507 lines
22 KiB
Matlab
507 lines
22 KiB
Matlab
function print_expectations(eqname, expectationmodelname, expectationmodelkind, withcalibration)
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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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% - eqname [string] Name of the equation.
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% - epxpectationmodelname [string] Name of the expectation model.
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% - expectationmodelkind [string] Kind of the expectation model.
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% - withcalibration [logical] Prints calibration if true.
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%
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% OUTPUTS
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% None
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%
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% REMARKS
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% print_expectations creates two text files
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%
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% - {expectationmodelname}-parameters.inc which contains the declaration of the parameters specific to the expectation model kind term.
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% - {expectationmodelname}-expression.inc which contains the expanded version of the expectation model kind term.
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%
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% These routines are saved under the {modfilename}/model/{expectationmodelkind} subfolder, and can be
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% used after in another mod file (ie included with the macro directive @#include).
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%
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% print_expectations also creates a matlab routine to evaluate the expectations (returning a dseries object).
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%
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% The variable expectationmodelkind can take two values 'var' or 'pac'.
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% Copyright © 2018-2023 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 nargin<4 || isempty(withcalibration)
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withcalibration = 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(eqname)==1 || ~ischar(eqname)
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error('First input argument must be a row character array.')
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end
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% Check that the second input is a row character array.
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if ~isrow(expectationmodelname)==1 || ~ischar(expectationmodelname)
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error('Second input argument must be a row character array.')
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end
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% Check that the third input is a row character array.
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if ~isrow(expectationmodelkind)==1 || ~ischar(expectationmodelkind)
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error('Third input argument must be a row character array.')
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end
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% Check that the value of the second input is correct.
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if ~ismember(expectationmodelkind, {'var', 'pac'})
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error('Wrong value for the second input argument.')
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end
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% Check that the model exists.
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switch expectationmodelkind
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case 'var'
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if ~isfield(M_.var_expectation, expectationmodelname)
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error('VAR_EXPECTATION_MODEL %s is not defined.', expectationmodelname)
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else
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expectationmodelfield = 'var_expectation';
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end
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case 'pac'
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if ~isfield(M_.pac, expectationmodelname)
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error('PAC_EXPECTATION_MODEL %s is not defined.', expectationmodelname)
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else
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expectationmodelfield = 'pac';
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end
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otherwise
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end
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% Get the expectation model description
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expectationmodel = M_.(expectationmodelfield).(expectationmodelname);
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% Get the name of the associated VAR model and test its existence.
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if isfield(expectationmodel, 'auxiliary_model_name') && ~isfield(M_.(expectationmodel.auxiliary_model_type), expectationmodel.auxiliary_model_name)
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switch expectationmodelkind
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case 'var'
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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'
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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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elseif isequal(expectationmodelkind, 'pac') && ~isfield(expectationmodel, 'auxiliary_model_name')
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error('print method does not work in PAC/MCE.')
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end
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auxmodel = M_.(expectationmodel.auxiliary_model_type).(expectationmodel.auxiliary_model_name);
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%
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% First print the list of parameters appearing in the VAR_EXPECTATION/PAC_EXPECTATION term.
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%
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if ~exist(sprintf('%s/model/%s', M_.fname, [expectationmodelkind '-expectations']), 'dir')
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mkdir(sprintf('%s/model/%s', M_.fname, [expectationmodelkind '-expectations']))
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end
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filename = sprintf('%s/model/%s/%s-parameters.inc', M_.fname, [expectationmodelkind '-expectations'], expectationmodelname);
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fid = fopen(filename, 'w');
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fprintf(fid, '// This file has been generated by dynare (%s).\n\n', datestr(now));
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switch expectationmodelkind
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case 'var'
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parameter_declaration = 'parameters';
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for i=1:length(expectationmodel.param_indices)
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parameter_declaration = sprintf('%s %s', parameter_declaration, M_.param_names{expectationmodel.param_indices(i)});
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end
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fprintf(fid, '%s;\n\n', parameter_declaration);
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if withcalibration
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for i=1:length(expectationmodel.param_indices)
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fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.param_indices(i)}, M_.params(expectationmodel.param_indices(i)));
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end
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end
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case 'pac'
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parameter_declaration = 'parameters';
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if isfield(expectationmodel, 'h_param_indices')
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for i=1:length(expectationmodel.h_param_indices)
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parameter_declaration = sprintf('%s %s', parameter_declaration, M_.param_names{expectationmodel.h_param_indices(i)});
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end
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else
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for j=1:length(expectationmodel.components)
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for i=1:length(expectationmodel.components(j).h_param_indices)
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parameter_declaration = sprintf('%s %s', parameter_declaration, M_.param_names{expectationmodel.components(j).h_param_indices(i)});
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end
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end
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end
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fprintf(fid, '%s;\n\n', parameter_declaration);
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if withcalibration
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if isfield(expectationmodel, 'h_param_indices')
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for i=1:length(expectationmodel.h_param_indices)
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fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.h_param_indices(i)}, M_.params(expectationmodel.h_param_indices(i)));
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end
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else
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for j=1:length(expectationmodel.components)
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for i=1:length(expectationmodel.components(j).h_param_indices)
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fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.components(j).h_param_indices(i)}, M_.params(expectationmodel.components(j).h_param_indices(i)));
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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 isfield(expectationmodel, 'growth_neutrality_param_index')
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fprintf(fid, '\n');
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fprintf(fid, 'parameters %s;\n\n', M_.param_names{expectationmodel.growth_neutrality_param_index});
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if withcalibration
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fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.growth_neutrality_param_index}, M_.params(expectationmodel.growth_neutrality_param_index));
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end
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growth_correction = true;
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else
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growth_correction = false;
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if isfield(expectationmodel, 'components')
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for j=1:length(expectationmodel.components)
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if isfield(expectationmodel.components(j), 'growth_neutrality_param_index') && ~isempty(expectationmodel.components(j).growth_neutrality_param_index)
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fprintf(fid, '\n');
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fprintf(fid, 'parameters %s;\n\n', M_.param_names{expectationmodel.components(j).growth_neutrality_param_index});
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if withcalibration
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fprintf(fid, '%s = %1.16f;\n', M_.param_names{expectationmodel.components(j).growth_neutrality_param_index}, M_.params(expectationmodel.components(j).growth_neutrality_param_index));
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end
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growth_correction = true;
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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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fclose(fid);
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skipline()
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fprintf('Parameters declarations and calibrations are saved in %s.\n', filename);
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%
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% Second print the expanded VAR_EXPECTATION/PAC_EXPECTATION term.
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%
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filename = sprintf('%s/model/%s/%s-expression.inc', M_.fname, [expectationmodelkind '-expectations'], expectationmodelname);
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fid = fopen(filename, 'w');
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fprintf(fid, '// This file has been generated by dynare (%s).\n', datestr(now));
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switch expectationmodelkind
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case 'var'
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expression = write_expectations(expectationmodelname, expectationmodelkind, true);
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case 'pac'
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[expression, growthneutralitycorrection] = write_expectations(expectationmodelname, expectationmodelkind, true);
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end
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fprintf(fid, '%s', expression);
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fclose(fid);
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fprintf('Expectation unrolled expression is saved in %s.\n', filename);
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%
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% Second bis print the PAC growth neutrality correction term (if any).
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%
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if isequal(expectationmodelkind, 'pac') && growth_correction
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filename = sprintf('%s/model/%s/%s-growth-neutrality-correction.inc', M_.fname, [expectationmodelkind '-expectations'], expectationmodelname);
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fid = fopen(filename, 'w');
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fprintf(fid, '// This file has been generated by dynare (%s).\n', datestr(now));
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fprintf(fid, '%s', growthneutralitycorrection);
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fclose(fid);
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fprintf('Growth neutrality correction is saved in %s.\n', filename);
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end
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%
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% Third print a routine for evaluating VAR_EXPECTATION/PAC_EXPECTATION term (returns a dseries object).
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%
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kind = [expectationmodelkind '_expectations'];
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ndir = sprintf('+%s/+%s/+%s', M_.fname, kind, expectationmodelname);
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if ~exist(ndir, 'dir')
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mkdir(sprintf('+%s/+%s/+%s', M_.fname, kind, expectationmodelname));
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end
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filename = sprintf('+%s/+%s/+%s/evaluate.m', M_.fname, kind, expectationmodelname);
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fid = fopen(filename, 'w');
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fprintf(fid, 'function ds = evaluate(dbase)\n\n');
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fprintf(fid, '%% Evaluates %s term (%s).\n', kind, expectationmodelname);
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fprintf(fid, '%%\n');
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fprintf(fid, '%% INPUTS\n');
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fprintf(fid, '%% - dbase [dseries] databse containing all the variables appearing in the auxiliary model for the expectation.\n');
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fprintf(fid, '%%\n');
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fprintf(fid, '%% OUTPUTS\n');
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fprintf(fid, '%% - ds [dseries] the expectation term .\n');
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fprintf(fid, '%%\n');
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fprintf(fid, '%% REMARKS\n');
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fprintf(fid, '%% The name of the appended variable in dbase is the declared name for the (PAC/VAR) expectation model.\n\n');
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fprintf(fid, '%% This file has been generated by dynare (%s).\n\n', datestr(now));
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fprintf(fid, 'ds = dseries();\n\n');
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id = 0;
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if isfield(expectationmodel, 'h_param_indices')
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decompose = false;
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else
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if isequal(expectationmodelkind, 'pac')
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decompose = true;
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else
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decompose = false;
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end
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end
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clear('expression');
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% Get coefficient values in the target (if any)
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if exist(sprintf('+%s/pac_target_coefficients.m', M_.fname), 'file')
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targetcoefficients = feval(sprintf('%s.pac_target_coefficients', M_.fname), expectationmodelname, M_.params);
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end
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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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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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% Constant in the VAR auxiliary model
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id = id+1;
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expression = sprintf('%1.16f', M_.params(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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% Constant in the VAR auxiliary model
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id = id+1;
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if isfield(expectationmodel, 'h_param_indices')
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constant = M_.params(expectationmodel.h_param_indices(id));
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else
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if decompose
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expressions = cell(length(expectationmodel.components), 1);
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for j=1:length(expectationmodel.components)
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expressions{j} = sprintf('%1.16f', M_.params(expectationmodel.components(j).h_param_indices(id)));
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end
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end
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constant = 0;
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for j=1:length(expectationmodel.components)
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constant = constant + targetcoefficients(j)*M_.params(expectationmodel.components(j).h_param_indices(id));
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end
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end
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if isfield(expectationmodel, 'h_param_indices')
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expression = sprintf('%1.16f', constant);
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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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[variable, transformations] = rewrite_aux_variable(variable, M_);
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switch expectationmodelkind
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case 'var'
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parameter = M_.params(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_.params(expectationmodel.h_param_indices(id));
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else
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parameter = 0;
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for k=1:length(expectationmodel.components)
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parameter = parameter+targetcoefficients(k)*M_.params(expectationmodel.components(k).h_param_indices(id));
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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)>0
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variable = sprintf('dbase.%s(-%d)', variable, timeindices(i));
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else
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variable = sprintf('dbase.%s', variable);
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end
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case 'pac'
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variable = sprintf('dbase.%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()', variable, transformations{k});
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end
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end
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if exist('expression','var')
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if parameter>=0
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expression = sprintf('%s+%1.16f*%s', expression, parameter, variable);
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elseif parameter<0
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expression = sprintf('%s-%1.16f*%s', expression, -parameter, variable);
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end
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else
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if parameter>=0
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expression = sprintf('%1.16f*%s', parameter, variable);
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elseif parameter<0
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expression = sprintf('-%1.16f*%s', -parameter, variable);
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end
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end
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if decompose
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for k=1:length(expectationmodel.components)
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parameter = M_.params(expectationmodel.components(k).h_param_indices(id));
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if parameter>=0
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expressions{k} = sprintf('%s+%1.16f*%s', expressions{k}, parameter, variable);
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else
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expressions{k} = sprintf('%s-%1.16f*%s', expressions{k}, -parameter, 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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if isequal(expectationmodelkind, 'pac') && growth_correction
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if isfield(expectationmodel, 'growth_neutrality_param_index')
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pgrowth = M_.params(expectationmodel.growth_neutrality_param_index);
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for iter = 1:numel(expectationmodel.growth_linear_comb)
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vgrowth='';
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variable = [];
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if expectationmodel.growth_linear_comb(iter).exo_id > 0
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variable = M_.exo_names{expectationmodel.growth_linear_comb(iter).exo_id};
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elseif expectationmodel.growth_linear_comb(iter).endo_id > 0
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variable = M_.endo_names{expectationmodel.growth_linear_comb(iter).endo_id};
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end
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if ~isempty(variable)
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[variable, transformations] = rewrite_aux_variable(variable, M_);
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if isempty(transformations)
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if expectationmodel.growth_linear_comb(iter).lag ~= 0
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variable = sprintf('%s(%d)', variable, expectationmodel.growth_linear_comb(iter).lag);
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end
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else
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for k=rows(transformations):-1:1
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if isequal(transformations{k,1}, 'lag')
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variable = sprintf('%s.lag(%u)', variable, -transformations{k,2});
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elseif isequal(transformations{k,1}, 'diff')
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if isempty(transformations{k,2})
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variable = sprintf('%s.diff()', variable);
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else
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variable = sprintf('%s.lag(%u).diff()', variable, transformations{k,2});
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end
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else
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variable = sprintf('%s.%s()', variable, transformations{k});
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end
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end
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end
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vgrowth = strcat('dbase.', variable);
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end
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if expectationmodel.growth_linear_comb(iter).param_id > 0
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if ~isempty(vgrowth)
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vgrowth = sprintf('%1.16f*%s',M_.params(expectationmodel.growth_linear_comb(iter).param_id), vgrowth);
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else
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vgrowth = num2str(M_.params(expectationmodel.growth_linear_comb(iter).param_id), '%1.16f');
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end
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end
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if abs(expectationmodel.growth_linear_comb(iter).constant) ~= 1
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if ~isempty(vgrowth)
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vgrowth = sprintf('%1.16f*%s', expectationmodel.growth_linear_comb(iter).constant, vgrowth);
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else
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vgrowth = num2str(expectationmodel.growth_linear_comb(iter).constant, '%1.16f');
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end
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end
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if iter > 1
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if expectationmodel.growth_linear_comb(iter).constant > 0
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linearCombination = sprintf('%s+%s', linearCombination, vgrowth);
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else
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linearCombination = sprintf('%s-%s', linearCombination, vgrowth);
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end
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else
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linearCombination = vgrowth;
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end
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end % loop over growth linear combination elements
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growthcorrection = sprintf('%1.16f*(%s)', pgrowth, linearCombination);
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else
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first = true;
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for i=1:length(expectationmodel.components)
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if ~isequal(expectationmodel.components(i).kind, 'll') && isfield(expectationmodel.components(i), 'growth_neutrality_param_index') && isfield(expectationmodel.components(i), 'growth_linear_comb') && ~isempty(expectationmodel.components(i).growth_linear_comb)
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pgrowth = targetcoefficients(i)*M_.params(expectationmodel.components(i).growth_neutrality_param_index);
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for iter = 1:numel(expectationmodel.components(i).growth_linear_comb)
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vgrowth='';
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variable=[];
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if expectationmodel.components(i).growth_linear_comb(iter).exo_id > 0
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variable = M_.exo_names{expectationmodel.components(i).growth_linear_comb(iter).exo_id};
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elseif expectationmodel.components(i).growth_linear_comb(iter).endo_id > 0
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variable = M_.endo_names{expectationmodel.components(i).growth_linear_comb(iter).endo_id};
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end
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if ~isempty(variable)
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[variable, transformations] = rewrite_aux_variable(variable, M_);
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if isempty(transformations)
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|
if expectationmodel.components(i).growth_linear_comb(iter).lag ~= 0
|
|
variable = sprintf('%s(%d)', variable, expectationmodel.components(i).growth_linear_comb(iter).lag);
|
|
end
|
|
else
|
|
for k=rows(transformations):-1:1
|
|
if isequal(transformations{k,1}, 'lag')
|
|
variable = sprintf('%s.lag(%u)', variable, -transformations{k,2});
|
|
elseif isequal(transformations{k,1}, 'diff')
|
|
if isempty(transformations{k,2})
|
|
variable = sprintf('%s.diff()', variable);
|
|
else
|
|
variable = sprintf('%s.lag(%u).diff()', variable, transformations{k,2});
|
|
end
|
|
else
|
|
variable = sprintf('%s.%s()', variable, transformations{k});
|
|
end
|
|
end
|
|
end
|
|
vgrowth = strcat('dbase.', variable);
|
|
end
|
|
if expectationmodel.components(i).growth_linear_comb(iter).param_id > 0
|
|
if ~isempty(vgrowth)
|
|
vgrowth = sprintf('%1.16f*%s',M_.params(expectationmodel.components(i).growth_linear_comb(iter).param_id), vgrowth);
|
|
else
|
|
vgrowth = num2str(M_.params(expectationmodel.components(i).growth_linear_comb(iter).param_id), '%1.16f');
|
|
end
|
|
end
|
|
if abs(expectationmodel.components(i).growth_linear_comb(iter).constant) ~= 1
|
|
if ~isempty(vgrowth)
|
|
vgrowth = sprintf('%1.16f*%s', expectationmodel.components(i).growth_linear_comb(iter).constant, vgrowth);
|
|
else
|
|
vgrowth = num2str(expectationmodel.components(i).growth_linear_comb(iter).constant, '%1.16f');
|
|
end
|
|
end
|
|
if iter > 1
|
|
if expectationmodel.components(i).growth_linear_comb(iter).constant > 0
|
|
linearCombination = sprintf('%s+%s', linearCombination, vgrowth);
|
|
else
|
|
linearCombination = sprintf('%s-%s', linearCombination, vgrowth);
|
|
end
|
|
else
|
|
linearCombination = vgrowth;
|
|
end
|
|
end % loop over growth linear combination elements
|
|
if first
|
|
growthcorrection = sprintf('%1.16f*(%s)', pgrowth, linearCombination);
|
|
first = false;
|
|
else
|
|
if pgrowth>0
|
|
growthcorrection = sprintf('%s+%1.16f*(%s)', growthcorrection, pgrowth, linearCombination);
|
|
elseif pgrowth<0
|
|
growthcorrection = sprintf('%s-%1.16f*(%s)', growthcorrection, -pgrowth, linearCombination);
|
|
end
|
|
end
|
|
end
|
|
end
|
|
end
|
|
expression = sprintf('%s+%s', expression, growthcorrection);
|
|
end % growth_correction
|
|
|
|
fprintf(fid, 'ds.%s = %s;\n', expectationmodelname, expression);
|
|
if exist('expressions', 'var')
|
|
for i=1:length(expressions)
|
|
fprintf(fid, 'ds.%s = %s;\n', M_.lhs{expectationmodel.components(i).aux_id}, expressions{i});
|
|
end
|
|
end
|
|
fclose(fid);
|
|
|
|
fprintf('Expectation dseries expression is saved in %s.\n', filename);
|
|
|
|
skipline();
|
|
|
|
rehash
|