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add pooled_fgls (with test)

time-shift
Houtan Bastani 2017-11-15 12:36:12 +01:00
parent 9f94bcf825
commit 96d716343d
5 changed files with 498 additions and 16 deletions
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83
matlab/ols/pooled_fgls.m Normal file
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@ -0,0 +1,83 @@
function pooled_fgls(ds, param_common, param_regex)
% function pooled_fgls(ds, param_common, param_regex)
% Run Pooled FGLS
% Apply parameter values found to corresponding parameter values in the
% other blocks of the model
%
% INPUTS
% ds [dseries] data to use in estimation
% param_common [cellstr] List of values to insert into param_regex,
% e.g. country codes {'FR', 'DE', 'IT'}
% param_regex [cellstr] Where '*' should be replaced by the first
% value in param_common
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% dynare must be run with the option: json=parse
% Copyright (C) 2017 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 <http://www.gnu.org/licenses/>.
global M_ oo_
pooled_ols(ds, param_common, param_regex, true, 'pooled_fgls');
neq = length(fieldnames(oo_.pooled_fgls.resid));
nobs = length(oo_.pooled_fgls.sample_range);
for i = 1:neq
ui = oo_.pooled_fgls.resid.(oo_.pooled_fgls.residnames{i});
for j = i:neq
uj = oo_.pooled_fgls.resid.(oo_.pooled_fgls.residnames{j});
M_.Sigma(i, j) = (ui'*uj)/nobs;
M_.Sigma(j, i) = M_.Sigma(i, j);
end
end
kLeye = kron(chol(M_.Sigma), eye(nobs));
[q, r] = qr(kLeye*oo_.pooled_fgls.X, 0);
oo_.pooled_fgls.beta = r\(q'*kLeye*oo_.pooled_fgls.Y);
param_names_trim = cellstr(M_.param_names);
regexcountries = ['(' strjoin(param_common(1:end),'|') ')'];
pbeta = oo_.pooled_fgls.pbeta;
assigned_idxs = false(size(pbeta));
for i = 1:length(param_regex)
beta_idx = strcmp(pbeta, strrep(param_regex{i}, '*', oo_.pooled_fgls.country_name));
assigned_idxs = assigned_idxs | beta_idx;
value = oo_.pooled_fgls.beta(beta_idx);
assert(~isempty(value));
M_.params(~cellfun(@isempty, regexp(param_names_trim, ...
strrep(param_regex{i}, '*', regexcountries)))) = value;
end
idxs = find(assigned_idxs == 0);
values = oo_.pooled_fgls.beta(idxs);
names = pbeta(idxs);
assert(length(values) == length(names));
for i = 1:length(idxs)
M_.params(strcmp(param_names_trim, names{i})) = values(i);
end
oo_.pooled_fgls = rmfield(oo_.pooled_fgls, 'X');
oo_.pooled_fgls = rmfield(oo_.pooled_fgls, 'Y');
oo_.pooled_fgls = rmfield(oo_.pooled_fgls, 'varcovar');
oo_.pooled_fgls = rmfield(oo_.pooled_fgls, 'residnames');
oo_.pooled_fgls = rmfield(oo_.pooled_fgls, 'pbeta');
oo_.pooled_fgls = rmfield(oo_.pooled_fgls, 'country_name');
end

86
matlab/ols/pooled_ols.m
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@ -1,15 +1,19 @@
function pooled_ols(ds, param_common, param_regex)
% function pooled_ols(ds, param_common, param_regex)
function pooled_ols(ds, param_common, param_regex, overlapping_dates, save_structure_name)
% pooled_ols(ds, param_common, param_regex, overlapping_dates, save_structure_name)
% Run Pooled OLS
% Apply parameter values found to corresponding parameter values in the
% other blocks of the model
%
% INPUTS
% ds [dseries] data to use in estimation
% param_common [cellstr] List of values to insert into param_regex,
% e.g. country codes {'FR', 'DE', 'IT'}
% param_regex [cellstr] Where '*' should be replaced by the first
% value in param_common
% ds [dseries] data to use in estimation
% param_common [cellstr] List of values to insert into param_regex,
% e.g. country codes {'FR', 'DE', 'IT'}
% param_regex [cellstr] Where '*' should be replaced by the first
% value in param_common
% overlapping_dates [bool] if the dates across the equations should
% overlap
% save_structure_name [string] Name of structure in oo_ to save results in
% (pooled_ols by default)
%
% OUTPUTS
% none
@ -38,6 +42,7 @@ global M_ oo_
% Check input arguments
assert(~isempty(ds) && isdseries(ds), 'The first argument must be a dseries');
if isempty(param_common) && isempty(param_regex)
disp('Performing OLS instead of Pooled OLS...')
dyn_ols(ds);
@ -46,12 +51,24 @@ end
assert(~isempty(param_common) && iscellstr(param_common), 'The second argument must be a cellstr');
assert(~isempty(param_regex) && iscellstr(param_regex), 'The third argument must be a cellstr');
if nargin < 4
overlapping_dates = false;
else
assert(islogical(overlapping_dates) && length(overlapping_dates) == 1, 'The fourth argument must be a bool');
end
if nargin < 5
save_structure_name = 'pooled_ols';
else
assert(ischar(save_structure_name), 'The fifth argument must be a string');
end
%% Read JSON
jsonfile = [M_.fname '_original.json'];
if exist(jsonfile, 'file') ~= 2
error('Could not find %s! Please use the json=parse option (See the Dynare invocation section in the reference manual).', jsonfile);
end
%% Read JSON
jsonmodel = loadjson(jsonfile);
jsonmodel = jsonmodel.model;
[lhs, rhs, lineno] = getEquationsByTags(jsonmodel);
@ -78,6 +95,8 @@ pbeta = {};
Y = [];
X = [];
startidxs = zeros(length(lhs), 1);
startdates = cell(length(lhs), 1);
enddates = cell(length(lhs), 1);
residnames = cell(length(lhs), 1);
for i = 1:length(lhs)
rhs_ = strsplit(rhs{i}, {'+','-','*','/','^','log(','ln(','log10(','exp(','(',')','diff('});
@ -161,14 +180,49 @@ for i = 1:length(lhs)
lp = min(ydata.lastobservedperiod, xjdata.lastobservedperiod);
startidxs(i) = length(Y) + 1;
startdates{i} = fp;
enddates{i} = lp;
Y(startidxs(i):startidxs(i)+lp-fp, 1) = ydata(fp:lp).data;
X(startidxs(i):startidxs(i)+lp-fp, pidxs) = xjdata(fp:lp).data;
end
if overlapping_dates
maxfp = max([startdates{:}]);
minlp = min([enddates{:}]);
nobs = minlp - maxfp;
newY = zeros(nobs*length(lhs), 1);
newX = zeros(nobs*length(lhs), columns(X));
newstartidxs = zeros(size(startidxs));
newstartidxs(1) = 1;
for i = 1:length(lhs)
if i == length(lhs)
yds = dseries(Y(startidxs(i):end), startdates{i});
xds = dseries(X(startidxs(i):end, :), startdates{i});
else
yds = dseries(Y(startidxs(i):startidxs(i+1)-1), startdates{i});
xds = dseries(X(startidxs(i):startidxs(i+1)-1, :), startdates{i});
end
newY(newstartidxs(i):newstartidxs(i) + nobs, 1) = yds(maxfp:minlp).data;
newX(newstartidxs(i):newstartidxs(i) + nobs, :) = xds(maxfp:minlp, :).data;
if i ~= length(lhs)
newstartidxs(i+1) = newstartidxs(i) + nobs + 1;
end
end
Y = newY;
X = newX;
startidxs = newstartidxs;
oo_.(save_structure_name).sample_range = maxfp:minlp;
oo_.(save_structure_name).residnames = residnames;
oo_.(save_structure_name).Y = Y;
oo_.(save_structure_name).X = X;
oo_.(save_structure_name).pbeta = pbeta;
oo_.(save_structure_name).country_name = country_name;
end
%% Estimation
% Estimated Parameters
[q, r] = qr(X, 0);
oo_.pooled_ols.beta = r\(q'*Y);
oo_.(save_structure_name).beta = r\(q'*Y);
% Assign parameter values back to parameters using param_regex & param_common
param_names_trim = cellfun(@strtrim, num2cell(M_.param_names(:,:),2), 'Uniform', 0);
@ -177,28 +231,28 @@ assigned_idxs = false(size(pbeta));
for i = 1:length(param_regex)
beta_idx = strcmp(pbeta, strrep(param_regex{i}, '*', country_name));
assigned_idxs = assigned_idxs | beta_idx;
value = oo_.pooled_ols.beta(beta_idx);
value = oo_.(save_structure_name).beta(beta_idx);
assert(~isempty(value));
M_.params(~cellfun(@isempty, regexp(param_names_trim, ...
strrep(param_regex{i}, '*', regexcountries)))) = value;
end
idxs = find(assigned_idxs == 0);
values = oo_.pooled_ols.beta(idxs);
values = oo_.(save_structure_name).beta(idxs);
names = pbeta(idxs);
assert(length(values) == length(names));
for i = 1:length(idxs)
M_.params(strcmp(param_names_trim, names{i})) = values(i);
end
residuals = Y - X * oo_.pooled_ols.beta;
residuals = Y - X * oo_.(save_structure_name).beta;
for i = 1:length(lhs)
if i == length(lhs)
oo_.pooled_ols.resid.(residnames{i}) = residuals(startidxs(i):end);
oo_.(save_structure_name).resid.(residnames{i}) = residuals(startidxs(i):end);
else
oo_.pooled_ols.resid.(residnames{i}) = residuals(startidxs(i):startidxs(i+1)-1);
oo_.(save_structure_name).resid.(residnames{i}) = residuals(startidxs(i):startidxs(i+1)-1);
end
oo_.pooled_ols.varcovar.(['eq' num2str(i)]) = oo_.pooled_ols.resid.(residnames{i})*oo_.pooled_ols.resid.(residnames{i})';
oo_.(save_structure_name).varcovar.(['eq' num2str(i)]) = oo_.(save_structure_name).resid.(residnames{i})*oo_.(save_structure_name).resid.(residnames{i})';
idx = find(strcmp(residnames{i}, M_exo_names_trim));
M_.Sigma_e(idx, idx) = var(oo_.pooled_ols.resid.(residnames{i}));
M_.Sigma_e(idx, idx) = var(oo_.(save_structure_name).resid.(residnames{i}));
end
end

147
tests/ECB/panel_var/panel_var_diff_NB_2country.mod Executable file
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@ -0,0 +1,147 @@
// --+ 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;
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 ;
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 ;
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 ;
U2_ESTN = u2_estn_u2_estn_L1 * U2_ESTN(-1)
+ res_U2_ESTN ;
U2_EHIC = u2_ehic_u2_ehic_L1 * U2_EHIC(-1)
+ res_U2_EHIC ;
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 ;
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 ;
DE_EHIC = de_ehic_de_ehic_L1 * DE_EHIC(-1)
+ res_DE_EHIC ;
end;
pooled_fgls(dseries('data_var.mat'), ...
{'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', ...
'*_ehic_*_ehic_L1'});

151
tests/ECB/pooled_fgls/panel_var_diff_NB_simulation_test.mod Normal file
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@ -0,0 +1,151 @@
// --+ 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);
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 ;
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 ;
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 ;
U2_ESTN = u2_estn_u2_estn_L1 * U2_ESTN(-1)
+ res_U2_ESTN ;
U2_EHIC = u2_ehic_u2_ehic_L1 * U2_EHIC(-1)
+ res_U2_EHIC ;
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 ;
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 ;
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;

47
tests/ECB/pooled_fgls/run_simulation_test.m Normal file
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close all
dynare panel_var_diff_NB_simulation_test.mod;
NSIMS = 1000;
calibrated_values = M_.params;
Sigma_e = M_.Sigma_e;
options_.bnlms.set_dynare_seed_to_default = false;
M_endo_names_trim = cellstr(M_.endo_names);
nparampool = length(M_.params);
BETA = zeros(NSIMS, nparampool);
for i=1:NSIMS
i
firstobs = rand(3, length(M_endo_names_trim));
M_.params = calibrated_values;
M_.Sigma_e = Sigma_e;
simdata = simul_backward_model(dseries(firstobs, dates('1995Q1'), M_endo_names_trim), 10000);
simdata = simdata(simdata.dates(5001:6000));
pooled_fgls(simdata, ...
{'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', ...
'*_ehic_*_ehic_L1'});
BETA(i, :) = M_.params';
oldsim = simdata;
end
mean(BETA)' - calibrated_values
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