Added a cherry-pick routine.
Extracts equations from a mod file and produce .inc files (equations, lists of parameters, endogenous variables and exogenous variables) that can be included in a mod file that will be used to simulate the model. If an innovation has a tag `(used='estimationonly')` it will be excluded from the generated files (ie list of shocks and equations).time-shift
Stéphane Adjemian (Charybdis)
parent
52eed33c42
commit
d3db092c81
GPG Key ID:
A6D44CB9C64CE77B
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@ -41,4 +41,4 @@ if nargin<3
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withcalibration = true;
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end
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print_expectations(eqname, pacexpectationmodelname, 'pac-expectations', withcalibration);
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print_expectations(eqname, pacexpectationmodelname, 'pac', withcalibration);
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@ -40,4 +40,4 @@ if nargin<2
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withcalibration = true;
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end
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print_expectations('fake', varexpectationmodelname, 'var-expectations', withcalibration);
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print_expectations('fake', varexpectationmodelname, 'var', withcalibratiyon);
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@ -0,0 +1,118 @@
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function cherrypick(infile, outfold, eqtags, noresids)
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% Extract some equations in infile (mod file used for estimation)
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% and write them in outfile (mod file used for simulation).
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%
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% INPUTS
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% - infile [string] Name of the mod file where all the equations used for estimation are available.
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% - outfold [string] Name of the folder where the generated files are saveda subset of the equations is to be printed.
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% - eqtags [cell] Equation tags of the selected equations.
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% - noresids [logical] Removes estimation residuals (not to be used in simulation) if true.
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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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% It is expected that the file infile.mod has already been run, and
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% that the associated JSON output is available.
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% Copyright (C) 2019 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 <http://www.gnu.org/licenses/>.
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global M_
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% Set default value
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if nargin<4
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noresids = true;
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end
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% Delete outfold subdirectory if it already exists
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if exist(outfold, 'dir')
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rmdir(outfold, 's');
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end
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% Create the subdirectoty where the generated files will be saved.
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mkdir(outfold);
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% Check that infile.mod and the related JSON output exist.
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if ~exist(sprintf('%s.mod', infile), 'file')
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error('Cannot find %s.mod.', infile)
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end
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if ~exist(sprintf('%s/model/json', infile), 'dir')
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error('Cannot find %s/model/json folder. Did you run %s.mod with the json option?', infile, infile);
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end
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% Create a new file.
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fid = fopen(sprintf('%s/model.inc', outfold), 'w');
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plist = {};
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elist = {};
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xlist = {};
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for i=1:length(eqtags)
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% Get the original equation.
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[LHS, RHS] = get_lhs_and_rhs(eqtags{i}, M_, true);
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% Get the parameters, endogenous and exogenous variables in the current equation.
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[pnames, enames, xnames] = get_variables_and_parameters_in_equation(LHS, RHS, M_);
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% Remove residual from equation if required.
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if noresids
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exogenous_variables_to_be_removed = ~ismember(xnames, M_.simulation_exo_names);
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if any(exogenous_variables_to_be_removed)
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switch sum(exogenous_variables_to_be_removed)
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case 1
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RHS = regexprep(RHS, sprintf('(\\ *)(+)(\\ *)%s', xnames{exogenous_variables_to_be_removed}), '');
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case 0
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% Nothing to do.
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otherwise
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error('Cannot remove more than one exogenous variable in an equation (%s).', eqtags{i})
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end
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xnames = setdiff(xnames, xnames{exogenous_variables_to_be_removed});
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end
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end
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% Unroll expectation terms if any.
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isvar = regexp(RHS, 'var_expectation\(model_name = (?<name>\w+)\)', 'names');
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ispac = regexp(RHS, 'pac_expectation\(model_name = (?<name>\w+)\)', 'names');
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if ~isempty(isvar)
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expression = write_expectations(eqtags{i}, isvar.name, 'var');
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RHS = strrep(RHS, sprintf('var_expectation(model_name = %s)', isvar.name), expression);
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else
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if ~isempty(ispac)
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expression = write_expectations(eqtags{i}, ispac.name, 'pac');
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RHS = strrep(RHS, sprintf('pac_expectation(model_name = %s)', ispac.name), expression);
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end
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end
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% Print equation.
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fprintf(fid, '%s = %s;\n\n', LHS, RHS);
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% Update lists of parameters, endogenous variables and exogenous variables.
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plist = union(plist, pnames);
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elist = union(elist, enames);
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xlist = union(xlist, xnames);
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end
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fclose(fid);
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fid = fopen(sprintf('%s/parameters.inc', outfold), 'w');
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fprintf(fid, 'parameters %s;', sprintf('%s ', plist{:}));
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fclose(fid);
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fid = fopen(sprintf('%s/endogenous.inc', outfold), 'w');
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fprintf(fid, 'var %s;', sprintf('%s ', elist{:}));
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fclose(fid);
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fid = fopen(sprintf('%s/exogenous.inc', outfold), 'w');
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fprintf(fid, 'varexo %s;', sprintf('%s ', xlist{:}));
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fclose(fid);
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@ -41,9 +41,21 @@ rhs_ = strsplit(rhs,{'+','-','*','/','^', ...
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'normcdf(', 'normpdf(', 'erf(', ...
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'diff(', 'adl(', '(', ')'});
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lhs_ = strsplit(lhs, {'+','-','*','/','^', ...
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'log(', 'log10(', 'ln(', 'exp(', ...
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'sqrt(', 'abs(', 'sign(', ...
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'sin(', 'cos(', 'tan(', 'asin(', 'acos(', 'atan(', ...
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'min(', 'max(', ...
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'normcdf(', 'normpdf(', 'erf(', ...
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'diff(', 'adl(', '(', ')'});
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% Filter out the numbers and punctuation.
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rhs_(cellfun(@(x) all(isstrprop(x, 'digit')+isstrprop(x, 'punct')), rhs_)) = [];
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% Filter out empty elements.
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rhs_(cellfun(@(x) all(isempty(x)), rhs_)) = [];
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lhs_(cellfun(@(x) all(isempty(x)), lhs_)) = [];
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% Get list of parameters.
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pnames = DynareModel.param_names;
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pnames = intersect(rhs_, pnames);
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@ -64,7 +76,7 @@ if ~isempty(id)
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end
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% Add lhs variable in first position of enames.
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enames = [lhs; enames];
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enames = [lhs_; enames];
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% Returns vector of indices for parameters endogenous and exogenous
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% variables if required.
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@ -20,7 +20,7 @@ function print_expectations(eqname, expectationmodelname, expectationmodelkind,
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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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% The variable expectationmodelkind can take two values 'var-expctations' or 'pac-expectations'.
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% The variable expectationmodelkind can take two values 'var' or 'pac'.
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% Copyright (C) 2018-2019 Dynare Team
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%
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@ -61,19 +61,19 @@ if ~isrow(expectationmodelkind)==1 || ~ischar(expectationmodelkind)
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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-expectations', 'pac-expectations'})
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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-expectations'
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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-expectations'
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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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@ -82,7 +82,7 @@ switch expectationmodelkind
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otherwise
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end
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if isequal(expectationmodelkind, 'pac-expectations')
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if isequal(expectationmodelkind, 'pac')
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% Get the equation tag (in M_.pac.(pacmodl).equations)
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eqtag = M_.pac.(expectationmodelname).tag_map{strcmp(M_.pac.(expectationmodelname).tag_map(:,1), eqname),2};
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end
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@ -93,9 +93,9 @@ 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(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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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-expectations'
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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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@ -106,20 +106,20 @@ auxmodel = M_.(expectationmodel.auxiliary_model_type).(expectationmodel.auxiliar
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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), 'dir')
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mkdir(sprintf('%s/model/%s', M_.fname, expectationmodelkind))
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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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if isequal(expectationmodelkind, 'pac-expectations')
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filename = sprintf('%s/model/%s/%s-%s-parameters.inc', M_.fname, expectationmodelkind, eqtag, expectationmodelname);
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if isequal(expectationmodelkind, 'pac')
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filename = sprintf('%s/model/%s/%s-%s-parameters.inc', M_.fname, [expectationmodelkind '-expectations'], eqtag, expectationmodelname);
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else
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filename = sprintf('%s/model/%s/%s-parameters.inc', M_.fname, expectationmodelkind, expectationmodelname);
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filename = sprintf('%s/model/%s/%s-parameters.inc', M_.fname, [expectationmodelkind '-expectations'], expectationmodelname);
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end
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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-expectations'
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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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@ -130,7 +130,7 @@ switch expectationmodelkind
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fprintf(fid, '%s = %s;\n', M_.param_names{expectationmodel.param_indices(i)}, num2str(M_.params(expectationmodel.param_indices(i)), 16));
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end
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end
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case 'pac-expectations'
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case 'pac'
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if ~isempty(expectationmodel.equations.(eqtag).h0_param_indices)
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parameter_declaration = 'parameters';
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for i=1:length(expectationmodel.equations.(eqtag).h0_param_indices)
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@ -177,80 +177,19 @@ fprintf('Parameters declarations and calibrations are saved in %s.\n', filename)
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% Second print the expanded VAR_EXPECTATION/PAC_EXPECTATION term.
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%
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if isequal(expectationmodelkind, 'pac-expectations')
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filename = sprintf('%s/model/%s/%s-%s-expression.inc', M_.fname, expectationmodelkind, eqtag, expectationmodelname);
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if isequal(expectationmodelkind, 'pac')
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filename = sprintf('%s/model/%s/%s-%s-expression.inc', M_.fname, [expectationmodelkind '-expectations'], eqtag, expectationmodelname);
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else
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filename = sprintf('%s/model/%s/%s-expression.inc', M_.fname, expectationmodelkind, expectationmodelname);
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filename = sprintf('%s/model/%s/%s-expression.inc', M_.fname, [expectationmodelkind '-expectations'], expectationmodelname);
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end
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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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id = 0;
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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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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-expectations'
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parameter = M_.param_names{expectationmodel.param_indices(id)};
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case 'pac-expectations'
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parameter = '';
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if ~isempty(expectationmodel.equations.(eqtag).h0_param_indices)
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parameter = M_.param_names{expectationmodel.equations.(eqtag).h0_param_indices(id)};
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end
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if ~isempty(expectationmodel.equations.(eqtag).h1_param_indices)
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if isempty(parameter)
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parameter = M_.param_names{expectationmodel.equations.(eqtag).h1_param_indices(id)};
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else
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parameter = sprintf('(%s+%s)', parameter, M_.param_names{expectationmodel.equations.(eqtag).h1_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-expectations'
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if i>1
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variable = sprintf('%s(-%d)', variable, i-1);
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end
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case 'pac-expectations'
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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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expression = sprintf('%s*%s\n', parameter, variable);
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else
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expression = sprintf('%s + %s*%s\n', expression, parameter, variable);
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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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expression = write_expectations(eqname, expectationmodelname, expectationmodelkind, true);
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case 'pac'
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[expression, growthneutralitycorrection] = write_expectations(eqname, expectationmodelname, expectationmodelkind, true);
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end
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fprintf(fid, '%s', expression);
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@ -262,11 +201,11 @@ fprintf('Expectation unrolled expression is saved in %s.\n', filename);
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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-expectations') && growth_correction
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filename = sprintf('%s/model/%s/%s-%s-growth-neutrality-correction.inc', M_.fname, expectationmodelkind, eqtag, expectationmodelname);
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if isequal(expectationmodelkind, 'pac') && growth_correction
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filename = sprintf('%s/model/%s/%s-%s-growth-neutrality-correction.inc', M_.fname, [expectationmodelkind '-expectations'], eqtag, 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*%s', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
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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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@ -274,20 +213,20 @@ 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 = strrep(expectationmodelkind, '-', '_');
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kind = [expectationmodelkind '_expectations'];
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mkdir(sprintf('+%s/+%s/+%s', M_.fname, kind, expectationmodelname));
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if isequal(expectationmodelkind, 'pac-expectations')
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if isequal(expectationmodelkind, 'pac')
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filename = sprintf('+%s/+%s/+%s/%s_evaluate.m', M_.fname, kind, expectationmodelname, eqtag);
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else
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filename = sprintf('+%s/+%s/+%s/evaluate.m', M_.fname, kind, expectationmodelname);
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end
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fid = fopen(filename, 'w');
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if isequal(expectationmodelkind, 'pac-expectations')
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if isequal(expectationmodelkind, 'pac')
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fprintf(fid, 'function ds = %s_evaluate(dbase)\n\n', eqtag);
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else
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fprintf(fid, 'function ds = evaluate(dbase)\n\n');
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end
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if isequal(expectationmodelkind, 'pac-expectations')
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if isequal(expectationmodelkind, 'pac')
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fprintf(fid, '%% Evaluates %s term (%s in %s).\n', kind, expectationmodelname, eqname);
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else
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fprintf(fid, '%% Evaluates %s term (%s).\n', kind, expectationmodelname);
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@ -334,9 +273,9 @@ for i=1:maxlag
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end
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end
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||||
switch expectationmodelkind
|
||||
case 'var-expectations'
|
||||
case 'var'
|
||||
parameter = M_.params(expectationmodel.param_indices(id));
|
||||
case 'pac-expectations'
|
||||
case 'pac'
|
||||
parameter = 0;
|
||||
if ~isempty(expectationmodel.equations.(eqtag).h0_param_indices)
|
||||
parameter = M_.params(expectationmodel.equations.(eqtag).h0_param_indices(id));
|
||||
|
|
@ -351,13 +290,13 @@ for i=1:maxlag
|
|||
otherwise
|
||||
end
|
||||
switch expectationmodelkind
|
||||
case 'var-expectations'
|
||||
case 'var'
|
||||
if i>1
|
||||
variable = sprintf('dbase.%s(-%d)', variable, i-1);
|
||||
else
|
||||
variable = sprintf('dbase.%s', variable);
|
||||
end
|
||||
case 'pac-expectations'
|
||||
case 'pac'
|
||||
variable = sprintf('dbase.%s(-%d)', variable, i);
|
||||
otherwise
|
||||
end
|
||||
|
|
@ -367,7 +306,7 @@ for i=1:maxlag
|
|||
end
|
||||
end
|
||||
if isequal(id, 1)
|
||||
if isequal(expectationmodelkind, 'pac-expectations') && growth_correction
|
||||
if isequal(expectationmodelkind, 'pac') && growth_correction
|
||||
pgrowth = M_.params(expectationmodel.growth_neutrality_param_index);
|
||||
vgrowth = expectationmodel.growth_str;
|
||||
switch expectationmodel.growth_type
|
||||
|
|
|
|||
|
|
@ -0,0 +1,35 @@
|
|||
function DynareModel = set_exogenous_variables_for_simulation(DynareModel)
|
||||
|
||||
% Appends the list of observed exogenous variables in Dynare's model structure (if any).
|
||||
%
|
||||
% INPUTS
|
||||
% - DynareModel [struct] Dynare's model global structure, M_.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - DynareModel [struct] Dynare's model global structure, M_.
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright (C) 2019 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
if isfield(DynareModel, 'exo_partitions')
|
||||
if isfield(DynareModel.exo_partitions, 'used')
|
||||
DynareModel.simulation_exo_names = DynareModel.exo_names(~strcmpi('estimationonly', DynareModel.exo_partitions.used));
|
||||
end
|
||||
end
|
||||
|
|
@ -0,0 +1,153 @@
|
|||
function [expression, growthneutralitycorrection] = write_expectations(eqname, expectationmodelname, expectationmodelkind, iscrlf)
|
||||
|
||||
% Prints the exansion of the VAR_EXPECTATION or PAC_EXPECTATION term in files.
|
||||
%
|
||||
% INPUTS
|
||||
% - eqname [string] Name of the equation.
|
||||
% - epxpectationmodelname [string] Name of the expectation model.
|
||||
% - expectationmodelkind [string] Kind of the expectation model ('var' or 'pac').
|
||||
% - iscrlf [string] Adds carriage return after each additive term if true.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - expression [string] Unrolled expectation expression.
|
||||
% - growthneutralitycorrection [string]
|
||||
|
||||
% Copyright (C) 2019 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
global M_
|
||||
|
||||
if ismember(expectationmodelkind, {'var', 'pac'})
|
||||
if isequal(expectationmodelkind, 'var')
|
||||
expectationmodelfield = 'var_expectation';
|
||||
else
|
||||
expectationmodelfield = 'pac';
|
||||
% Get the equation tag (in M_.pac.(pacmodl).equations)
|
||||
eqtag = M_.pac.(expectationmodelname).tag_map{strcmp(M_.pac.(expectationmodelname).tag_map(:,1), eqname),2};
|
||||
end
|
||||
else
|
||||
error('Value of third input argument must be ''var'' or ''pac''.')
|
||||
end
|
||||
|
||||
expectationmodel = M_.(expectationmodelfield).(expectationmodelname);
|
||||
|
||||
if nargout>1 && isequal(expectationmodelkind, 'var')
|
||||
error('Cannot return more than one argument if the expectation model is a VAR.')
|
||||
end
|
||||
|
||||
if nargin<4
|
||||
iscrlf = false;
|
||||
end
|
||||
|
||||
% Get the name of the associated VAR model and test its existence.
|
||||
if ~isfield(M_.(expectationmodel.auxiliary_model_type), expectationmodel.auxiliary_model_name)
|
||||
switch expectationmodelkind
|
||||
case 'var-expectations'
|
||||
error('Unknown VAR/TREND_COMPONENT model (%s) in VAR_EXPECTATION_MODEL (%s)!', expectationmodel.auxiliary_model_name, expectationmodelname)
|
||||
case 'pac-expectations'
|
||||
error('Unknown VAR/TREND_COMPONENT model (%s) in PAC_EXPECTATION_MODEL (%s)!', expectationmodel.auxiliary_model_name, expectationmodelname)
|
||||
otherwise
|
||||
end
|
||||
end
|
||||
|
||||
auxmodel = M_.(expectationmodel.auxiliary_model_type).(expectationmodel.auxiliary_model_name);
|
||||
|
||||
maxlag = max(auxmodel.max_lag);
|
||||
if isequal(expectationmodel.auxiliary_model_type, 'trend_component')
|
||||
% Need to add a lag since the error correction equations are rewritten in levels.
|
||||
maxlag = maxlag+1;
|
||||
end
|
||||
|
||||
id = 0;
|
||||
|
||||
for i=1:maxlag
|
||||
for j=1:length(auxmodel.list_of_variables_in_companion_var)
|
||||
id = id+1;
|
||||
variable = auxmodel.list_of_variables_in_companion_var{j};
|
||||
transformations = {};
|
||||
ida = get_aux_variable_id(variable);
|
||||
op = 0;
|
||||
while ida
|
||||
op = op+1;
|
||||
if isequal(M_.aux_vars(ida).type, 8)
|
||||
transformations(op) = {'diff'};
|
||||
variable = M_.endo_names{M_.aux_vars(ida).orig_index};
|
||||
ida = get_aux_variable_id(variable);
|
||||
elseif isequal(M_.aux_vars(ida).type, 10)
|
||||
transformations(op) = {M_.aux_vars(ida).unary_op};
|
||||
variable = M_.endo_names{M_.aux_vars(ida).orig_index};
|
||||
ida = get_aux_variable_id(variable);
|
||||
else
|
||||
error('This case is not implemented.')
|
||||
end
|
||||
end
|
||||
switch expectationmodelkind
|
||||
case 'var'
|
||||
parameter = M_.param_names{expectationmodel.param_indices(id)};
|
||||
case 'pac'
|
||||
parameter = '';
|
||||
if ~isempty(expectationmodel.equations.(eqtag).h0_param_indices)
|
||||
parameter = M_.param_names{expectationmodel.equations.(eqtag).h0_param_indices(id)};
|
||||
end
|
||||
if ~isempty(expectationmodel.equations.(eqtag).h1_param_indices)
|
||||
if isempty(parameter)
|
||||
parameter = M_.param_names{expectationmodel.equations.(eqtag).h1_param_indices(id)};
|
||||
else
|
||||
parameter = sprintf('(%s+%s)', parameter, M_.param_names{expectationmodel.equations.(eqtag).h1_param_indices(id)});
|
||||
end
|
||||
end
|
||||
otherwise
|
||||
end
|
||||
switch expectationmodelkind
|
||||
case 'var'
|
||||
if i>1
|
||||
variable = sprintf('%s(-%d)', variable, i-1);
|
||||
end
|
||||
case 'pac'
|
||||
variable = sprintf('%s(-%d)', variable, i);
|
||||
otherwise
|
||||
end
|
||||
if ~isempty(transformations)
|
||||
for k=length(transformations):-1:1
|
||||
variable = sprintf('%s(%s)', transformations{k}, variable);
|
||||
end
|
||||
end
|
||||
if isequal(id, 1)
|
||||
if iscrlf
|
||||
expression = sprintf('%s*%s\n', parameter, variable);
|
||||
else
|
||||
expression = sprintf('%s*%s', parameter, variable);
|
||||
end
|
||||
else
|
||||
if iscrlf
|
||||
expression = sprintf('%s + %s*%s\n', expression, parameter, variable);
|
||||
else
|
||||
expression = sprintf('%s + %s*%s', expression, parameter, variable);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
if isfield(expectationmodel, 'growth_neutrality_param_index')
|
||||
growthneutralitycorrection = sprintf('%s*%s', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
|
||||
else
|
||||
growthneutralitycorrection = '';
|
||||
end
|
||||
|
||||
if nargout==1 && ~isempty(growthneutralitycorrection)
|
||||
expression = sprintf('%s + %s', expression, growthneutralitycorrection);
|
||||
end
|
||||
|
|
@ -1 +1 @@
|
|||
Subproject commit e0acc669bdfce02baa94a0ff0372d72578fba177
|
||||
Subproject commit 9632b18c247ca16cdc8d6fbe71bc036b3d0dc435
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
#!/bin/sh
|
||||
|
||||
rm -rf example
|
||||
rm -rf +example
|
||||
rm -f example.log
|
||||
rm -f *.mat
|
||||
rm -f *.m
|
||||
rm -f *.dat
|
||||
|
|
@ -0,0 +1,115 @@
|
|||
// --+ options: json=compute, stochastic +--
|
||||
|
||||
var x1 x2 x1bar x2bar z y x u v s;
|
||||
|
||||
varexo ex1
|
||||
ex2
|
||||
ex1bar (used='estimationonly')
|
||||
ex2bar (used='estimationonly')
|
||||
ez
|
||||
ey
|
||||
ex
|
||||
eu
|
||||
ev
|
||||
es;
|
||||
|
||||
parameters
|
||||
rho_1 rho_2 rho_3 rho_4
|
||||
a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
|
||||
a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
|
||||
e_c_m c_z_1 c_z_2 c_z_dx2 c_z_u c_z_dv c_z_s cx cy beta
|
||||
lambda;
|
||||
|
||||
rho_1 = .9;
|
||||
rho_2 = -.2;
|
||||
rho_3 = .4;
|
||||
rho_4 = -.3;
|
||||
|
||||
|
||||
a_x1_0 = -.9;
|
||||
a_x1_1 = .4;
|
||||
a_x1_2 = .3;
|
||||
a_x1_x2_1 = .1;
|
||||
a_x1_x2_2 = .2;
|
||||
|
||||
a_x2_0 = -.9;
|
||||
a_x2_1 = .2;
|
||||
a_x2_2 = -.1;
|
||||
a_x2_x1_1 = -.1;
|
||||
a_x2_x1_2 = .2;
|
||||
|
||||
beta = .2;
|
||||
e_c_m = .5;
|
||||
c_z_1 = .2;
|
||||
c_z_2 = -.1;
|
||||
c_z_dx2 = .3;
|
||||
c_z_u = .3;
|
||||
c_z_dv = .4;
|
||||
c_z_s = -.2;
|
||||
cx = 1.0;
|
||||
cy = 1.0;
|
||||
|
||||
|
||||
lambda = 0.5; // Share of optimizing agents.
|
||||
|
||||
trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
|
||||
|
||||
pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
|
||||
|
||||
model;
|
||||
|
||||
[name='eq:u']
|
||||
s = .3*s(-1) - .1*s(-2) + es;
|
||||
|
||||
[name='eq:diff(v)']
|
||||
diff(v) = .5*diff(v(-1)) + ev;
|
||||
|
||||
[name='eq:u']
|
||||
u = .5*u(-1) - .2*u(-2) + eu;
|
||||
|
||||
[name='eq:y']
|
||||
y = rho_1*y(-1) + rho_2*y(-2) + ey;
|
||||
|
||||
[name='eq:x']
|
||||
x = rho_3*x(-1) + rho_4*x(-2) + ex;
|
||||
|
||||
[name='eq:x1']
|
||||
diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
|
||||
|
||||
[name='eq:x2']
|
||||
diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
|
||||
|
||||
[name='eq:x1bar']
|
||||
x1bar = x1bar(-1) + ex1bar;
|
||||
|
||||
[name='eq:x2bar']
|
||||
x2bar = x2bar(-1) + ex2bar;
|
||||
|
||||
[name='zpac']
|
||||
diff(z) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + c_z_s*s + c_z_dv*diff(v) ) + (1-lambda)*( cy*y + cx*x) + c_z_u*u + c_z_dx2*diff(x2) + ez;
|
||||
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var ex1 = 1.0;
|
||||
var ex2 = 1.0;
|
||||
var ex1bar = 1.0;
|
||||
var ex2bar = 1.0;
|
||||
var ez = 1.0;
|
||||
var ey = 0.1;
|
||||
var ex = 0.1;
|
||||
var eu = 0.05;
|
||||
var ev = 0.05;
|
||||
var es = 0.07;
|
||||
end;
|
||||
|
||||
// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
|
||||
pac.initialize('pacman');
|
||||
|
||||
// Update the parameters of the PAC expectation model (h0 and h1 vectors).
|
||||
pac.update.expectation('pacman');
|
||||
|
||||
// Select a subset of the equations and print the equations, the list of parameters, endogenous
|
||||
// variables and exogenous variables in .inc files under ./simulation-files folder. Note that
|
||||
// innovations ex1bar and ex2bar will not appear in the equations.
|
||||
cherrypick('example', 'simulation-files', {'zpac', 'eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'}, true);
|
||||
Loading…
Reference in New Issue