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

Merge branch 'dynare-globals_and_namespace' 

Ref. !2219
covariance-quadratic-approximation
Sébastien Villemot 2023-12-14 18:29:28 +01:00
parent 565667c6b7 f05a2de89e
commit 19dcd4a0f2
No known key found for this signature in database
GPG Key ID: 2CECE9350ECEBE4A
165 changed files with 502 additions and 796 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

52
license.txt
View File

@ -113,7 +113,7 @@ Copyright: 1995 E.G.Tsionas
2015-2017 Dynare Team
License: GPL-3+
Files: matlab/endogenous_prior.m
Files: matlab/estimation/endogenous_prior.m
Copyright: 2011 Lawrence J. Christiano, Mathias Trabandt and Karl Walentin
2013-2017 Dynare Team
License: GPL-3+
@ -128,7 +128,7 @@ Copyright: 2016 Benjamin Born and Johannes Pfeifer
2016-2017 Dynare Team
License: GPL-3+
Files: matlab/commutation.m matlab/duplication.m
Files: matlab/+pruned_SS/commutation.m matlab/+pruned_SS/duplication.m
Copyright: 1997 Tom Minka <minka@microsoft.com>
2019-2020 Dynare Team
License: GPL-3+
@ -141,7 +141,7 @@ Comment: The original author gave authorization to change
the license from BSD-2-clause to GPL-3+ and redistribute
it under GPL-3+ with Dynare.
Files: matlab/uperm.m
Files: matlab/+pruned_SS/uperm.m
Copyright: 2014 Bruno Luong <brunoluong@yahoo.com>
2020 Dynare Team
License: GPL-3+
@ -149,7 +149,7 @@ Comment: The original author gave authorization to change
the license from BSD-2-clause to GPL-3+ and redistribute
it under GPL-3+ with Dynare.
Files: matlab/prodmom.m matlab/bivmom.m
Files: matlab/+pruned_SS/prodmom.m matlab/+pruned_SS/bivmom.m
Copyright: 2008-2015 Raymond Kan <kan@chass.utoronto.ca>
2019-2020 Dynare Team
License: GPL-3+
@ -161,33 +161,33 @@ Comment: The author gave authorization to redistribute
Journal of Multivariate Analysis, 2008, vol. 99, issue 3,
pages 542-554.
Files: matlab/gsa/Morris_Measure_Groups.m
matlab/gsa/Sampling_Function_2.m
Files: matlab/+gsa/Morris_Measure_Groups.m
matlab/+gsa/Sampling_Function_2.m
Copyright: 2005 European Commission
2012-2017 Dynare Team
2012-2013 Dynare Team
License: GPL-3+
Comment: Written by Jessica Cariboni and Francesca Campolongo
Joint Research Centre, The European Commission,
Files: matlab/gsa/cumplot.m
matlab/gsa/filt_mc_.m
matlab/gsa/gsa_skewness.m
matlab/gsa/log_trans_.m
matlab/gsa/map_calibration.m
matlab/gsa/map_ident_.m
matlab/gsa/mcf_analysis.m
matlab/gsa/myboxplot.m
matlab/gsa/prior_draw_gsa.m
matlab/gsa/redform_map.m
matlab/gsa/redform_screen.m
matlab/gsa/scatter_mcf.m
matlab/gsa/smirnov.m
matlab/gsa/stab_map_.m
matlab/gsa/stab_map_1.m
matlab/gsa/stab_map_2.m
matlab/gsa/stand_.m
matlab/gsa/tcrit.m
matlab/gsa/teff.m
Files: matlab/+gsa/cumplot.m
matlab/+gsa/monte_carlo_filtering.m
matlab/+gsa/skewness.m
matlab/+gsa/log_trans_.m
matlab/+gsa/map_calibration.m
matlab/+gsa/map_identification.m
matlab/+gsa/monte_carlo_filtering_analysis.m
matlab/+gsa/boxplot.m
matlab/+gsa/prior_draw.m
matlab/+gsa/reduced_form_mapping.m
matlab/+gsa/reduced_form_screening.m
matlab/+gsa/scatter_mcf.m
matlab/+gsa/smirnov_test.m
matlab/+gsa/stability_mapping.m
matlab/+gsa/stability_mapping_univariate.m
matlab/+gsa/stability_mapping_bivariate.m
matlab/+gsa/standardize_columns.m
matlab/+gsa/tcrit.m
matlab/+gsa/teff.m
Copyright: 2011-2018 European Commission
2011-2023 Dynare Team
License: GPL-3+

0
matlab/gsa/Morris_Measure_Groups.m → matlab/+gsa/Morris_Measure_Groups.m
View File

0
matlab/gsa/Sampling_Function_2.m → matlab/+gsa/Sampling_Function_2.m
View File

4
matlab/gsa/myboxplot.m → matlab/+gsa/boxplot.m
View File

@ -1,5 +1,5 @@
function sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
% sout = myboxplot (data,notched,symbol,vertical,maxwhisker)
function sout = boxplot (data,notched,symbol,vertical,maxwhisker)
% sout = boxplot (data,notched,symbol,vertical,maxwhisker)
% Creates a box plot
% Copyright © 2010-2023 Dynare Team

0
matlab/gsa/cumplot.m → matlab/+gsa/cumplot.m
View File

8
matlab/gsa/log_trans_.m → matlab/+gsa/log_transform.m
View File

@ -1,5 +1,5 @@
function [yy, xdir, isig, lam]=log_trans_(y0,xdir0,isig,lam)
% [yy, xdir, isig, lam]=log_trans_(y0,xdir0,isig,lam)
function [yy, xdir, isig, lam]=log_transform(y0,xdir0,isig,lam)
% [yy, xdir, isig, lam]=log_transform(y0,xdir0,isig,lam)
% Conduct automatic log transformation lam(yy/isig+lam)
% Inputs:
% - y0 [double] series to transform
@ -56,10 +56,10 @@ end
if nargin==1
xdir0='';
end
f=@(lam,y)gsa_skewness(log(y+lam));
f=@(lam,y)gsa.skewness(log(y+lam));
isig=1;
if ~(max(y0)<0 || min(y0)>0)
if gsa_skewness(y0)<0
if gsa.skewness(y0)<0
isig=-1;
y0=-y0;
end

16
matlab/gsa/map_calibration.m → matlab/+gsa/map_calibration.m
View File

@ -229,7 +229,7 @@ if ~isempty(indx_irf)
if ~options_.nograph && length(time_matrix{plot_indx(ij)})==1
set(0,'currentfigure',h1),
subplot(nrow,ncol, plot_indx(ij)),
hc = cumplot(mat_irf{ij}(:,ik));
hc = gsa.cumplot(mat_irf{ij}(:,ik));
a=axis;
delete(hc);
x1val=max(endo_prior_restrictions.irf{ij,4}(1),a(1));
@ -237,7 +237,7 @@ if ~isempty(indx_irf)
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
hold all,
set(hp,'FaceColor', [0.7 0.8 1])
hc = cumplot(mat_irf{ij}(:,ik));
hc = gsa.cumplot(mat_irf{ij}(:,ik));
set(hc,'color','k','linewidth',2)
hold off,
% hold off,
@ -259,7 +259,7 @@ if ~isempty(indx_irf)
end
options_mcf.title = atitle0;
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, M_, options_, bayestopt_, estim_params_);
end
end
for ij=1:nbr_irf_couples
@ -316,7 +316,7 @@ if ~isempty(indx_irf)
options_mcf.title = atitle0;
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, M_, options_, bayestopt_, estim_params_);
end
end
end
@ -434,7 +434,7 @@ if ~isempty(indx_moment)
if ~options_.nograph && length(time_matrix{plot_indx(ij)})==1
set(0,'currentfigure',h2);
subplot(nrow,ncol,plot_indx(ij)),
hc = cumplot(mat_moment{ij}(:,ik));
hc = gsa.cumplot(mat_moment{ij}(:,ik));
a=axis; delete(hc),
% hist(mat_moment{ij}),
x1val=max(endo_prior_restrictions.moment{ij,4}(1),a(1));
@ -442,7 +442,7 @@ if ~isempty(indx_moment)
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceColor', [0.7 0.8 1])
hold all
hc = cumplot(mat_moment{ij}(:,ik));
hc = gsa.cumplot(mat_moment{ij}(:,ik));
set(hc,'color','k','linewidth',2)
hold off
title([endo_prior_restrictions.moment{ij,1},' vs ',endo_prior_restrictions.moment{ij,2},'(',leg,')'],'interpreter','none'),
@ -463,7 +463,7 @@ if ~isempty(indx_moment)
end
options_mcf.title = atitle0;
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat, indx1, indx2, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(xmat, indx1, indx2, options_mcf, M_, options_, bayestopt_, estim_params_);
end
end
for ij=1:nbr_moment_couples
@ -520,7 +520,7 @@ if ~isempty(indx_moment)
end
options_mcf.title = atitle0;
if ~isempty(indx1) && ~isempty(indx2)
mcf_analysis(xmat, indx1, indx2, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(xmat, indx1, indx2, options_mcf, M_, options_, bayestopt_, estim_params_);
end
end
end

44
matlab/gsa/map_ident_.m → matlab/+gsa/map_identification.m
View File

@ -1,5 +1,5 @@
function map_ident_(OutputDirectoryName,opt_gsa,M_,oo_,options_,estim_params_,bayestopt_)
% map_ident_(OutputDirectoryName,opt_gsa,M_,oo_,options_,estim_params_,bayestopt_)
function map_identification(OutputDirectoryName,opt_gsa,M_,oo_,options_,estim_params_,bayestopt_)
% map_identification(OutputDirectoryName,opt_gsa,M_,oo_,options_,estim_params_,bayestopt_)
% Inputs
% - OutputDirectoryName [string] name of the output directory
% - opt_gsa [structure] GSA options structure
@ -58,16 +58,16 @@ fname_ = M_.fname;
if opt_gsa.load_ident_files==0
mss = yys(bayestopt_.mfys,:);
mss = teff(mss(:,istable),Nsam,istable);
yys = teff(yys(dr.order_var,istable),Nsam,istable);
mss = gsa.teff(mss(:,istable),Nsam,istable);
yys = gsa.teff(yys(dr.order_var,istable),Nsam,istable);
if exist('T','var')
[vdec, cc, ac] = mc_moments(T, lpmatx, dr, M_, options_, estim_params_);
[vdec, cc, ac] = gsa.monte_carlo_moments(T, lpmatx, dr, M_, options_, estim_params_);
else
return
end
if opt_gsa.morris==2
pdraws = dynare_identification(M_,oo_,options_,bayestopt_,estim_params_,options_.options_ident,[lpmatx lpmat(istable,:)]);
pdraws = identification.run(M_,oo_,options_,bayestopt_,estim_params_,options_.options_ident,[lpmatx lpmat(istable,:)]);
if ~isempty(pdraws) && max(max(abs(pdraws-[lpmatx lpmat(istable,:)])))==0
disp(['Sample check OK. Largest difference: ', num2str(max(max(abs(pdraws-[lpmatx lpmat(istable,:)]))))]),
clear pdraws;
@ -84,7 +84,7 @@ if opt_gsa.load_ident_files==0
end
iplo=iplo+1;
subplot(2,3,iplo)
myboxplot(squeeze(vdec(:,j,:))',[],'.',[],10)
gsa.boxplot(squeeze(vdec(:,j,:))',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:size(options_.varobs,1))
set(gca,'xlim',[0.5 size(options_.varobs,1)+0.5])
set(gca,'ylim',[-2 102])
@ -105,11 +105,11 @@ if opt_gsa.load_ident_files==0
end
end
for j=1:size(cc,1)
cc(j,j,:)=stand_(squeeze(log(cc(j,j,:))))./2;
cc(j,j,:)=gsa.standardize_columns(squeeze(log(cc(j,j,:))))./2;
end
[vdec, ~, ir_vdec, ic_vdec] = teff(vdec,Nsam,istable);
[cc, ~, ir_cc, ic_cc] = teff(cc,Nsam,istable);
[ac, ~, ir_ac, ic_ac] = teff(ac,Nsam,istable);
[vdec, ~, ir_vdec, ic_vdec] = gsa.teff(vdec,Nsam,istable);
[cc, ~, ir_cc, ic_cc] = gsa.teff(cc,Nsam,istable);
[ac, ~, ir_ac, ic_ac] = gsa.teff(ac,Nsam,istable);
nc1= size(T,2);
endo_nbr = M_.endo_nbr;
@ -123,7 +123,7 @@ if opt_gsa.load_ident_files==0
[Aa,Bb] = kalman_transition_matrix(dr,iv,ic);
A = zeros(size(Aa,1),size(Aa,2)+size(Aa,1),length(istable));
if ~isempty(lpmatx)
M_=set_shocks_param(M_,estim_params_,lpmatx(1,:));
M_=gsa.set_shocks_param(M_,estim_params_,lpmatx(1,:));
end
A(:,:,1)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
for j=2:length(istable)
@ -131,14 +131,14 @@ if opt_gsa.load_ident_files==0
dr.ghu = T(:, (nc1-M_.exo_nbr+1):end, j);
[Aa,Bb] = kalman_transition_matrix(dr, iv, ic);
if ~isempty(lpmatx)
M_=set_shocks_param(M_,estim_params_,lpmatx(j,:));
M_=gsa.set_shocks_param(M_,estim_params_,lpmatx(j,:));
end
A(:,:,j)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
end
clear T
clear lpmatx
[yt, j0]=teff(A,Nsam,istable);
[yt, j0]=gsa.teff(A,Nsam,istable);
yt = [yys yt];
if opt_gsa.morris==2
clear TAU A
@ -155,7 +155,7 @@ if opt_gsa.morris==1
if opt_gsa.load_ident_files==0
SAMorris=NaN(npT,3,size(vdec,2));
for i=1:size(vdec,2)
[~, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], vdec(:,i),nliv);
[~, SAMorris(:,:,i)] = gsa.Morris_Measure_Groups(npT, [lpmat0 lpmat], vdec(:,i),nliv);
end
SAvdec = squeeze(SAMorris(:,1,:))';
save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAvdec','vdec','ir_vdec','ic_vdec')
@ -164,7 +164,7 @@ if opt_gsa.morris==1
end
hh_fig = dyn_figure(options_.nodisplay,'name','Screening identification: variance decomposition');
myboxplot(SAvdec,[],'.',[],10)
gsa.boxplot(SAvdec,[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:npT)
set(gca,'xlim',[0.5 npT+0.5])
ydum = get(gca,'ylim');
@ -190,7 +190,7 @@ if opt_gsa.morris==1
ccac = [mss cc ac];
SAMorris=NaN(npT,3,size(ccac,2));
for i=1:size(ccac,2)
[~, SAMorris(:,:,i)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], [ccac(:,i)],nliv);
[~, SAMorris(:,:,i)] = gsa.Morris_Measure_Groups(npT, [lpmat0 lpmat], [ccac(:,i)],nliv);
end
SAcc = squeeze(SAMorris(:,1,:))';
SAcc = SAcc./(max(SAcc,[],2)*ones(1,npT));
@ -202,7 +202,7 @@ if opt_gsa.morris==1
end
hh_fig=dyn_figure(options_.nodisplay,'name','Screening identification: theoretical moments');
myboxplot(SAcc,[],'.',[],10)
gsa.boxplot(SAcc,[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:npT)
set(gca,'xlim',[0.5 npT+0.5])
set(gca,'ylim',[0 1])
@ -223,7 +223,7 @@ if opt_gsa.morris==1
if opt_gsa.load_ident_files==0
SAMorris=NaN(npT,3,j0);
for j=1:j0
[~, SAMorris(:,:,j)] = Morris_Measure_Groups(npT, [lpmat0 lpmat], yt(:,j),nliv);
[~, SAMorris(:,:,j)] = gsa.Morris_Measure_Groups(npT, [lpmat0 lpmat], yt(:,j),nliv);
end
SAM = squeeze(SAMorris(1:end,1,:));
@ -249,7 +249,7 @@ if opt_gsa.morris==1
load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAnorm')
end
hh_fig=dyn_figure(options_.nodisplay,'name','Screening identification: model');
myboxplot(SAnorm',[],'.',[],10)
gsa.boxplot(SAnorm',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:npT)
set(gca,'xlim',[0.5 npT+0.5])
set(gca,'ylim',[0 1])
@ -297,7 +297,7 @@ else % main effects analysis
catch
EET=[];
end
ccac = stand_([mss cc ac]);
ccac = gsa.standardize_columns([mss cc ac]);
[pcc, dd] = eig(cov(ccac(istable,:)));
[latent, isort] = sort(-diag(dd));
latent = -latent;
@ -314,7 +314,7 @@ else % main effects analysis
if itrans==0
y0 = ccac(istable,j);
elseif itrans==1
y0 = log_trans_(ccac(istable,j));
y0 = gsa.log_transform(ccac(istable,j));
else
y0 = trank(ccac(istable,j));
end

50
matlab/gsa/filt_mc_.m → matlab/+gsa/monte_carlo_filtering.m
View File

@ -1,5 +1,5 @@
function [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_,dataset_,dataset_info,M_,oo_,options_,bayestopt_,estim_params_)
% [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_,dataset_,dataset_info,M_,oo_,options_,bayestopt_,estim_params_
function [rmse_MC, ixx] = monte_carlo_filtering(OutDir,options_gsa_,dataset_,dataset_info,M_,oo_,options_,bayestopt_,estim_params_)
% [rmse_MC, ixx] = monte_carlo_filtering(OutDir,options_gsa_,dataset_,dataset_info,M_,oo_,options_,bayestopt_,estim_params_
% Inputs:
% - OutputDirectoryName [string] name of the output directory
% - options_gsa_ [structure] GSA options
@ -288,7 +288,7 @@ options_scatter.OutputDirectoryName = OutDir;
options_scatter.amcf_name = asname;
options_scatter.amcf_title = atitle;
options_scatter.title = tmp_title;
scatter_analysis(r2_MC, x,options_scatter, options_);
gsa.scatter_analysis(r2_MC, x,options_scatter, options_);
% end of visual scatter analysis
if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only
@ -320,7 +320,7 @@ if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only
options_mcf.nobeha_title_latex = 'worse posterior kernel';
end
mcf_analysis(x, ipost(1:nfilt), ipost(nfilt+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(x, ipost(1:nfilt), ipost(nfilt+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
if options_.opt_gsa.pprior
anam = 'rmse_prior_lik';
atitle = 'RMSE prior: Log Likelihood Kernel';
@ -338,7 +338,7 @@ if ~options_.opt_gsa.ppost && options_.opt_gsa.lik_only
options_mcf.nobeha_title_latex = 'worse likelihood';
end
mcf_analysis(x, ilik(1:nfilt), ilik(nfilt+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(x, ilik(1:nfilt), ilik(nfilt+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
else
if options_.opt_gsa.ppost
@ -367,9 +367,9 @@ else
SS = zeros(npar+nshock, length(vvarvecm));
for j = 1:npar+nshock
for i = 1:length(vvarvecm)
[~, P] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j), alpha);
[H1] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,1);
[H2] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,-1);
[~, P] = gsa.smirnov_test(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j), alpha);
[H1] = gsa.smirnov_test(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,1);
[H2] = gsa.smirnov_test(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,-1);
if H1==0 && H2==0
SS(j,i)=1;
elseif H1==0
@ -382,7 +382,7 @@ else
for i = 1:length(vvarvecm)
for l = 1:length(vvarvecm)
if l~=i && PP(j,i)<alpha && PP(j,l)<alpha
[~,P] = smirnov(x(ixx(1:nfilt0(i),i),j),x(ixx(1:nfilt0(l),l),j), alpha);
[~,P] = gsa.smirnov_test(x(ixx(1:nfilt0(i),i),j),x(ixx(1:nfilt0(l),l),j), alpha);
PPV(i,l,j) = P;
elseif l==i
PPV(i,l,j) = PP(j,i);
@ -407,11 +407,11 @@ else
hh_fig=dyn_figure(options_.nodisplay,'name',[temp_name,' ',int2str(ifig)]);
end
subplot(3,3,i-9*(ifig-1))
h=cumplot(lnprior(ixx(1:nfilt0(i),i)));
h=gsa.cumplot(lnprior(ixx(1:nfilt0(i),i)));
set(h,'color','blue','linewidth',2)
hold on, h=cumplot(lnprior);
hold on, h=gsa.cumplot(lnprior);
set(h,'color','k','linewidth',1)
h=cumplot(lnprior(ixx(nfilt0(i)+1:end,i)));
h=gsa.cumplot(lnprior(ixx(nfilt0(i)+1:end,i)));
set(h,'color','red','linewidth',2)
if options_.TeX
title(vvarvecm_tex{i},'interpreter','latex')
@ -459,11 +459,11 @@ else
hh_fig = dyn_figure(options_.nodisplay,'Name',[temp_name,' ',int2str(ifig)]);
end
subplot(3,3,i-9*(ifig-1))
h=cumplot(likelihood(ixx(1:nfilt0(i),i)));
h=gsa.cumplot(likelihood(ixx(1:nfilt0(i),i)));
set(h,'color','blue','linewidth',2)
hold on, h=cumplot(likelihood);
hold on, h=gsa.cumplot(likelihood);
set(h,'color','k','linewidth',1)
h=cumplot(likelihood(ixx(nfilt0(i)+1:end,i)));
h=gsa.cumplot(likelihood(ixx(nfilt0(i)+1:end,i)));
set(h,'color','red','linewidth',2)
if options_.TeX
title(vvarvecm_tex{i},'interpreter','latex')
@ -514,11 +514,11 @@ else
hh_fig = dyn_figure(options_.nodisplay,'Name',[temp_name,' ',int2str(ifig)]);
end
subplot(3,3,i-9*(ifig-1))
h=cumplot(logpo2(ixx(1:nfilt0(i),i)));
h=gsa.cumplot(logpo2(ixx(1:nfilt0(i),i)));
set(h,'color','blue','linewidth',2)
hold on, h=cumplot(logpo2);
hold on, h=gsa.cumplot(logpo2);
set(h,'color','k','linewidth',1)
h=cumplot(logpo2(ixx(nfilt0(i)+1:end,i)));
h=gsa.cumplot(logpo2(ixx(nfilt0(i)+1:end,i)));
set(h,'color','red','linewidth',2)
if options_.TeX
title(vvarvecm_tex{i},'interpreter','latex')
@ -756,7 +756,7 @@ else
options_mcf.nobeha_title_latex = ['worse fit of ' vvarvecm_tex{iy}];
end
options_mcf.title = ['the fit of ' vvarvecm{iy}];
mcf_analysis(x, ixx(1:nfilt0(iy),iy), ixx(nfilt0(iy)+1:end,iy), options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(x, ixx(1:nfilt0(iy),iy), ixx(nfilt0(iy)+1:end,iy), options_mcf, M_, options_, bayestopt_, estim_params_);
end
for iy = 1:length(vvarvecm)
ipar = find(any(squeeze(PPV(iy,:,:))<alpha));
@ -764,20 +764,20 @@ else
hh_fig = dyn_figure(options_.nodisplay,'name',[temp_name,' observed variable ', vvarvecm{iy}]);
for j=1+5*(ix-1):min(length(ipar),5*ix)
subplot(2,3,j-5*(ix-1))
h0=cumplot(x(:,ipar(j)));
h0=gsa.cumplot(x(:,ipar(j)));
set(h0,'color',[0 0 0])
hold on,
iobs=find(squeeze(PPV(iy,:,ipar(j)))<alpha);
for i = 1:length(vvarvecm)
if any(iobs==i) || i==iy
h0=cumplot(x(ixx(1:nfilt0(i),i),ipar(j)));
h0=gsa.cumplot(x(ixx(1:nfilt0(i),i),ipar(j)));
if ~isoctave
hcmenu = uicontextmenu;
uimenu(hcmenu,'Label',vvarvecm{i});
set(h0,'uicontextmenu',hcmenu)
end
else
h0=cumplot(x(ixx(1:nfilt0(i),i),ipar(j))*NaN);
h0=gsa.cumplot(x(ixx(1:nfilt0(i),i),ipar(j))*NaN);
end
set(h0,'color',a00(i,:),'linewidth',2)
end
@ -829,15 +829,15 @@ else
hh_fig = dyn_figure(options_.nodisplay,'name',[temp_name,' estimated params and shocks ',int2str(ix)]);
for j=1+5*(ix-1):min(size(snam2,1),5*ix)
subplot(2,3,j-5*(ix-1))
h0=cumplot(x(:,nsnam(j)));
h0=gsa.cumplot(x(:,nsnam(j)));
set(h0,'color',[0 0 0])
hold on,
npx=find(SP(nsnam(j),:)==0);
for i = 1:length(vvarvecm)
if any(npx==i)
h0=cumplot(x(ixx(1:nfilt0(i),i),nsnam(j))*NaN);
h0=gsa.cumplot(x(ixx(1:nfilt0(i),i),nsnam(j))*NaN);
else
h0=cumplot(x(ixx(1:nfilt0(i),i),nsnam(j)));
h0=gsa.cumplot(x(ixx(1:nfilt0(i),i),nsnam(j)));
if ~isoctave
hcmenu = uicontextmenu;
uimenu(hcmenu,'Label', vvarvecm{i});

18
matlab/gsa/mcf_analysis.m → matlab/+gsa/monte_carlo_filtering_analysis.m
View File

@ -1,5 +1,5 @@
function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, M_, options_, bayestopt_, estim_params_)
% indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, M_, options_, bayestopt_, estim_params_)
function indmcf = monte_carlo_filtering_analysis(lpmat, ibeha, inobeha, options_mcf, M_, options_, bayestopt_, estim_params_)
% indmcf = monte_carlo_filtering_analysis(lpmat, ibeha, inobeha, options_mcf, M_, options_, bayestopt_, estim_params_)
% Inputs:
% - lpmat [double] Monte Carlo matrix
% - ibeha [integer] index of behavioural runs
@ -66,7 +66,7 @@ if isfield(options_mcf,'xparam1')
end
OutputDirectoryName = options_mcf.OutputDirectoryName;
[proba, dproba] = stab_map_1(lpmat, ibeha, inobeha, [],fname_, options_, bayestopt_.name, estim_params_,0);
[proba, dproba] = gsa.stability_mapping_univariate(lpmat, ibeha, inobeha, [],fname_, options_, bayestopt_.name, estim_params_,0);
indmcf=find(proba<pvalue_ks);
[~,jtmp] = sort(proba(indmcf),1,'ascend');
indmcf = indmcf(jtmp);
@ -87,11 +87,11 @@ end
if length(ibeha)>10 && length(inobeha)>10
if options_.TeX
indcorr1 = stab_map_2(lpmat(ibeha,:),alpha2, pvalue_corr, M_, options_, bayestopt_, estim_params_, beha_title, beha_title_latex);
indcorr2 = stab_map_2(lpmat(inobeha,:),alpha2, pvalue_corr, M_, options_, bayestopt_, estim_params_, nobeha_title, nobeha_title_latex);
indcorr1 = gsa.stability_mapping_bivariate(lpmat(ibeha,:),alpha2, pvalue_corr, M_, options_, bayestopt_, estim_params_, beha_title, beha_title_latex);
indcorr2 = gsa.stability_mapping_bivariate(lpmat(inobeha,:),alpha2, pvalue_corr, M_, options_, bayestopt_, estim_params_, nobeha_title, nobeha_title_latex);
else
indcorr1 = stab_map_2(lpmat(ibeha,:),alpha2, pvalue_corr, M_, options_, bayestopt_, estim_params_, beha_title);
indcorr2 = stab_map_2(lpmat(inobeha,:),alpha2, pvalue_corr, M_, options_, bayestopt_, estim_params_, nobeha_title);
indcorr1 = gsa.stability_mapping_bivariate(lpmat(ibeha,:),alpha2, pvalue_corr, M_, options_, bayestopt_, estim_params_, beha_title);
indcorr2 = gsa.stability_mapping_bivariate(lpmat(inobeha,:),alpha2, pvalue_corr, M_, options_, bayestopt_, estim_params_, nobeha_title);
end
indcorr = union(indcorr1(:), indcorr2(:));
indcorr = indcorr(~ismember(indcorr(:),indmcf));
@ -104,11 +104,11 @@ if ~isempty(indmcf) && ~options_.nograph
xx=xparam1(indmcf);
end
if options_.TeX
scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names_tex(indmcf), ...
gsa.scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names_tex(indmcf), ...
'.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,xx, options_, ...
beha_title, nobeha_title, beha_title_latex, nobeha_title_latex)
else
scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names_tex(indmcf), ...
gsa.scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names_tex(indmcf), ...
'.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,xx, options_, ...
beha_title, nobeha_title)
end

10
matlab/gsa/mc_moments.m → matlab/+gsa/monte_carlo_moments.m
View File

@ -1,5 +1,5 @@
function [vdec, cc, ac] = mc_moments(mm, ss, dr, M_, options_, estim_params_)
% [vdec, cc, ac] = mc_moments(mm, ss, dr, M_, options_,estim_params_)
function [vdec, cc, ac] = monte_carlo_moments(mm, ss, dr, M_, options_, estim_params_)
% [vdec, cc, ac] = monte_carlo_moments(mm, ss, dr, M_, options_,estim_params_)
% Conduct Monte Carlo simulation of second moments for GSA
% Inputs:
% - dr [structure] decision rules
@ -32,7 +32,7 @@ function [vdec, cc, ac] = mc_moments(mm, ss, dr, M_, options_, estim_params_)
[~, nc1, nsam] = size(mm);
nobs=length(options_.varobs);
disp('mc_moments: Computing theoretical moments ...')
disp('monte_carlo_moments: Computing theoretical moments ...')
h = dyn_waitbar(0,'Theoretical moments ...');
vdec = zeros(nobs,M_.exo_nbr,nsam);
cc = zeros(nobs,nobs,nsam);
@ -42,9 +42,9 @@ for j=1:nsam
dr.ghx = mm(:, 1:(nc1-M_.exo_nbr),j);
dr.ghu = mm(:, (nc1-M_.exo_nbr+1):end, j);
if ~isempty(ss)
M_=set_shocks_param(M_,estim_params_,ss(j,:));
M_=gsa.set_shocks_param(M_,estim_params_,ss(j,:));
end
[vdec(:,:,j), corr, autocorr] = th_moments(dr,options_,M_);
[vdec(:,:,j), corr, autocorr] = gsa.th_moments(dr,options_,M_);
cc(:,:,j)=triu(corr);
dum=NaN(nobs,nobs*options_.ar);
for i=1:options_.ar

3
matlab/gsa/prior_draw_gsa.m → matlab/+gsa/prior_draw.m
View File

@ -1,4 +1,5 @@
function pdraw = prior_draw_gsa(M_,bayestopt_,options_,estim_params_,init,rdraw)
function pdraw = prior_draw(M_,bayestopt_,options_,estim_params_,init,rdraw)
% pdraw = prior_draw(M_,bayestopt_,options_,estim_params_,init,rdraw)
% Draws from the prior distributions for use with Sensitivity Toolbox for DYNARE
%
% INPUTS

0
matlab/gsa/priorcdf.m → matlab/+gsa/priorcdf.m
View File

40
matlab/gsa/redform_map.m → matlab/+gsa/reduced_form_mapping.m
View File

@ -1,5 +1,5 @@
function redform_map(dirname,options_gsa_,M_,estim_params_,options_,bayestopt_,oo_)
% redform_map(dirname,options_gsa_,M_,estim_params_,options_,bayestopt_,oo_)
function reduced_form_mapping(dirname,options_gsa_,M_,estim_params_,options_,bayestopt_,oo_)
% reduced_form_mapping(dirname,options_gsa_,M_,estim_params_,options_,bayestopt_,oo_)
% Inputs:
% - dirname [string] name of the output directory
% - options_gsa_ [structure] GSA options_
@ -85,7 +85,7 @@ options_mcf.fname_ = M_.fname;
options_mcf.OutputDirectoryName = adir;
if ~exist('T','var')
stab_map_(dirname,options_gsa_,M_,oo_,options_,bayestopt_,estim_params_);
gsa.stability_mapping(dirname,options_gsa_,M_,oo_,options_,bayestopt_,estim_params_);
if pprior
load([dirname,filesep,M_.fname,'_prior'],'T');
else
@ -182,14 +182,14 @@ for j = 1:length(anamendo)
end
if ~options_.nograph
hf=dyn_figure(options_.nodisplay,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs ', namexo]);
hc = cumplot(y0);
hc = gsa.cumplot(y0);
a=axis; delete(hc);
x1val=max(threshold(1),a(1));
x2val=min(threshold(2),a(2));
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceColor', [0.7 0.8 1])
hold all,
hc = cumplot(y0);
hc = gsa.cumplot(y0);
set(hc,'color','k','linewidth',2)
hold off,
if options_.TeX
@ -218,7 +218,7 @@ for j = 1:length(anamendo)
options_mcf.OutputDirectoryName = xdir;
if ~isempty(iy) && ~isempty(iyc)
fprintf(['%4.1f%% of the ',type,' support matches ',atitle0,'\n'],length(iy)/length(y0)*100)
icheck = mcf_analysis(x0, iy, iyc, options_mcf, M_, options_, bayestopt_, estim_params_);
icheck = gsa.monte_carlo_filtering_analysis(x0, iy, iyc, options_mcf, M_, options_, bayestopt_, estim_params_);
lpmat=x0(iy,:);
if nshocks
@ -349,14 +349,14 @@ for j = 1:length(anamendo)
end
if ~options_.nograph
hf=dyn_figure(options_.nodisplay,'name',['Reduced Form Mapping (Monte Carlo Filtering): ',namendo,' vs lagged ', namlagendo]);
hc = cumplot(y0);
hc = gsa.cumplot(y0);
a=axis; delete(hc);
x1val=max(threshold(1),a(1));
x2val=min(threshold(2),a(2));
hp = patch([x1val x2val x2val x1val],a([3 3 4 4]),'b');
set(hp,'FaceColor', [0.7 0.8 1])
hold all,
hc = cumplot(y0);
hc = gsa.cumplot(y0);
set(hc,'color','k','linewidth',2)
hold off
if options_.TeX
@ -387,7 +387,7 @@ for j = 1:length(anamendo)
if ~isempty(iy) && ~isempty(iyc)
fprintf(['%4.1f%% of the ',type,' support matches ',atitle0,'\n'],length(iy)/length(y0)*100)
icheck = mcf_analysis(x0, iy, iyc, options_mcf, M_, options_, bayestopt_, estim_params_);
icheck = gsa.monte_carlo_filtering_analysis(x0, iy, iyc, options_mcf, M_, options_, bayestopt_, estim_params_);
lpmat=x0(iy,:);
if nshocks
@ -476,9 +476,9 @@ end
if isempty(threshold) && ~options_.nograph
hh_fig=dyn_figure(options_.nodisplay,'name','Reduced Form GSA');
if ilog==0
myboxplot(si',[],'.',[],10)
gsa.boxplot(si',[],'.',[],10)
else
myboxplot(silog',[],'.',[],10)
gsa.boxplot(silog',[],'.',[],10)
end
xlabel(' ')
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:np)
@ -513,7 +513,7 @@ if options_map.prior_range
x0(:,j)=(x0(:,j)-pd(j,3))./(pd(j,4)-pd(j,3));
end
else
x0=priorcdf(x0,pshape, pd(:,1), pd(:,2), pd(:,3), pd(:,4));
x0=gsa.priorcdf(x0,pshape, pd(:,1), pd(:,2), pd(:,3), pd(:,4));
end
if ilog
@ -549,7 +549,7 @@ if iload==0
ipred = setdiff(1:nrun,ifit);
if ilog
[~, ~, isig, lam] = log_trans_(y0(iest));
[~, ~, isig, lam] = gsa.log_transform(y0(iest));
y1 = log(y0*isig+lam);
end
if ~options_.nograph
@ -571,9 +571,9 @@ if iload==0
title(options_map.title,'interpreter','none')
subplot(222)
if ilog
hc = cumplot(y1);
hc = gsa.cumplot(y1);
else
hc = cumplot(y0);
hc = gsa.cumplot(y0);
end
set(hc,'color','k','linewidth',2)
title([options_map.title ' CDF'],'interpreter','none')
@ -620,7 +620,7 @@ if iload==0
if nfit<nrun
if ilog
yf = ss_anova_fcast(x0(ipred,:), gsa1);
yf = log_trans_(yf,'',isig,lam)+ss_anova_fcast(x0(ipred,:), gsax);
yf = gsa.log_transform(yf,'',isig,lam)+ss_anova_fcast(x0(ipred,:), gsax);
else
yf = ss_anova_fcast(x0(ipred,:), gsa_);
end
@ -657,7 +657,7 @@ function gsa2 = log2level_map(gsa1, isig, lam)
nest=length(gsa1.y);
np = size(gsa1.x0,2);
gsa2=gsa1;
gsa2.y = log_trans_(gsa1.y,'',isig,lam);
gsa2.y = gsa.log_transform(gsa1.y,'',isig,lam);
gsa2.fit = (exp(gsa1.fit)-lam)*isig;
gsa2.f0 = mean(gsa2.fit);
gsa2.out.SSE = sum((gsa2.fit-gsa2.y).^2);
@ -727,7 +727,7 @@ for jt=1:10
indy{jt}=find( (y0>post_deciles(jt)) & (y0<=post_deciles(jt+1)));
leg{jt}=[int2str(jt) '-dec'];
end
[proba] = stab_map_1(x0, indy{1}, indy{end}, [], fname, options_, parnames, estim_params_,0);
[proba] = gsa.stability_mapping_univariate(x0, indy{1}, indy{end}, [], fname, options_, parnames, estim_params_,0);
indmcf=find(proba<options_mcf.pvalue_ks);
if isempty(indmcf)
[~,jtmp] = sort(proba,1,'ascend');
@ -747,7 +747,7 @@ for jx=1:nbr_par
subplot(nrow,ncol,jx)
hold off
for jt=1:10
h=cumplot(x0(indy{jt},indmcf(jx)));
h=gsa.cumplot(x0(indy{jt},indmcf(jx)));
set(h,'color', cmap(jt,:), 'linewidth', 2)
hold all
end
@ -782,7 +782,7 @@ if nargin<5
end
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([figpath '.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by redform_map.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by reduced_form_mapping.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');

16
matlab/gsa/redform_screen.m → matlab/+gsa/reduced_form_screening.m
View File

@ -1,5 +1,5 @@
function redform_screen(dirname, options_gsa_, estim_params_, M_, dr, options_, bayestopt_)
% redform_screen(dirname, options_gsa_, estim_params_, M_, dr, options_, bayestopt_)
function reduced_form_screening(dirname, options_gsa_, estim_params_, M_, dr, options_, bayestopt_)
% reduced_form_screening(dirname, options_gsa_, estim_params_, M_, dr, options_, bayestopt_)
% Conduct reduced form screening
% Inputs:
% - dirname [string] name of the output directory
@ -72,7 +72,7 @@ for j=1:size(anamendo,1)
namexo_tex = anamexo_tex{jx};
iexo = strmatch(namexo, M_.exo_names, 'exact');
if ~isempty(iexo)
y0=teff(T(iendo,iexo+nspred,:), kn, istable);
y0=gsa.teff(T(iendo,iexo+nspred,:), kn, istable);
if ~isempty(y0)
if mod(iplo,9)==0
ifig = ifig+1;
@ -82,7 +82,7 @@ for j=1:size(anamendo,1)
iplo = iplo+1;
js = js+1;
subplot(3, 3, iplo)
[~, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0, nliv);
[~, SAMorris] = gsa.Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0, nliv);
SAM = squeeze(SAMorris(nshock+1:end,1));
SA(:,js) = SAM./(max(SAM)+eps);
[~, iso] = sort(-SA(:,js));
@ -122,7 +122,7 @@ for j=1:size(anamendo,1)
ilagendo=strmatch(namlagendo, M_.endo_names(dr.order_var(M_.nstatic+1:M_.nstatic+nsok)), 'exact');
if ~isempty(ilagendo)
y0=teff(T(iendo,ilagendo,:),kn,istable);
y0=gsa.teff(T(iendo,ilagendo,:),kn,istable);
if ~isempty(y0)
if mod(iplo,9)==0
ifig=ifig+1;
@ -132,7 +132,7 @@ for j=1:size(anamendo,1)
iplo=iplo+1;
js=js+1;
subplot(3,3,iplo),
[~, SAMorris] = Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
[~, SAMorris] = gsa.Morris_Measure_Groups(np+nshock, [lpmat0 lpmat], y0,nliv);
SAM = squeeze(SAMorris(nshock+1:end,1));
SA(:,js)=SAM./(max(SAM)+eps);
[~, iso] = sort(-SA(:,js));
@ -166,7 +166,7 @@ for j=1:size(anamendo,1)
end
hh_fig=dyn_figure(options_.nodisplay,'Name','Reduced form screening');
myboxplot(SA',[],'.',[],10)
gsa.boxplot(SA',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:np)
set(gca,'xlim',[0.5 np+0.5])
set(gca,'ylim',[0 1])
@ -191,7 +191,7 @@ if nargin<6
end
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([figpath '.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by redform_screen.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by reduced_form_screening.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');

18
matlab/dynare_sensitivity.m → matlab/+gsa/run.m
View File

@ -1,5 +1,5 @@
function x0=dynare_sensitivity(M_,oo_,options_,bayestopt_,estim_params_,options_gsa)
% x0=dynare_sensitivity(M_,oo_,options_,bayestopt_,estim_params_,options_gsa)
function x0=run(M_,oo_,options_,bayestopt_,estim_params_,options_gsa)
% x0=run(M_,oo_,options_,bayestopt_,estim_params_,options_gsa)
% Frontend to the Sensitivity Analysis Toolbox for DYNARE
% Inputs:
% - M_ [structure] Matlab's structure describing the model
@ -306,7 +306,7 @@ if (options_gsa.load_stab || options_gsa.load_rmse || options_gsa.load_redform)
end
if options_gsa.stab && ~options_gsa.ppost
x0 = stab_map_(OutputDirectoryName,options_gsa,M_,oo_,options_,bayestopt_,estim_params_);
x0 = gsa.stability_mapping(OutputDirectoryName,options_gsa,M_,oo_,options_,bayestopt_,estim_params_);
if isempty(x0)
skipline()
disp('Sensitivity computations stopped: no parameter set provided a unique solution')
@ -316,11 +316,11 @@ end
options_.opt_gsa = options_gsa;
if ~isempty(options_gsa.moment_calibration) || ~isempty(options_gsa.irf_calibration)
map_calibration(OutputDirectoryName, M_, options_, oo_, estim_params_,bayestopt_);
gsa.map_calibration(OutputDirectoryName, M_, options_, oo_, estim_params_,bayestopt_);
end
if options_gsa.identification
map_ident_(OutputDirectoryName,options_gsa,M_,oo_,options_,estim_params_,bayestopt_);
gsa.map_identification(OutputDirectoryName,options_gsa,M_,oo_,options_,estim_params_,bayestopt_);
end
if options_gsa.redform && ~isempty(options_gsa.namendo)
@ -346,10 +346,10 @@ if options_gsa.redform && ~isempty(options_gsa.namendo)
save([OutputDirectoryName filesep M_.fname '_mc.mat'],'lpmat','lpmat0','istable','iunstable','iwrong','iindeterm')
options_gsa.load_stab=1;
x0 = stab_map_(OutputDirectoryName,options_gsa,M_,oo_,options_,bayestopt_,estim_params_);
x0 = gsa.stability_mapping(OutputDirectoryName,options_gsa,M_,oo_,options_,bayestopt_,estim_params_);
end
if options_gsa.morris==1
redform_screen(OutputDirectoryName,options_gsa, estim_params_, M_, oo_.dr, options_, bayestopt_);
gsa.reduced_form_screening(OutputDirectoryName,options_gsa, estim_params_, M_, oo_.dr, options_, bayestopt_);
else
% check existence of the SS_ANOVA toolbox
if isempty(options_gsa.threshold_redform) && ~(exist('gsa_sdp','file')==6 || exist('gsa_sdp','file')==2)
@ -360,7 +360,7 @@ if options_gsa.redform && ~isempty(options_gsa.namendo)
fprintf('After obtaining the files, you need to unpack them and set a Matlab Path to those files.\n')
error('SS-ANOVA-R Toolbox missing!')
end
redform_map(OutputDirectoryName,options_gsa,M_,estim_params_,options_,bayestopt_,oo_);
gsa.reduced_form_mapping(OutputDirectoryName,options_gsa,M_,estim_params_,options_,bayestopt_,oo_);
end
end
% RMSE mapping
@ -415,7 +415,7 @@ if options_gsa.rmse
end
end
clear a;
filt_mc_(OutputDirectoryName,options_gsa,dataset_,dataset_info,M_,oo_,options_,bayestopt_,estim_params_);
gsa.monte_carlo_filtering(OutputDirectoryName,options_gsa,dataset_,dataset_info,M_,oo_,options_,bayestopt_,estim_params_);
end
options_.opt_gsa = options_gsa;

4
matlab/gsa/scatter_analysis.m → matlab/+gsa/scatter_analysis.m
View File

@ -50,8 +50,8 @@ if ~options_.nograph
xx=xparam1;
end
if options_.TeX
scatter_plots(lpmat, xdata, param_names_tex, '.', [fname_, '_', amcf_name], OutputDirectoryName, amcf_title, xx, options_)
gsa.scatter_plots(lpmat, xdata, param_names_tex, '.', [fname_, '_', amcf_name], OutputDirectoryName, amcf_title, xx, options_)
else
scatter_plots(lpmat, xdata, param_names, '.', [fname_, '_', amcf_name], OutputDirectoryName, amcf_title, xx, options_)
gsa.scatter_plots(lpmat, xdata, param_names, '.', [fname_, '_', amcf_name], OutputDirectoryName, amcf_title, xx, options_)
end
end

4
matlab/gsa/scatter_mcf.m → matlab/+gsa/scatter_mcf.m
View File

@ -96,10 +96,10 @@ for i = 1:p
for j = 1:p
h = axes('position',[fL(i),fL(p+1-j),ffl,ffl]);
if i==j
h1=cumplot(X(:,j));
h1=gsa.cumplot(X(:,j));
set(h1,'color',[0 0 1],'LineWidth',1.5)
hold on,
h2=cumplot(Y(:,j));
h2=gsa.cumplot(Y(:,j));
set(h2,'color',[1 0 0],'LineWidth',1.5)
if ~isempty(xparam1)
hold on, plot(xparam1([j j]),[0 1],'k--')

2
matlab/gsa/scatter_plots.m → matlab/+gsa/scatter_plots.m
View File

@ -86,7 +86,7 @@ for i = 1:p
for j = 1:p
h = axes('position',[fL(i),fL(p+1-j),ffl,ffl]);
if i==j
h1=cumplot(X(:,j));
h1=gsa.cumplot(X(:,j));
set(h,'Tag','cumplot')
set(h1,'color',[0 0 1],'LineWidth',1.5)
if ~isempty(xparam1)

0
matlab/gsa/set_shocks_param.m → matlab/+gsa/set_shocks_param.m
View File

4
matlab/gsa/gsa_skewness.m → matlab/+gsa/skewness.m
View File

@ -1,5 +1,5 @@
function s=gsa_skewness(y)
% s=gsa_skewness(y)
function s=skewness(y)
% s=skewness(y)
% Compute normalized skewness of y
% Inputs:
% - y [double] input vector

6
matlab/gsa/smirnov.m → matlab/+gsa/smirnov_test.m
View File

@ -1,7 +1,7 @@
function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
function [H,prob,d] = smirnov_test(x1 , x2 , alpha, iflag )
% [H,prob,d] = smirnov_test(x1 , x2 , alpha, iflag )
% Smirnov test for 2 distributions
% [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
% marco.ratto@ec.europa.eu

22
matlab/gsa/stab_map_.m → matlab/+gsa/stability_mapping.m
View File

@ -1,5 +1,5 @@
function x0 = stab_map_(OutputDirectoryName,opt_gsa,M_,oo_,options_,bayestopt_,estim_params_)
% x0 = stab_map_(OutputDirectoryName,opt_gsa,M_,oo_,options_,bayestopt_,estim_params_)
function x0 = stability_mapping(OutputDirectoryName,opt_gsa,M_,oo_,options_,bayestopt_,estim_params_)
% x0 = stability_mapping(OutputDirectoryName,opt_gsa,M_,oo_,options_,bayestopt_,estim_params_)
% Mapping of stability regions in the prior ranges applying
% Monte Carlo filtering techniques.
%
@ -37,7 +37,7 @@ function x0 = stab_map_(OutputDirectoryName,opt_gsa,M_,oo_,options_,bayestopt_,e
% 3) Bivariate plots of significant correlation patterns
% ( abs(corrcoef) > alpha2) under the stable and unacceptable subsets
%
% USES qmc_sequence, stab_map_1, stab_map_2
% USES qmc_sequence, gsa.stability_mapping_univariate, gsa.stability_mapping_bivariate
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
@ -147,7 +147,7 @@ if fload==0 %run new MC
yys=zeros(length(dr_.ys),Nsam);
if opt_gsa.morris == 1
[lpmat] = Sampling_Function_2(nliv, np+nshock, ntra, ones(np+nshock, 1), zeros(np+nshock,1), []);
[lpmat] = gsa.Sampling_Function_2(nliv, np+nshock, ntra, ones(np+nshock, 1), zeros(np+nshock,1), []);
lpmat = lpmat.*(nliv-1)/nliv+1/nliv/2;
Nsam=size(lpmat,1);
lpmat0 = lpmat(:,1:nshock);
@ -167,7 +167,7 @@ if fload==0 %run new MC
end
end
end
prior_draw_gsa(M_,bayestopt_,options_,estim_params_,1); %initialize
gsa.prior_draw(M_,bayestopt_,options_,estim_params_,1); %initialize
if pprior
for j=1:nshock
if opt_gsa.morris~=1
@ -184,7 +184,7 @@ if fload==0 %run new MC
lpmat(:,j)=lpmat(:,j).*(upper_bound-lower_bound)+lower_bound;
end
else
xx=prior_draw_gsa(M_,bayestopt_,options_,estim_params_,0,[lpmat0 lpmat]);
xx=gsa.prior_draw(M_,bayestopt_,options_,estim_params_,0,[lpmat0 lpmat]);
lpmat0=xx(:,1:nshock);
lpmat=xx(:,nshock+1:end);
clear xx;
@ -500,7 +500,7 @@ if ~isempty(iunstable) || ~isempty(iwrong)
options_mcf.nobeha_title_latex = 'NO unique Stable Saddle-Path';
end
options_mcf.title = 'unique solution';
mcf_analysis(lpmat, istable, itmp, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(lpmat, istable, itmp, options_mcf, M_, options_, bayestopt_, estim_params_);
if ~isempty(iindeterm)
itmp = isolve(~ismember(isolve,iindeterm));
@ -513,7 +513,7 @@ if ~isempty(iunstable) || ~isempty(iwrong)
options_mcf.nobeha_title_latex = 'indeterminacy';
end
options_mcf.title = 'indeterminacy';
mcf_analysis(lpmat, itmp, iindeterm, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(lpmat, itmp, iindeterm, options_mcf, M_, options_, bayestopt_, estim_params_);
end
if ~isempty(ixun)
@ -527,7 +527,7 @@ if ~isempty(iunstable) || ~isempty(iwrong)
options_mcf.nobeha_title_latex = 'explosive solution';
end
options_mcf.title = 'instability';
mcf_analysis(lpmat, itmp, ixun, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(lpmat, itmp, ixun, options_mcf, M_, options_, bayestopt_, estim_params_);
end
inorestriction = istable(~ismember(istable,irestriction)); % violation of prior restrictions
@ -543,7 +543,7 @@ if ~isempty(iunstable) || ~isempty(iwrong)
options_mcf.nobeha_title_latex = 'inability to find a solution';
end
options_mcf.title = 'inability to find a solution';
mcf_analysis(lpmat, itmp, iwrong, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(lpmat, itmp, iwrong, options_mcf, M_, options_, bayestopt_, estim_params_);
end
if ~isempty(irestriction)
@ -576,7 +576,7 @@ if ~isempty(iunstable) || ~isempty(iwrong)
options_mcf.nobeha_title_latex = 'NO prior IRF/moment calibration';
end
options_mcf.title = 'prior restrictions';
mcf_analysis([lpmat0 lpmat], irestriction, inorestriction, options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis([lpmat0 lpmat], irestriction, inorestriction, options_mcf, M_, options_, bayestopt_, estim_params_);
iok = irestriction(1);
x0 = [lpmat0(iok,:)'; lpmat(iok,:)'];
else

4
matlab/gsa/stab_map_2.m → matlab/+gsa/stability_mapping_bivariate.m
View File

@ -1,5 +1,5 @@
function indcorr = stab_map_2(x,alpha2, pvalue_crit, M_,options_,bayestopt_,estim_params_, case_name_plain, case_name_latex, dirname,xparam1,figtitle,fig_caption_latex)
% indcorr = stab_map_2(x,alpha2, pvalue_crit, M_,options_,bayestopt_,estim_params_, fnam, fnam_latex, dirname,xparam1,figtitle,fig_caption_latex)
function indcorr = stability_mapping_bivariate(x,alpha2, pvalue_crit, M_,options_,bayestopt_,estim_params_, case_name_plain, case_name_latex, dirname,xparam1,figtitle,fig_caption_latex)
% indcorr = stability_mapping_bivariate(x,alpha2, pvalue_crit, M_,options_,bayestopt_,estim_params_, fnam, fnam_latex, dirname,xparam1,figtitle,fig_caption_latex)
% Inputs:
% - x
% - alpha2

16
matlab/gsa/stab_map_1.m → matlab/+gsa/stability_mapping_univariate.m
View File

@ -1,5 +1,5 @@
function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, fname_, options_, parnames, estim_params_, iplot, ipar, dirname, pcrit, atitle)
% [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, fname_, options_, parnames, estim_params_, iplot, ipar, dirname, pcrit, atitle)
function [proba, dproba] = stability_mapping_univariate(lpmat, ibehaviour, inonbehaviour, aname, fname_, options_, parnames, estim_params_, iplot, ipar, dirname, pcrit, atitle)
% [proba, dproba] = stability_mapping_univariate(lpmat, ibehaviour, inonbehaviour, aname, fname_, options_, parnames, estim_params_, iplot, ipar, dirname, pcrit, atitle)
% Inputs:
% - lpmat [double] Monte Carlo matrix
% - ibehaviour [integer] index of behavioural runs
@ -18,7 +18,7 @@ function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, f
%
% Plots: dotted lines for BEHAVIOURAL
% solid lines for NON BEHAVIOURAL
% USES smirnov
% USES gsa.smirnov_test.m
%
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
@ -71,7 +71,7 @@ end
proba=NaN(npar,1);
dproba=NaN(npar,1);
for j=1:npar
[~,P,KSSTAT] = smirnov(lpmat(ibehaviour,j),lpmat(inonbehaviour,j));
[~,P,KSSTAT] = gsa.smirnov_test(lpmat(ibehaviour,j),lpmat(inonbehaviour,j));
proba(j)=P;
dproba(j)=KSSTAT;
end
@ -88,12 +88,12 @@ if iplot && ~options_.nograph
for j=1+12*(i-1):min(nparplot,12*i)
subplot(3,4,j-12*(i-1))
if ~isempty(ibehaviour)
h=cumplot(lpmat(ibehaviour,j));
h=gsa.cumplot(lpmat(ibehaviour,j));
set(h,'color',[0 0 1], 'linestyle',':','LineWidth',1.5)
end
hold on
if ~isempty(inonbehaviour)
h=cumplot(lpmat(inonbehaviour,j));
h=gsa.cumplot(lpmat(inonbehaviour,j));
set(h,'color',[0 0 0],'LineWidth',1.5)
end
title([ftit{j},'. p-value ', num2str(proba(ipar(j)),2)],'interpreter','none')
@ -102,7 +102,7 @@ if iplot && ~options_.nograph
dyn_saveas(hh_fig,[dirname,filesep,fname_,'_',aname,'_SA_',int2str(i)],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([dirname,filesep,fname_,'_',aname,'_SA_',int2str(i) '.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by stab_map_1.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by gsa.stability_mapping_univariate.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
@ -117,7 +117,7 @@ if iplot && ~options_.nograph
dyn_saveas(hh_fig,[dirname,filesep,fname_,'_',aname,'_SA'],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([dirname,filesep,fname_,'_',aname,'_SA.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by stab_map_1.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by gsa.stability_mapping_univariate.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');

4
matlab/gsa/stand_.m → matlab/+gsa/standardize_columns.m
View File

@ -1,5 +1,5 @@
function [y, meany, stdy] = stand_(x)
% [y, meany, stdy] = stand_(x)
function [y, meany, stdy] = standardize_columns(x)
% [y, meany, stdy] = standardize_columns(x)
% Standardise a matrix by columns
%
% [x,my,sy]=stand(y)

0
matlab/gsa/tcrit.m → matlab/+gsa/tcrit.m
View File

0
matlab/gsa/teff.m → matlab/+gsa/teff.m
View File

0
matlab/gsa/th_moments.m → matlab/+gsa/th_moments.m
View File

62
matlab/identification_analysis.m → matlab/+identification/analysis.m
View File

@ -1,5 +1,5 @@
function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide_dynamic, derivatives_info, info, error_indicator] = identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, init)
% [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide_dynamic, derivatives_info, info, error_indicator] = identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, init)
function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide_dynamic, derivatives_info, info, error_indicator] = analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, init)
% [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide_dynamic, derivatives_info, info, error_indicator] = analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, init)
% -------------------------------------------------------------------------
% This function wraps all identification analysis, i.e. it
% (1) wraps functions for the theoretical identification analysis based on moments (Iskrev, 2010),
@ -58,18 +58,18 @@ function [ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide
% indicator on problems
% -------------------------------------------------------------------------
% This function is called by
% * dynare_identification.m
% * identification.run
% -------------------------------------------------------------------------
% This function calls
% * [M_.fname,'.dynamic']
% * dseries
% * dsge_likelihood.m
% * dyn_vech
% * ident_bruteforce
% * identification_checks
% * identification_checks_via_subsets
% * identification.bruteforce
% * identification.checks
% * identification.checks_via_subsets
% * isoctave
% * get_identification_jacobians (previously getJJ)
% * identification.get_jacobians (previously getJJ)
% * matlab_ver_less_than
% * prior_bounds
% * resol
@ -120,7 +120,7 @@ if ~isempty(estim_params_)
M_ = set_all_parameters(params,estim_params_,M_);
end
%get options (see dynare_identification.m for description of options)
%get options (see identification.run.m for description of options)
nlags = options_ident.ar;
advanced = options_ident.advanced;
replic = options_ident.replic;
@ -142,7 +142,7 @@ error_indicator.identification_spectrum=0;
if info(1) == 0 %no errors in solution
% Compute parameter Jacobians for identification analysis
[~, ~, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dSPECTRUM_NO_MEAN, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params_, M_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
[~, ~, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dSPECTRUM_NO_MEAN, dMINIMAL, derivatives_info] = identification.get_jacobians(estim_params_, M_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if isempty(dMINIMAL)
% Komunjer and Ng is not computed if (1) minimality conditions are not fullfilled or (2) there are more shocks and measurement errors than observables, so we need to reset options
error_indicator.identification_minimal = 1;
@ -206,7 +206,7 @@ if info(1) == 0 %no errors in solution
options_ident_local.no_identification_spectrum = 1; %do not recompute dSPECTRUM
options_ident_local.ar = nlags; %store new lag number
options_.ar = nlags; %store new lag number
[~, ~, ~, ~, ~, ~, MOMENTS, dMOMENTS, ~, ~, ~, ~] = get_identification_jacobians(estim_params_, M_, options_, options_ident_local, indpmodel, indpstderr, indpcorr, indvobs, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
[~, ~, ~, ~, ~, ~, MOMENTS, dMOMENTS, ~, ~, ~, ~] = identification.get_jacobians(estim_params_, M_, options_, options_ident_local, indpmodel, indpstderr, indpcorr, indvobs, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
ind_dMOMENTS = (find(max(abs(dMOMENTS'),[],1) > tol_deriv)); %new index with non-zero rows
end
@ -305,7 +305,7 @@ if info(1) == 0 %no errors in solution
options_.analytic_derivation = analytic_derivation; %reset option
AHess = -AHess; %take negative of hessian
if min(eig(AHess))<-tol_rank
error('identification_analysis: Analytic Hessian is not positive semi-definite!')
error('identification.analysis: Analytic Hessian is not positive semi-definite!')
end
ide_hess.AHess = AHess; %store asymptotic Hessian
%normalize asymptotic hessian
@ -313,9 +313,9 @@ if info(1) == 0 %no errors in solution
iflag = any((deltaM.*deltaM)==0); %check if all second-order derivatives wrt to a single parameter are nonzero
tildaM = AHess./((deltaM)*(deltaM')); %this normalization is for numerical purposes
if iflag || rank(AHess)>rank(tildaM)
[ide_hess.cond, ide_hess.rank, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification_checks(AHess, 0, tol_rank, tol_sv, totparam_nbr);
[ide_hess.cond, ide_hess.rank, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification.checks(AHess, 0, tol_rank, tol_sv, totparam_nbr);
else %use normalized version if possible
[ide_hess.cond, ide_hess.rank, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification_checks(tildaM, 0, tol_rank, tol_sv, totparam_nbr);
[ide_hess.cond, ide_hess.rank, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification.checks(tildaM, 0, tol_rank, tol_sv, totparam_nbr);
end
indok = find(max(ide_hess.indno,[],1)==0);
ide_uncert_unnormaliz(indok) = sqrt(diag(inv(AHess(indok,indok))))';
@ -325,7 +325,7 @@ if info(1) == 0 %no errors in solution
diag_chh = sum(si_dREDUCEDFORM(:,ind1)'.*temp1)';
ind1 = ind1(ind1>stderrparam_nbr+corrparam_nbr);
cdynamic = si_dDYNAMIC(:,ind1-stderrparam_nbr-corrparam_nbr)*((AHess(ind1,ind1))\si_dDYNAMIC(:,ind1-stderrparam_nbr-corrparam_nbr)');
flag_score = 1; %this is used for the title in plot_identification.m
flag_score = 1; %this is used for the title in identification.plot.m
catch
%Asymptotic Hessian via simulation
if options_.order > 1
@ -336,7 +336,7 @@ if info(1) == 0 %no errors in solution
options_.periods = periods+100;
end
replic = max([replic, length(ind_dMOMENTS)*3]);
cmm = simulated_moment_uncertainty(ind_dMOMENTS, periods, replic,options_,M_,oo_); %covariance matrix of moments
cmm = identification.simulated_moment_uncertainty(ind_dMOMENTS, periods, replic,options_,M_,oo_); %covariance matrix of moments
sd = sqrt(diag(cmm));
cc = cmm./(sd*sd');
[VV,DD,WW] = eig(cc);
@ -350,9 +350,9 @@ if info(1) == 0 %no errors in solution
iflag = any((deltaM.*deltaM)==0);
tildaM = MIM./((deltaM)*(deltaM'));
if iflag || rank(MIM)>rank(tildaM)
[ide_hess.cond, ide_hess.rank, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification_checks(MIM, 0, tol_rank, tol_sv, totparam_nbr);
[ide_hess.cond, ide_hess.rank, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification.checks(MIM, 0, tol_rank, tol_sv, totparam_nbr);
else %use normalized version if possible
[ide_hess.cond, ide_hess.rank, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification_checks(tildaM, 0, tol_rank, tol_sv, totparam_nbr);
[ide_hess.cond, ide_hess.rank, ide_hess.ind0, ide_hess.indno, ide_hess.ino, ide_hess.Mco, ide_hess.Pco] = identification.checks(tildaM, 0, tol_rank, tol_sv, totparam_nbr);
end
indok = find(max(ide_hess.indno,[],1)==0);
ind1 = find(ide_hess.ind0);
@ -363,7 +363,7 @@ if info(1) == 0 %no errors in solution
if ~isempty(indok)
ide_uncert_unnormaliz(indok) = (sqrt(diag(inv(tildaM(indok,indok))))./deltaM(indok))'; %sqrt(diag(inv(MIM(indok,indok))))';
end
flag_score = 0; %this is used for the title in plot_identification.m
flag_score = 0; %this is used for the title in identification.plot.m
end % end of computing sample information matrix for identification strength measure
ide_strength_dMOMENTS(indok) = (1./(ide_uncert_unnormaliz(indok)'./abs(params(indok)'))); %this is s_i in Ratto and Iskrev (2011, p.13)
@ -375,7 +375,7 @@ if info(1) == 0 %no errors in solution
if size(quant,1)==1
si_dMOMENTSnorm = abs(quant).*normaliz_prior_std;
else
si_dMOMENTSnorm = vnorm(quant).*normaliz_prior_std;
si_dMOMENTSnorm = identification.vnorm(quant).*normaliz_prior_std;
end
iy = find(diag_chh);
ind_dREDUCEDFORM = ind_dREDUCEDFORM(iy);
@ -385,7 +385,7 @@ if info(1) == 0 %no errors in solution
if size(quant,1)==1
si_dREDUCEDFORMnorm = abs(quant).*normaliz_prior_std;
else
si_dREDUCEDFORMnorm = vnorm(quant).*normaliz_prior_std;
si_dREDUCEDFORMnorm = identification.vnorm(quant).*normaliz_prior_std;
end
else
si_dREDUCEDFORMnorm = [];
@ -399,7 +399,7 @@ if info(1) == 0 %no errors in solution
if size(quant,1)==1
si_dDYNAMICnorm = abs(quant).*normaliz_prior_std(stderrparam_nbr+corrparam_nbr+1:end);
else
si_dDYNAMICnorm = vnorm(quant).*normaliz_prior_std(stderrparam_nbr+corrparam_nbr+1:end);
si_dDYNAMICnorm = identification.vnorm(quant).*normaliz_prior_std(stderrparam_nbr+corrparam_nbr+1:end);
end
else
si_dDYNAMICnorm=[];
@ -465,11 +465,11 @@ if info(1) == 0 %no errors in solution
ide_moments.MOMENTS = MOMENTS;
if advanced
% here we do not normalize (i.e. we set norm_dMOMENTS=1) as the OLS in ident_bruteforce is very sensitive to norm_dMOMENTS
[ide_moments.pars, ide_moments.cosndMOMENTS] = ident_bruteforce(M_.dname,M_.fname,dMOMENTS(ind_dMOMENTS,:), max_dim_cova_group, options_.TeX, options_ident.name_tex, options_ident.tittxt, tol_deriv);
% here we do not normalize (i.e. we set norm_dMOMENTS=1) as the OLS in identification.bruteforce is very sensitive to norm_dMOMENTS
[ide_moments.pars, ide_moments.cosndMOMENTS] = identification.bruteforce(M_.dname,M_.fname,dMOMENTS(ind_dMOMENTS,:), max_dim_cova_group, options_.TeX, options_ident.name_tex, options_ident.tittxt, tol_deriv);
end
%here we focus on the unnormalized S and V, which is then used in plot_identification.m and for prior_mc > 1
%here we focus on the unnormalized S and V, which is then used in identification.plot.m and for prior_mc > 1
[~, S, V] = svd(dMOMENTS(ind_dMOMENTS,:),0);
if size(S,1) == 1
S = S(1); % edge case that S is not a matrix but a row vector
@ -522,9 +522,9 @@ if info(1) == 0 %no errors in solution
%% Perform identification checks, i.e. find out which parameters are involved
if checks_via_subsets
% identification_checks_via_subsets is only for debugging
% identification.checks_via_subsets is only for debugging
[ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal] = ...
identification_checks_via_subsets(ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, totparam_nbr, modparam_nbr, options_ident, error_indicator);
identification.checks_via_subsets(ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, totparam_nbr, modparam_nbr, options_ident, error_indicator);
if ~error_indicator.identification_minimal
ide_minimal.minimal_state_space=1;
else
@ -532,19 +532,19 @@ if info(1) == 0 %no errors in solution
end
else
[ide_dynamic.cond, ide_dynamic.rank, ide_dynamic.ind0, ide_dynamic.indno, ide_dynamic.ino, ide_dynamic.Mco, ide_dynamic.Pco, ide_dynamic.jweak, ide_dynamic.jweak_pair] = ...
identification_checks(dDYNAMIC(ind_dDYNAMIC,:)./norm_dDYNAMIC, 1, tol_rank, tol_sv, modparam_nbr);
identification.checks(dDYNAMIC(ind_dDYNAMIC,:)./norm_dDYNAMIC, 1, tol_rank, tol_sv, modparam_nbr);
if ~options_ident.no_identification_reducedform && ~error_indicator.identification_reducedform
[ide_reducedform.cond, ide_reducedform.rank, ide_reducedform.ind0, ide_reducedform.indno, ide_reducedform.ino, ide_reducedform.Mco, ide_reducedform.Pco, ide_reducedform.jweak, ide_reducedform.jweak_pair] = ...
identification_checks(dREDUCEDFORM(ind_dREDUCEDFORM,:)./norm_dREDUCEDFORM, 1, tol_rank, tol_sv, totparam_nbr);
identification.checks(dREDUCEDFORM(ind_dREDUCEDFORM,:)./norm_dREDUCEDFORM, 1, tol_rank, tol_sv, totparam_nbr);
end
if ~options_ident.no_identification_moments && ~error_indicator.identification_moments
[ide_moments.cond, ide_moments.rank, ide_moments.ind0, ide_moments.indno, ide_moments.ino, ide_moments.Mco, ide_moments.Pco, ide_moments.jweak, ide_moments.jweak_pair] = ...
identification_checks(dMOMENTS(ind_dMOMENTS,:)./norm_dMOMENTS, 1, tol_rank, tol_sv, totparam_nbr);
identification.checks(dMOMENTS(ind_dMOMENTS,:)./norm_dMOMENTS, 1, tol_rank, tol_sv, totparam_nbr);
end
if ~options_ident.no_identification_minimal
if ~error_indicator.identification_minimal
[ide_minimal.cond, ide_minimal.rank, ide_minimal.ind0, ide_minimal.indno, ide_minimal.ino, ide_minimal.Mco, ide_minimal.Pco, ide_minimal.jweak, ide_minimal.jweak_pair] = ...
identification_checks(dMINIMAL(ind_dMINIMAL,:)./norm_dMINIMAL, 2, tol_rank, tol_sv, totparam_nbr);
identification.checks(dMINIMAL(ind_dMINIMAL,:)./norm_dMINIMAL, 2, tol_rank, tol_sv, totparam_nbr);
ide_minimal.minimal_state_space=1;
else
ide_minimal.minimal_state_space=0;
@ -552,7 +552,7 @@ if info(1) == 0 %no errors in solution
end
if ~options_ident.no_identification_spectrum && ~error_indicator.identification_spectrum
[ide_spectrum.cond, ide_spectrum.rank, ide_spectrum.ind0, ide_spectrum.indno, ide_spectrum.ino, ide_spectrum.Mco, ide_spectrum.Pco, ide_spectrum.jweak, ide_spectrum.jweak_pair] = ...
identification_checks(tilda_dSPECTRUM, 3, tol_rank, tol_sv, totparam_nbr);
identification.checks(tilda_dSPECTRUM, 3, tol_rank, tol_sv, totparam_nbr);
end
end
end

4
matlab/ident_bruteforce.m → matlab/+identification/bruteforce.m
View File

@ -18,7 +18,7 @@ function [pars, cosnJ] = ident_bruteforce(dname,fname,J, max_dim_cova_group, TeX
% cosnJ : cosn of each column with the selected group of columns
% -------------------------------------------------------------------------
% This function is called by
% * identification_analysis.m
% * identification.analysis.m
% =========================================================================
% Copyright © 2009-2023 Dynare Team
%
@ -67,7 +67,7 @@ for ll = 1:max_dim_cova_group
cosnJ2=zeros(size(tmp2,1),1);
b=[];
for jj = 1:size(tmp2,1)
[cosnJ2(jj,1), b(:,jj)] = cosn([J(:,ii),J(:,tmp2(jj,:))]);
[cosnJ2(jj,1), b(:,jj)] = identification.cosn([J(:,ii),J(:,tmp2(jj,:))]);
end
cosnJ(ii,ll) = max(cosnJ2(:,1));
if cosnJ(ii,ll)>tol_deriv

14
matlab/identification_checks.m → matlab/+identification/checks.m
View File

@ -1,5 +1,5 @@
function [condX, rankX, ind0, indno, ixno, Mco, Pco, jweak, jweak_pair] = identification_checks(X, test_flag, tol_rank, tol_sv, param_nbr)
% function [condX, rankX, ind0, indno, ixno, Mco, Pco, jweak, jweak_pair] = identification_checks(X, test_flag, tol_rank, tol_sv, param_nbr)
function [condX, rankX, ind0, indno, ixno, Mco, Pco, jweak, jweak_pair] = checks(X, test_flag, tol_rank, tol_sv, param_nbr)
% function [condX, rankX, ind0, indno, ixno, Mco, Pco, jweak, jweak_pair] = checks(X, test_flag, tol_rank, tol_sv, param_nbr)
% -------------------------------------------------------------------------
% Checks rank criteria of identification tests and finds out parameter sets
% that are not identifiable via the nullspace, pairwise correlation
@ -24,10 +24,10 @@ function [condX, rankX, ind0, indno, ixno, Mco, Pco, jweak, jweak_pair] = identi
% * jweak_pair [(vech) matrix] gives 1 if a couple parameters has Pco=1 (with tolerance tol_rank)
% -------------------------------------------------------------------------
% This function is called by
% * identification_analysis.m
% * identification.analysis.m
% -------------------------------------------------------------------------
% This function calls
% * cosn
% * identification.cosn
% * dyn_vech
% * vnorm
% =========================================================================
@ -75,7 +75,7 @@ end
% find non-zero columns at machine precision
if size(Xpar,1) > 1
ind1 = find(vnorm(Xpar) >= eps);
ind1 = find(identification.vnorm(Xpar) >= eps);
else
ind1 = find(abs(Xpar) >= eps); % if only one parameter
end
@ -141,7 +141,7 @@ if test_flag == 0 || test_flag == 3 % G is a Gram matrix and hence should be a c
else
Mco = NaN(param_nbr,1);
for ii = 1:size(Xparnonzero,2)
Mco(ind1(ii),:) = cosn([Xparnonzero(:,ii) , Xparnonzero(:,find([1:1:size(Xparnonzero,2)]~=ii)), Xrest]);
Mco(ind1(ii),:) = identification.cosn([Xparnonzero(:,ii) , Xparnonzero(:,find([1:1:size(Xparnonzero,2)]~=ii)), Xrest]);
end
end
@ -176,7 +176,7 @@ if test_flag ~= 0
for ii = 1:size(Xparnonzero,2)
Pco(ind1(ii),ind1(ii)) = 1;
for jj = ii+1:size(Xparnonzero,2)
Pco(ind1(ii),ind1(jj)) = cosn([Xparnonzero(:,ii),Xparnonzero(:,jj),Xrest]);
Pco(ind1(ii),ind1(jj)) = identification.cosn([Xparnonzero(:,ii),Xparnonzero(:,jj),Xrest]);
Pco(ind1(jj),ind1(ii)) = Pco(ind1(ii),ind1(jj));
end
end

8
matlab/identification_checks_via_subsets.m → matlab/+identification/checks_via_subsets.m
View File

@ -1,5 +1,5 @@
function [ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal] = identification_checks_via_subsets(ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, totparam_nbr, modparam_nbr, options_ident,error_indicator)
%[ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal] = identification_checks_via_subsets(ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, totparam_nbr, modparam_nbr, options_ident,error_indicator)
function [ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal] = checks_via_subsets(ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, totparam_nbr, modparam_nbr, options_ident,error_indicator)
%[ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal] = checks_via_subsets(ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, totparam_nbr, modparam_nbr, options_ident,error_indicator)
% -------------------------------------------------------------------------
% Finds problematic sets of paramters via checking the necessary rank condition
% of the Jacobians for all possible combinations of parameters. The rank is
@ -50,7 +50,7 @@ function [ide_dynamic, ide_reducedform, ide_moments, ide_spectrum, ide_minimal]
% * rank: [integer] rank of Jacobian
% -------------------------------------------------------------------------
% This function is called by
% * identification_analysis.m
% * identification.analysis.m
% =========================================================================
% Copyright © 2019-2021 Dynare Team
%
@ -161,7 +161,7 @@ end
% initialize for spectrum criteria
if ~no_identification_spectrum && ~error_indicator.identification_spectrum
dSPECTRUM = ide_spectrum.tilda_dSPECTRUM; %tilda dSPECTRUM is normalized dSPECTRUM matrix in identification_analysis.m
dSPECTRUM = ide_spectrum.tilda_dSPECTRUM; %tilda dSPECTRUM is normalized dSPECTRUM matrix in identification.analysis.m
%alternative normalization
%dSPECTRUM = ide_spectrum.dSPECTRUM;
%dSPECTRUM(ide_spectrum.ind_dSPECTRUM,:) = dSPECTRUM(ide_spectrum.ind_dSPECTRUM,:)./ide_spectrum.norm_dSPECTRUM; %normalize

2
matlab/cosn.m → matlab/+identification/cosn.m
View File

@ -17,7 +17,7 @@ function [co, b, yhat] = cosn(H)
% * y [n by 1] predicted endogenous values given ols estimation
% -------------------------------------------------------------------------
% This function is called by
% * identification_checks.m
% * identification.checks.m
% * ident_bruteforce.m
% =========================================================================
% Copyright © 2008-2019 Dynare Team

8
matlab/disp_identification.m → matlab/+identification/display.m
View File

@ -1,5 +1,5 @@
function disp_identification(pdraws, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, name, options_ident)
% disp_identification(pdraws, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, name, options_ident)
function display(pdraws, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, name, options_ident)
% display(pdraws, ide_reducedform, ide_moments, ide_spectrum, ide_minimal, name, options_ident)
% -------------------------------------------------------------------------
% This function displays all identification analysis to the command line
% =========================================================================
@ -26,7 +26,7 @@ function disp_identification(pdraws, ide_reducedform, ide_moments, ide_spectrum,
% * all output is printed on the command line
% -------------------------------------------------------------------------
% This function is called by
% * dynare_identification.m
% * identification.run
% =========================================================================
% Copyright © 2010-2021 Dynare Team
%
@ -207,7 +207,7 @@ for jide = 1:4
end
end
%% display problematic parameters computed by identification_checks_via_subsets
%% display problematic parameters computed by identification.checks_via_subsets
elseif checks_via_subsets
if ide.rank < size(Jacob,2)
no_warning_message_display = 0;

6
matlab/fjaco.m → matlab/+identification/fjaco.m
View File

@ -30,7 +30,7 @@ function fjac = fjaco(f,x,varargin)
ff=feval(f,x,varargin{:});
tol = eps.^(1/3); %some default value
if strcmp(func2str(f),'get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification_numerical_objective')
if strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective')
tol= varargin{4}.dynatol.x;
end
h = tol.*max(abs(x),1);
@ -40,12 +40,12 @@ fjac = NaN(length(ff),length(x));
for j=1:length(x)
xx = x;
xx(j) = xh1(j); f1=feval(f,xx,varargin{:});
if isempty(f1) && (strcmp(func2str(f),'get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification_numerical_objective') )
if isempty(f1) && (strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective') )
[~,info]=feval(f,xx,varargin{:});
disp_info_error_identification_perturbation(info,j);
end
xx(j) = xh0(j); f0=feval(f,xx,varargin{:});
if isempty(f0) && (strcmp(func2str(f),'get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification_numerical_objective') )
if isempty(f0) && (strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective') )
[~,info]=feval(f,xx,varargin{:});
disp_info_error_identification_perturbation(info,j)
end

22
matlab/get_identification_jacobians.m → matlab/+identification/get_jacobians.m
View File

@ -1,5 +1,5 @@
function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dSPECTRUM_NO_MEAN, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dSPECTRUM_NO_MEAN, dMINIMAL, derivatives_info] = get_identification_jacobians(estim_params, M_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dSPECTRUM_NO_MEAN, dMINIMAL, derivatives_info] = get_jacobians(estim_params, M_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dMOMENTS, dSPECTRUM, dSPECTRUM_NO_MEAN, dMINIMAL, derivatives_info] = get_jacobians(estim_params, M_, options_, options_ident, indpmodel, indpstderr, indpcorr, indvobs, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% previously getJJ.m in Dynare 4.5
% Sets up the Jacobians needed for identification analysis
% =========================================================================
@ -84,7 +84,7 @@ function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dM
%
% -------------------------------------------------------------------------
% This function is called by
% * identification_analysis.m
% * identification.analysis.m
% -------------------------------------------------------------------------
% This function calls
% * commutation
@ -94,7 +94,7 @@ function [MEAN, dMEAN, REDUCEDFORM, dREDUCEDFORM, DYNAMIC, dDYNAMIC, MOMENTS, dM
% * fjaco
% * get_perturbation_params_derivs (previously getH)
% * get_all_parameters
% * identification_numerical_objective (previously thet2tau)
% * identification.numerical_objective (previously thet2tau)
% * pruned_state_space_system
% * vec
% =========================================================================
@ -153,7 +153,7 @@ obs_nbr = length(indvobs);
d2flag = 0; % do not compute second parameter derivatives
% Get Jacobians (wrt selected params) of steady state, dynamic model derivatives and perturbation solution matrices for all endogenous variables
dr.derivs = get_perturbation_params_derivs(M_, options_, estim_params, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indpmodel, indpstderr, indpcorr, d2flag);
dr.derivs = identification.get_perturbation_params_derivs(M_, options_, estim_params, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indpmodel, indpstderr, indpcorr, d2flag);
[I,~] = find(lead_lag_incidence'); %I is used to select nonzero columns of the Jacobian of endogenous variables in dynamic model files
yy0 = dr.ys(I); %steady state of dynamic (endogenous and auxiliary variables) in lead_lag_incidence order
@ -230,7 +230,7 @@ elseif order == 3
end
% Get (pruned) state space representation:
pruned = pruned_state_space_system(M_, options_, dr, indvobs, nlags, useautocorr, 1);
pruned = pruned_SS.pruned_state_space_system(M_, options_, dr, indvobs, nlags, useautocorr, 1);
MEAN = pruned.E_y;
dMEAN = pruned.dE_y;
%storage for Jacobians used in dsge_likelihood.m for analytical Gradient and Hession of likelihood (only at order=1)
@ -258,7 +258,7 @@ if ~no_identification_moments
if kronflag == -1
%numerical derivative of autocovariogram
dMOMENTS = fjaco(str2func('identification_numerical_objective'), xparam1, 1, estim_params, M_, options_, indpmodel, indpstderr, indvobs, useautocorr, nlags, grid_nbr, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); %[outputflag=1]
dMOMENTS = identification.fjaco(str2func('identification.numerical_objective'), xparam1, 1, estim_params, M_, options_, indpmodel, indpstderr, indvobs, useautocorr, nlags, grid_nbr, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); %[outputflag=1]
dMOMENTS = [dMEAN; dMOMENTS]; %add Jacobian of steady state of VAROBS variables
else
dMOMENTS = zeros(obs_nbr + obs_nbr*(obs_nbr+1)/2 + nlags*obs_nbr^2 , totparam_nbr);
@ -315,7 +315,7 @@ if ~no_identification_spectrum
IA = eye(size(pruned.A,1));
if kronflag == -1
%numerical derivative of spectral density
dOmega_tmp = fjaco(str2func('identification_numerical_objective'), xparam1, 2, estim_params, M_, options_, indpmodel, indpstderr, indvobs, useautocorr, nlags, grid_nbr, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); %[outputflag=2]
dOmega_tmp = identification.fjaco(str2func('identification.numerical_objective'), xparam1, 2, estim_params, M_, options_, indpmodel, indpstderr, indvobs, useautocorr, nlags, grid_nbr, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); %[outputflag=2]
kk = 0;
for ig = 1:length(freqs)
kk = kk+1;
@ -333,7 +333,7 @@ if ~no_identification_spectrum
dC = reshape(pruned.dC,size(pruned.dC,1)*size(pruned.dC,2),size(pruned.dC,3));
dD = reshape(pruned.dD,size(pruned.dD,1)*size(pruned.dD,2),size(pruned.dD,3));
dVarinov = reshape(pruned.dVarinov,size(pruned.dVarinov,1)*size(pruned.dVarinov,2),size(pruned.dVarinov,3));
K_obs_exo = commutation(obs_nbr,size(pruned.Varinov,1));
K_obs_exo = pruned_SS.commutation(obs_nbr,size(pruned.Varinov,1));
for ig=1:length(freqs)
z = tneg(ig);
zIminusA = (z*IA - pruned.A);
@ -400,7 +400,7 @@ if ~no_identification_minimal
SYS.dC = dr.derivs.dghx(pruned.indy,:,:);
SYS.D = dr.ghu(pruned.indy,:);
SYS.dD = dr.derivs.dghu(pruned.indy,:,:);
[CheckCO,minnx,SYS] = get_minimal_state_representation(SYS,1);
[CheckCO,minnx,SYS] = identification.get_minimal_state_representation(SYS,1);
if CheckCO == 0
warning_KomunjerNg = 'WARNING: Komunjer and Ng (2011) failed:\n';
@ -423,7 +423,7 @@ if ~no_identification_minimal
dvechSig = dvechSig(indvechSig,:);
Inx = eye(minnx);
Inu = eye(exo_nbr);
[~,Enu] = duplication(exo_nbr);
[~,Enu] = pruned_SS.duplication(exo_nbr);
KomunjerNg_DL = [dminA; dminB; dminC; dminD; dvechSig];
KomunjerNg_DT = [kron(transpose(minA),Inx) - kron(Inx,minA);
kron(transpose(minB),Inx);

2
matlab/get_minimal_state_representation.m → matlab/+identification/get_minimal_state_representation.m
View File

@ -53,7 +53,7 @@ function [CheckCO,minns,minSYS] = get_minimal_state_representation(SYS, derivs_f
% Jacobian (wrt to all parameters) of measurement matrix minD
% -------------------------------------------------------------------------
% This function is called by
% * get_identification_jacobians.m (previously getJJ.m)
% * identification.get_jacobians.m (previously getJJ.m)
% -------------------------------------------------------------------------
% This function calls
% * check_minimality (embedded)

96
matlab/get_perturbation_params_derivs.m → matlab/+identification/get_perturbation_params_derivs.m
View File

@ -88,7 +88,7 @@ function DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr
% -------------------------------------------------------------------------
% This function is called by
% * dsge_likelihood.m
% * get_identification_jacobians.m
% * identification.get_jacobians.m
% -------------------------------------------------------------------------
% This function calls
% * [fname,'.dynamic']
@ -191,7 +191,7 @@ if order > 1 && analytic_derivation_mode == 1
analytic_derivation_mode = 0; fprintf('As order > 1, reset ''analytic_derivation_mode'' to 0\n');
end
numerical_objective_fname = str2func('get_perturbation_params_derivs_numerical_objective');
numerical_objective_fname = str2func('identification.get_perturbation_params_derivs_numerical_objective');
idx_states = nstatic+(1:nspred); %index for state variables, in DR order
modparam_nbr = length(indpmodel); %number of selected model parameters
stderrparam_nbr = length(indpstderr); %number of selected stderr parameters
@ -295,7 +295,7 @@ if analytic_derivation_mode == -1
% - perturbation solution matrices: dghx, dghu, dghxx, dghxu, dghuu, dghs2, dghxxx, dghxxu, dghxuu, dghuuu, dghxss, dghuss
%Parameter Jacobian of covariance matrix and solution matrices (wrt selected stderr, corr and model paramters)
dSig_gh = fjaco(numerical_objective_fname, xparam1, 'perturbation_solution', estim_params_, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
dSig_gh = identification.fjaco(numerical_objective_fname, xparam1, 'perturbation_solution', estim_params_, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
ind_Sigma_e = (1:exo_nbr^2);
ind_ghx = ind_Sigma_e(end) + (1:endo_nbr*nspred);
ind_ghu = ind_ghx(end) + (1:endo_nbr*exo_nbr);
@ -348,7 +348,7 @@ if analytic_derivation_mode == -1
end
%Parameter Jacobian of dynamic model derivatives (wrt selected model parameters only)
dYss_g = fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
dYss_g = identification.fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
ind_Yss = 1:endo_nbr;
if options_.discretionary_policy || options_.ramsey_policy
ind_g1 = ind_Yss(end) + (1:M_.eq_nbr*yy0ex0_nbr);
@ -374,7 +374,7 @@ if analytic_derivation_mode == -1
% Hessian (wrt paramters) of steady state and first-order solution matrices ghx and Om
% note that hessian_sparse.m (contrary to hessian.m) does not take symmetry into account, but focuses already on unique values
options_.order = 1; %make sure only first order
d2Yss_KalmanA_Om = hessian_sparse(numerical_objective_fname, xparam1, gstep, 'Kalman_Transition', estim_params_, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
d2Yss_KalmanA_Om = identification.hessian_sparse(numerical_objective_fname, xparam1, gstep, 'Kalman_Transition', estim_params_, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
options_.order = order; %make sure to set back
ind_KalmanA = ind_Yss(end) + (1:endo_nbr^2);
DERIVS.d2KalmanA = d2Yss_KalmanA_Om(ind_KalmanA, indp2tottot2); %only unique elements
@ -394,7 +394,7 @@ if analytic_derivation_mode == -2
% The parameter derivatives of perturbation solution matrices are computed analytically below (analytic_derivation_mode=0)
if order == 3
[~, g1, g2, g3] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1);
g3 = unfold_g3(g3, yy0ex0_nbr);
g3 = identification.unfold_g3(g3, yy0ex0_nbr);
elseif order == 2
[~, g1, g2] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1);
elseif order == 1
@ -405,7 +405,7 @@ if analytic_derivation_mode == -2
% computation of d2Yss and d2g1
% note that hessian_sparse does not take symmetry into account, i.e. compare hessian_sparse.m to hessian.m, but focuses already on unique values, which are duplicated below
options_.order = 1; %d2flag requires only first order
d2Yss_g1 = hessian_sparse(numerical_objective_fname, modparam1, gstep, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); % d2flag requires only first-order
d2Yss_g1 = identification.hessian_sparse(numerical_objective_fname, modparam1, gstep, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); % d2flag requires only first-order
options_.order = order; %make sure to set back the order
d2Yss = reshape(full(d2Yss_g1(1:endo_nbr,:)), [endo_nbr modparam_nbr modparam_nbr]); %put into tensor notation
for j=1:endo_nbr
@ -431,7 +431,7 @@ if analytic_derivation_mode == -2
end
%Parameter Jacobian of dynamic model derivatives (wrt selected model parameters only)
dYss_g = fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
dYss_g = identification.fjaco(numerical_objective_fname, modparam1, 'dynamic_model', estim_params_model, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
ind_Yss = 1:endo_nbr;
ind_g1 = ind_Yss(end) + (1:endo_nbr*yy0ex0_nbr);
dYss = dYss_g(ind_Yss,:); %in tensor notation, wrt selected model parameters only
@ -447,20 +447,22 @@ if analytic_derivation_mode == -2
clear dYss_g
elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
%% Analytical computation of Jacobian and Hessian (wrt selected model parameters) of steady state, i.e. dYss and d2Yss
[~, g1_static] = feval([fname,'.static'], ys, exo_steady_state', params); %g1_static is [endo_nbr by endo_nbr] first-derivative (wrt all endogenous variables) of static model equations f, i.e. df/dys, in declaration order
try
rp_static = feval([fname,'.static_params_derivs'], ys, exo_steady_state', params); %rp_static is [endo_nbr by param_nbr] first-derivative (wrt all model parameters) of static model equations f, i.e. df/dparams, in declaration order
catch
if ~exist(['+' fname filesep 'static_params_derivs.m'],'file')
error('For analytical parameter derivatives ''static_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
end
if ~exist(['+' fname filesep 'dynamic_params_derivs.m'],'file')
error('For analytical parameter derivatives ''dynamic_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
end
%% Analytical computation of Jacobian and Hessian (wrt selected model parameters) of steady state, i.e. dYss and d2Yss
[~, g1_static] = feval([fname,'.static'], ys, exo_steady_state', params); %g1_static is [endo_nbr by endo_nbr] first-derivative (wrt all endogenous variables) of static model equations f, i.e. df/dys, in declaration order
rp_static = feval([fname,'.static_params_derivs'], ys, exo_steady_state', params); %rp_static is [endo_nbr by param_nbr] first-derivative (wrt all model parameters) of static model equations f, i.e. df/dparams, in declaration order
dys = -g1_static\rp_static; %use implicit function theorem (equation 5 of Ratto and Iskrev (2012) to compute [endo_nbr by param_nbr] first-derivative (wrt all model parameters) of steady state for all endogenous variables analytically, note that dys is in declaration order
d2ys = zeros(endo_nbr, param_nbr, param_nbr); %initialize in tensor notation, note that d2ys is only needed for d2flag, i.e. for g1pp
if d2flag
[~, ~, g2_static] = feval([fname,'.static'], ys, exo_steady_state', params); %g2_static is [endo_nbr by endo_nbr^2] second derivative (wrt all endogenous variables) of static model equations f, i.e. d(df/dys)/dys, in declaration order
if order < 3
[~, g1, g2, g3] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1); %note that g3 does not contain symmetric elements
g3 = unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
else
T = NaN(sum(dynamic_tmp_nbr(1:5)));
T = feval([fname, '.dynamic_g4_tt'], T, ys(I), exo_steady_state', params, ys, 1);
@ -468,20 +470,16 @@ elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
g2 = feval([fname, '.dynamic_g2'], T, ys(I), exo_steady_state', params, ys, 1, false); %g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g3 = feval([fname, '.dynamic_g3'], T, ys(I), exo_steady_state', params, ys, 1, false); %note that g3 does not contain symmetric elements
g4 = feval([fname, '.dynamic_g4'], T, ys(I), exo_steady_state', params, ys, 1, false); %note that g4 does not contain symmetric elements
g3 = unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3, %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g4 = unfold_g4(g4, yy0ex0_nbr); %add symmetric elements to g4, %g4 is [endo_nbr by yy0ex0_nbr^4] fourth-derivative (wrt all dynamic variables) of dynamic model equations, i.e. ((d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3, %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g4 = identification.unfold_g4(g4, yy0ex0_nbr); %add symmetric elements to g4, %g4 is [endo_nbr by yy0ex0_nbr^4] fourth-derivative (wrt all dynamic variables) of dynamic model equations, i.e. ((d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
end
%g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
%g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
%g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
try
[~, g1p_static, rpp_static] = feval([fname,'.static_params_derivs'], ys, exo_steady_state', params);
%g1p_static is [endo_nbr by endo_nbr by param_nbr] first derivative (wrt all model parameters) of first-derivative (wrt all endogenous variables) of static model equations f, i.e. (df/dys)/dparams, in declaration order
%rpp_static is [#second_order_residual_terms by 4] and contains nonzero values and corresponding indices of second derivatives (wrt all model parameters) of static model equations f, i.e. d(df/dparams)/dparams, in declaration order, where
% column 1 contains equation number; column 2 contains first parameter; column 3 contains second parameter; column 4 contains value of derivative
catch
error('For analytical parameter derivatives ''static_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
end
[~, g1p_static, rpp_static] = feval([fname,'.static_params_derivs'], ys, exo_steady_state', params);
%g1p_static is [endo_nbr by endo_nbr by param_nbr] first derivative (wrt all model parameters) of first-derivative (wrt all endogenous variables) of static model equations f, i.e. (df/dys)/dparams, in declaration order
%rpp_static is [#second_order_residual_terms by 4] and contains nonzero values and corresponding indices of second derivatives (wrt all model parameters) of static model equations f, i.e. d(df/dparams)/dparams, in declaration order, where
% column 1 contains equation number; column 2 contains first parameter; column 3 contains second parameter; column 4 contains value of derivative
rpp_static = get_all_resid_2nd_derivs(rpp_static, endo_nbr, param_nbr); %make full matrix out of nonzero values and corresponding indices
%rpp_static is [endo_nbr by param_nbr by param_nbr] second derivatives (wrt all model parameters) of static model equations, i.e. d(df/dparams)/dparams, in declaration order
if isempty(find(g2_static))
@ -525,58 +523,42 @@ elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
end
if d2flag
try
if order < 3
[~, g1p, ~, g1pp, g2p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
else
[~, g1p, ~, g1pp, g2p, g3p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
end
catch
error('For analytical parameter derivatives ''dynamic_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
if order < 3
[~, g1p, ~, g1pp, g2p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
else
[~, g1p, ~, g1pp, g2p, g3p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
end
%g1pp are nonzero values and corresponding indices of second-derivatives (wrt all model parameters) of first-derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(d(df/dyy0ex0)/dparam)/dparam, rows are in declaration order, first column in declaration order
d2Yss = d2ys(order_var,indpmodel,indpmodel); %[endo_nbr by mod_param_nbr by mod_param_nbr], put into DR order and focus only on selected model parameters
else
if order == 1
try
[~, g1p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
%g1p is [endo_nbr by yy0ex0_nbr by param_nbr] first-derivative (wrt all model parameters) of first-derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dparam, rows are in declaration order, column in lead_lag_incidence order
catch
error('For analytical parameter derivatives ''dynamic_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
end
[~, g1p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
%g1p is [endo_nbr by yy0ex0_nbr by param_nbr] first-derivative (wrt all model parameters) of first-derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dparam, rows are in declaration order, column in lead_lag_incidence order
[~, g1, g2 ] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1);
%g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
%g2 is [endo_nbr by yy0ex0_nbr^2] second derivatives (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
elseif order == 2
try
[~, g1p, ~, ~, g2p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
%g1p is [endo_nbr by yy0ex0_nbr by param_nbr] first-derivative (wrt all model parameters) of first-derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dparam, rows are in declaration order, column in lead_lag_incidence order
%g2p are nonzero values and corresponding indices of first-derivative (wrt all model parameters) of second-derivatives (wrt all dynamic variables) of dynamic model equations, i.e. d(d(df/dyy0ex0)/dyy0ex0)/dparam, rows are in declaration order, first and second column in declaration order
catch
error('For analytical parameter derivatives ''dynamic_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
end
[~, g1p, ~, ~, g2p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
%g1p is [endo_nbr by yy0ex0_nbr by param_nbr] first-derivative (wrt all model parameters) of first-derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dparam, rows are in declaration order, column in lead_lag_incidence order
%g2p are nonzero values and corresponding indices of first-derivative (wrt all model parameters) of second-derivatives (wrt all dynamic variables) of dynamic model equations, i.e. d(d(df/dyy0ex0)/dyy0ex0)/dparam, rows are in declaration order, first and second column in declaration order
[~, g1, g2, g3] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1); %note that g3 does not contain symmetric elements
g3 = unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
%g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
%g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
%g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
elseif order == 3
try
[~, g1p, ~, ~, g2p, g3p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
%g1p is [endo_nbr by yy0ex0_nbr by param_nbr] first-derivative (wrt all model parameters) of first-derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dparam, rows are in declaration order, column in lead_lag_incidence order
%g2p are nonzero values and corresponding indices of first-derivative (wrt all model parameters) of second-derivatives (wrt all dynamic variables) of dynamic model equations, i.e. d(d(df/dyy0ex0)/dyy0ex0)/dparam, rows are in declaration order, first and second column in declaration order
%g3p are nonzero values and corresponding indices of first-derivative (wrt all model parameters) of third-derivatives (wrt all dynamic variables) of dynamic model equations, i.e. d(d(d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dparam, rows are in declaration order, first, second and third column in declaration order
catch
error('For analytical parameter derivatives ''dynamic_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
end
[~, g1p, ~, ~, g2p, g3p] = feval([fname,'.dynamic_params_derivs'], ys(I), exo_steady_state', params, ys, 1, dys, d2ys);
%g1p is [endo_nbr by yy0ex0_nbr by param_nbr] first-derivative (wrt all model parameters) of first-derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dparam, rows are in declaration order, column in lead_lag_incidence order
%g2p are nonzero values and corresponding indices of first-derivative (wrt all model parameters) of second-derivatives (wrt all dynamic variables) of dynamic model equations, i.e. d(d(df/dyy0ex0)/dyy0ex0)/dparam, rows are in declaration order, first and second column in declaration order
%g3p are nonzero values and corresponding indices of first-derivative (wrt all model parameters) of third-derivatives (wrt all dynamic variables) of dynamic model equations, i.e. d(d(d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dparam, rows are in declaration order, first, second and third column in declaration order
T = NaN(sum(dynamic_tmp_nbr(1:5)));
T = feval([fname, '.dynamic_g4_tt'], T, ys(I), exo_steady_state', params, ys, 1);
g1 = feval([fname, '.dynamic_g1'], T, ys(I), exo_steady_state', params, ys, 1, false); %g1 is [endo_nbr by yy0ex0_nbr first derivative (wrt all dynamic variables) of dynamic model equations, i.e. df/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g2 = feval([fname, '.dynamic_g2'], T, ys(I), exo_steady_state', params, ys, 1, false); %g2 is [endo_nbr by yy0ex0_nbr^2] second derivative (wrt all dynamic variables) of dynamic model equations, i.e. d(df/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g3 = feval([fname, '.dynamic_g3'], T, ys(I), exo_steady_state', params, ys, 1, false); %note that g3 does not contain symmetric elements
g4 = feval([fname, '.dynamic_g4'], T, ys(I), exo_steady_state', params, ys, 1, false); %note that g4 does not contain symmetric elements
g3 = unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3, %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g4 = unfold_g4(g4, yy0ex0_nbr); %add symmetric elements to g4, %g4 is [endo_nbr by yy0ex0_nbr^4] fourth-derivative (wrt all dynamic variables) of dynamic model equations, i.e. ((d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3, %g3 is [endo_nbr by yy0ex0_nbr^3] third-derivative (wrt all dynamic variables) of dynamic model equations, i.e. (d(df/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
g4 = identification.unfold_g4(g4, yy0ex0_nbr); %add symmetric elements to g4, %g4 is [endo_nbr by yy0ex0_nbr^4] fourth-derivative (wrt all dynamic variables) of dynamic model equations, i.e. ((d(df/dyy0ex0)/dyy0ex0)/dyy0ex0)/dyy0ex0, rows are in declaration order, columns in lead_lag_incidence order
end
end
% Parameter Jacobian of steady state in different orderings, note dys is in declaration order
@ -801,7 +783,7 @@ if analytic_derivation_mode == 1
dghu = [zeros(endo_nbr*exo_nbr, stderrparam_nbr+corrparam_nbr) dghu];
% Compute dOm = dvec(ghu*Sigma_e*ghu') from expressions 34 in Iskrev (2010) Appendix A
dOm = kron(I_endo,ghu*Sigma_e)*(commutation(endo_nbr, exo_nbr)*dghu)...
dOm = kron(I_endo,ghu*Sigma_e)*(pruned_SS.commutation(endo_nbr, exo_nbr)*dghu)...
+ kron(ghu,ghu)*reshape(dSigma_e, exo_nbr^2, totparam_nbr) + kron(ghu*Sigma_e,I_endo)*dghu;
% Put into tensor notation

2
matlab/get_perturbation_params_derivs_numerical_objective.m → matlab/+identification/get_perturbation_params_derivs_numerical_objective.m
View File

@ -95,7 +95,7 @@ if strcmp(outputflag,'dynamic_model')
out = [Yss; g1(:); g2(:)];
elseif options_.order == 3
[~, g1, g2, g3] = feval([M_.fname,'.dynamic'], ys(I), exo_steady_state', M_.params, ys, 1);
g3 = unfold_g3(g3, length(ys(I))+M_.exo_nbr);
g3 = identification.unfold_g3(g3, length(ys(I))+M_.exo_nbr);
out = [Yss; g1(:); g2(:); g3(:)];
end
end

0
matlab/hessian_sparse.m → matlab/+identification/hessian_sparse.m
View File

10
matlab/identification_numerical_objective.m → matlab/+identification/numerical_objective.m
View File

@ -1,5 +1,5 @@
function out = identification_numerical_objective(params, outputflag, estim_params_, M_, options_, indpmodel, indpstderr, indvar, useautocorr, nlags, grid_nbr, dr, steady_state, exo_steady_state, exo_det_steady_state)
% out = identification_numerical_objective(params, outputflag, estim_params_, M_, options_, indpmodel, indpstderr, indvar, useautocorr, nlags, grid_nbr, dr, steady_state, exo_steady_state, exo_det_steady_state)
function out = numerical_objective(params, outputflag, estim_params_, M_, options_, indpmodel, indpstderr, indvar, useautocorr, nlags, grid_nbr, dr, steady_state, exo_steady_state, exo_det_steady_state)
% out = numerical_objective(params, outputflag, estim_params_, M_, options_, indpmodel, indpstderr, indvar, useautocorr, nlags, grid_nbr, dr, steady_state, exo_steady_state, exo_det_steady_state)
% -------------------------------------------------------------------------
% Objective function to compute numerically the Jacobians used for identification analysis
% Previously this function was called thet2tau.m
@ -22,7 +22,7 @@ function out = identification_numerical_objective(params, outputflag, estim_para
% OUTPUTS
% out: dependent on outputflag
% * 0: out = [Yss; vec(A); vec(B); dyn_vech(Sig_e)]; of indvar variables only, in DR order. This is needed to compute dTAU and Komunjer and Ng's D.
% Note that Jacobian of Om is computed in get_identification_Jacobians.m (previously getJJ.m) or get_first_order_solution_params_deriv.m (previously getH.m) from Jacobian of B and Sigma_e, because this is more efficient due to some testing with analytical derivatives from An and Schorfheide model
% Note that Jacobian of Om is computed in identification.get_jacobians.m (previously getJJ.m) or get_first_order_solution_params_deriv.m (previously getH.m) from Jacobian of B and Sigma_e, because this is more efficient due to some testing with analytical derivatives from An and Schorfheide model
% * 1: out = [vech(cov(Y_t,Y_t)); vec(cov(Y_t,Y_{t-1}); ...; vec(cov(Y_t,Y_{t-nlags})] of indvar variables, in DR order. This is needed to compute Iskrev's J.
% * 2: out = vec(spectral density) with dimension [var_nbr^2*grid_nbr,1] Spectral density of indvar variables evaluated at (grid_nbr/2+1) discretized points in the interval [0;pi]. This is needed for Qu and Tkachenko's G.
% * -1: out = g1(:); of all variables, in DR order. This is needed to compute dLRE.
@ -32,7 +32,7 @@ function out = identification_numerical_objective(params, outputflag, estim_para
% Jacobian of the dynamic model equations, and Y_t selected variables
% -------------------------------------------------------------------------
% This function is called by
% * get_identification_jacobians.m (previously getJJ.m)
% * identification.get_jacobians.m (previously getJJ.m)
% -------------------------------------------------------------------------
% This function calls
% * [M_.fname,'.dynamic']
@ -80,7 +80,7 @@ end
%% compute Kalman transition matrices and steady state with updated parameters
[dr,info,M_.params] = compute_decision_rules(M_,options_,dr, steady_state, exo_steady_state, exo_det_steady_state);
options_ = rmfield(options_,'options_ident');
pruned = pruned_state_space_system(M_, options_, dr, indvar, nlags, useautocorr, 0);
pruned = pruned_SS.pruned_state_space_system(M_, options_, dr, indvar, nlags, useautocorr, 0);
%% out = [vech(cov(Y_t,Y_t)); vec(cov(Y_t,Y_{t-1}); ...; vec(cov(Y_t,Y_{t-nlags})] of indvar variables, in DR order. This is Iskrev (2010)'s J matrix.
if outputflag == 1

26
matlab/plot_identification.m → matlab/+identification/plot.m
View File

@ -1,5 +1,5 @@
function plot_identification(M_, params, idemoments, idehess, idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, fname, options_, estim_params_, bayestopt_, tit_TeX, name_tex)
% plot_identification(M_, params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, fname, options_, estim_params_, bayestopt_, tit_TeX, name_tex)
function plot(M_, params, idemoments, idehess, idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, fname, options_, estim_params_, bayestopt_, tit_TeX, name_tex)
% plot(M_, params,idemoments,idehess,idemodel, idelre, advanced, tittxt, name, IdentifDirectoryName, fname, options_, estim_params_, bayestopt_, tit_TeX, name_tex)
%
% INPUTS
% o M_ [structure] model
@ -156,7 +156,7 @@ if SampleSize == 1
end
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([IdentifDirectoryName '/' fname '_ident_strength_' tittxt1,'.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by identification.plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
@ -203,7 +203,7 @@ if SampleSize == 1
dyn_saveas(hh_fig,[IdentifDirectoryName '/' fname '_sensitivity_' tittxt1 ],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([IdentifDirectoryName '/' fname '_sensitivity_' tittxt1,'.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by identification.plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
@ -262,7 +262,7 @@ if SampleSize == 1
dyn_saveas(hh_fig,[ IdentifDirectoryName '/' fname '_ident_collinearity_' tittxt1 '_' int2str(j) ],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([ IdentifDirectoryName '/' fname '_ident_collinearity_' tittxt1 '_' int2str(j),'.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by identification.plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
@ -329,7 +329,7 @@ if SampleSize == 1
dyn_saveas(f1,[ IdentifDirectoryName '/' fname '_ident_pattern_' tittxt1 '_1' ],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([ IdentifDirectoryName '/' fname '_ident_pattern_' tittxt1 '_1','.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by identification.plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
@ -344,7 +344,7 @@ if SampleSize == 1
dyn_saveas(f2,[ IdentifDirectoryName '/' fname '_ident_pattern_' tittxt1 '_2' ],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([ IdentifDirectoryName '/' fname '_ident_pattern_' tittxt1 '_2.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by identification.plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
@ -392,7 +392,7 @@ else
dyn_saveas(hh_fig,[ IdentifDirectoryName '/' fname '_MC_sensitivity' ],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([ IdentifDirectoryName '/' fname '_MC_sensitivity.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by identification.plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
@ -450,17 +450,17 @@ else
options_mcf.title = 'MC Highest Condition Number LRE Model';
ncut=floor(SampleSize/10*9);
[~,is]=sort(idelre.cond);
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
options_mcf.amcf_name = 'MC_HighestCondNumberModel';
options_mcf.amcf_title = 'MC Highest Condition Number Model Solution';
options_mcf.title = 'MC Highest Condition Number Model Solution';
[~,is]=sort(idemodel.cond);
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
options_mcf.amcf_name = 'MC_HighestCondNumberMoments';
options_mcf.amcf_title = 'MC Highest Condition Number Model Moments';
options_mcf.title = 'MC Highest Condition Number Model Moments';
[~,is]=sort(idemoments.cond);
mcf_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
gsa.monte_carlo_filtering_analysis(params, is(1:ncut), is(ncut+1:end), options_mcf, M_, options_, bayestopt_, estim_params_);
if nparam<5
f1 = dyn_figure(options_.nodisplay,'Name',[tittxt,' - MC Identification patterns (moments): HIGHEST SV']);
@ -514,7 +514,7 @@ else
dyn_saveas(f1,[IdentifDirectoryName '/' fname '_MC_ident_pattern_1' ],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([IdentifDirectoryName '/' fname '_MC_ident_pattern_1.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by identification.plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
@ -529,7 +529,7 @@ else
dyn_saveas(f2,[ IdentifDirectoryName '/' fname '_MC_ident_pattern_2' ],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([ IdentifDirectoryName '/' fname '_MC_ident_pattern_2.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by plot_identification.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by identification.plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');

92
matlab/dynare_identification.m → matlab/+identification/run.m
View File

@ -1,5 +1,5 @@
function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(M_,oo_,options_,bayestopt_,estim_params_,options_ident, pdraws0)
% [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = dynare_identification(options_ident, pdraws0)
function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = run(M_,oo_,options_,bayestopt_,estim_params_,options_ident, pdraws0)
% [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, STO_si_dREDUCEDFORM, STO_si_dMOMENTS, STO_dSPECTRUM, STO_dMINIMAL] = run(options_ident, pdraws0)
% -------------------------------------------------------------------------
% This function is called, when the user specifies identification(...); in the mod file. It prepares all identification analysis:
% (1) set options, local and persistent variables for a new identification
@ -32,19 +32,19 @@ function [pdraws, STO_REDUCEDFORM, STO_MOMENTS, STO_DYNAMIC, STO_si_dDYNAMIC, ST
% -------------------------------------------------------------------------
% This function is called by
% * driver.m
% * map_ident_.m
% * gsa.map_identification.m
% -------------------------------------------------------------------------
% This function calls
% * checkpath
% * disp_identification
% * identification.display
% * dyn_waitbar
% * dyn_waitbar_close
% * get_all_parameters
% * get_posterior_parameters
% * get_the_name
% * identification_analysis
% * identification.analysis
% * isoctave
% * plot_identification
% * identification.plot
% * dprior.draw
% * set_default_option
% * set_prior
@ -95,7 +95,7 @@ end
options_ident = set_default_option(options_ident,'gsa_sample_file',0);
% 0: do not use sample file
% 1: triggers gsa prior sample
% 2: triggers gsa Monte-Carlo sample (i.e. loads a sample corresponding to pprior=0 and ppost=0 in dynare_sensitivity options)
% 2: triggers gsa Monte-Carlo sample (i.e. loads a sample corresponding to pprior=0 and ppost=0 in sensitivity.run options)
% FILENAME: use sample file in provided path
options_ident = set_default_option(options_ident,'parameter_set','prior_mean');
% 'calibration': use values in M_.params and M_.Sigma_e to update estimated stderr, corr and model parameters (get_all_parameters)
@ -140,7 +140,7 @@ options_ident = set_default_option(options_ident,'tol_rank','robust');
options_ident = set_default_option(options_ident,'tol_deriv',1.e-8);
% tolerance level for selecting columns of non-zero derivatives
options_ident = set_default_option(options_ident,'tol_sv',1.e-3);
% tolerance level for selecting non-zero singular values in identification_checks.m
% tolerance level for selecting non-zero singular values in identification.checks.m
options_ident = set_default_option(options_ident,'schur_vec_tol',1e-11);
% tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix.
@ -181,7 +181,7 @@ if (isfield(options_ident,'no_identification_strength') && options_ident.no_ide
options_ident.no_identification_moments = 0;
end
%overwrite setting, as dynare_sensitivity does not make use of spectrum and minimal system
%overwrite setting, as sensitivity.run does not make use of spectrum and minimal system
if isfield(options_,'opt_gsa') && isfield(options_.opt_gsa,'identification') && options_.opt_gsa.identification == 1
options_ident.no_identification_minimal = 1;
options_ident.no_identification_spectrum = 1;
@ -308,12 +308,12 @@ options_.options_ident = [];
options_ident = set_default_option(options_ident,'analytic_derivation_mode', options_.analytic_derivation_mode); % if not set by user, inherit default global one
% 0: efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012)
% 1: kronecker products method to compute analytical derivatives as in Iskrev (2010) (only for order=1)
% -1: numerical two-sided finite difference method to compute numerical derivatives of all identification Jacobians using function identification_numerical_objective.m (previously thet2tau.m)
% -1: numerical two-sided finite difference method to compute numerical derivatives of all identification Jacobians using function identification.numerical_objective.m (previously thet2tau.m)
% -2: numerical two-sided finite difference method to compute numerically dYss, dg1, dg2, dg3, d2Yss and d2g1, the identification Jacobians are then computed analytically as with 0
if options_.discretionary_policy || options_.ramsey_policy
if options_ident.analytic_derivation_mode~=-1
fprintf('dynare_identification: discretionary_policy and ramsey_policy require analytic_derivation_mode=-1. Resetting the option.')
fprintf('identification.run: discretionary_policy and ramsey_policy require analytic_derivation_mode=-1. Resetting the option.')
options_ident.analytic_derivation_mode=-1;
end
end
@ -384,7 +384,7 @@ else % no estimated_params block, choose all model parameters and all stderr par
name_tex = cellfun(@(x) horzcat('$ SE_{', x, '} $'), M_.exo_names_tex, 'UniformOutput', false);
name_tex = vertcat(name_tex, cellfun(@(x) horzcat('$ ', x, ' $'), M_.param_names_tex, 'UniformOutput', false));
if ~isequal(M_.H,0)
fprintf('\ndynare_identification:: Identification does not support measurement errors (yet) and will ignore them in the following. To test their identifiability, instead define them explicitly as varexo and provide measurement equations in the model definition.\n')
fprintf('\nidentification.run:: Identification does not support measurement errors (yet) and will ignore them in the following. To test their identifiability, instead define them explicitly as varexo and provide measurement equations in the model definition.\n')
end
end
options_ident.name_tex = name_tex;
@ -402,13 +402,13 @@ end
% settings dependent on number of parameters
options_ident = set_default_option(options_ident,'max_dim_cova_group',min([2,totparam_nbr-1]));
options_ident.max_dim_cova_group = min([options_ident.max_dim_cova_group,totparam_nbr-1]);
% In brute force search (ident_bruteforce.m) when advanced=1 this option sets the maximum dimension of groups of parameters that best reproduce the behavior of each single model parameter
% In brute force search (identification.bruteforce.m) when advanced=1 this option sets the maximum dimension of groups of parameters that best reproduce the behavior of each single model parameter
options_ident = set_default_option(options_ident,'checks_via_subsets',0);
% 1: uses identification_checks_via_subsets.m to compute problematic parameter combinations
% 0: uses identification_checks.m to compute problematic parameter combinations [default]
% 1: uses identification.checks_via_subsets.m to compute problematic parameter combinations
% 0: uses identification.checks.m to compute problematic parameter combinations [default]
options_ident = set_default_option(options_ident,'max_dim_subsets_groups',min([4,totparam_nbr-1]));
% In identification_checks_via_subsets.m, when checks_via_subsets=1, this option sets the maximum dimension of groups of parameters for which the corresponding rank criteria is checked
% In identification.checks_via_subsets.m, when checks_via_subsets=1, this option sets the maximum dimension of groups of parameters for which the corresponding rank criteria is checked
% store identification options
@ -471,7 +471,7 @@ if iload <=0
options_ident.tittxt = parameters; %title text for graphs and figures
% perform identification analysis for single point
[ide_moments_point, ide_spectrum_point, ide_minimal_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, derivatives_info_point, info, error_indicator_point] = ...
identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end implies initialization of persistent variables
identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end implies initialization of persistent variables
if info(1)~=0
% there are errors in the solution algorithm
message = get_error_message(info,options_);
@ -488,7 +488,7 @@ if iload <=0
options_ident.tittxt = 'Random_prior_params'; %title text for graphs and figures
% perform identification analysis
[ide_moments_point, ide_spectrum_point, ide_minimal_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, derivatives_info_point, info, error_indicator_point] = ...
identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1);
identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1);
end
end
if info(1)
@ -513,10 +513,10 @@ if iload <=0
save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point', 'store_options_ident');
save([IdentifDirectoryName '/' fname '_' parameters '_identif.mat'], 'ide_moments_point', 'ide_spectrum_point', 'ide_minimal_point', 'ide_hess_point', 'ide_reducedform_point', 'ide_dynamic_point', 'store_options_ident');
% display results of identification analysis
disp_identification(params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident);
identification.display(params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident);
if ~options_ident.no_identification_strength && ~options_.nograph && ~error_indicator_point.identification_strength && ~error_indicator_point.identification_moments
% plot (i) identification strength and sensitivity measure based on the moment information matrix and (ii) plot advanced analysis graphs
plot_identification(M_,params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, ...
identification.plot(M_,params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, ...
IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, parameters_TeX, name_tex);
end
@ -529,7 +529,7 @@ if iload <=0
file_index = 0; % initialize counter for files (if options_.MaxNumberOfBytes is reached, we store results in files)
options_MC = options_ident; %store options structure for Monte Carlo analysis
options_MC.advanced = 0; %do not run advanced checking in a Monte Carlo analysis
options_ident.checks_via_subsets = 0; % for Monte Carlo analysis currently only identification_checks and not identification_checks_via_subsets is supported
options_ident.checks_via_subsets = 0; % for Monte Carlo analysis currently only identification.checks and not identification.checks_via_subsets is supported
else
iteration = 1; % iteration equals SampleSize and we are finished
pdraws = []; % to have output object otherwise map_ident.m may crash
@ -543,7 +543,7 @@ if iload <=0
options_ident.tittxt = []; % clear title text for graphs and figures
% run identification analysis
[ide_moments, ide_spectrum, ide_minimal, ide_hess, ide_reducedform, ide_dynamic, ide_derivatives_info, info, error_indicator] = ...
identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_MC, dataset_info, prior_exist, 0); % the 0 implies that we do not initialize persistent variables anymore
identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,params, indpmodel, indpstderr, indpcorr, options_MC, dataset_info, prior_exist, 0); % the 0 implies that we do not initialize persistent variables anymore
if iteration==0 && info(1)==0 % preallocate storage in the first admissable run
delete([IdentifDirectoryName '/' fname '_identif_*.mat']) % delete previously saved results
@ -801,26 +801,26 @@ if iload <=0
end
for irun=1:max([maxrun_dDYNAMIC, maxrun_dREDUCEDFORM, maxrun_dMOMENTS, maxrun_dSPECTRUM, maxrun_dMINIMAL])
iter=iter+1;
% note that this is not the same si_dDYNAMICnorm as computed in identification_analysis
% note that this is not the same si_dDYNAMICnorm as computed in identification.analysis
% given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure
si_dDYNAMICnorm(iter,:) = vnorm(STO_si_dDYNAMIC(:,:,irun)./repmat(normalize_STO_DYNAMIC,1,totparam_nbr-(stderrparam_nbr+corrparam_nbr))).*normaliz1((stderrparam_nbr+corrparam_nbr)+1:end);
si_dDYNAMICnorm(iter,:) = identification.vnorm(STO_si_dDYNAMIC(:,:,irun)./repmat(normalize_STO_DYNAMIC,1,totparam_nbr-(stderrparam_nbr+corrparam_nbr))).*normaliz1((stderrparam_nbr+corrparam_nbr)+1:end);
if ~options_MC.no_identification_reducedform && ~isempty(STO_si_dREDUCEDFORM)
% note that this is not the same si_dREDUCEDFORMnorm as computed in identification_analysis
% note that this is not the same si_dREDUCEDFORMnorm as computed in identification.analysis
% given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure
si_dREDUCEDFORMnorm(iter,:) = vnorm(STO_si_dREDUCEDFORM(:,:,irun)./repmat(normalize_STO_REDUCEDFORM,1,totparam_nbr)).*normaliz1;
si_dREDUCEDFORMnorm(iter,:) = identification.vnorm(STO_si_dREDUCEDFORM(:,:,irun)./repmat(normalize_STO_REDUCEDFORM,1,totparam_nbr)).*normaliz1;
end
if ~options_MC.no_identification_moments && ~isempty(STO_si_dMOMENTS)
% note that this is not the same si_dMOMENTSnorm as computed in identification_analysis
% note that this is not the same si_dMOMENTSnorm as computed in identification.analysis
% given that we have the MC sample of the Jacobians, we also normalize by the std of the sample of Jacobian entries, to get a fully standardized sensitivity measure
si_dMOMENTSnorm(iter,:) = vnorm(STO_si_dMOMENTS(:,:,irun)./repmat(normalize_STO_MOMENTS,1,totparam_nbr)).*normaliz1;
si_dMOMENTSnorm(iter,:) = identification.vnorm(STO_si_dMOMENTS(:,:,irun)./repmat(normalize_STO_MOMENTS,1,totparam_nbr)).*normaliz1;
end
if ~options_MC.no_identification_spectrum && ~isempty(STO_dSPECTRUM)
% note that this is not the same dSPECTRUMnorm as computed in identification_analysis
dSPECTRUMnorm(iter,:) = vnorm(STO_dSPECTRUM(:,:,irun)); %not yet used
% note that this is not the same dSPECTRUMnorm as computed in identification.analysis
dSPECTRUMnorm(iter,:) = identification.vnorm(STO_dSPECTRUM(:,:,irun)); %not yet used
end
if ~options_MC.no_identification_minimal && ~isempty(STO_dMINIMAL)
% note that this is not the same dMINIMALnorm as computed in identification_analysis
dMINIMALnorm(iter,:) = vnorm(STO_dMINIMAL(:,:,irun)); %not yet used
% note that this is not the same dMINIMALnorm as computed in identification.analysis
dMINIMALnorm(iter,:) = identification.vnorm(STO_dMINIMAL(:,:,irun)); %not yet used
end
end
end
@ -847,7 +847,7 @@ else
options_.options_ident = options_ident;
end
%% if dynare_identification is called as it own function (not through identification command) and if we load files
%% if identification.run is called as it own function (not through identification command) and if we load files
if nargout>3 && iload
filnam = dir([IdentifDirectoryName '/' fname '_identif_*.mat']);
STO_si_dDYNAMIC = [];
@ -876,10 +876,10 @@ end
if iload
%if previous analysis is loaded
fprintf(['Testing %s\n',parameters]);
disp_identification(ide_hess_point.params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident);
identification.display(ide_hess_point.params, ide_reducedform_point, ide_moments_point, ide_spectrum_point, ide_minimal_point, name, options_ident);
if ~options_.nograph && ~error_indicator_point.identification_strength && ~error_indicator_point.identification_moments
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs
plot_identification(M_,ide_hess_point.params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, ...
identification.plot(M_,ide_hess_point.params, ide_moments_point, ide_hess_point, ide_reducedform_point, ide_dynamic_point, options_ident.advanced, parameters, name, ...
IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, [], name_tex);
end
end
@ -890,11 +890,11 @@ if SampleSize > 1
%print results to console but make sure advanced=0
advanced0 = options_ident.advanced;
options_ident.advanced = 0;
disp_identification(pdraws, IDE_REDUCEDFORM, IDE_MOMENTS, IDE_SPECTRUM, IDE_MINIMAL, name, options_ident);
identification.display(pdraws, IDE_REDUCEDFORM, IDE_MOMENTS, IDE_SPECTRUM, IDE_MINIMAL, name, options_ident);
options_ident.advanced = advanced0; % reset advanced setting
if ~options_.nograph && isfield(ide_hess_point,'ide_strength_dMOMENTS')
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs
plot_identification(M_, pdraws, IDE_MOMENTS, ide_hess_point, IDE_REDUCEDFORM, IDE_DYNAMIC, options_ident.advanced, 'MC sample ', name, ...
identification.plot(M_, pdraws, IDE_MOMENTS, ide_hess_point, IDE_REDUCEDFORM, IDE_DYNAMIC, options_ident.advanced, 'MC sample ', name, ...
IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, [], name_tex);
end
%advanced display and plots for MC Sample, i.e. look at draws with highest/lowest condition number
@ -912,15 +912,15 @@ if SampleSize > 1
if ~iload
options_ident.tittxt = tittxt; %title text for graphs and figures
[ide_moments_max, ide_spectrum_max, ide_minimal_max, ide_hess_max, ide_reducedform_max, ide_dynamic_max, derivatives_info_max, info_max, error_indicator_max] = ...
identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jmax,:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end initializes some persistent variables
identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jmax,:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end initializes some persistent variables
save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_hess_max', 'ide_moments_max', 'ide_spectrum_max', 'ide_minimal_max','ide_reducedform_max', 'ide_dynamic_max', 'jmax', '-append');
end
advanced0 = options_ident.advanced; options_ident.advanced = 1; % make sure advanced setting is on
disp_identification(pdraws(jmax,:), ide_reducedform_max, ide_moments_max, ide_spectrum_max, ide_minimal_max, name, options_ident);
identification.display(pdraws(jmax,:), ide_reducedform_max, ide_moments_max, ide_spectrum_max, ide_minimal_max, name, options_ident);
options_ident.advanced = advanced0; %reset advanced setting
if ~options_.nograph && ~error_indicator_max.identification_strength && ~error_indicator_max.identification_moments
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs
plot_identification(M_, pdraws(jmax,:), ide_moments_max, ide_hess_max, ide_reducedform_max, ide_dynamic_max, 1, tittxt, name, ...
identification.plot(M_, pdraws(jmax,:), ide_moments_max, ide_hess_max, ide_reducedform_max, ide_dynamic_max, 1, tittxt, name, ...
IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, tittxt, name_tex);
end
@ -931,15 +931,15 @@ if SampleSize > 1
if ~iload
options_ident.tittxt = tittxt; %title text for graphs and figures
[ide_moments_min, ide_spectrum_min, ide_minimal_min, ide_hess_min, ide_reducedform_min, ide_dynamic_min, ~, ~, error_indicator_min] = ...
identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jmin,:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end initializes persistent variables
identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jmin,:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1); %the 1 at the end initializes persistent variables
save([IdentifDirectoryName '/' fname '_identif.mat'], 'ide_hess_min', 'ide_moments_min','ide_spectrum_min','ide_minimal_min','ide_reducedform_min', 'ide_dynamic_min', 'jmin', '-append');
end
advanced0 = options_ident.advanced; options_ident.advanced = 1; % make sure advanced setting is on
disp_identification(pdraws(jmin,:), ide_reducedform_min, ide_moments_min, ide_spectrum_min, ide_minimal_min, name, options_ident);
identification.display(pdraws(jmin,:), ide_reducedform_min, ide_moments_min, ide_spectrum_min, ide_minimal_min, name, options_ident);
options_ident.advanced = advanced0; %reset advanced setting
if ~options_.nograph && ~error_indicator_min.identification_strength && ~error_indicator_min.identification_moments
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs
plot_identification(M_, pdraws(jmin,:),ide_moments_min,ide_hess_min,ide_reducedform_min,ide_dynamic_min,1,tittxt,name,...
identification.plot(M_, pdraws(jmin,:),ide_moments_min,ide_hess_min,ide_reducedform_min,ide_dynamic_min,1,tittxt,name,...
IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, tittxt,name_tex);
end
% reset nodisplay option
@ -954,14 +954,14 @@ if SampleSize > 1
if ~iload
options_ident.tittxt = tittxt; %title text for graphs and figures
[ide_moments_(j), ide_spectrum_(j), ide_minimal_(j), ide_hess_(j), ide_reducedform_(j), ide_dynamic_(j), derivatives_info_(j), info_resolve, error_indicator_j] = ...
identification_analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jcrit(j),:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1);
identification.analysis(M_,options_,oo_,bayestopt_,estim_params_,pdraws(jcrit(j),:), indpmodel, indpstderr, indpcorr, options_ident, dataset_info, prior_exist, 1);
end
advanced0 = options_ident.advanced; options_ident.advanced = 1; %make sure advanced setting is on
disp_identification(pdraws(jcrit(j),:), ide_reducedform_(j), ide_moments_(j), ide_spectrum_(j), ide_minimal_(j), name, options_ident);
identification.display(pdraws(jcrit(j),:), ide_reducedform_(j), ide_moments_(j), ide_spectrum_(j), ide_minimal_(j), name, options_ident);
options_ident.advanced = advanced0; % reset advanced
if ~options_.nograph && ~error_indicator_j.identification_strength && ~error_indicator_j.identification_moments
% plot (i) identification strength and sensitivity measure based on the sample information matrix and (ii) advanced analysis graphs
plot_identification(M_, pdraws(jcrit(j),:), ide_moments_(j), ide_hess_(j), ide_reducedform_(j), ide_dynamic_(j), 1, tittxt, name, ...
identification.plot(M_, pdraws(jcrit(j),:), ide_moments_(j), ide_hess_(j), ide_reducedform_(j), ide_dynamic_(j), 1, tittxt, name, ...
IdentifDirectoryName, M_.fname, options_, estim_params_, bayestopt_, tittxt, name_tex);
end
end

0
matlab/simulated_moment_uncertainty.m → matlab/+identification/simulated_moment_uncertainty.m
View File

0
matlab/unfold_g3.m → matlab/+identification/unfold_g3.m
View File

0
matlab/unfold_g4.m → matlab/+identification/unfold_g4.m
View File

0
matlab/vnorm.m → matlab/+identification/vnorm.m
View File

6
matlab/+mom/objective_function.m
View File

@ -150,11 +150,11 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
stderrparam_nbr = estim_params_.nvx; % number of stderr parameters
corrparam_nbr = estim_params_.ncx; % number of corr parameters
totparam_nbr = stderrparam_nbr+corrparam_nbr+modparam_nbr;
oo_.dr.derivs = get_perturbation_params_derivs(M_, options_mom_, estim_params_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, indpmodel, indpstderr, indpcorr, 0); %analytic derivatives of perturbation matrices
oo_.dr.derivs = identification.get_perturbation_params_derivs(M_, options_mom_, estim_params_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, indpmodel, indpstderr, indpcorr, 0); %analytic derivatives of perturbation matrices
oo_.mom.model_moments_params_derivs = NaN(options_mom_.mom.mom_nbr,totparam_nbr);
pruned_state_space = pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 1);
pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 1);
else
pruned_state_space = pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 0);
pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 0);
end
oo_.mom.model_moments = NaN(options_mom_.mom.mom_nbr,1);
for jm = 1:size(M_.matched_moments,1)

4
matlab/+osr/objective.m
View File

@ -64,9 +64,9 @@ if ~options_.analytic_derivation
loss = full(weights(:)'*vx(:));
else
totparam_nbr=length(i_params);
oo_.dr.derivs = get_perturbation_params_derivs(M_, options_, [], oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, i_params, [], [], 0); %analytic derivatives of perturbation matrices
oo_.dr.derivs = identification.get_perturbation_params_derivs(M_, options_, [], oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, i_params, [], [], 0); %analytic derivatives of perturbation matrices
pruned_state_space = pruned_state_space_system(M_, options_, oo_.dr, i_var, 0, 0, 1);
pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_, oo_.dr, i_var, 0, 0, 1);
vx = pruned_state_space.Var_y + pruned_state_space.E_y*pruned_state_space.E_y';
dE_yy = pruned_state_space.dVar_y;
for jp=1:length(i_params)

2
matlab/+pruned_SS/Q6_plication.m
View File

@ -49,7 +49,7 @@ for i1=1:p
for i4=i3:p
for i5=i4:p
for i6=i5:p
idx = uperm([i6 i5 i4 i3 i2 i1]);
idx = pruned_SS.uperm([i6 i5 i4 i3 i2 i1]);
for r = 1:size(idx,1)
ii1 = idx(r,1); ii2= idx(r,2); ii3=idx(r,3); ii4=idx(r,4); ii5=idx(r,5); ii6=idx(r,6);
n = ii1 + (ii2-1)*p + (ii3-1)*p^2 + (ii4-1)*p^3 + (ii5-1)*p^4 + (ii6-1)*p^5;

2
matlab/commutation.m → matlab/+pruned_SS/commutation.m
View File

@ -14,7 +14,7 @@ function k = commutation(n, m, sparseflag)
% -------------------------------------------------------------------------
% This function is called by
% * get_perturbation_params_derivs.m (previously getH.m)
% * get_identification_jacobians.m (previously getJJ.m)
% * identification.get_jacobians.m (previously getJJ.m)
% * pruned_state_space_system.m
% -------------------------------------------------------------------------
% This function calls

2
matlab/duplication.m → matlab/+pruned_SS/duplication.m
View File

@ -11,7 +11,7 @@ function [Dp,DpMPinv] = duplication(p)
% DpMPinv: Moore-Penroze inverse of Dp
% -------------------------------------------------------------------------
% This function is called by
% * get_identification_jacobians.m (previously getJJ.m)
% * identification.get_jacobians.m (previously getJJ.m)
% =========================================================================
% Copyright © 1997 Tom Minka <minka@microsoft.com>
% Copyright © 2019 Dynare Team

16
matlab/pruned_state_space_system.m → matlab/+pruned_SS/pruned_state_space_system.m
View File

@ -80,8 +80,8 @@ function pruned_state_space = pruned_state_space_system(M_, options_, dr, indy,
% parameter Jacobian of E_y
% -------------------------------------------------------------------------
% This function is called by
% * get_identification_jacobians.m
% * identification_numerical_objective.m
% * identification.get_jacobians.m
% * identification.numerical_objective.m
% -------------------------------------------------------------------------
% This function calls
% * allVL1.m
@ -418,7 +418,7 @@ if order > 1
hx_hu = kron(hx,hu);
hu_hu = kron(hu,hu);
I_xx = eye(x_nbr^2);
K_x_x = commutation(x_nbr,x_nbr,1);
K_x_x = pruned_SS.commutation(x_nbr,x_nbr,1);
invIx_hx = (eye(x_nbr)-hx)\eye(x_nbr);
%Compute unique fourth order product moments of u, i.e. unique(E[kron(kron(kron(u,u),u),u)],'stable')
@ -596,9 +596,9 @@ if order > 1
if order > 2
% Some common and useful objects for order > 2
if isempty(K_u_xx)
K_u_xx = commutation(u_nbr,x_nbr^2,1);
K_u_ux = commutation(u_nbr,u_nbr*x_nbr,1);
K_xx_x = commutation(x_nbr^2,x_nbr);
K_u_xx = pruned_SS.commutation(u_nbr,x_nbr^2,1);
K_u_ux = pruned_SS.commutation(u_nbr,u_nbr*x_nbr,1);
K_xx_x = pruned_SS.commutation(x_nbr^2,x_nbr);
end
hx_hss2 = kron(hx,1/2*hss);
hu_hss2 = kron(hu,1/2*hss);
@ -627,7 +627,7 @@ if order > 1
E_xf_xfxs = Var_z(id_z3_xf_xf, id_z2_xs ) + E_xfxf(:)*E_xs'; %this is E[kron(xf,xf)*xs']
E_xf_xfxf_xf = Var_z(id_z3_xf_xf, id_z3_xf_xf) + E_xfxf(:)*E_xfxf(:)'; %this is E[kron(xf,xf)*kron(xf,xf)']
E_xrdxf = reshape(invIxx_hx_hx*vec(...
hxx*reshape( commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*hx'...
hxx*reshape( pruned_SS.commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*hx'...
+ hxu*kron(E_xs,E_uu)*hu'...
+ 1/6*hxxx*reshape(E_xf_xfxf_xf,x_nbr^3,x_nbr)*hx'...
+ 1/6*huuu*reshape(QPu*E_u_u_u_u,u_nbr^3,u_nbr)*hu'...
@ -655,7 +655,7 @@ if order > 1
dE_xf_xfxf_xf(:,:,jp2) = dVar_z(id_z3_xf_xf , id_z3_xf_xf , jp2) + vec(dE_xfxf(:,:,jp2))*E_xfxf(:)' + E_xfxf(:)*vec(dE_xfxf(:,:,jp2))';
dE_xrdxf(:,:,jp2) = reshape(invIxx_hx_hx*vec(...
dhx(:,:,jp2)*E_xrdxf*hx' + hx*E_xrdxf*dhx(:,:,jp2)'...
+ dhxx(:,:,jp2)*reshape( commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*hx' + hxx*reshape( commutation(x_nbr^2,x_nbr,1)*vec(dE_xf_xfxs(:,:,jp2)), x_nbr^2,x_nbr)*hx' + hxx*reshape( commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*dhx(:,:,jp2)'...
+ dhxx(:,:,jp2)*reshape( pruned_SS.commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*hx' + hxx*reshape( pruned_SS.commutation(x_nbr^2,x_nbr,1)*vec(dE_xf_xfxs(:,:,jp2)), x_nbr^2,x_nbr)*hx' + hxx*reshape( pruned_SS.commutation(x_nbr^2,x_nbr,1)*E_xf_xfxs(:), x_nbr^2,x_nbr)*dhx(:,:,jp2)'...
+ dhxu(:,:,jp2)*kron(E_xs,E_uu)*hu' + hxu*kron(dE_xs(:,jp2),E_uu)*hu' + hxu*kron(E_xs,dE_uu(:,:,jp2))*hu' + hxu*kron(E_xs,E_uu)*dhu(:,:,jp2)'...
+ 1/6*dhxxx(:,:,jp2)*reshape(E_xf_xfxf_xf,x_nbr^3,x_nbr)*hx' + 1/6*hxxx*reshape(dE_xf_xfxf_xf(:,:,jp2),x_nbr^3,x_nbr)*hx' + 1/6*hxxx*reshape(E_xf_xfxf_xf,x_nbr^3,x_nbr)*dhx(:,:,jp2)'...
+ 1/6*dhuuu(:,:,jp2)*reshape(QPu*E_u_u_u_u,u_nbr^3,u_nbr)*hu' + 1/6*huuu*reshape(dE_u_u_u_u_jp2,u_nbr^3,u_nbr)*hu' + 1/6*huuu*reshape(QPu*E_u_u_u_u,u_nbr^3,u_nbr)*dhu(:,:,jp2)'...

2
matlab/+pruned_SS/quadruplication.m
View File

@ -49,7 +49,7 @@ for l=1:p
for k=l:p
for j=k:p
for i=j:p
idx = uperm([i j k l]);
idx = pruned_SS.uperm([i j k l]);
for r = 1:size(idx,1)
ii = idx(r,1); jj= idx(r,2); kk=idx(r,3); ll=idx(r,4);
n = ii + (jj-1)*p + (kk-1)*p^2 + (ll-1)*p^3;

96
matlab/uperm.m → matlab/+pruned_SS/uperm.m
View File

@ -1,49 +1,49 @@
function p = uperm(a)
% Return all unique permutations of possibly-repeating array elements
% =========================================================================
% Copyright © 2014 Bruno Luong <brunoluong@yahoo.com>
% Copyright © 2020 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/>.
% =========================================================================
% Original author: Bruno Luong <brunoluong@yahoo.com>, April 20, 2014
% https://groups.google.com/d/msg/comp.soft-sys.matlab/yQKVPTYrv6Q/gw1MzNd9sYkJ
% https://stackoverflow.com/a/42810388
[u, ~, J] = unique(a);
p = u(up(J, length(a)));
function p = up(J, n)
ktab = histc(J,1:max(J));
l = n;
p = zeros(1, n);
s = 1;
for i=1:length(ktab)
k = ktab(i);
c = nchoosek(1:l, k);
m = size(c,1);
[t, ~] = find(~p.');
t = reshape(t, [], s);
c = t(c,:)';
s = s*m;
r = repmat((1:s)',[1 k]);
q = accumarray([r(:) c(:)], i, [s n]);
p = repmat(p, [m 1]) + q;
l = l - k;
end
end
function p = uperm(a)
% Return all unique permutations of possibly-repeating array elements
% =========================================================================
% Copyright © 2014 Bruno Luong <brunoluong@yahoo.com>
% Copyright © 2020 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/>.
% =========================================================================
% Original author: Bruno Luong <brunoluong@yahoo.com>, April 20, 2014
% https://groups.google.com/d/msg/comp.soft-sys.matlab/yQKVPTYrv6Q/gw1MzNd9sYkJ
% https://stackoverflow.com/a/42810388
[u, ~, J] = unique(a);
p = u(up(J, length(a)));
function p = up(J, n)
ktab = histc(J,1:max(J));
l = n;
p = zeros(1, n);
s = 1;
for i=1:length(ktab)
k = ktab(i);
c = nchoosek(1:l, k);
m = size(c,1);
[t, ~] = find(~p.');
t = reshape(t, [], s);
c = t(c,:)';
s = s*m;
r = repmat((1:s)',[1 k]);
q = accumarray([r(:) c(:)], i, [s n]);
p = repmat(p, [m 1]) + q;
l = l - k;
end
end
end % uperm

0
matlab/simul_static_model.m → matlab/backward/simul_static_model.m
View File

63
matlab/cellofchar2mfile.m
View File

@ -1,63 +0,0 @@
function cellofchar2mfile(fname, c, cname)
% Write a cell of char in a matlab script.
%
% INPUTS
% - fname [string] name of the file where c is to be saved.
% - c [cell] a two dimensional cell of char.
%
% OUTPUTS
% None.
% Copyright © 2015-2017 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 <https://www.gnu.org/licenses/>.
[pathstr,name,ext] = fileparts(fname);
if isempty(ext)
fname = [pathstr, name, '.m']
else
if ~isequal(ext, '.m')
error(['The first argument needs to be the name of a matlab script (with an .m extension)!'])
end
end
if ~iscell(c)
error('The second input argument must be a cell!')
end
if ndims(c)>2
error(['The cell passed has a second argument cannot have more than two dimensions!'])
end
variablename = inputname(2);
if isempty(variablename) && nargin<3
error(['You must pass the name of the cell (second input argument) as a string in the third input argument!'])
end
if nargin>2
if isvarname(cname)
variablename = cname;
else
error('The third input argument must be a valid variable name!')
end
end
fid = fopen(fname,'w');
fprintf(fid, '%s = %s;', variablename, writecellofchar(c));
fclose(fid);

2
matlab/check_model.m
View File

@ -2,7 +2,7 @@ function check_model(M_)
% check_model(M_)
% Performs various consistency checks on the model
% Copyright (C) 2005-2033 Dynare Team
% Copyright (C) 2005-2023 Dynare Team
%
% This file is part of Dynare.
%

50
matlab/clear_persistent_variables.m
View File

@ -1,5 +1,5 @@
function clear_persistent_variables(folder, writelistofroutinestobecleared)
% clear_persistent_variables(folder, writelistofroutinestobecleared)
% Clear all the functions with persistent variables in directory folder (and subdirectories).
% Copyright © 2015-2019 Dynare Team
@ -60,3 +60,51 @@ end
list_of_functions_to_be_cleared;
clear(list_of_functions{:});
function cellofchar2mfile(fname, c, cname)
% Write a cell of char in a matlab script.
%
% INPUTS
% - fname [string] name of the file where c is to be saved.
% - c [cell] a two dimensional cell of char.
%
% OUTPUTS
% None.
[pathstr,name,ext] = fileparts(fname);
if isempty(ext)
fname = [pathstr, name, '.m'];
else
if ~isequal(ext, '.m')
error(['The first argument needs to be the name of a matlab script (with an .m extension)!'])
end
end
if ~iscell(c)
error('The second input argument must be a cell!')
end
if ndims(c)>2
error(['The cell passed has a second argument cannot have more than two dimensions!'])
end
variablename = inputname(2);
if isempty(variablename) && nargin<3
error(['You must pass the name of the cell (second input argument) as a string in the third input argument!'])
end
if nargin>2
if isvarname(cname)
variablename = cname;
else
error('The third input argument must be a valid variable name!')
end
end
fid = fopen(fname,'w');
fprintf(fid, '%s = %s;', variablename, writecellofchar(c));
fclose(fid);

0
matlab/print_moments_implied_prior.m → matlab/cli/print_moments_implied_prior.m
View File

32
matlab/compute_model_moments.m
View File

@ -1,32 +0,0 @@
function moments=compute_model_moments(dr,M_,options_)
%
% INPUTS
% dr: structure describing model solution
% M_: structure of Dynare options
% options_
%
% OUTPUTS
% moments: a cell array containing requested moments
%
% SPECIAL REQUIREMENTS
% none
% Copyright © 2008-2017 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 <https://www.gnu.org/licenses/>.
[ivar,vartan,options_] = get_variables_list(options_,M_);
moments = th_autocovariances(dr,ivar,M_,options_,options_.nodecomposition);

24
matlab/convergence_diagnostics/mcmc_ifac.m
View File

@ -69,3 +69,27 @@ for i=(Nc/2)+1: Nc+1
end
Parzen=Parzen';
Ifac= 1+2*sum(Parzen(:).* AcorrXSIM);
function acf = dyn_autocorr(y, ar)
% function acf = dyn_autocorr(y, ar)
% autocorrelation function of y
%
% INPUTS
% y: time series
% ar: # of lags
%
% OUTPUTS
% acf: autocorrelation for lags 1 to ar
%
% SPECIAL REQUIREMENTS
% none
y=y(:);
acf = NaN(ar+1,1);
acf(1)=1;
m = mean(y);
sd = std(y,1);
for i=1:ar
acf(i+1) = (y(i+1:end)-m)'*(y(1:end-i)-m)./((size(y,1))*sd^2);
end

40
matlab/dyn_autocorr.m
View File

@ -1,40 +0,0 @@
function acf = dyn_autocorr(y, ar)
% function acf = dyn_autocorr(y, ar)
% autocorrelation function of y
%
% INPUTS
% y: time series
% ar: # of lags
%
% OUTPUTS
% acf: autocorrelation for lags 1 to ar
%
% SPECIAL REQUIREMENTS
% none
% Copyright © 2015-16 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/>.
y=y(:);
acf = NaN(ar+1,1);
acf(1)=1;
m = mean(y);
sd = std(y,1);
for i=1:ar
acf(i+1) = (y(i+1:end)-m)'*(y(1:end-i)-m)./((size(y,1))*sd^2);
end

31
matlab/dyn_diag_vech.m
View File

@ -1,31 +0,0 @@
function d = dyn_diag_vech(Vector)
% This function returns the diagonal elements of a symmetric matrix
% stored in vech form
%
% INPUTS
% Vector [double] a m*1 vector.
%
% OUTPUTS
% d [double] a n*1 vector, where n solves n*(n+1)/2=m.
% Copyright © 2010-2017 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 <https://www.gnu.org/licenses/>.
m = length(Vector);
n = (sqrt(1+8*m)-1)/2;
k = cumsum(1:n);
d = Vector(k);

6
matlab/dynare_config.m
View File

@ -47,6 +47,7 @@ p = {'/../contrib/ms-sbvar/TZcode/MatlabFiles/' ; ...
'/AIM/' ; ...
'/backward/' ; ...
'/cli/' ; ...
'/conditional_forecasts/'; ...
'/convergence_diagnostics/' ; ...
'/discretionary_policy/' ; ...
'/distributions/' ; ...
@ -57,19 +58,24 @@ p = {'/../contrib/ms-sbvar/TZcode/MatlabFiles/' ; ...
'/gsa/' ; ...
'/kalman/' ; ...
'/kalman/likelihood' ; ...
'/latex/' ; ...
'/lmmcp/' ; ...
'/modules/dseries/src/' ; ...
'/reporting/' ; ...
'/matrix_solver/'; ...
'/moments/'; ...
'/ms-sbvar/' ; ...
'/ms-sbvar/identification/' ; ...
'/nonlinear-filters/' ; ...
'/ols/' ; ...
'/optimal_policy/' ; ...
'/optimization/' ; ...
'/pac-tools/' ; ...
'/parallel/' ; ...
'/partial_information/' ; ...
'/perfect-foresight-models/' ; ...
'/shock_decomposition/' ; ...
'/stochastic_solver/' ; ...
'/utilities/dataset/' ; ...
'/utilities/doc/' ; ...
'/utilities/estimation/' ; ...

66
matlab/dynare_gradient.m
View File

@ -1,66 +0,0 @@
function [F,G] = dynare_gradient(fcn,x,epsilon,varargin)
% Computes the gradient of a function from R^m in R^n.
%
% INPUTS:
% fcn [string] name of the matlab's function.
% x [double] m*1 vector (where the gradient is evaluated).
% epsilon [double] scalar or m*1 vector of steps.
%
% OUTPUTS:
% F [double] n*1 vector, evaluation of the function at x.
% G [double] n*m matrix, evaluation of the gradient at x.
%
% OUTPUTS
%
% Copyright © 2010-2017 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 <https://www.gnu.org/licenses/>.
% Evaluate the function at x.
F = feval(fcn, x, varargin{:});
% (G)Set dimensions.
m = length(x);
n = length(F);
% Initialization of the gradient.
G = NaN(length(F),length(x));
if length(epsilon==1)
H = epsilon*eye(m);
else
H = diag(epsilon);
end
% Compute the gradient.
for i=1:m
if size(x,1)>size(x,2)
h = H(i,:);
else
h = H(:,i);
end
[Fh,~,~,flag] = feval(fcn, x+transpose(h), varargin{:});
if flag
G(:,i) = (Fh-F)/epsilon;
else
[Fh,~,~,flag] = feval(fcn, x-transpose(h), varargin{:});
if flag
G(:,i) = (F-Fh)/epsilon;
else
error('-- Bad gradient --')
end
end
end

0
matlab/cartesian_product_of_sets.m → matlab/ep/cartesian_product_of_sets.m
View File

0
matlab/cubature_with_gaussian_weight.m → matlab/ep/cubature_with_gaussian_weight.m
View File

0
matlab/gauss_hermite_weights_and_nodes.m → matlab/ep/gauss_hermite_weights_and_nodes.m
View File

0
matlab/gauss_legendre_weights_and_nodes.m → matlab/ep/gauss_legendre_weights_and_nodes.m
View File

4
matlab/estimation/dsge_likelihood.m
View File

@ -482,14 +482,14 @@ if analytic_derivation
old_analytic_derivation_mode = options_.analytic_derivation_mode;
options_.analytic_derivation_mode = kron_flag;
if full_Hess
DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], true);
DERIVS = identification.get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], true);
indD2T = reshape(1:M_.endo_nbr^2, M_.endo_nbr, M_.endo_nbr);
indD2Om = dyn_unvech(1:M_.endo_nbr*(M_.endo_nbr+1)/2);
D2T = DERIVS.d2KalmanA(indD2T(iv,iv),:);
D2Om = DERIVS.d2Om(dyn_vech(indD2Om(iv,iv)),:);
D2Yss = DERIVS.d2Yss(iv,:,:);
else
DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], false);
DERIVS = identification.get_perturbation_params_derivs(M_, options_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indparam, indexo, [], false);
end
DT = zeros(M_.endo_nbr, M_.endo_nbr, size(DERIVS.dghx,3));
DT(:,M_.nstatic+(1:M_.nspred),:) = DERIVS.dghx;

2
matlab/estimation/dsge_simulated_theoretical_covariance.m
View File

@ -132,7 +132,7 @@ for file = 1:NumberOfDrawsFiles
if ~options_.pruning
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
else
pruned_state_space = pruned_state_space_system(M_, options_, dr, obs_var, options_.ar, 1, 0);
pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_, dr, obs_var, options_.ar, 1, 0);
tmp{1} = pruned_state_space.Var_y;
for i=1:nar
tmp{i+1} = pruned_state_space.Corr_yi(:,:,i);

29
matlab/evaluate_dynamic_model.m
View File

@ -1,29 +0,0 @@
function residuals = evaluate_dynamic_model(dynamicmodel, endogenousvariables, exogenousvariables, params, steadystate, leadlagincidence, samplesize)
% Copyright © 2016 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/>.
ny = length(steadystate);
periods = rows(exogenousvariables);
residuals = zeros(ny,samplesize);
icols = find(leadlagincidence');
for t = 2:samplesize+1
residuals(:,t-1) = dynamicmodel(endogenousvariables(icols), exogenousvariables, params, steadystate, t);
icols = icols + ny;
end

0
matlab/DsgeSmoother.m → matlab/kalman/DsgeSmoother.m
View File

0
matlab/evaluate_smoother.m → matlab/kalman/evaluate_smoother.m
View File

0
matlab/missing_DiffuseKalmanSmootherH1_Z.m → matlab/kalman/missing_DiffuseKalmanSmootherH1_Z.m
View File

0
matlab/missing_DiffuseKalmanSmootherH3_Z.m → matlab/kalman/missing_DiffuseKalmanSmootherH3_Z.m
View File

0
matlab/save_display_classical_smoother_results.m → matlab/kalman/save_display_classical_smoother_results.m
View File

0
matlab/store_smoother_results.m → matlab/kalman/store_smoother_results.m
View File

0
matlab/collect_latex_files.m → matlab/latex/collect_latex_files.m
View File

0
matlab/dyn_latex_table.m → matlab/latex/dyn_latex_table.m
View File

0
matlab/isbayes.m → matlab/latex/isbayes.m
View File

0
matlab/write_latex_definitions.m → matlab/latex/write_latex_definitions.m
View File

0
matlab/write_latex_parameter_table.m → matlab/latex/write_latex_parameter_table.m
View File

0
matlab/write_latex_prior_table.m → matlab/latex/write_latex_prior_table.m
View File

2
matlab/list_of_functions_to_be_cleared.m
View File

@ -1,2 +1,2 @@
list_of_functions = {'discretionary_policy_1', 'dsge_var_likelihood', 'dyn_first_order_solver', 'dyn_waitbar', 'ep_residuals', 'evaluate_likelihood', 'prior_draw_gsa', 'identification_analysis', 'computeDLIK', 'univariate_computeDLIK', 'metropolis_draw', 'flag_implicit_skip_nan', 'mr_hessian', 'masterParallel', 'auxiliary_initialization', 'auxiliary_particle_filter', 'conditional_filter_proposal', 'conditional_particle_filter', 'gaussian_filter', 'gaussian_filter_bank', 'gaussian_mixture_filter', 'gaussian_mixture_filter_bank', 'Kalman_filter', 'online_auxiliary_filter', 'pruned_state_space_system', 'sequential_importance_particle_filter', 'solve_model_for_online_filter', 'prior_draw', 'priordens',...
list_of_functions = {'discretionary_policy_1', 'dsge_var_likelihood', 'dyn_first_order_solver', 'dyn_waitbar', 'ep_residuals', 'evaluate_likelihood', '+gsa/prior_draw.m', '+identification/analysis.m', 'computeDLIK', 'univariate_computeDLIK', 'metropolis_draw', 'flag_implicit_skip_nan', 'mr_hessian', 'masterParallel', 'auxiliary_initialization', 'auxiliary_particle_filter', 'conditional_filter_proposal', 'conditional_particle_filter', 'gaussian_filter', 'gaussian_filter_bank', 'gaussian_mixture_filter', 'gaussian_mixture_filter_bank', 'Kalman_filter', 'online_auxiliary_filter', 'pruned_state_space_system', 'sequential_importance_particle_filter', 'solve_model_for_online_filter', 'prior_draw', 'priordens',...
'+occbin/solver.m','+occbin/mkdatap_anticipated_dyn.m','+occbin/mkdatap_anticipated_2constraints_dyn.m','+occbin/match_function.m','+occbin/solve_one_constraint.m','+occbin/solve_two_constraint.m','+occbin/plot/shock_decomposition.m'};

0
matlab/mcp_func.m → matlab/lmmcp/mcp_func.m
View File

0
matlab/fastgensylv.m → matlab/matrix_solver/fastgensylv.m
View File

0
matlab/gensylv_fp.m → matlab/matrix_solver/gensylv_fp.m
View File

0
matlab/lyapunov_solver.m → matlab/matrix_solver/lyapunov_solver.m
View File

0
matlab/lyapunov_symm.m → matlab/matrix_solver/lyapunov_symm.m
View File

0
matlab/quadratic_matrix_equation_solver.m → matlab/matrix_solver/quadratic_matrix_equation_solver.m
View File

0
matlab/sylvester3.m → matlab/matrix_solver/sylvester3.m
View File

0
matlab/sylvester3a.m → matlab/matrix_solver/sylvester3a.m
View File

0
matlab/UnivariateSpectralDensity.m → matlab/moments/UnivariateSpectralDensity.m
View File

0
matlab/add_filter_subtitle.m → matlab/moments/add_filter_subtitle.m
View File

0
matlab/compute_moments_varendo.m → matlab/moments/compute_moments_varendo.m
View File

0
matlab/conditional_variance_decomposition.m → matlab/moments/conditional_variance_decomposition.m
View File

0
matlab/conditional_variance_decomposition_ME_mc_analysis.m → matlab/moments/conditional_variance_decomposition_ME_mc_analysis.m
View File

Some files were not shown because too many files have changed in this diff Show More