1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
|
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 © 2019-2021 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';
% 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;
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;
expression = sprintf('%s+%s', M_.param_names{expectationmodel.equations.(eqtag).h0_param_indices(id)}, ...
M_.param_names{expectationmodel.equations.(eqtag).h1_param_indices(id)});
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'
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 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 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')
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
growthneutralitycorrection = '';
end
if nargout==1 && ~isempty(growthneutralitycorrection)
expression = sprintf('%s + %s', expression, growthneutralitycorrection);
end
|
