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function [expression, growthneutralitycorrection] = write_expectations(expectationmodelname, expectationmodelkind, iscrlf, aggregate)
% Prints the exansion of the VAR_EXPECTATION or PAC_EXPECTATION term in files.
%
% INPUTS
% - 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 © 2019-2023 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 <https://www.gnu.org/licenses/>.
global M_
if ismember(expectationmodelkind, {'var', 'pac'})
if isequal(expectationmodelkind, 'var')
expectationmodelfield = 'var_expectation';
else
expectationmodelfield = 'pac';
end
else
error('Value of third input argument must be ''var'' or ''pac''.')
end
expectationmodel = M_.(expectationmodelfield).(expectationmodelname);
if isfield(expectationmodel, 'model_consistent_expectations') && expectationmodel.model_consistent_expectations
expression = '';
if nargout>1
growthneutralitycorrection = 0;
end
return
end
if nargout>1 && isequal(expectationmodelkind, 'var')
error('Cannot return more than one argument if the expectation model is a VAR.')
end
if nargin<3
iscrlf = false;
aggregate = true;
end
if nargin<4
aggregate = true;
end
if isfield(expectationmodel, 'h_param_indices')
% Disaggregation requires components...
aggregate = true;
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;
if isequal(expectationmodelkind, 'var')
timeindices = (0:(maxlag-1))+abs(expectationmodel.time_shift);
end
if isequal(expectationmodelkind, 'var') && isequal(expectationmodel.auxiliary_model_type, 'var')
id = id+1;
expression = sprintf('%s', M_.param_names{expectationmodel.param_indices(id)});
end
if isequal(expectationmodelkind, 'pac') && isequal(expectationmodel.auxiliary_model_type, 'var')
id = id+1;
if isfield(expectationmodel, 'h_param_indices')
expression = sprintf('%s', M_.param_names{expectationmodel.h_param_indices(id)});
else
if aggregate
if isequal(expectationmodel.components(1).coeff_str, '1')
expression = sprintf('%s', M_.param_names{expectationmodel.components(1).h_param_indices(id)});
else
expression = sprintf('%s*%s', expectationmodel.components(1).coeff_str, M_.param_names{expectationmodel.components(1).h_param_indices(id)});
end
for i=2:length(expectationmodel.components)
if isequal(expectationmodel.components(i).coeff_str, '1')
expression = sprintf('%s+%s', expression, M_.param_names{expectationmodel.components(i).h_param_indices(id)});
else
expression = sprintf('%s+%s*%s', expression, expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).h_param_indices(id)});
end
end
else
expression = cell(length(expectationmodel.components), 1);
for i=1:length(expectationmodel.components)
expression(i) = {M_.param_names{expectationmodel.components(i).h_param_indices(id)}};
end
end
end
end
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'
if isfield(expectationmodel, 'h_param_indices')
parameter = M_.param_names{expectationmodel.h_param_indices(id)};
else
if aggregate
% TODO Check if we can have parameters entering with a minus sign in the linear combination defining the target.
if isequal(expectationmodel.components(1).coeff_str, '1')
parameter = M_.param_names{expectationmodel.components(1).h_param_indices(id)};
else
parameter = sprintf('%s*%s', expectationmodel.components(1).coeff_str, M_.param_names{expectationmodel.components(1).h_param_indices(id)});
end
for k=2:length(expectationmodel.components)
if isequal(expectationmodel.components(k).coeff_str, '1')
parameter = sprintf('%s+%s', parameter, M_.param_names{expectationmodel.components(k).h_param_indices(id)});
else
parameter = sprintf('%s+%s*%s', parameter, expectationmodel.components(k).coeff_str, M_.param_names{expectationmodel.components(k).h_param_indices(id)});
end
end
parameter = sprintf('(%s)', parameter);
else
parameter = cell(length(expectationmodel.components), 1);
for k=1:length(expectationmodel.components)
parameter(k) = {M_.param_names{expectationmodel.components(k).h_param_indices(id)}};
end
end
end
otherwise
end
switch expectationmodelkind
case 'var'
if timeindices(i)
variable = sprintf('%s(-%d)', variable, timeindices(i));
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 aggregate
if iscrlf
expression = sprintf('%s*%s\n', parameter, variable);
else
expression = sprintf('%s*%s', parameter, variable);
end
else
for k=1:length(expectationmodel.components)
if iscrlf
expression(k) = {sprintf('%s*%s\n', parameter{k}, variable)};
else
expression(k) = {sprintf('%s*%s', parameter{k}, variable)};
end
end
end
else
if aggregate
if iscrlf
expression = sprintf('%s + %s*%s\n', expression, parameter, variable);
else
expression = sprintf('%s + %s*%s', expression, parameter, variable);
end
else
for k=1:length(expectationmodel.components)
if iscrlf
expression(k) = {sprintf('%s + %s*%s\n', expression{k}, parameter{k}, variable)};
else
expression(k) = {sprintf('%s + %s*%s', expression{k}, parameter{k}, variable)};
end
end
end
end
end
end
if aggregate
growthneutralitycorrection = '';
else
growthneutralitycorrection = {};
end
if isfield(expectationmodel, 'growth_neutrality_param_index')
if numel(expectationmodel.growth_linear_comb) == 1
growthneutralitycorrection = sprintf('%s*%s', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
else
growthneutralitycorrection = sprintf('%s*(%s)', M_.param_names{expectationmodel.growth_neutrality_param_index}, expectationmodel.growth_str);
end
else
if isfield(expectationmodel, 'components')
if aggregate
growthneutralitycorrection = '';
for i=1:length(expectationmodel.components)
if ~isequal(expectationmodel.components(i).kind, 'll')
if isfield(expectationmodel.components(i), 'growth_neutrality_param_index')
if isempty(growthneutralitycorrection)
if ~isempty(expectationmodel.components(i).growth_str)
if isequal(expectationmodel.components(i).coeff_str, '1')
if numel(expectationmodel.components(i).growth_linear_comb) == 1
growthneutralitycorrection = sprintf('%s*%s', M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
else
growthneutralitycorrection = sprintf('%s*(%s)', M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
end
else
if numel(expectationmodel.components(i).growth_linear_comb) == 1
growthneutralitycorrection = sprintf('%s*%s*%s', expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
else
growthneutralitycorrection = sprintf('%s*%s*(%s)', expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
end
end
end
else
if ~isempty(expectationmodel.components(i).growth_str)
if isequal(expectationmodel.components(i).coeff_str, '1')
if numel(expectationmodel.components(i).growth_linear_comb) == 1
growthneutralitycorrection = sprintf('%s+%s*%s', growthneutralitycorrection, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
else
growthneutralitycorrection = sprintf('%s+%s*(%s)', growthneutralitycorrection, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
end
else
if numel(expectationmodel.components(i).growth_linear_comb) == 1
growthneutralitycorrection = sprintf('%s+%s*%s*%s', growthneutralitycorrection, expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
else
growthneutralitycorrection = sprintf('%s+%s*%s*(%s)', growthneutralitycorrection, expectationmodel.components(i).coeff_str, M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str);
end
end
end
end
end % if growth neutrality correction for this component
end % if non stationary component
end
else
growthneutralitycorrection = repmat({''}, length(expectationmodel.components), 1);
for i=1:length(growthneutralitycorrection)
if ~isequal(expectationmodel.components(i).kind, 'll')
if isfield(expectationmodel.components(i), 'growth_neutrality_param_index')
if ~isempty(expectationmodel.components(i).growth_str)
if numel(expectationmodel.components(i).growth_linear_comb) == 1
growthneutralitycorrection(i) = {sprintf('%s*%s', M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str)};
else
growthneutralitycorrection(i) = {sprintf('%s*(%s)', M_.param_names{expectationmodel.components(i).growth_neutrality_param_index}, expectationmodel.components(i).growth_str)};
end
end
end % if growth neutrality correction for this component
end % if non stationary component
end
end % if aggregate
end
end
if nargout==1 && ~isempty(growthneutralitycorrection)
expression = sprintf('%s + %s', expression, growthneutralitycorrection);
end
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