stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Encoding fixes.

time-shift
Stéphane Adjemian (Charybdis) 2019-11-25 11:48:09 +01:00
parent 75a929051f
commit 7e7f57d7d1
5 changed files with 892 additions and 892 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
matlab/surgibbs.m
View File

@ -132,7 +132,7 @@ oo_.surgibbs.(model_name).posterior.mean.beta = (sum(oo_.surgibbs.(model_name).b
oo_.surgibbs.(model_name).posterior.variance.beta = cov(oo_.surgibbs.(model_name).betadraws);
% Yhat
oo_.surgibbs.(model_name).Yhat = X*oo_.surgibbs.(model_name).posterior.mean.beta;
oo_.surgibbs.(model_name).Yhat = X*oo_.surgibbs.(model_name).posterior.mean.beta;
% Residuals
oo_.surgibbs.(model_name).resid = Y - oo_.surgibbs.(model_name).Yhat;
@ -181,7 +181,7 @@ if ~options_.noprint
sprintf('No. Equations: %d', oo_.surgibbs.(model_name).neqs), ...
sprintf('No. Independent Variables: %d', size(X, 2)), ...
sprintf('Observations: %d', oo_.surgibbs.(model_name).dof)};
afterward = {sprintf('s^2: %f', oo_.surgibbs.(model_name).s2), sprintf('R^2: %f', oo_.surgibbs.(model_name).R2)};
dyn_table(ttitle, preamble, afterward, param_names,...
{'Posterior mean', 'Posterior std.'}, 4,...

446
tests/ecb/SUR/panel_var_diff_NB_simulation_test.mod
View File

@ -1,223 +1,223 @@
// --+ 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(-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;
options_.noprint = 1;
calibrated_values = M_.params;
verbatim;
Sigma_e = M_.Sigma_e;
end;
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
simdata = sur(simdata{idxs});
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
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
good = [-0.826686196809409
-0.346753563700393
0.063013991583949
0.074802596658698
-0.017440119721953
-0.127090614348862
0.025293280404460
0.524290302468866
-0.117611206771440
-0.027776224547132
-0.156590828735908
0.054039707976331
0.276257666502046
1.000417289621684
0.999336865129450
-0.803258152338916
-0.309594948488168
0.051602756230521
0.039275481081030
0.024897596371662
-0.096310133845385
-0.022630284059365
0.461683465196454
-0.110278113383114
1.000000000000000];
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
// --+ 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(-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;
options_.noprint = 1;
calibrated_values = M_.params;
verbatim;
Sigma_e = M_.Sigma_e;
end;
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
simdata = sur(simdata{idxs});
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
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
good = [-0.826686196809409
-0.346753563700393
0.063013991583949
0.074802596658698
-0.017440119721953
-0.127090614348862
0.025293280404460
0.524290302468866
-0.117611206771440
-0.027776224547132
-0.156590828735908
0.054039707976331
0.276257666502046
1.000417289621684
0.999336865129450
-0.803258152338916
-0.309594948488168
0.051602756230521
0.039275481081030
0.024897596371662
-0.096310133845385
-0.022630284059365
0.461683465196454
-0.110278113383114
1.000000000000000];
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

444
tests/ecb/SUR/panel_var_diff_NB_simulation_zero_eq.mod
View File

@ -1,222 +1,222 @@
// --+ 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_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 = 0 ;
[name = 'eq5']
U2_EHIC = 0 ;
[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(-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_DE_Q_YED = 0.005;
var res_DE_G_YER = 0.005;
var res_DE_EHIC = 0.005;
end;
NSIMS = 1;
options_.noprint = 1;
calibrated_values = M_.params;
verbatim;
Sigma_e = M_.Sigma_e;
end;
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));
% fill zeroed-out variables with random data to ensure they are not being referenced
simdata.U2_EHIC = dseries(rand(1000, 1), simdata.firstdate, 'U2_EHIC');
simdata.U2_ESTN = dseries(rand(1000, 1), simdata.firstdate, 'U2_ESTN');
names = regexp(simdata.name, 'res\w*');
idxs = [];
for j=1:length(names)
if isempty(names{j})
idxs = [idxs j];
end
end
simdata = sur(simdata{idxs});
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
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
good = [-0.806260229719792
-0.354371215026078
0.067618717060638
0.088053231576354
-0.057161898696690
-0.136999143481575
0.052047088887385
0.524022502978068
-0.110211572532150
-0.041415758049800
-0.162997606880957
0.091000830792721
0.281196660545012
1.000000000000000
1.000000000000000
-0.823354793011220
-0.337521089381118
0.025493177305917
0.059513905441361
0.016845886773011
-0.151537340795868
0.004832174649536
0.430883913061161
-0.071743697511067
1.000000000000000];
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
// --+ 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_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 = 0 ;
[name = 'eq5']
U2_EHIC = 0 ;
[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(-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_DE_Q_YED = 0.005;
var res_DE_G_YER = 0.005;
var res_DE_EHIC = 0.005;
end;
NSIMS = 1;
options_.noprint = 1;
calibrated_values = M_.params;
verbatim;
Sigma_e = M_.Sigma_e;
end;
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));
% fill zeroed-out variables with random data to ensure they are not being referenced
simdata.U2_EHIC = dseries(rand(1000, 1), simdata.firstdate, 'U2_EHIC');
simdata.U2_ESTN = dseries(rand(1000, 1), simdata.firstdate, 'U2_ESTN');
names = regexp(simdata.name, 'res\w*');
idxs = [];
for j=1:length(names)
if isempty(names{j})
idxs = [idxs j];
end
end
simdata = sur(simdata{idxs});
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
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
good = [-0.806260229719792
-0.354371215026078
0.067618717060638
0.088053231576354
-0.057161898696690
-0.136999143481575
0.052047088887385
0.524022502978068
-0.110211572532150
-0.041415758049800
-0.162997606880957
0.091000830792721
0.281196660545012
1.000000000000000
1.000000000000000
-0.823354793011220
-0.337521089381118
0.025493177305917
0.059513905441361
0.016845886773011
-0.151537340795868
0.004832174649536
0.430883913061161
-0.071743697511067
1.000000000000000];
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

446
tests/ecb/SUR/sur_noniterative.mod
View File

@ -1,223 +1,223 @@
// --+ 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(-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;
options_.noprint = 1;
calibrated_values = M_.params;
verbatim;
Sigma_e = M_.Sigma_e;
end;
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
simdata = sur(simdata{idxs}, {}, {}, 'mymodel', true);
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
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
good = [-0.826686383251952
-0.346754226737713
0.063012625739192
0.074803008678581
-0.017406695286611
-0.127075466955954
0.025273119798253
0.524311285013928
-0.117653504652072
-0.027795769946178
-0.156599054434094
0.054048691636616
0.276250884768198
1.000417819589230
0.999336022919911
-0.803262845969268
-0.309598590696623
0.051598925732525
0.039275097813797
0.024902442384228
-0.096310332265968
-0.022625946626029
0.461672601960238
-0.110232456667380
1.000000000000000];
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
// --+ 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(-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;
options_.noprint = 1;
calibrated_values = M_.params;
verbatim;
Sigma_e = M_.Sigma_e;
end;
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
simdata = sur(simdata{idxs}, {}, {}, 'mymodel', true);
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
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
good = [-0.826686383251952
-0.346754226737713
0.063012625739192
0.074803008678581
-0.017406695286611
-0.127075466955954
0.025273119798253
0.524311285013928
-0.117653504652072
-0.027795769946178
-0.156599054434094
0.054048691636616
0.276250884768198
1.000417819589230
0.999336022919911
-0.803262845969268
-0.309598590696623
0.051598925732525
0.039275097813797
0.024902442384228
-0.096310332265968
-0.022625946626029
0.461672601960238
-0.110232456667380
1.000000000000000];
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

444
tests/ecb/SUR/sur_params_noniterative.mod
View File

@ -1,222 +1,222 @@
// --+ 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(-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;
options_.noprint = 1;
calibrated_values = M_.params;
verbatim;
Sigma_e = M_.Sigma_e;
end;
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
simdata = sur(simdata{idxs}, {'u2_q_yed_u2_g_yer_L1', 'u2_estn_u2_estn_L1', 'u2_ehic_u2_ehic_L1', 'de_q_yed_de_g_yer_L1', 'u2_g_yer_u2_g_yer_L1', 'u2_stn_u2_q_yed_L1', 'u2_q_yed_ecm_u2_q_yed_L1', 'de_g_yer_u2_stn_L1'}, {}, 'mymodel', true);
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
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
good = [-0.811559435201128
-0.323715338568976
0.073666282610569
0.058397703958446
0.018989604697742
-0.109597659887432
0.003766796763203
0.510389966839712
-0.072235928612349
-0.043850066260836
-0.153283917138772
0.058739144948151
0.292121949736756
1.000413662363949
0.999337514406012
-0.822375165893149
-0.323715338568977
0.046096335070402
0.058397703958446
0.018989604697742
-0.109597659887433
0.003766796763203
0.480506381923643
-0.111002148299648
1.000000000000000];
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
// --+ 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(-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;
options_.noprint = 1;
calibrated_values = M_.params;
verbatim;
Sigma_e = M_.Sigma_e;
end;
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
simdata = sur(simdata{idxs}, {'u2_q_yed_u2_g_yer_L1', 'u2_estn_u2_estn_L1', 'u2_ehic_u2_ehic_L1', 'de_q_yed_de_g_yer_L1', 'u2_g_yer_u2_g_yer_L1', 'u2_stn_u2_q_yed_L1', 'u2_q_yed_ecm_u2_q_yed_L1', 'de_g_yer_u2_stn_L1'}, {}, 'mymodel', true);
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
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
good = [-0.811559435201128
-0.323715338568976
0.073666282610569
0.058397703958446
0.018989604697742
-0.109597659887432
0.003766796763203
0.510389966839712
-0.072235928612349
-0.043850066260836
-0.153283917138772
0.058739144948151
0.292121949736756
1.000413662363949
0.999337514406012
-0.822375165893149
-0.323715338568977
0.046096335070402
0.058397703958446
0.018989604697742
-0.109597659887433
0.003766796763203
0.480506381923643
-0.111002148299648
1.000000000000000];
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