140 lines
5.0 KiB
Matlab
140 lines
5.0 KiB
Matlab
function [pacmodl, lhs, rhs, pnames, enames, xnames, rname, pid, eid, xid, pnames_, ipnames_, params, data, islaggedvariables] = init(M_, oo_, eqname, params, data, range)
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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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% Get the original equation to be estimated
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[LHS, RHS] = get_lhs_and_rhs(eqname, M_, true);
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% Check that the equation has a PAC expectation term.
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if ~contains(RHS, 'pac_expectation', 'IgnoreCase', true)
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error('This is not a PAC equation.')
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end
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% Get the name of the PAC model.
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pattern = '(\(model_name\s*=\s*)(?<name>\w+)\)';
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pacmodl = regexp(RHS, pattern, 'names');
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pacmodl = pacmodl.name;
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pacmodel = M_.pac.(pacmodl);
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% Get the transformed equation to be estimated.
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[lhs, rhs, json] = get_lhs_and_rhs(eqname, M_);
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% Get definition of aux. variable for pac expectation...
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if isfield(pacmodel, 'aux_id')
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auxrhs = {M_.lhs{pacmodel.aux_id}, json.model{pacmodel.aux_id}.rhs};
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elseif isfield(pacmodel, 'components')
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auxrhs = cell(length(pacmodel.components), 2);
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for i=1:length(pacmodel.components)
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auxrhs{i,1} = M_.lhs{pacmodel.components(i).aux_id};
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auxrhs{i,2} = sprintf('(%s)', json.model{pacmodel.components(i).aux_id}.rhs);
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end
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else
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error('Cannot find auxiliary variables for PAC expectation.')
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end
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% ... and substitute in rhs.
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for i=1:rows(auxrhs)
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rhs = strrep(rhs, auxrhs{i,1}, auxrhs{i,2});
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end
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% Get pacmodel properties
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pacmodel = M_.pac.(pacmodl);
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% Get the parameters and variables in the PAC equation.
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[pnames, enames, xnames, pid, eid, xid] = get_variables_and_parameters_in_equation(lhs, rhs, M_);
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% Get list and indices of estimated parameters.
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ipnames_ = get_estimated_parameters_indices(params, pnames, eqname, M_);
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% If equation is estimated by recursive OLS, ensure that the error
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% correction parameter comes first, followed by the autoregressive
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% parameters (in increasing order w.r.t. the lags).
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stack = dbstack;
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ipnames__ = ipnames_; % The user provided order.
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if isequal(stack(2).name, 'iterative_ols')
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ipnames_ = [pacmodel.ec.params; pacmodel.ar.params'];
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if isfield(pacmodel, 'optim_additive')
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ipnames_ = [ipnames_; pacmodel.optim_additive.params(~isnan(pacmodel.optim_additive.params))'];
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end
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if isfield(pacmodel, 'additive')
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ipnames_ = [ipnames_; pacmodel.additive.params(~isnan(pacmodel.additive.params))'];
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end
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if isfield(pacmodel, 'share_of_optimizing_agents_index')
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ipnames_ = [ipnames_; pacmodel.share_of_optimizing_agents_index];
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end
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for i=1:length(ipnames_)
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if ~ismember(ipnames_(i), ipnames__)
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% This parameter is not estimated.
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ipnames_(i) = NaN;
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end
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end
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end
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% Remove calibrated parameters (if any).
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ipnames_(isnan(ipnames_)) = [];
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% Reorder params if needed.
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[~, permutation] = ismember(ipnames__, ipnames_);
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pnames_ = fieldnames(params);
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pnames_ = pnames_(permutation);
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params = orderfields(params, permutation);
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% Add the auxiliary variables in the dataset.
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if M_.endo_nbr>M_.orig_endo_nbr
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data = feval([M_.fname '.dynamic_set_auxiliary_series'], data, M_.params);
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end
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% Check that the data for endogenous variables have values.
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if any(isnan(data{enames{:}}(range).data(:)))
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error('Some variable values are missing in the database.')
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end
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% Set the number of exogenous variables.
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xnbr = length(xnames);
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% Test if we have a residual and get its name (-> rname).
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if isequal(xnbr, 1)
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rname = M_.exo_names{strcmp(xnames{1}, M_.exo_names)};
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if ~all(isnan(data{xnames{1}}.data))
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error('The residual (%s) must have NaN values in the provided database.', xnames{1})
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end
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else
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% We have observed exogenous variables in the PAC equation.
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tmp = data{xnames{:}}(range).data;
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idx = find(all(~isnan(tmp))); % Indices for the observed exogenous variables.
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if isequal(length(idx), length(xnames))
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error('There is no residual in this equation, all the exogenous variables are observed.')
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else
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if length(idx)<length(xnames)-1
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error('It is not allowed to have more than one residual in a PAC equation')
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end
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irname = setdiff(1:length(xnames), idx);
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rname = xnames{irname};
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end
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end
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% Remove residuals from the equation.
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%
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% Note that a plus or minus will remain in the equation, but this seems to
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% be without consequence.
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rhs = regexprep(rhs, rname, '');
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% Create a dummy variable
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islaggedvariables = contains(rhs, '(-1)'); |