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|
// --+ options: json=compute +--
/* REMARK
** ------
**
** You need to have the first line on top of the mod file. The options defined on this line are passed
** to the dynare command (you can add other options, separated by spaces or commas). The option defined
** here is mandatory for the decomposition. It forces Dynare to output another representation of the
** model in JSON file (additionaly to the matlab files) which is used here to manipulate the equations.
*/
var
U2_Q_YED
U2_G_YER
U2_STN
U2_ESTN
U2_EHIC
DE_Q_YED
DE_G_YER
DE_EHIC
;
varexo
res_U2_Q_YED
res_U2_G_YER
res_U2_STN
res_U2_ESTN
res_U2_EHIC
res_DE_Q_YED
res_DE_G_YER
res_DE_EHIC
;
parameters
u2_q_yed_ecm_u2_q_yed_L1
u2_q_yed_ecm_u2_stn_L1
u2_q_yed_u2_g_yer_L1
u2_q_yed_u2_stn_L1
u2_g_yer_ecm_u2_q_yed_L1
u2_g_yer_ecm_u2_stn_L1
u2_g_yer_u2_q_yed_L1
u2_g_yer_u2_g_yer_L1
u2_g_yer_u2_stn_L1
u2_stn_ecm_u2_q_yed_L1
u2_stn_ecm_u2_stn_L1
u2_stn_u2_q_yed_L1
u2_stn_u2_g_yer_L1
u2_estn_u2_estn_L1
u2_ehic_u2_ehic_L1
de_q_yed_ecm_de_q_yed_L1
de_q_yed_ecm_u2_stn_L1
de_q_yed_de_g_yer_L1
de_q_yed_u2_stn_L1
de_g_yer_ecm_de_q_yed_L1
de_g_yer_ecm_u2_stn_L1
de_g_yer_de_q_yed_L1
de_g_yer_de_g_yer_L1
de_g_yer_u2_stn_L1
de_ehic_de_ehic_L1
;
u2_q_yed_ecm_u2_q_yed_L1 = -0.82237516589315 ;
u2_q_yed_ecm_u2_stn_L1 = -0.323715338568976 ;
u2_q_yed_u2_g_yer_L1 = 0.0401361895021084 ;
u2_q_yed_u2_stn_L1 = 0.058397703958446 ;
u2_g_yer_ecm_u2_q_yed_L1 = 0.0189896046977421 ;
u2_g_yer_ecm_u2_stn_L1 = -0.109597659887432 ;
u2_g_yer_u2_q_yed_L1 = 0.0037667967632025 ;
u2_g_yer_u2_g_yer_L1 = 0.480506381923644 ;
u2_g_yer_u2_stn_L1 = -0.0722359286123494 ;
u2_stn_ecm_u2_q_yed_L1 = -0.0438500662608356 ;
u2_stn_ecm_u2_stn_L1 = -0.153283917138772 ;
u2_stn_u2_q_yed_L1 = 0.0328744983772825 ;
u2_stn_u2_g_yer_L1 = 0.292121949736756 ;
u2_estn_u2_estn_L1 = 1 ;
u2_ehic_u2_ehic_L1 = 1 ;
de_q_yed_ecm_de_q_yed_L1 = -0.822375165893149 ;
de_q_yed_ecm_u2_stn_L1 = -0.323715338568977 ;
de_q_yed_de_g_yer_L1 = 0.0401361895021082 ;
de_q_yed_u2_stn_L1 = 0.0583977039584461 ;
de_g_yer_ecm_de_q_yed_L1 = 0.0189896046977422 ;
de_g_yer_ecm_u2_stn_L1 = -0.109597659887433 ;
de_g_yer_de_q_yed_L1 = 0.00376679676320256;
de_g_yer_de_g_yer_L1 = 0.480506381923643 ;
de_g_yer_u2_stn_L1 = -0.0722359286123494 ;
de_ehic_de_ehic_L1 = 1 ;
model(linear);
[name = 'eq1']
diff(U2_Q_YED) = u2_q_yed_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
+ u2_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ u2_q_yed_u2_g_yer_L1 * diff(U2_G_YER(-1))
+ u2_q_yed_u2_stn_L1 * diff(U2_STN(-1))
+ res_U2_Q_YED ;
[name = 'eq2']
diff(U2_G_YER) = u2_g_yer_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
+ u2_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ u2_g_yer_u2_q_yed_L1 * diff(U2_Q_YED(-1))
+ u2_g_yer_u2_g_yer_L1 * diff(U2_G_YER(-1))
+ u2_g_yer_u2_stn_L1 * diff(U2_STN(-1))
+ res_U2_G_YER ;
[name = 'eq3']
diff(U2_STN) = u2_stn_ecm_u2_q_yed_L1 * (U2_Q_YED(-1) - U2_EHIC(-1))
+ u2_stn_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ u2_stn_u2_q_yed_L1 * diff(U2_Q_YED(-1))
+ u2_stn_u2_g_yer_L1 * diff(U2_G_YER(-1))
+ res_U2_STN ;
[name = 'eq4']
U2_ESTN = u2_estn_u2_estn_L1 * U2_ESTN(-1)
+ res_U2_ESTN ;
[name = 'eq5']
U2_EHIC = u2_ehic_u2_ehic_L1 * U2_EHIC(-1)
+ res_U2_EHIC ;
[name = 'eq6']
diff(DE_Q_YED) = de_q_yed_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
+ de_q_yed_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ de_q_yed_de_g_yer_L1 * diff(DE_G_YER(-1))
+ de_q_yed_u2_stn_L1 * diff(U2_STN(-1))
+ res_DE_Q_YED ;
[name = 'eq7']
diff(DE_G_YER) = de_g_yer_ecm_de_q_yed_L1 * (DE_Q_YED(-1) - DE_EHIC(-1))
+ de_g_yer_ecm_u2_stn_L1 * (U2_STN(-1) - U2_ESTN(-1))
+ de_g_yer_de_q_yed_L1 * diff(DE_Q_YED(-1))
+ de_g_yer_de_g_yer_L1 * diff(DE_G_YER(-1))
+ de_g_yer_u2_stn_L1 * diff(U2_STN(-1))
+ res_DE_G_YER ;
[name = 'eq8']
DE_EHIC = de_ehic_de_ehic_L1 * DE_EHIC(-1)
+ res_DE_EHIC ;
end;
shocks;
var res_U2_Q_YED = 0.005;
var res_U2_G_YER = 0.005;
var res_U2_STN = 0.005;
var res_U2_ESTN = 0.005;
var res_U2_EHIC = 0.005;
var res_DE_Q_YED = 0.005;
var res_DE_G_YER = 0.005;
var res_DE_EHIC = 0.005;
end;
NSIMS = 1;
calibrated_values = M_.params;
Sigma_e = M_.Sigma_e;
options_.bnlms.set_dynare_seed_to_default = false;
nparampool = length(M_.params);
BETA = zeros(NSIMS, nparampool);
for i=1:NSIMS
firstobs = rand(3, length(M_.endo_names));
M_.params = calibrated_values;
M_.Sigma_e = Sigma_e;
simdata = simul_backward_model(dseries(firstobs, dates('1995Q1'), M_.endo_names), 10000);
simdata = simdata(simdata.dates(5001:6000));
names=regexp(simdata.name, 'res\w*');
idxs = [];
for j=1:length(names)
if isempty(names{j})
idxs = [idxs j];
end
end
pooled_fgls(simdata{idxs}, ...
{'de','u2'}, ...
{'*_q_yed_ecm_*_q_yed_L1', ...
'*_q_yed_ecm_u2_stn_L1', ...
'*_q_yed_*_g_yer_L1', ...
'*_q_yed_u2_stn_L1', ...
'*_g_yer_ecm_*_q_yed_L1', ...
'*_g_yer_ecm_u2_stn_L1', ...
'*_g_yer_*_q_yed_L1', ...
'*_g_yer_*_g_yer_L1', ...
'*_g_yer_u2_stn_L1'}, ...
{}, '', ...
{'*_q_yed_ecm_*_q_yed_L1', ...
'*_q_yed_ecm_u2_stn_L1', ...
'*_q_yed_*_g_yer_L1', ...
'*_q_yed_u2_stn_L1', ...
'de_ehic_de_ehic_L1'});
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'pooled_fgls');
end
if NSIMS > 1
if max(abs(mean(BETA)' - calibrated_values)) > 1e-2
error(['sum(abs(mean(BETA)'' - calibrated_values)) ' num2str(sum(abs(mean(BETA)' - calibrated_values)))]);
end
else
if isoctave
good = [-8.310501956997751e-01
-3.225294235017088e-01
1.865216033306362e-02
5.663863653611149e-02
1.898960469774210e-02
-1.095976598874320e-01
3.766796763202500e-03
4.805063819236440e-01
-7.223592861234940e-02
-4.385006626083560e-02
-1.532839171387720e-01
3.287449837728250e-02
2.921219497367560e-01
1.000000000000000e+00
1.000000000000000e+00
-8.310501956997751e-01
-3.225294235017088e-01
1.865216033306362e-02
5.663863653611149e-02
1.898960469774220e-02
-1.095976598874330e-01
3.766796763202560e-03
4.805063819236430e-01
-7.223592861234940e-02
9.987547807197997e-01];
else
good = [-0.814720065821038
-0.327476517170325
0.058494818795786
0.057190907485572
0.018989604697742
-0.109597659887432
0.003766796763203
0.480506381923644
-0.072235928612349
-0.043850066260836
-0.153283917138772
0.032874498377282
0.292121949736756
1.000000000000000
1.000000000000000
-0.814720065821038
-0.327476517170325
0.058494818795786
0.057190907485572
0.018989604697742
-0.109597659887433
0.003766796763203
0.480506381923643
-0.072235928612349
0.999222744717732];
end
if max(abs(BETA' - good)) > 1e-14
error(['sum of BETA'' - good was: ' num2str(sum(abs(BETA' - good)))]);
end
return
end
for i=1:nparampool
figure
hold on
title(strrep(M_.param_names(i,:), '_', '\_'));
histogram(BETA(:,i),50);
line([calibrated_values(i) calibrated_values(i)], [0 NSIMS/10], 'LineWidth', 2, 'Color', 'r');
hold off
end
|
