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Tidying up Partial information extension to stoch_simul, PCL_Part_info_irf.m and PCL_Part_info_moments.m

time-shift
George Perendia 2010-03-24 23:26:05 +00:00
parent 667d5ac262
commit 0be001d194
3 changed files with 38 additions and 50 deletions
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45
matlab/partial_information/PCL_Part_info_irf.m
View File

@ -1,11 +1,11 @@
function [irfmat,irfst]=PCL_Part_info_irf( H, varobs, M_, dr, irfpers,ii)
function y=PCL_Part_info_irf( H, varobs, ivar, M_, dr, irfpers,ii)
% sets up parameters and calls part-info kalman filter
% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
% suit partial information RE solution in accordance with, and based on, the
% Pearlman, Currie and Levine 1986 solution.
% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
% Copyright (C) 2006-2010 Dynare Team
% Copyright (C) 2001-20010 Dynare Team
%
% This file is part of Dynare.
%
@ -49,16 +49,6 @@ function [irfmat,irfst]=PCL_Part_info_irf( H, varobs, M_, dr, irfpers,ii)
end
end
if exist( 'irfpers')==1
if ~isempty(irfpers)
if irfpers<=0, irfpers=20, end;
else
irfpers=20;
end
else
irfpers=20;
end
ss=size(G1,1);
pd=ss-size(nmat,1);
SDX=M_.Sigma_e^0.5; % =SD,not V-COV, of Exog shocks or M_.Sigma_e^0.5 num_exog x num_exog matrix
@ -86,8 +76,8 @@ function [irfmat,irfst]=PCL_Part_info_irf( H, varobs, M_, dr, irfpers,ii)
% o(t)=K1*[eps(t)' s(t-1)' x(t-1)']' + K2*x(t) where
% K1=[L1*H1 L1*G11 L1*G12] K2=L1*G13+L2
G12=G1(NX+1:ss-2*FL_RANK,:);
KK1=L1*G12;
G12=G1(NX+1:ss-2*FL_RANK,:);
KK1=L1*G12;
K1=KK1(:,1:ss-FL_RANK);
K2=KK1(:,ss-FL_RANK+1:ss)+L2;
@ -97,13 +87,11 @@ function [irfmat,irfst]=PCL_Part_info_irf( H, varobs, M_, dr, irfpers,ii)
A12=G1(1:pd, pd+1:end);
A21=G1(pd+1:end,1:pd);
Lambda= nmat*A12+A22;
%A11_A12Nmat= A11-A12*nmat % test
I_L=inv(Lambda);
BB=A12*inv(A22);
FF=K2*inv(A22);
QQ=BB*U22*BB' + U11;
UFT=U22*FF';
% kf_param structure:
AA=A11-BB*A21;
CCCC=A11-A12*nmat; % F in new notation
DD=K1-FF*A21; % H in new notation
@ -138,25 +126,22 @@ function [irfmat,irfst]=PCL_Part_info_irf( H, varobs, M_, dr, irfpers,ii)
DPDR=DD*PP*DD'+RR;
I_DPDR=inv(DPDR);
PDIDPDRD=PP*DD'*I_DPDR*DD;
%GG=[CCCC (AA-CCCC)*(eye(ss-FL_RANK)-PP*DD'*I_DQDR*DD); zeros(ss-FL_RANK) AA*(eye(ss-FL_RANK)-PP*DD'*I_DQDR*DD)];
GG=[CCCC (AA-CCCC)*(eye(ss-FL_RANK)-PDIDPDRD); zeros(ss-FL_RANK) AA*(eye(ss-FL_RANK)-PDIDPDRD)];
imp=[impact(1:ss-FL_RANK,:); impact(1:ss-FL_RANK,:)];
% Calculate IRFs of observable series
%The extra term in leads to
%LL0=[EE (H-EE)(I-PH^T(HPH^T+V)^{-1}H)].
%Then in the case of observing all variables without noise (V=0), this
% leads to LL0=[EE 0].
I_PD=(eye(ss-FL_RANK)-PDIDPDRD);
LL0=[ EE (DD-EE)*I_PD];
%OVV = [ zeros( size(dr.PI_TT1,1), NX ) dr.PI_TT1 dr.PI_TT2];
VV = [ dr.PI_TT1 dr.PI_TT2];
stderr=diag(M_.Sigma_e^0.5);
irfmat=zeros(size(dr.PI_TT1 ,1),irfpers);
irfst=zeros(size(GG,1),irfpers);
irfst(:,1)=stderr(ii)*imp(:,ii);
for jj=2:irfpers;
irfst(:,jj)=GG*irfst(:,jj-1); % xjj=f irfstjj-2
irfmat=zeros(size(dr.PI_TT1 ,1),irfpers+1);
irfst=zeros(size(GG,1),irfpers+1);
irfst(:,1)=stderr(ii)*imp(:,ii);
for jj=2:irfpers+1;
irfst(:,jj)=GG*irfst(:,jj-1);
irfmat(:,jj-1)=VV*irfst(NX+1:ss-FL_RANK,jj);
%irfmat(:,jj)=LL0*irfst(:,jj);
end
save ([M_.fname '_PCL_PtInfoIRFs_' num2str(ii) '_' deblank(exo_names(ii,:))], 'irfmat','irfst');
end
y=zeros(M_.endo_nbr,irfpers);
y(:,:)=irfmat(:,1:irfpers);
save ([M_.fname '_PCL_PtInfoIRFs_' num2str(ii) '_' deblank(exo_names(ii,:))], 'irfmat','irfst');

11
matlab/partial_information/PCL_Part_info_moments.m
View File

@ -1,11 +1,11 @@
function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
function AutoCOR_YRk=PCL_Part_info_irmoments( H, varobs, dr,ivar)
% sets up parameters and calls part-info kalman filter
% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
% suit partial information RE solution in accordance with, and based on, the
% Pearlman, Currie and Levine 1986 solution.
% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
% Copyright (C) 2006-2010 Dynare Team
% Copyright (C) 2001-20010 Dynare Team
%
% This file is part of Dynare.
%
@ -30,7 +30,7 @@ function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
global M_ options_ oo_
warning_old_state = warning;
warning off
[junk,OBS] = ismember(varobs,M_.endo_names,'rows');
G1=dr.PI_ghx;
@ -38,7 +38,6 @@ function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
nmat=dr.PI_nmat;
CC=dr.PI_CC;
NX=M_.exo_nbr; % no of exogenous varexo shock variables.
% NETA=dr.nfwrd+dr.nboth; % total no of exp. errors set to no of forward looking equations
FL_RANK=dr.PI_FL_RANK;
NY=M_.endo_nbr;
if isempty(OBS)
@ -80,7 +79,6 @@ function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
MM1=MM(1:ss-FL_RANK,:);
U11=MM1*MM1';
% SDX
U22=0;
% determine K1 and K2 observation mapping matrices
% This uses the fact that measurements are given by L1*s(t)+L2*x(t)
@ -101,7 +99,6 @@ function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
A12=G1(1:pd, pd+1:end);
A21=G1(pd+1:end,1:pd);
Lambda= nmat*A12+A22;
%A11_A12Nmat= A11-A12*nmat % test
I_L=inv(Lambda);
BB=A12*inv(A22);
FF=K2*inv(A22);
@ -141,7 +138,6 @@ function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
DPDR=DD*PP*DD'+RR;
I_DPDR=inv(DPDR);
%GG=[ CCCC, zeros(pd,NETA); -nmat*CCCC, zeros(NETA,NETA)];
PDIDPDRD=PP*DD'*I_DPDR*DD;
MSIG=disclyap_fast(CCCC, CCCC*PDIDPDRD*PP*CCCC');
@ -166,7 +162,6 @@ function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
end
if options_.nocorr == 0
diagSqrtCovYR0=sqrt(diagCovYR0);
%COR_Y= diag(diagSqrtCovYR0)*COV_YR0*diag(diagSqrtCovYR0);
DELTA=inv(diag(diagSqrtCovYR0));
COR_Y= DELTA*COV_YR0*DELTA;
title = 'CORRELATION OF SIMULATED VARIABLES';

32
matlab/stoch_simul.m
View File

@ -30,9 +30,9 @@ elseif options_.order == 3
end
if options_.partial_information == 1 || options_.ACES_solver == 1
ACES_solver = 1;
PI_PCL_solver = 1;
else
ACES_solver = 0;
PI_PCL_solver = 0;
end
TeX = options_.TeX;
@ -50,7 +50,7 @@ end
check_model;
if ACES_solver
if PI_PCL_solver
[oo_.dr, info] = PCL_resol(oo_.steady_state,0);
else
[oo_.dr, info] = resol(oo_.steady_state,0);
@ -82,18 +82,26 @@ if ~options_.noprint
disp(' ')
disp(' SOLUTION UNDER PARTIAL INFORMATION')
disp(' ')
disp(' OBSERVED VARIABLES')
for i=1:size(options_.varobs,1)
disp([' ' options_.varobs(i,:)])
if isfield(options_,'varobs')&& ~isempty(options_.varobs)
PCL_varobs=options_.varobs;
disp(' OBSERVED VARIABLES')
else
PCL_varobs=var_list;
disp(' VAROBS LIST NOT SPECIFIED')
disp(' ASSUMED OBSERVED VARIABLES')
end
for i=1:size(PCL_varobs,1)
disp([' ' PCL_varobs(i,:)])
end
end
disp(' ')
if options_.order <= 2 && ~ACES_solver
if options_.order <= 2 && ~PI_PCL_solver
disp_dr(oo_.dr,options_.order,var_list);
end
end
if options_.periods > 0 && ~ACES_solver
if options_.periods > 0 && ~PI_PCL_solver
if options_.periods < options_.drop
disp(['STOCH_SIMUL error: The horizon of simulation is shorter' ...
' than the number of observations to be DROPed'])
@ -105,8 +113,8 @@ if options_.periods > 0 && ~ACES_solver
end
if options_.nomoments == 0
if ACES_solver
PCL_Part_info_moments (0, options_.varobs, oo_.dr, i_var);
if PI_PCL_solver
PCL_Part_info_moments (0, PCL_varobs, oo_.dr, i_var);
elseif options_.periods == 0
disp_th_moments(oo_.dr,var_list);
else
@ -133,8 +141,8 @@ if options_.irf
end
for i=1:M_.exo_nbr
if SS(i,i) > 1e-13
if ACES_solver
y=PCL_Part_info_irf (0, options_.varobs, M_, oo_.dr, options_.irf, i);
if PI_PCL_solver
y=PCL_Part_info_irf (0, PCL_varobs, i_var, M_, oo_.dr, options_.irf, i);
else
y=irf(oo_.dr,cs(M_.exo_names_orig_ord,i), options_.irf, options_.drop, ...
options_.replic, options_.order);