to rewrite the model as a VAR(1) model. Let
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% Yₜ = [Xₜ; Zₜ] be the vertical concatenation of vectors
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% Xₜ (variables with EC) and Zₜ (trends). We have
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%
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% Yₜ = Σᵢ₌₁ᵖ⁺¹ Bᵢ Yₜ₋ᵢ + [εₜ; ηₜ]
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%
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% with
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%
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% B₁ = [I+Λ+A₁, -Λ; 0, I]
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%
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% Bᵢ = [Aᵢ-Aᵢ₋₁, 0; 0, 0] for i = 2,…, p
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% and
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% Bₚ₊₁ = -[Aₚ, 0; 0, 0]
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%
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% where the dimensions of I and 0 matrices can easily be
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% deduced from the number of EC and trend equations.
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% Check that the lhs of candidate ecm equations are at least first differences.
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difference_orders_in_error_correction_eq = zeros(m, 1);
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for i=1:m
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difference_orders_in_error_correction_eq(i) = get_difference_order(M_.trend_component.(auxiliary_model_name).lhs(ecm_eqnums_in_auxiliary_model(i)));
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end
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if any(~difference_orders_in_error_correction_eq)
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error('Model %s is not a Trend component model! LHS variables should be in difference', auxiliary_model_name)
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end
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% Get the trend variables indices (lhs variables in trend equations).
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[~, id_trend_in_var, ~] = intersect(M_.trend_component.(auxiliary_model_name).eqn, trend_eqnums);
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trend_variables = reshape(M_.trend_component.(auxiliary_model_name).lhs(id_trend_in_var), q, 1);
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% Get the rhs variables in trend equations.
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trend_autoregressive_variables = zeros(q, 1);
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for i=1:q
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% Check that there is only one variable on the rhs and update trend_autoregressive_variables.
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v = M_.trend_component.(auxiliary_model_name).rhs.vars_at_eq{id_trend_in_var(i)}.var;
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if length(v)~=1
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error('A trend equation (%s) must have only one variable on the RHS!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
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end
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oo_.var.(var_model_name).CompanionMatrix(1:n, p*n+(1:n)) = B(:,:,p+1);
|
||||
M_.var.(var_model_name).list_of_variables_in_companion_var = M_.endo_names(M_.var.(var_model_name).lhs);
|
||||
variables_rewritten_in_levels = M_.var.(var_model_name).list_of_variables_in_companion_var(ecm_eqnums_in_auxiliary_model);
|
||||
for i=1:m
|
||||
id = get_aux_variable_id(variables_rewritten_in_levels{i});
|
||||
if id
|
||||
auxinfo = M_.aux_vars(id);
|
||||
if auxinfo.type==8
|
||||
M_.var.(var_model_name).list_of_variables_in_companion_var(ecm_eqnums_in_auxiliary_model(i)) = ...
|
||||
{M_.endo_names{auxinfo.orig_index}};
|
||||
else
|
||||
error('This is a bug. Please contact the Dynare Team.')
|
||||
trend_autoregressive_variables(i) = v;
|
||||
% Check that the variables on lhs and rhs have the same difference orders.
|
||||
if get_difference_order(trend_variables(i))~=get_difference_order(trend_autoregressive_variables(i))
|
||||
error('In a trend equation (%s) LHS and RHS variables must have the same difference orders!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
|
||||
end
|
||||
% Check that the trend equation is autoregressive.
|
||||
if isdiff(v)
|
||||
if ~M_.aux_vars(get_aux_variable_id(v)).type==9
|
||||
error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
|
||||
else
|
||||
if M_.aux_vars(get_aux_variable_id(v)).orig_index~=trend_variables(i)
|
||||
error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
|
||||
end
|
||||
end
|
||||
else
|
||||
if get_aux_variable_id(v) && M_.aux_vars(get_aux_variable_id(v)).endo_index~=trend_variables(i)
|
||||
error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
|
||||
end
|
||||
end
|
||||
end
|
||||
% Get the EC matrix (the EC term is assumend to be in t-1).
|
||||
%
|
||||
% TODO: Check that the EC term is the difference between the
|
||||
% endogenous variable and the trend variable.
|
||||
%
|
||||
A0 = oo_.trend_component.(auxiliary_model_name).ec(ecm_eqnums_in_auxiliary_model,:,1);
|
||||
% Get the AR matrices.
|
||||
AR = oo_.trend_component.(auxiliary_model_name).ar(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,:);
|
||||
% Build B matrices (VAR in levels)
|
||||
B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,1) = eye(m)+A0+AR(:,:,1);
|
||||
B(ecm_eqnums_in_auxiliary_model,trend_eqnums_in_auxiliary_model) = -A0;
|
||||
B(trend_eqnums_in_auxiliary_model,trend_eqnums_in_auxiliary_model) = eye(q);
|
||||
for i=2:p
|
||||
B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,i) = AR(:,:,i)-AR(:,:,i-1);
|
||||
end
|
||||
B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,p+1) = -AR(:,:,p);
|
||||
% Write Companion matrix
|
||||
oo_.trend_component.(auxiliary_model_name).CompanionMatrix = zeros(size(B, 1)*size(B, 3));
|
||||
for i=1:p
|
||||
oo_.trend_component.(auxiliary_model_name).CompanionMatrix(1:n, (i-1)*n+(1:n)) = B(:,:,i);
|
||||
oo_.trend_component.(auxiliary_model_name).CompanionMatrix(i*n+(1:n),(i-1)*n+(1:n)) = eye(n);
|
||||
end
|
||||
oo_.trend_component.(auxiliary_model_name).CompanionMatrix(1:n, p*n+(1:n)) = B(:,:,p+1);
|
||||
M_.trend_component.(auxiliary_model_name).list_of_variables_in_companion_var = M_.endo_names(M_.trend_component.(auxiliary_model_name).lhs);
|
||||
variables_rewritten_in_levels = M_.trend_component.(auxiliary_model_name).list_of_variables_in_companion_var(ecm_eqnums_in_auxiliary_model);
|
||||
for i=1:m
|
||||
id = get_aux_variable_id(variables_rewritten_in_levels{i});
|
||||
if id
|
||||
auxinfo = M_.aux_vars(id);
|
||||
if auxinfo.type==8
|
||||
M_.trend_component.(auxiliary_model_name).list_of_variables_in_companion_var(ecm_eqnums_in_auxiliary_model(i)) = ...
|
||||
{M_.endo_names{auxinfo.orig_index}};
|
||||
else
|
||||
error('This is a bug. Please contact the Dynare Team.')
|
||||
end
|
||||
else
|
||||
error('This is a bug. Please contact the Dynare Team.')
|
||||
end
|
||||
else
|
||||
error('It is not possible to cast the VECM model in a companion representation! Use undiff option.')
|
||||
end
|
||||
end
|
||||
|
|
@ -63,7 +63,7 @@ if nargout>1
|
|||
switch auxmodel
|
||||
case 'var'
|
||||
h1 = A_1*A_b*(kron(iota(m, m)'*inv(eye(m)-G), H')*(tmp\kron(iota(m, m), iota(n, idns))));
|
||||
case 'vecm'
|
||||
case 'trend_component'
|
||||
h1 = A_1*A_b*(kron(iota(m, m)'*inv(eye(m)-G), (H'-eye(length(H))))*(tmp\kron(iota(m, m), iota(n, idns))));
|
||||
end
|
||||
end
|
||||
|
|
|
|||
Loading…
Reference in New Issue