Make rool for trend_component_model in var_expectation.
Not yet working, a bug in the preprocessor remains to be fixed. The preprocessor does not create the correct number of reduced form parameters for VAR_EXPECTATION when the auxiliary model is a trend component model, because it ignores the fact that the model has to be rewritten in levels.time-shift
Stéphane Adjemia (Scylla)
parent
e25b6a0a18
commit
83c9b0d5b6
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@ -44,23 +44,23 @@ end
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varexpectationmodel = DynareModel.var_expectation.(varexpectationmodelname);
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% Get the name of the associated VAR model and test its existence.
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if ~isfield(DynareModel.var, varexpectationmodel.auxiliary_model_name)
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if ~isfield(DynareModel.(varexpectationmodel.auxiliary_model_type), varexpectationmodel.auxiliary_model_name)
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error('Unknown VAR (%s) in VAR_EXPECTATION_MODEL (%s)!', varexpectationmodel.auxiliary_model_name, varexpectationmodelname)
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end
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varmodel = DynareModel.var.(varexpectationmodel.auxiliary_model_name);
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auxmodel = DynareModel.(varexpectationmodel.auxiliary_model_type).(varexpectationmodel.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, varexpectationmodel.auxiliary_model_name)
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error('VAR model %s has to be estimated or calibrated first!', varexpectationmodel.auxiliary_model_name)
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if ~isfield(DynareOutput.(varexpectationmodel.auxiliary_model_type), varexpectationmodel.auxiliary_model_name)
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error('Auxiliary model %s has to be estimated or calibrated first!', varexpectationmodel.auxiliary_model_name)
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end
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varcalib = DynareOutput.var.(varexpectationmodel.auxiliary_model_name);
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auxcalib = DynareOutput.(varexpectationmodel.auxiliary_model_type).(varexpectationmodel.auxiliary_model_name);
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if ~isfield(varcalib, 'CompanionMatrix') || any(isnan(varcalib.CompanionMatrix(:)))
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message = sprintf('VAR model %s has to be estimated first.', varexpectationmodel.auxiliary_model_name);
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message = sprintf('s\nPlease use get_companion_matrix command first.', message);
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error(message)
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if ~isfield(auxcalib, 'CompanionMatrix') || any(isnan(auxcalib.CompanionMatrix(:)))
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message = sprintf('Auxiliary model %s has to be estimated first.', varexpectationmodel.auxiliary_model_name);
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message = sprintf('%s\nPlease use get_companion_matrix command first.', message);
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error(message);
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end
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% Set discount factor
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@ -85,7 +85,7 @@ if discountfactor>1
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end
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% Set variable_id in VAR model
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variable_id_in_var = find(varexpectationmodel.variable_id==varmodel.lhs);
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variable_id_in_var = find(varexpectationmodel.variable_id==auxmodel.lhs);
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% Get the horizon parameter.
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horizon = varexpectationmodel.horizon;
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@ -121,7 +121,7 @@ if wrong_horizon_parameter
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end
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% Get the companion matrix
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CompanionMatrix = varcalib.CompanionMatrix;
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CompanionMatrix = auxcalib.CompanionMatrix;
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% Get the dimension of the problem.
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n = length(CompanionMatrix);
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@ -0,0 +1,45 @@
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function initialize(varexpectationmodel)
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% Initialize a VAR_EXPECTATION_MODEL.
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%
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% INPUTS
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% - varepxpectationmodel [string] Name of the VAR expectation model.
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%
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% OUTPUTS
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% None
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%
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% SPECIAL REQUIREMENTS
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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_.var_expectation.(varexpectationmodel).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_.var_expectation.(varexpectationmodel).auxiliary_model_type = auxiliary_model_type;
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get_companion_matrix(auxiliary_model_name, auxiliary_model_type);
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@ -51,9 +51,8 @@ foo = .5*foo(-1) + var_expectation(varexp);
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end;
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// Build the companion matrix of the VAR model (toto).
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get_companion_matrix('toto');
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// Initialize the VAR expectation model, will build the companion matrix of the VAR.
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var_expectation.initialize('varexp')
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// Update VAR_EXPECTATION reduced form parameters
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var_expectation.update('varexp');
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@ -51,9 +51,8 @@ foo = .5*foo(-1) + var_expectation(varexp);
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end;
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// Build the companion matrix of the VAR model (toto).
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get_companion_matrix('toto');
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// Initialize the VAR expectation model, will build the companion matrix of the VAR.
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var_expectation.initialize('varexp')
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// Update VAR_EXPECTATION reduced form parameters
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var_expectation.update('varexp');
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@ -50,10 +50,8 @@ y = d*y(-2) + e*z(-1) + e_y;
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foo = .5*foo(-1) + var_expectation(varexp);
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end;
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// Build the companion matrix of the VAR model (toto).
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get_companion_matrix('toto');
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// Initialize the VAR expectation model, will build the companion matrix of the VAR.
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var_expectation.initialize('varexp')
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// Update VAR_EXPECTATION reduced form parameters
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var_expectation.update('varexp');
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@ -50,10 +50,8 @@ y = d*y(-2) + e*z(-1) + e_y;
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foo = .5*foo(-1) + var_expectation(varexp);
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end;
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// Build the companion matrix of the VAR model (toto).
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get_companion_matrix('toto');
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// Initialize the VAR expectation model, will build the companion matrix of the VAR.
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var_expectation.initialize('varexp')
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// Update VAR_EXPECTATION reduced form parameters
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var_expectation.update('varexp');
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@ -38,7 +38,6 @@ var_model(model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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var_expectation_model(model_name = varexp, variable = x, auxiliary_model_name = toto, horizon = 15:50, discount = beta) ;
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model;
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[ name = 'X' ]
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x = a*x(-1) + b*x(-2) + c*z(-2) + e_x;
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@ -50,10 +49,8 @@ y = d*y(-2) + e*z(-1) + e_y;
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foo = .5*foo(-1) + var_expectation(varexp);
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end;
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// Build the companion matrix of the VAR model (toto).
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get_companion_matrix('toto');
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// Initialize the VAR expectation model, will build the companion matrix of the VAR.
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var_expectation.initialize('varexp')
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// Update VAR_EXPECTATION reduced form parameters
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var_expectation.update('varexp');
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@ -0,0 +1,86 @@
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// --+ options: stochastic,json=compute +--
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var foo x1 x2 x1bar x2bar;
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varexo ex1 ex2 ex1bar ex2bar;
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parameters a_x1_0 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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beta ;
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a_x1_0 = -.9;
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a_x1_0_ = -.8;
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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 = 1/(1+.02);
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// Define a TREND_COMPONENT model from a subset of equations in the model block.
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trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], trends=['eq:x1bar', 'eq:x2bar']);
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/* Define a VAR_EXPECTATION_MODEL
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** ------------------------------
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**
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** model_name: the name of the VAR_EXPECTATION_MODEL (mandatory).
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** auxiliary_model_name: the name of the VAR model used for the expectations (mandatory).
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** variable: the name of the variable to be forecasted (mandatory).
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** horizon: the horizon forecast (mandatory).
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** discount: the discount factor, which can be a value or a declared parameter (default is 1.0, no discounting).
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**
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**
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** The `horizon` parameter can be an integer in which case the (discounted) `horizon` step ahead forecast
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** is computed using the VAR model `var_model_name`. Alternatively, `horizon` can be a range. In this case
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** VAR_EXPECTATION_MODEL returns a discounted sum of expected values. If `horizon` is set equal to the range
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** 0:Inf, then VAR_EXPECTATION_MODEL computes:
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**
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** ∑ βʰ Eₜ[yₜ₊ₕ]
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**
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** where the sum is over h=0,…,∞ and the conditional expectations are computed with VAR model `var_model_name`.
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*/
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var_expectation_model(model_name = varexp, variable = x1bar, auxiliary_model_name = toto, horizon = 15:50, discount = beta) ;
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model;
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[name='eq:x1', data_type='nonstationary']
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diff(x1) = a_x1_0*(x1(-1)-x1bar(-1))+a_x1_0_*(x2(-1)-x2bar(-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', data_type='nonstationary']
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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', data_type='nonstationary']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar', data_type='nonstationary']
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x2bar = x2bar(-1) + ex2bar;
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foo = .5*foo(-1) + var_expectation(varexp);
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end;
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// Initialize the VAR expectation model, will build the companion matrix of the VAR.
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var_expectation.initialize('varexp')
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// Update VAR_EXPECTATION reduced form parameters
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var_expectation.update('varexp');
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/*
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** REMARK The VAR model is such that x depends on past values of x
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** (xₜ₋₁ and xₜ₋₂) and on zₜ₋₂. Consequently the reduced
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** form parameters associated to yₜ₋₁, yₜ₋₂ have to be zero.
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*/
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weights = M_.params(M_.var_expectation.varexp.param_indices);
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if weights(2) || ~weights(3) || weights(5) || ~weights(1) || ~weights(4) || ~weights(6)
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error('Wrong reduced form parameter for VAR_EXPECTATION_MODEL')
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end
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