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Fix evaluate routine if PAC's growth option is constant.

last-period-simul
Stéphane Adjemian (Charybdis) 2022-01-17 16:52:28 +01:00
parent 7f075ca109
commit 1fd4904ccc
Signed by: stepan
GPG Key ID: 295C1FE89E17EB3C
3 changed files with 110 additions and 34 deletions
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74
matlab/print_expectations.m
View File

@ -361,32 +361,35 @@ if isequal(expectationmodelkind, 'pac') && growth_correction
pgrowth = M_.params(expectationmodel.growth_neutrality_param_index);
for iter = 1:numel(expectationmodel.growth_linear_comb)
vgrowth='';
variable = [];
if expectationmodel.growth_linear_comb(iter).exo_id > 0
variable = M_.exo_names{expectationmodel.growth_linear_comb(iter).exo_id};
elseif expectationmodel.growth_linear_comb(iter).endo_id > 0
variable = M_.endo_names{expectationmodel.growth_linear_comb(iter).endo_id};
end
[variable, transformations] = rewrite_aux_variable(variable, M_);
if isempty(transformations)
if expectationmodel.growth_linear_comb(iter).lag ~= 0
variable = sprintf('%s(%d)', variable, expectationmodel.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.%s()', variable, transformations{k,1});
if ~isempty(variable)
[variable, transformations] = rewrite_aux_variable(variable, M_);
if isempty(transformations)
if expectationmodel.growth_linear_comb(iter).lag ~= 0
variable = sprintf('%s(%d)', variable, expectationmodel.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.%s()', variable, transformations{k,1});
else
variable = sprintf('%s(-%u).%s()', variable, transformations{k,2}, transformations{k,1});
end
else
variable = sprintf('%s(-%u).%s()', variable, transformations{k,2}, transformations{k,1});
variable = sprintf('%s.%s()', variable, transformations{k});
end
else
variable = sprintf('%s.%s()', variable, transformations{k});
end
end
vgrowth = strcat('dbase.', variable);
end
vgrowth = strcat('dbase.', variable);
if expectationmodel.growth_linear_comb(iter).param_id > 0
if ~isempty(vgrowth)
vgrowth = sprintf('%1.16f*%s',M_.params(expectationmodel.growth_linear_comb(iter).param_id), vgrowth);
@ -408,8 +411,8 @@ if isequal(expectationmodelkind, 'pac') && growth_correction
linearCombination = sprintf('%s-%s', linearCombination, vgrowth);
end
else
linearCombination = vgrowth;
end
linearCombination = vgrowth;
end
end % loop over growth linear combination elements
growthcorrection = sprintf('%1.16f*(%s)', pgrowth, linearCombination);
else
@ -419,32 +422,35 @@ if isequal(expectationmodelkind, 'pac') && growth_correction
pgrowth = targetcoefficients(i)*M_.params(expectationmodel.components(i).growth_neutrality_param_index);
for iter = 1:numel(expectationmodel.components(i).growth_linear_comb)
vgrowth='';
variable=[];
if expectationmodel.components(i).growth_linear_comb(iter).exo_id > 0
variable = M_.exo_names{expectationmodel.components(i).growth_linear_comb(iter).exo_id};
elseif expectationmodel.components(i).growth_linear_comb(iter).endo_id > 0
variable = M_.endo_names{expectationmodel.components(i).growth_linear_comb(iter).endo_id};
end
[variable, transformations] = rewrite_aux_variable(variable, M_);
if isempty(transformations)
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.%s()', variable, transformations{k,1});
if ~isempty(variable)
[variable, transformations] = rewrite_aux_variable(variable, M_);
if isempty(transformations)
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.%s()', variable, transformations{k,1});
else
variable = sprintf('%s(-%u).%s()', variable, transformations{k,2}, transformations{k,1});
end
else
variable = sprintf('%s(-%u).%s()', variable, transformations{k,2}, transformations{k,1});
variable = sprintf('%s.%s()', variable, transformations{k});
end
else
variable = sprintf('%s.%s()', variable, transformations{k});
end
end
vgrowth = strcat('dbase.', variable);
end
vgrowth = strcat('dbase.', variable);
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);

1
tests/Makefile.am
View File

@ -531,6 +531,7 @@ ECB_MODFILES = \
pac/var-11e/example1.mod \
pac/var-12/example1.mod \
pac/var-12/example2.mod \
pac/var-12/example4.mod \
pac/var-12/example11.mod \
pac/var-12/example12.mod \
pac/trend-component-1/example1.mod \

69
tests/pac/var-12/example4.mod Normal file
View File

@ -0,0 +1,69 @@
// --+ options: json=compute, stochastic +--
var y x z v;
varexo ex ey ez ;
parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 d_y; // VAR parameters
parameters beta e_c_m c_z_1 c_z_2; // PAC equation parameters
a_y_1 = .2;
a_y_2 = .3;
b_y_1 = .1;
b_y_2 = .4;
b_x_1 = -.1;
b_x_2 = -.2;
d_y = .5;
beta = .9;
e_c_m = .1;
c_z_1 = .7;
c_z_2 = -.3;
var_model(model_name=toto, structural, eqtags=['eq:x', 'eq:y']);
pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
pac_target_info(pacman);
target v;
auxname_target_nonstationary vns;
component y;
growth 0;
auxname pv_y_;
kind dl;
component x;
growth diff(x(-2));
auxname pv_dx_;
kind dd;
end;
model;
[name='eq:y']
y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
[name='eq:x']
diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ex ;
[name='eq:v']
v = x + d_y*y ;
[name='eq:pac']
diff(z) = e_c_m*(pac_target_nonstationary(pacman)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
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');
// Print expanded PAC_EXPECTATION term.
pac.print('pacman', 'eq:pac');