307 lines
15 KiB
Matlab
307 lines
15 KiB
Matlab
function [expression, growthneutralitycorrection] = write_expectations(expectationmodelname, expectationmodelkind, iscrlf, aggregate)
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% Prints the exansion of the VAR_EXPECTATION or PAC_EXPECTATION term in files.
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%
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% INPUTS
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% - epxpectationmodelname [string] Name of the expectation model.
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% - expectationmodelkind [string] Kind of the expectation model ('var' or 'pac').
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% - iscrlf [string] Adds carriage return after each additive term if true.
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%
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% OUTPUTS
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% - expression [string] Unrolled expectation expression.
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% - growthneutralitycorrection [string]
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% Copyright © 2019-2023 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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if ismember(expectationmodelkind, {'var', 'pac'})
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if isequal(expectationmodelkind, 'var')
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expectationmodelfield = 'var_expectation';
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else
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expectationmodelfield = 'pac';
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end
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else
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error('Value of third input argument must be ''var'' or ''pac''.')
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end
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expectationmodel = M_.(expectationmodelfield).(expectationmodelname);
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if isfield(expectationmodel, 'model_consistent_expectations') && expectationmodel.model_consistent_expectations
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expression = '';
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if nargout>1
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growthneutralitycorrection = 0;
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end
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return
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end
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if nargout>1 && isequal(expectationmodelkind, 'var')
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error('Cannot return more than one argument if the expectation model is a VAR.')
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end
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if nargin<3
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iscrlf = false;
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aggregate = true;
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end
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if nargin<4
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aggregate = true;
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end
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if isfield(expectationmodel, 'h_param_indices')
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% Disaggregation requires components...
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aggregate = true;
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end
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% Get the name of the associated VAR model and test its existence.
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if ~isfield(M_.(expectationmodel.auxiliary_model_type), expectationmodel.auxiliary_model_name)
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switch expectationmodelkind
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case 'var-expectations'
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error('Unknown VAR/TREND_COMPONENT model (%s) in VAR_EXPECTATION_MODEL (%s)!', expectationmodel.auxiliary_model_name, expectationmodelname)
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case 'pac-expectations'
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error('Unknown VAR/TREND_COMPONENT model (%s) in PAC_EXPECTATION_MODEL (%s)!', expectationmodel.auxiliary_model_name, expectationmodelname)
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otherwise
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end
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end
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auxmodel = M_.(expectationmodel.auxiliary_model_type).(expectationmodel.auxiliary_model_name);
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maxlag = max(auxmodel.max_lag);
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if isequal(expectationmodel.auxiliary_model_type, 'trend_component')
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% Need to add a lag since the error correction equations are rewritten in levels.
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maxlag = maxlag+1;
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end
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id = 0;
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if isequal(expectationmodelkind, 'var')
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timeindices = (0:(maxlag-1))+abs(expectationmodel.time_shift);
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end
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if isequal(expectationmodelkind, 'var') && isequal(expectationmodel.auxiliary_model_type, 'var')
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id = id+1;
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expression = sprintf('%s', M_.param_names{expectationmodel.param_indices(id)});
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end
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if isequal(expectationmodelkind, 'pac') && isequal(expectationmodel.auxiliary_model_type, 'var')
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id = id+1;
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if isfield(expectationmodel, 'h_param_indices')
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expression = sprintf('%s', M_.param_names{expectationmodel.h_param_indices(id)});
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else
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if aggregate
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if isequal(expectationmodel.components(1).coeff_str, '1')
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expression = sprintf('%s', M_.param_names{expectationmodel.components(1).h_param_indices(id)});
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else
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expression = sprintf('%s*%s', expectationmodel.components(1).coeff_str, M_.param_names{expectationmodel.components(1).h_param_indices(id)});
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end
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for i=2:length(expectationmodel.components)
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if isequal(expectationmodel.components(i).coeff_str, '1')
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expression = sprintf('%s+%s', expression, M_.param_names{expectationmodel.components(i).h_param_indices(id)});
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else
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expression = sprintf('%s+%s*%s', expression, expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).h_param_indices(id)});
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end
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end
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else
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expression = cell(length(expectationmodel.components), 1);
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for i=1:length(expectationmodel.components)
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expression(i) = {M_.param_names{expectationmodel.components(i).h_param_indices(id)}};
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end
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end
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end
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end
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for i=1:maxlag
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for j=1:length(auxmodel.list_of_variables_in_companion_var)
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id = id+1;
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variable = auxmodel.list_of_variables_in_companion_var{j};
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transformations = {};
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ida = get_aux_variable_id(variable);
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op = 0;
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while ida
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op = op+1;
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if isequal(M_.aux_vars(ida).type, 8)
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transformations(op) = {'diff'};
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variable = M_.endo_names{M_.aux_vars(ida).orig_index};
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ida = get_aux_variable_id(variable);
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elseif isequal(M_.aux_vars(ida).type, 10)
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transformations(op) = {M_.aux_vars(ida).unary_op};
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variable = M_.endo_names{M_.aux_vars(ida).orig_index};
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ida = get_aux_variable_id(variable);
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else
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error('This case is not implemented.')
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end
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end
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switch expectationmodelkind
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case 'var'
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parameter = M_.param_names{expectationmodel.param_indices(id)};
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case 'pac'
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if isfield(expectationmodel, 'h_param_indices')
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parameter = M_.param_names{expectationmodel.h_param_indices(id)};
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else
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if aggregate
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% TODO Check if we can have parameters entering with a minus sign in the linear combination defining the target.
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if isequal(expectationmodel.components(1).coeff_str, '1')
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parameter = M_.param_names{expectationmodel.components(1).h_param_indices(id)};
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else
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parameter = sprintf('%s*%s', expectationmodel.components(1).coeff_str, M_.param_names{expectationmodel.components(1).h_param_indices(id)});
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end
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for k=2:length(expectationmodel.components)
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if isequal(expectationmodel.components(k).coeff_str, '1')
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parameter = sprintf('%s+%s', parameter, M_.param_names{expectationmodel.components(k).h_param_indices(id)});
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else
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parameter = sprintf('%s+%s*%s', parameter, expectationmodel.components(k).coeff_str, M_.param_names{expectationmodel.components(k).h_param_indices(id)});
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end
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end
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parameter = sprintf('(%s)', parameter);
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else
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parameter = cell(length(expectationmodel.components), 1);
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for k=1:length(expectationmodel.components)
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parameter(k) = {M_.param_names{expectationmodel.components(k).h_param_indices(id)}};
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end
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end
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end
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otherwise
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end
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switch expectationmodelkind
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case 'var'
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if timeindices(i)
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variable = sprintf('%s(-%d)', variable, timeindices(i));
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end
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case 'pac'
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variable = sprintf('%s(-%d)', variable, i);
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otherwise
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end
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if ~isempty(transformations)
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for k=length(transformations):-1:1
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variable = sprintf('%s(%s)', transformations{k}, variable);
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end
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end
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if isequal(id, 1)
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if aggregate
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if iscrlf
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expression = sprintf('%s*%s\n', parameter, variable);
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else
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expression = sprintf('%s*%s', parameter, variable);
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end
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else
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for k=1:length(expectationmodel.components)
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if iscrlf
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expression(k) = {sprintf('%s*%s\n', parameter{k}, variable)};
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else
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expression(k) = {sprintf('%s*%s', parameter{k}, variable)};
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end
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end
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end
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else
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if aggregate
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if iscrlf
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expression = sprintf('%s + %s*%s\n', expression, parameter, variable);
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else
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expression = sprintf('%s + %s*%s', expression, parameter, variable);
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end
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else
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for k=1:length(expectationmodel.components)
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if iscrlf
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expression(k) = {sprintf('%s + %s*%s\n', expression{k}, parameter{k}, variable)};
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else
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expression(k) = {sprintf('%s + %s*%s', expression{k}, parameter{k}, variable)};
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end
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end
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end
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end
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end
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end
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if aggregate
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growthneutralitycorrection = '';
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else
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growthneutralitycorrection = {};
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end
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if isfield(expectationmodel, 'growth_neutrality_param_index')
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if numel(expectationmodel.growth_linear_comb) == 1
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growthneutralitycorrection = sprintf('%s*%s', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
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else
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growthneutralitycorrection = sprintf('%s*(%s)', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
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end
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else
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if isfield(expectationmodel, 'components')
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if aggregate
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growthneutralitycorrection = '';
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for i=1:length(expectationmodel.components)
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if ~isequal(expectationmodel.components(i).kind, 'll')
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if isfield(expectationmodel.components(i), 'growth_neutrality_param_index')
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if isempty(growthneutralitycorrection)
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if ~isempty(expectationmodel.components(i).growth_str)
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if isequal(expectationmodel.components(i).coeff_str, '1')
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if numel(expectationmodel.components(i).growth_linear_comb) == 1
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growthneutralitycorrection = sprintf('%s*%s', M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
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else
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growthneutralitycorrection = sprintf('%s*(%s)', M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
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end
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else
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if numel(expectationmodel.components(i).growth_linear_comb) == 1
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growthneutralitycorrection = sprintf('%s*%s*%s', expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
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else
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growthneutralitycorrection = sprintf('%s*%s*(%s)', expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
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end
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end
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end
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else
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if ~isempty(expectationmodel.components(i).growth_str)
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if isequal(expectationmodel.components(i).coeff_str, '1')
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if numel(expectationmodel.components(i).growth_linear_comb) == 1
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growthneutralitycorrection = sprintf('%s+%s*%s', growthneutralitycorrection, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
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else
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growthneutralitycorrection = sprintf('%s+%s*(%s)', growthneutralitycorrection, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
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end
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else
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if numel(expectationmodel.components(i).growth_linear_comb) == 1
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growthneutralitycorrection = sprintf('%s+%s*%s*%s', growthneutralitycorrection, expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
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else
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growthneutralitycorrection = sprintf('%s+%s*%s*(%s)', growthneutralitycorrection, expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
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end
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end
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end
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end
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end % if growth neutrality correction for this component
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end % if non stationary component
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end
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else
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growthneutralitycorrection = repmat({''}, length(expectationmodel.components), 1);
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for i=1:length(growthneutralitycorrection)
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if ~isequal(expectationmodel.components(i).kind, 'll')
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if isfield(expectationmodel.components(i), 'growth_neutrality_param_index')
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if ~isempty(expectationmodel.components(i).growth_str)
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if numel(expectationmodel.components(i).growth_linear_comb) == 1
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growthneutralitycorrection(i) = {sprintf('%s*%s', M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str)};
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else
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growthneutralitycorrection(i) = {sprintf('%s*(%s)', M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str)};
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end
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end
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end % if growth neutrality correction for this component
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end % if non stationary component
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end
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end % if aggregate
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end
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end
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if nargout==1 && ~isempty(growthneutralitycorrection)
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expression = sprintf('%s + %s', expression, growthneutralitycorrection);
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end
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