2019-03-14 11:04:10 +01:00
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function [expression, growthneutralitycorrection] = write_expectations(eqname, expectationmodelname, expectationmodelkind, iscrlf)
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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 ('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 (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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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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% 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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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 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<4
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iscrlf = false;
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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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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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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'
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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'
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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 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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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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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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2019-10-07 16:45:24 +02:00
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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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2019-03-14 11:04:10 +01:00
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else
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growthneutralitycorrection = '';
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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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