128 lines
6.5 KiB
Matlab
128 lines
6.5 KiB
Matlab
function parameters(pacname)
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% Updates the parameters of a PAC Model Consistent Expectations.
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%
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% INPUTS
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% - pacname [string] Name of the pac equation.
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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 © 2019-2021 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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global M_
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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('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(M_.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 = M_.pac.(pacname);
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% Check that we are dealing with PAC/MCE
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if ~pacmodel.model_consistent_expectations
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error('This function is to be used only for PAC Model Consistent Expectations.')
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end
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% Show the equations where this PAC model is used.
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fprintf('PAC model %s is used in equation %s.\n', pacname, pacmodel.eq_name);
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skipline()
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% Get Error correction and autoregressive parameters in PAC equation
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params = NaN(2+pacmodel.max_lag, 1);
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params(1) = M_.params(pacmodel.ec.params);
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params(1+(1:pacmodel.max_lag)) = M_.params(pacmodel.ar.params);
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params(end) = M_.params(pacmodel.discount_index);
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[G, alpha, beta] = buildGmatrixWithAlphaAndBeta(params);
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M_.params(pacmodel.mce.alpha) = flip(alpha);
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if isfield(pacmodel, 'growth_neutrality_param_index')
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if isfield(pacmodel, 'non_optimizing_behaviour')
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gamma = M_.params(pacmodel.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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A = [alpha; 1];
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A_1 = polyval(A, 1.0);
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A_b = polyval(A, beta);
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m = length(alpha);
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d = A_1*A_b*(iota(m, m)'*inv((eye(m)-G)*(eye(m)-G))*iota(m, m));
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cc = 1/gamma-(sum(params(2:end-1))+d);
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ll = 0;
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if isfield(pacmodel, '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(pacmodel.optim_additive.params)
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if isnan(pacmodel.optim_additive.params(i)) && islogical(pacmodel.optim_additive.bgp{i}) && pacmodel.optim_additive.bgp{i}
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tmp0 = tmp0 + pacmodel.optim_additive.scaling_factor(i);
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elseif ~isnan(pacmodel.optim_additive.params(i)) && islogical(pacmodel.optim_additive.bgp{i}) && pacmodel.optim_additive.bgp{i}
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tmp0 = tmp0 + M_.params(pacmodel.optim_additive.params(i))*equations.(eqtag).optim_additive.scaling_factor(i);
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elseif ~islogical(pacmodel.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(pacmodel, 'non_optimizing_behaviour') && isfield(pacmodel.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(pacmodel.non_optimizing_behaviour.params)
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if isnan(pacmodel.non_optimizing_behaviour.params(i)) && islogical(pacmodel.non_optimizing_behaviour.bgp{i}) && pacmodel.non_optimizing_behaviour.bgp{i}
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tmp0 = tmp0 + pacmodel.non_optimizing_behaviour.scaling_factor(i);
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elseif ~isnan(pacmodel.non_optimizing_behaviour.params(i)) && islogical(pacmodel.non_optimizing_behaviour.bgp{i}) && pacmodel.non_optimizing_behaviour.bgp{i}
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tmp0 = tmp0 + M_.params(pacmodel.non_optimizing_behaviour.params(i))*pacmodel.non_optimizing_behaviour.scaling_factor(i);
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elseif ~islogical(pacmodel.non_optimizing_behaviour.bgp{i}) && isnumeric(pacmodel.non_optimizing_behaviour.bgp{i}) && isnan(pacmodel.non_optimizing_behaviour.params(i))
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tmp1 = tmp1 + pacmodel.non_optimizing_behaviour.scaling_factor(i)*pacmodel.non_optimizing_behaviour.bgp{i};
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elseif ~islogical(pacmodel.non_optimizing_behaviour.bgp{i}) && isnumeric(pacmodel.non_optimizing_behaviour.bgp{i}) && ~isnan(pacmodel.non_optimizing_behaviour.params(i))
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tmp1 = tmp1 + pacmodel.non_optimizing_behaviour.scaling_factor(i)*pacmodel.non_optimizing_behaviour.params(i)*pacmodel.non_optimizing_behaviour.bgp{i};
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end
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end
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cc = cc - (1-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(pacmodel, '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(pacmodel.additive.params)
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if isnan(pacmodel.additive.params(i)) && islogical(pacmodel.additive.bgp{i}) && pacmodel.additive.bgp{i}
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tmp0 = tmp0 + pacmodel.additive.scaling_factor(i);
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elseif ~isnan(pacmodel.additive.params(i)) && islogical(pacmodel.additive.bgp{i}) && pacmodel.additive.bgp{i}
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tmp0 = tmp0 + M_.params(pacmodel.additive.params(i))*pacmodel.additive.scaling_factor(i);
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elseif ~islogical(pacmodel.additive.bgp{i}) && isnumeric(pacmodel.additive.bgp{i}) && isnan(pacmodel.additive.params(i))
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tmp1 = tmp1 + pacmodel.additive.scaling_factor(i)*pacmodel.additive.bgp{i};
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elseif ~islogical(pacmodel.additive.bgp{i}) && isnumeric(pacmodel.additive.bgp{i}) && ~isnan(pacmodel.additive.params(i))
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tmp1 = tmp1 + pacmodel.additive.scaling_factor(i)*pacmodel.additive.params(i)*pacmodel.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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M_.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 |