245 lines
13 KiB
Matlab
245 lines
13 KiB
Matlab
function DynareModel = parameters(pacname, DynareModel, DynareOutput, verbose)
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% Updates the parameters of a PAC equation.
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%
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% INPUTS
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% - pacname [string] Name of the pac equation.
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% - DynareModel [struct] M_ global structure (model properties)
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% - DynareOutput [struct] oo_ global structure (model results)
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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 © 2018-2021 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 <https://www.gnu.org/licenses/>.
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if nargin<4
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verbose = true;
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end
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% Check that the first input is a row character array.
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if ~isrow(pacname)==1 || ~ischar(pacname)
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error('First input argument must be a row character array!')
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end
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% Check the name of the PAC model.
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if ~isfield(DynareModel.pac, pacname)
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error('PAC model %s is not defined in the model block!', pacname)
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end
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% Get PAC model description
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pacmodel = DynareModel.pac.(pacname);
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if pacmodel.model_consistent_expectations
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error('This function cannot be used with Model Consistent Expectation. Try pac.mce.parameters instead.')
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end
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% Get the name of the associated auxiliary model (VAR or TREND_COMPONENT) model and test its existence.
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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.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
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% Check that we have the values of the VAR or TREND_COMPONENT matrices.
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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.(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('Auxiliary model %s has to be estimated first.', pacmodel.auxiliary_model_name)
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end
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% Show the equations where this PAC model is used.
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number_of_pac_eq = size(pacmodel.tag_map, 1);
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if verbose
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fprintf('PAC model %s is used in %u equation(s):\n', pacname, number_of_pac_eq);
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skipline()
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for i=1:number_of_pac_eq
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fprintf(' - %s\n', pacmodel.tag_map{i,1});
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end
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skipline()
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end
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equations = pacmodel.equations;
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for e=1:number_of_pac_eq
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eqtag = pacmodel.tag_map{e,2};
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% Build the vector of PAC parameters (ECM parameter + autoregressive parameters).
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pacvalues = DynareModel.params([equations.(eqtag).ec.params; equations.(eqtag).ar.params(1:equations.(eqtag).max_lag)']);
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% Get the indices for the stationary/nonstationary variables in the VAR system.
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id = find(strcmp(DynareModel.endo_names{equations.(eqtag).ec.vars(equations.(eqtag).ec.istarget)}, varmodel.list_of_variables_in_companion_var));
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if isempty(id)
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% Find the auxiliary variables if any
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ad = find(cell2mat(cellfun(@(x) isauxiliary(x, [8 10]), varmodel.list_of_variables_in_companion_var, 'UniformOutput', false)));
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if isempty(ad)
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error('Cannot find the trend variable in the Companion VAR/VECM model.')
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else
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for i=1:length(ad)
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auxinfo = DynareModel.aux_vars(get_aux_variable_id(varmodel.list_of_variables_in_companion_var{ad(i)}));
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if isequal(auxinfo.endo_index, equations.(eqtag).ec.vars(equations.(eqtag).ec.istarget))
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id = ad(i);
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break
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end
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if isequal(auxinfo.type, 8) && isequal(auxinfo.orig_index, equations.(eqtag).ec.vars(equations.(eqtag).ec.istarget))
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id = ad(i);
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break
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end
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end
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end
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if isempty(id)
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error('Cannot find the trend variable in the Companion VAR/VECM model.')
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end
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end
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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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if varmodel.isconstant
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idns = idns+1;
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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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if varmodel.isconstant
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ids = ids+1;
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end
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end
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else
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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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beta = DynareModel.params(pacmodel.discount_index);
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% Is growth argument passed to pac_expectation?
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if isfield(pacmodel, 'growth_str')
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growth_flag = true;
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else
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growth_flag = false;
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end
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% Get h0 and h1 vectors (plus the parameter for the growth neutrality correction).
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if growth_flag
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[h0, h1, growthneutrality] = hVectors([pacvalues; beta], varcalib.CompanionMatrix, ids, idns, pacmodel.auxiliary_model_type);
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else
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[h0, h1] = hVectors([pacvalues; beta], varcalib.CompanionMatrix, ids, idns, pacmodel.auxiliary_model_type);
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end
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% Update the parameters related to the stationary components.
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if ~isempty(h0)
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if isequal(pacmodel.auxiliary_model_type, 'var')
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if DynareModel.var.(pacmodel.auxiliary_model_name).isconstant
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DynareModel.params(equations.(eqtag).h0_param_indices) = h0;
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else
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DynareModel.params(equations.(eqtag).h0_param_indices(1)) = .0;
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DynareModel.params(equations.(eqtag).h0_param_indices(2:end)) = h0;
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end
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else
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DynareModel.params(equations.(eqtag).h0_param_indices) = h0;
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end
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DynareModel.params(pacmodel.equations.(eqtag).h0_param_indices) = h0;
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else
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if ~isempty(equations.(eqtag).h0_param_indices)
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DynareModel.params(equations.(eqtag).h0_param_indices) = .0;
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end
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end
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% Update the parameters related to the nonstationary components.
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if ~isempty(h1)
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if isequal(pacmodel.auxiliary_model_type, 'var')
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if DynareModel.var.(pacmodel.auxiliary_model_name).isconstant
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DynareModel.params(equations.(eqtag).h1_param_indices) = h1;
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else
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DynareModel.params(equations.(eqtag).h1_param_indices(1)) = .0;
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DynareModel.params(equations.(eqtag).h1_param_indices(2:end)) = h1;
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end
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else
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DynareModel.params(equations.(eqtag).h1_param_indices) = h1;
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end
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else
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if ~isempty(equations.(eqtag).h1_param_indices)
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DynareModel.params(equations.(eqtag).h1_param_indices) = .0;
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end
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end
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% Update the parameter related to the growth neutrality correction.
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if isfield(equations.(eqtag), 'non_optimizing_behaviour')
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gamma = DynareModel.params(equations.(eqtag).share_of_optimizing_agents_index);
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else
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gamma = 1.0;
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end
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if growth_flag
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% Growth neutrality as returned by hVector is valid iff
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% there is no exogenous variables in the model and in the
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% absence of non optimizing agents.
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gg = -(growthneutrality-1); % Finite sum of autoregressive parameters + infinite sum of the coefficients in the PAC expectation term.
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cc = 1.0/gamma-gg; % First adjustment of the growth neutrality correction (should also be divided by gamma, done below at the end of this section).
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% We may have to further change the correction if we have nonzero mean exogenous variables.
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ll = 0.0;
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if isfield(equations.(eqtag), 'optim_additive')
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% Exogenous variables are present in the λ part (optimizing agents).
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tmp0 = 0;
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for i=1:length(equations.(eqtag).optim_additive.params)
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if isnan(equations.(eqtag).optim_additive.params(i)) && islogical(equations.(eqtag).optim_additive.bgp{i}) && equations.(eqtag).optim_additive.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).optim_additive.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).optim_additive.params(i)) && islogical(equations.(eqtag).optim_additive.bgp{i}) && equations.(eqtag).optim_additive.bgp{i}
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tmp0 = tmp0 + DynareModel.params(equations.(eqtag).optim_additive.params(i))*equations.(eqtag).optim_additive.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).optim_additive.bgp{i})
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error('It is not possible to provide a value for the mean of an exogenous variable appearing in the optimal part of the PAC equation.')
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end
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end
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cc = cc - tmp0;
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end
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if gamma<1
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if isfield(equations.(eqtag), 'non_optimizing_behaviour.params')
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% Exogenous variables are present in the 1-λ part (rule of thumb agents).
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tmp0 = 0;
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tmp1 = 0;
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for i=1:length(equations.(eqtag).non_optimizing_behaviour.params)
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if isnan(equations.(eqtag).non_optimizing_behaviour.params(i)) && islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && equations.(eqtag).non_optimizing_behaviour.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).non_optimizing_behaviour.params(i)) && islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && equations.(eqtag).non_optimizing_behaviour.bgp{i}
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tmp0 = tmp0 + DynareModel.params(equations.(eqtag).non_optimizing_behaviour.params(i))*equations.(eqtag).non_optimizing_behaviour.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnumeric(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnan(equations.(eqtag).non_optimizing_behaviour.params(i))
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tmp1 = tmp1 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i)*equations.(eqtag).non_optimizing_behaviour.bgp{i};
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elseif ~islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnumeric(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && ~isnan(equations.(eqtag).non_optimizing_behaviour.params(i))
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tmp1 = tmp1 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i)*equations.(eqtag).non_optimizing_behaviour.params(i)*equations.(eqtag).non_optimizing_behaviour.bgp{i};
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end
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end
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cc = cc - (1.0-gamma)*tmp0/gamma;
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ll = -(1.0-gamma)*tmp1/gamma; % TODO: ll should be added as a constant in the PAC equation (under the λ part) when unrolling pac_expectation.
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end
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end
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if isfield(equations.(eqtag), 'additive')
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% Exogenous variables are present outside of the λ and (1-λ) parts (or we have exogenous variables in a "pure" PAC equation.
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tmp0 = 0;
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tmp1 = 0;
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for i=1:length(equations.(eqtag).additive.params)
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if isnan(equations.(eqtag).additive.params(i)) && islogical(equations.(eqtag).additive.bgp{i}) && equations.(eqtag).additive.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).additive.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).additive.params(i)) && islogical(equations.(eqtag).additive.bgp{i}) && equations.(eqtag).additive.bgp{i}
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tmp0 = tmp0 + DynareModel.params(equations.(eqtag).additive.params(i))*equations.(eqtag).additive.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).additive.bgp{i}) && isnumeric(equations.(eqtag).additive.bgp{i}) && isnan(equations.(eqtag).additive.params(i))
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tmp1 = tmp1 + equations.(eqtag).additive.scaling_factor(i)*equations.(eqtag).additive.bgp{i};
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elseif ~islogical(equations.(eqtag).additive.bgp{i}) && isnumeric(equations.(eqtag).additive.bgp{i}) && ~isnan(equations.(eqtag).additive.params(i))
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tmp1 = tmp1 + equations.(eqtag).additive.scaling_factor(i)*equations.(eqtag).additive.params(i)*equations.(eqtag).additive.bgp{i};
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end
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end
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cc = cc - tmp0/gamma;
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ll = ll - tmp1/gamma; % TODO: ll should be added as a constant in the PAC equation (under the λ part) when unrolling pac_expectation.
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end
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DynareModel.params(pacmodel.growth_neutrality_param_index) = cc; % Multiplies the variable or expression provided though the growth option in command pac_model.
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end
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end |