%function msstart2(options_)
% This .m file is called for point graphics or error bands and
% It starts for both data and Bayesian estimation (when IxEstimat==0,
% no estimation but only data analysis), which allows you to set
% (a) available data range,
% (b) sample range,
% (c) rearrangement of actual data such as mlog, qg, yg
% (d) conditions of shocks 'Cms',
% (c) conditions of variables 'nconstr',
% (e) soft conditions 'nbancon,'
% (f) produce point conditional forecast (at least conditions on variables).
%
% February 2004
% Copyright © 2011-2017 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 .
% ** ONLY UNDER UNIX SYSTEM
%path(path,'/usr2/f1taz14/mymatlab')
%addpath('C:\SoftWDisk\MATLAB6p5\toolbox\cstz')
msstart_setup
%===========================================
% Exordium II
%===========================================
%options_.ms.ncsk = 0; % conditional directly on shoocks. Unlike Cms, not on variables that back out shocks
%options_.ms.nstd = 6; % number of standard deviations to cover the range in which distributions are put to bins
%options_.ms.ninv = 1000; % the number of bins for grouping, say, impulse responses
%options_.ms.Indxcol = [1:nvar]; % a vector of random columns in which MC draws are made.
%
%options_.ms.indxparr = 1; % 1: parameters random; 0: no randomness in parameters
% Note, when 0, there is no effect from the values of options_.ms.IndxAp, options_.ms.Aband, etc.
%options_.ms.indxovr = 0; % 1: distributions for other variables of interest; 0: no distribution.
% Example: joint distribution of a(1) and a(2). Only for specific purposes
%options_.ms.Aband = 1; % 1: error bands with only A0 and A+ random.
%options_.ms.IndxAp = 1; % 1: generate draws of A+; 0: no such draws.
% Note: when options_.ms.IndxAp=0, there is no effect from the values of options_.ms.options_.ms.options_.ms.options_.ms.indximf, IndxFore,
% or options_.ms.apband.
%options_.ms.apband = 1; % 1: error bands for A+; 0: no error bands for A+.
%*** The following (impulse responses and forecasts) is used only if options_.ms.IndxAp=1
%options_.ms.indximf = 1; % 1: generate draws of impulse responses; 0: no such draws (thus no effect
% from options_.ms.imfband)
%options_.ms.imfband = 1; % 1: error bands for impulse responses; 0: no error bands
%options_.ms.indxfore = 0; % 1: generate draws of forecasts; 0: no such draws (thus no effect from options_.ms.foreband)
%options_.ms.foreband = 0; % 1: error bands for out-of-sample forecasts; 0: no error bands
%
%options_.ms.indxgforhat = 1; % 1: plot unconditoinal forecasts; 0: no such plot
rnum = nvar; % number of rows in the graph
cnum = 1; % number of columns in the graph
if rnum*cnum>
nconstr=0 ; %6*nconstr1;
options_.ms.eq_ms = []; % location of MS equation; if [], all shocks
PorR = [4*ones(nconstr1,1);2*ones(nconstr1,1);3*ones(nconstr1,1)]; % the variable conditioned. 1: Pcm; 3: FFR; 4: CPI
PorR = [PorR;1*ones(nconstr1,1);5*ones(nconstr1,1);6*ones(nconstr1,1)];
%PorR = [3 5]; % the variable conditioned. 3: FFR; 5: CPI
%PorR = 3;
%%%%----------------------------------------
% Conditions directly on future shocks
%
%options_.ms.cms = 0 % 1: condition on ms shocks; 0: disable this and "fidcnderr.m" gives
% unconditional forecasts if nconstr = 0 as well; <<>>
%options_.ms.ncms = 0; % number of the stance of policy; 0 if no tightening or loosening
%options_.ms.eq_cms = 1; % location of MS shocks
options_.ms.tlindx = 1*ones(1,options_.ms.ncms); % 1-by-options_.ms.ncms vector; 1: tightening; 0: loosen
options_.ms.tlnumber = [0.5 0.5 0 0]; %94:4 % [2 2 1.5 1.5]; %79:9 %[1.5 1.5 1 1]; 90:9
% 1-by-options_.ms.ncms vector; cut-off point for MS shocks
TLmean = zeros(1,options_.ms.ncms);
% unconditional, i.e., 0 mean, for the final report in the paper
if options_.ms.cms
options_.ms.eq_ms = [];
% At least at this point, it makes no sense to have DLS type of options_.ms.eq_ms; 10/12/98
if all(isfinite(options_.ms.tlnumber))
for k=1:options_.ms.ncms
TLmean(k) = lcnmean(options_.ms.tlnumber(k),options_.ms.tlindx(k));
% shock mean magnitude. 1: tight; 0: loose
% Never used for any subsequent computation but
% simply used for the final report in the paper.
%options_.ms.tlnumber(k) = fzero('lcutoff',0,[],[],TLmean(k))
% get an idea about the cutoff point given TLmean instead
end
end
else
options_.ms.ncms = 0; % only for the use of the graph by msprobg.m
options_.ms.tlnumber = NaN*ones(1,options_.ms.ncms);
% -infinity, only for the use of the graph by msprobg.m
end
%%%%----------------------------------------
% Soft conditions on variables
%
%cnum = 0 % # of band condtions; when 0, disable this option
% Note (different from "fidencon") that each condition corres. to variable
%options_.ms.banact = 1; % 1: use infor on actual; 0: preset without infor on actual
if cnum
banindx = cell(cnum,1); % index for each variable or conditon
banstp = cell(cnum,1); % steps: annual in general
banvar = zeros(cnum,1); % varables: annual in general
banval = cell(cnum,1); % band value (each variable occupy a cell)
badval{1} = zeros(length(banstp{1}),2); % 2: lower or higher bound
banstp{1} = 1:4; % 3 or 4 years
banvar(1) = 3; % 3: FFR; 5: CPI
if ~options_.ms.banact
for i=1:length(banstp{1})
banval{1}(i,:) = [5.0 10.0];
end
end
end
%
pause(1)
skipline()
disp('For uncondtional forecasts, set nconstr = options_.ms.cms = cnum = 0')
pause(1)
%
%=================================================
%====== End of exordium ==========================
%=================================================
%(1)--------------------------------------
% Further data analysis
%(1)--------------------------------------
%
if (options_.ms.freq==12)
nStart=(yrStart-options_.ms.initial_year )*12+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
nEnd=(yrEnd-options_.ms.final_year )*12+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
elseif (options_.ms.freq==4)
nStart=(yrStart-options_.ms.initial_year )*4+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
nEnd=(yrEnd-options_.ms.final_year )*4+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
elseif (options_.ms.freq==1)
nStart=(yrStart-options_.ms.initial_year )*1+qmStart-options_.ms.initial_subperiod ; % positive number of months at the start
nEnd=(yrEnd-options_.ms.final_year )*1+qmEnd-options_.ms.final_subperiod ; % negative number of months towards end
else
error('Error: this code is only good for monthly/quarterly/yearly data!!!')
return
end
%
if nEnd>0 || nStart<0
disp('Warning: this particular sample consider is out of bounds of the data!!!')
return
end
%*** Note, both xdgel and xdata have the same start with the specific sample
xdgel=options_.data(nStart+1:nData+nEnd,options_.ms.vlist);
% gel: general options_.data within sample (nSample)
if ~(nSample==size(xdgel,1))
warning('The sample size (including options_.ms.nlags ) and data are incompatible')
disp('Check to make sure nSample and size(xdgel,1) are the same')
return
end
%
baddata = find(isnan(xdgel));
if ~isempty(baddata)
warning('Some data for this selected sample are actually unavailable.')
disp('Hit any key to continue, or ctrl-c to abort')
pause
end
%
if options_.ms.initial_subperiod ==1
yrB = options_.ms.initial_year ; qmB = options_.ms.initial_subperiod ;
else
yrB = options_.ms.initial_year +1; qmB = 1;
end
yrF = options_.ms.final_year ; qmF = options_.ms.final_subperiod ;
[Mdate,tmp] = fn_calyrqm(options_.ms.freq,[options_.ms.initial_year options_.ms.initial_subperiod ],[options_.ms.final_year options_.ms.final_subperiod ]);
xdatae=[Mdate options_.data(1:nData,options_.ms.vlist)];
% beyond sample into forecast horizon until the end of the data options_.ms.final_year :options_.ms.final_subperiod
% Note: may contain NaN data. So must be careful about its use
%=========== Obtain prior-period, period-to-last period, and annual growth rates
[yactyrge,yactyre,yactqmyge,yactqmge,yactqme] = fn_datana(xdatae,options_.ms.freq,options_.ms.log_var,options_.ms.percent_var,[yrB qmB],[yrF qmF]);
qdates = zeros(size(yactqmyge,1),1);
for ki=1:length(qdates)
qdates(ki) = yactqmyge(1,1) + (yactqmyge(1,2)+ki-2)/options_.ms.freq;
end
for ki=1:nvar
figure
plot(qdates, yactqmyge(:,2+ki)/100)
xlabel(options_.ms.varlist{ki})
end
save outactqmygdata.prn yactqmyge -ascii
%=========== Write the output on the screen or to a file in an organized way ==============
%disp([sprintf('%4.0f %2.0f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n',yactyrge')])
spstr1 = 'disp([sprintf(';
spstr2 = '%4.0f %2.0f';
yactyrget=yactyrge';
for ki=1:length(options_.ms.vlist)
if ki==length(options_.ms.vlist)
spstr2 = [spstr2 ' %8.3f\n'];
else
spstr2 = [spstr2 ' %8.3f'];
end
end
spstr = [spstr1 'spstr2' ', yactyrget)])'];
eval(spstr)
%
fid = fopen('outyrqm.prn','w');
%fprintf(fid,'%4.0f %2.0f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n',yactyrge');
fpstr1 = 'fprintf(fid,';
fpstr2 = '%4.0f %2.0f';
for ki=1:nvar
if ki==nvar
fpstr2 = [fpstr2 ' %8.3f\n'];
else
fpstr2 = [fpstr2 ' %8.3f'];
end
end
fpstr = [fpstr1 'fpstr2' ', yactyrget);'];
eval(fpstr)
fclose(fid);
if options_.ms.indxestima
%(2)----------------------------------------------------------------------------
% Estimation
% ML forecast and impulse responses
% Hard or soft conditions for conditional forecasts
%(2)----------------------------------------------------------------------------
%
%* Arranged data information, WITHOUT dummy obs when 0 after mu is used. See fn_rnrprior_covres_dobs.m for using the dummy
% observations as part of an explicit prior.
[xtx,xty,yty,fss,phi,y,ncoef,xr,Bh] = fn_dataxy(nvar,options_.ms.nlags ,xdgel,mu,0,nexo);
if qmStart+options_.ms.nlags -options_.ms.dummy_obs >0
qmStartEsti = rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
if (~qmStartEsti)
qmStartEsti = options_.ms.freq;
end
yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq+0.01));
% + 0.01 (or any number < 1) is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==?*options_.ms.freq doesn't give us an extra year forward.
else
qmStartEsti = options_.ms.freq + rem(qmStart+options_.ms.nlags -options_.ms.dummy_obs ,options_.ms.freq); % dummy observations are included in the sample.
if (qmStart+options_.ms.nlags -options_.ms.dummy_obs ==0)
yrStartEsti = yrStart - 1; % one year back.
else
yrStartEsti = yrStart + floor((qmStart+options_.ms.nlags -options_.ms.dummy_obs )/(options_.ms.freq-0.01));
% - 0.01 (or any number < 1) is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==-?*options_.ms.freq give us an extra year back.
end
end
dateswd = fn_dataext([yrStartEsti qmStartEsti],[yrEnd qmEnd],xdatae(:,[1:2])); % dates with dummies
phie = [dateswd phi];
ye = [dateswd y];
%* Obtain linear restrictions
[Uiconst,Viconst,n0,np,ixmC0Pres] = feval(options_.ms.restriction_fname,nvar,nexo,options_.ms );
if min(n0)==0
skipline()
warning('A0: restrictions in dlrprior.m give no free parameter in one of equations')
disp('Press ctrl-c to abort')
pause
elseif min(np)==0
skipline()
warning('Ap: Restrictions in dlrprior.m give no free parameter in one of equations')
disp('Press ctrl-c to abort')
pause
end
if options_.ms.contemp_reduced_form
Uiconst=cell(nvar,1); Viconst=cell(ncoef,1);
for kj=1:nvar
Uiconst{kj} = eye(nvar); Viconst{kj} = eye(ncoef);
end
end
if options_.ms.bayesian_prior
%*** Obtains asymmetric prior (with no linear restrictions) with dummy observations as part of an explicit prior (i.e,
% reflected in Hpmulti and Hpinvmulti). See Forecast II, pp.69a-69b for details.
if 1 % Liquidity effect prior on both MS and MD equations.
[Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior_covres_dobs(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu,indxDummy,hpmsmd,indxmsmdeqn);
else
[Pi,H0multi,Hpmulti,H0invmulti,Hpinvmulti] = fn_rnrprior(nvar,options_.ms.freq,options_.ms.nlags ,xdgel,mu);
end
%*** Combines asymmetric prior with linear restrictions
[Ptld,H0invtld,Hpinvtld] = fn_rlrprior(Uiconst,Viconst,Pi,H0multi,Hpmulti,nvar);
%*** Obtains the posterior matrices for estimation and inference
[Pmat,H0inv,Hpinv] = fn_rlrpostr(xtx,xty,yty,Ptld,H0invtld,Hpinvtld,Uiconst,Viconst);
if options_.ms.contemp_reduced_form
%*** Obtain the ML estimate
A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
A0hat=inv(A0hatinv);
a0indx = find(A0hat);
else
%*** Obtain the ML estimate
% load idenml
x = 10*rand(sum(n0),1);
H0 = eye(sum(n0));
crit = 1.0e-9;
nit = 10000;
%
[fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
A0hatinv = inv(A0hat);
fhat
xhat
grad
itct
fcount
retcodehat
save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
end
else
%*** Obtain the posterior matrices for estimation and inference
[Pmat,H0inv,Hpinv] = fn_dlrpostr(xtx,xty,yty,Uiconst,Viconst);
if options_.ms.contemp_reduced_form
%*** Obtain the ML estimate
A0hatinv = chol(H0inv{1}/fss); % upper triangular but lower triangular choleski
A0hat=inv(A0hatinv);
a0indx = find(A0hat);
else
%*** Obtain the ML estimate
% load idenml
x = 10*rand(sum(n0),1);
H0 = eye(sum(n0));
crit = 1.0e-9;
nit = 10000;
%
[fhat,xhat,grad,Hhat,itct,fcount,retcodehat] = ...
csminwel('fn_a0freefun',x,H0,'fn_a0freegrad',crit,nit,Uiconst,nvar,n0,fss,H0inv);
A0hat = fn_tran_b2a(xhat,Uiconst,nvar,n0)
A0hatinv = inv(A0hat);
fhat
xhat
grad
itct
fcount
retcodehat
save outm.mat xhat A0hat A0hatinv grad fhat itct itct fcount retcodehat
end
end
%**** impulse responses
swish = A0hatinv; % each column corresponds to an equation
if options_.ms.contemp_reduced_form
xhat = A0hat(a0indx);
Bhat=Pmat{1};
Fhat = Bhat*A0hat
ghat = NaN;
else
xhat = fn_tran_a2b(A0hat,Uiconst,nvar,n0);
[Fhat,ghat] = fn_gfmean(xhat,Pmat,Viconst,nvar,ncoef,n0,np);
if options_.ms.cross_restrictions
Fhatur0P = Fhat; % ur: unrestriced across A0 and A+
for ki = 1:size(ixmC0Pres,1) % loop through the number of equations in which
% cross-A0-A+ restrictions occur. See St. Louis Note p.5.
ixeq = ixmC0Pres{ki}(1,1); % index for the jth equation in consideration.
Lit = Viconst{ixeq}(ixmC0Pres{ki}(:,2),:); % transposed restriction matrix Li
% V_j(i,:) in f_j(i) = V_j(i,:)*g_j
ci = ixmC0Pres{ki}(:,4) .* A0hat(ixmC0Pres{ki}(:,3),ixeq);
% s * a_j(h) in the restriction f_j(i) = s * a_j(h).
LtH = Lit/Hpinv{ixeq};
HLV = LtH'/(LtH*Lit');
gihat = Viconst{ixeq}'*Fhatur0P(:,ixeq);
Fhat(:,ixeq) = Viconst{ixeq}*(gihat + HLV*(ci-Lit*gihat));
end
end
Fhat
Bhat = Fhat/A0hat; % ncoef-by-nvar reduced form lagged parameters.
end
nn = [nvar options_.ms.nlags imstp];
imfhat = fn_impulse(Bhat,swish,nn); % in the form that is congenial to RATS
imf3hat=reshape(imfhat,size(imfhat,1),nvar,nvar);
% imf3: row--steps, column--nvar responses, 3rd dimension--nvar shocks
imf3shat=permute(imf3hat,[1 3 2]);
% imf3s: permuted so that row--steps, column--nvar shocks,
% 3rd dimension--nvar responses
% Note: reshape(imf3s(1,:,:),nvar,nvar) = A0in (columns -- equations)
if options_.ms.indxgimfhat
figure
end
scaleout = fn_imcgraph(imfhat,nvar,imstp,xlab,ylab,options_.ms.indxgimfhat);
imfstd = max(abs(scaleout)'); % row: nvar (largest number); used for standard deviations
%
% %**** save stds. of both data and impulse responses in idfile1
% temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
% save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
% %
% %**** save stds. of both data and impulse responses in idfile1
% temp = [std(yactqmyge(:,3:end)); std(yactyrge(:,3:end)); imfstd]; %<<>>
% save idenyimstd.prn temp -ascii % export forecast and impulse response to the file "idenyimstd.prn", 3-by-nvar
% if options_.ms.indxparr
% idfile1='idenyimstd';
% end
%=====================================
% Now, out-of-sample forecasts. Note: Hm1t does not change with A0.
%=====================================
%
% * updating the last row of X (phi) with the current (last row of) y.
tcwx = nvar*options_.ms.nlags ; % total coefficeint without exogenous variables
phil = phi(size(phi,1),:);
phil(nvar+1:tcwx) = phil(1:tcwx-nvar);
phil(1:nvar) = y(end,:);
%*** exogenous variables excluding constant terms
if (nexo>1)
Xexoe = fn_dataext([yrEnd qmEnd],[yrEnd qmEnd],xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
phil(1,tcwx+1:tcwx+nexo-1) = Xexoe(1,3:end);
end
%
%*** ML unconditional point forecast
nn = [nvar options_.ms.nlags nfqm];
if nexo<2
yforehat = fn_forecast(Bhat,phil,nn); % nfqm-by-nvar, in log
else
Xfexoe = fn_dataext(fdates(1,:),fdates(numel(fdates),:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
%Xfexoe = fn_dataext(fdates(1,:),fdates(end,:),xdatae(:,[1:2 2+nvar+1:2+nvar+nexo-1]));
yforehat = fn_forecast(Bhat,phil,nn,nexo,Xfexoe(:,3:end)); % nfqm-by-nvar, in log
end
yforehate = [fdates yforehat];
%
yact1e = fn_dataext([yrEnd-nayr 1],[yrEnd qmEnd],xdatae(:,1:nvar+2));
if options_.ms.real_pseudo_forecast
%yact2e = fn_dataext([yrEnd-nayr 1],E2yrqm,xdatae);
yact2e = fn_dataext([yrEnd-nayr 1],[fdates(end,1) options_.ms.freq],xdatae(:,1:nvar+2));
else
yact2e=yact1e;
end
yafhate = [yact1e; yforehate]; % actual and forecast
%
%===== Converted to mg, qg, and calendar yg
%
[yafyrghate,yafyrhate,yafqmyghate] = fn_datana(yafhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
% actual and forecast growth rates
[yact2yrge,yact2yre,yact2qmyge] = fn_datana(yact2e,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
% only actual growth rates
yafyrghate
if options_.ms.indxgforhat
keyindx = [1:nvar];
conlab=['unconditional'];
figure
yafyrghate(:,3:end) = yafyrghate(:,3:end)/100;
yact2yrge(:,3:end) = yact2yrge(:,3:end)/100;
fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
end
%-------------------------------------------------
% Setup for point conditional forecast
% ML Conditional Forecast
%-------------------------------------------------
%
%% See Zha's note "Forecast (1)" p. 5, RATS manual (some errors in RATS), etc.
%
%% Some notations: y(t+1) = y(t)B1 + e(t+1)inv(A0). e(t+1) is 1-by-n.
%% Let r(t+1)=e(t+1)inv(A0) + e(t+2)C + .... where inv(A0) is impulse
%% response at t=1, C at t=2, etc. The row of inv(A0) or C is
%% all responses to one shock.
%% Let r be q-by-1 (such as r(1) = r(t+1)
%% = y(t+1) (constrained) - y(t+1) (forecast)).
%% Use impulse responses to find out R (k-by-q) where k=nvar*nsteps
%% where nsteps the largest constrained step. The key of the program
%% is to creat R using impulse responses
%% Optimal solution for shock e where R'*e=r and e is k-by-1 is
%% e = R*inv(R'*R)*r.
%
if (nconstr > 0)
%*** initializing
stepcon=cell(nconstr,1); % initializing, value y conditioned
valuecon=zeros(nconstr,1); % initializing, value y conditioned
varcon=zeros(nconstr,1); % initializing, endogous variables conditioned
varcon(:)=PorR; % 1: Pcm; 3: FFR; 5: CPI
%
for i=1:nconstr
if i<=nconstr1
stepcon{i}=i; % FFR
elseif i<=2*nconstr1
stepcon{i}=i-nconstr1; % FFR
elseif i<=3*nconstr1
stepcon{i}=i-2*nconstr1; % FFR
elseif i<=4*nconstr1
stepcon{i}=i-3*nconstr1; % FFR
elseif i<=5*nconstr1
stepcon{i}=i-4*nconstr1; % FFR
elseif i<=6*nconstr1
stepcon{i}=i-5*nconstr1; % FFR
end
end
% for i=1:nconstr
% stepcon{i}=i; % FFR
% end
% bend=12;
% stepcon{1}=[1:bend]'; % average over
% stepcon{nconstr1+1}=[1:options_.ms.freq-qmSub]'; % average over the remaing months in 1st forecast year
% stepcon{nconstr1+2}=[options_.ms.freq-qmSub+1:options_.ms.freq-qmSub+12]'; % average over 12 months next year
% stepcon{nconstr1+3}=[options_.ms.freq-qmSub+13:options_.ms.freq-qmSub+24]'; % average over 12 months. 3rd year
% stepcon{nconstr1+4}=[options_.ms.freq-qmSub+25:options_.ms.freq-qmSub+36]'; % average over 12 months. 4th year
% %**** avearage condition over, say, options_.ms.freq periods
% if qmEnd==options_.ms.freq
% stepcon{1}=[1:options_.ms.freq]'; % average over the remaing periods in 1st forecast year
% else
% stepcon{1}=[1:options_.ms.freq-qmEnd]'; % average over the remaing periods in 1st forecast year
% end
% for kj=2:nconstr
% stepcon{kj}=[length(stepcon{kj-1})+1:length(stepcon{kj-1})+options_.ms.freq]'; % average over 12 months next year
% end
if options_.ms.real_pseudo_forecast
% %*** conditions in every period
% for i=1:nconstr
% valuecon(i) = yact(actup+i,varcon(i));
% %valuecon(i) = mean( yact(actup+1:actup+bend,varcon(i)) );
% %valuecon(i) = 0.060; % 95:01
% %valuecon(i) = (0.0475+0.055)/2; % 94:10
% end
% %*** average condtions over,say, options_.ms.freq periods.
% for i=nconstr1+1:nconstr1+nconstr2
% i=1;
% valuecon(nconstr1+i) = ( ( mean(ylast12Cal(:,varcon(nconstr1+i)),1) + ...
% log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100) )*options_.ms.freq - ...
% yCal_1(:,varcon(nconstr1+i)) ) ./ length(stepcon{nconstr1+i});
% % the same as unconditional "yactCalyg" 1st calendar year
% i=2;
% valuecon(nconstr1+i) = mean(ylast12Cal(:,varcon(nconstr1+i))) + ...
% log(1+yactCalyg(yAg-yFg+1,varcon(nconstr1+i))/100) ...
% + log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
% % the same as actual "yactCalgy" 2nd calendar year
% i=3;
% valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
% log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
% % the same as actual "yactCalgy" 3rd calendar year
% %i=4;
% %valuecon(nconstr1+i) = valuecon(nconstr1+i-1) + ...
% % log(1+yactCalyg(yAg-yFg+i,varcon(nconstr1+i))/100);
% % the same as actual "yactCalgy" 4th calendar year
% end
%*** conditions in every period
vpntM = fn_dataext(E1yrqm, E2yrqm,xdatae); % point value matrix with dates
% vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
for i=1:nconstr
if i<=nconstr1
valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates
elseif i<=2*nconstr1
valuecon(i) = vpntM(i-nconstr1,2+varcon(i));
elseif i<=3*nconstr1
valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i));
elseif i<=4*nconstr1
valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i));
elseif i<=5*nconstr1
valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i));
elseif i<=6*nconstr1
valuecon(i) = vpntM(i-5*nconstr1,2+varcon(i));
end
end
% %*** average condtions over,say, options_.ms.freq periods.
% if qmEnd==options_.ms.freq
% vaveM = fn_dataext([yrEnd+1 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
% valuecon(1) = vaveM(1,2+varcon(1)); % 2: first 2 elements are dates
% else
% vaveM = fn_dataext([yrEnd 0],[yrEnd+options_.forecast 0],yact2yre); % average value matrix with dates
% yactrem = fn_dataext([yrEnd qmEnd+1],[yrEnd options_.ms.freq],xdatae);
% valuecon(1) = sum(yactrem(:,2+varcon(1)),1)/length(stepcon{1});
% % 2: first 2 elements are dates
% end
% for kj=2:nconstr
% valuecon(kj) = vaveM(kj,2+varcon(kj)); % 2: first 2 elements are dates
% end
else
vpntM = dataext([yrEnd qmEnd+1],[yrEnd qmEnd+2],xdatae); % point value matrix with dates
for i=1:nconstr
if i<=nconstr1
valuecon(i) = vpntM(i,2+varcon(i)); % 2: first 2 elements are dates; Poil
elseif i<=2*nconstr1
valuecon(i) = vpntM(i-nconstr1,2+varcon(i)); % 2: first 2 elements are dates; M2
elseif i<=3*nconstr1
valuecon(i) = vpntM(i-2*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; FFR
elseif i<=4*nconstr1
valuecon(i) = vpntM(i-3*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; CPI
elseif i<=5*nconstr1
valuecon(i) = vpntM(i-4*nconstr1,2+varcon(i)); % 2: first 2 elements are dates; U
elseif i<=5*nconstr1+nconstr2
valuecon(i)=xdata(end,5)+(i-5*nconstr1)*log(1.001)/options_.ms.freq; %CPI
elseif i<=5*nconstr1+2*nconstr2
valuecon(i)=0.0725; %FFR
else
valuecon(i)=xdata(end,6)+(i-5*nconstr1-2*nconstr2)*0.01/nfqm; %U
end
end
%valuecon(i) = 0.060; % 95:01
end
else
valuecon = [];
stepcon = [];
varcon = [];
end
nstepsm = 0; % initializing, the maximum step in all constraints
for i=1:nconstr
nstepsm = max([nstepsm max(stepcon{i})]);
end
if cnum
if options_.ms.real_pseudo_forecast && options_.ms.banact
for i=1:length(banstp{1})
banval{1}(1:length(banstp{1}),1) = ...
yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) - 2;
banval{1}(1:length(banstp{1}),2) = ...
yactCalyg(yAg-yFg+1:yAg-yFg+length(banstp{1}),banvar(1)) + 2;
end
end
end
%===================================================
% ML conditional forecast
%===================================================
%/*
[ychat,Estr,rcon] = fn_fcstidcnd(valuecon,stepcon,varcon,nstepsm,...
nconstr,options_.ms.eq_ms,nvar,options_.ms.nlags ,phil,0,0,yforehat,imf3shat,A0hat,Bhat,...
nfqm,options_.ms.tlindx,options_.ms.tlnumber,options_.ms.ncms,options_.ms.eq_cms);
ychate = [fdates ychat];
yachate = [yact1e; ychate]; % actual and condtional forecast
%===== Converted to mg, qg, and calendar yg
[yacyrghate,yacyrhate,yacqmyghate] = fn_datana(yachate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
% actual and conditional forecast growth rates
if options_.ms.indxgdls && nconstr
keyindx = [1:nvar];
% conlab=['conditional on' ylab{PorR(1)}];
conlab=['v-conditions'];
figure
fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
end
if options_.ms.ncsk
Estr = zeros(nfqm,nvar);
Estr(1:2,:) = [
-2.1838 -1.5779 0.53064 -0.099425 -0.69269 -1.0391
1.9407 3.3138 -0.10563 -0.55457 -0.68772 1.3534
];
Estr(3:6,3) = [0.5*ones(1,4)]'; % MD shocks
Estr(3:10,2) = [1.5 1.5 1.5*ones(1,6)]'; % MS shocks
%Estr(3:6,6) = 1*ones(4,1); % U shocks
%Estr(8:11,4) = 1*ones(4,1); % y shocks
%Estr(3:10,2) = [2.5 2.5 1.5*ones(1,6)]'; % MS shocks alone
nn = [nvar options_.ms.noptions_.ms.nlags nfqm];
ycEhat = forefixe(A0hat,Bhat,phil,nn,Estr);
ycEhate = [fdates ycEhat];
yacEhate = [yact1e; ycEhate]; % actual and condtional forecast
%===== Converted to mg, qg, and calendar yg
[yacEyrghate,yacEyrhate,yacEqmyghate] = datana(yacEhate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
% actual and conditional forecast growth rates
disp([sprintf('%4.0f %2.0f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n',yacEyrghate')])
if 1
keyindx = [1:nvar];
% conlab=['conditional on' ylab{PorR(1)}];
conlab=['shock-conditions'];
figure
gyrfore(yacEyrghate,yact2yrge,keyindx,rnum,cnum,ylab,forelabel,conlab)
end
end
%-----------------------------------------------------------
% Compute structural shocks for the whole sample period excluding dummy observations.
%-----------------------------------------------------------
ywod = y(options_.ms.dummy_obs +1:end,:); % without dummy observations
phiwod=phi(options_.ms.dummy_obs +1:end,:); % without dummy observations
eplhat=ywod*A0hat-phiwod*Fhat;
qmStartWod = mod(qmStart+options_.ms.nlags ,options_.ms.freq);
if (~qmStartWod)
qmStartWod = options_.ms.freq;
end
yrStartWod = yrStart + floor((qmStart+options_.ms.nlags -1)/options_.ms.freq);
dateswod = fn_dataext([yrStartWod qmStartWod],[yrEnd qmEnd],xdatae(:,[1:2]));
eplhate = [dateswod eplhat];
Aphat = Fhat;
end
%----------------------------------------
% Tests for LR, HQ, Akaike, Schwarz. The following gives a guidance.
% But the computation has to be done in a different M file by exporting fhat's
% from different idfile's.
%----------------------------------------
%
%if ~options_.ms.contemp_reduced_form
% SpHR=A0in'*A0in;
%end
%%
%if ~isnan(SpHR) && ~options_.ms.contemp_reduced_form
% warning(' ')
% disp('Make sure you run the program with options_.ms.contemp_reduced_form =1 first.')
% disp('Otherwise, the following test results such as Schwartz are incorrect.')
% disp('All other results such as A0ml and imfs, however, are correct.')
% disp('Press anykey to contintue or ctrl-c to stop now')
% pause
% load SpHUout
% logLHU=-fss*sum(log(diag(chol(SpHU)))) -0.5*fss*nvar % unrestricted logLH
% logLHR=-fhat % restricted logLH
% tra = reshape(SpHU,nvar*nvar,1)'*reshape(A0*A0',nvar*nvar,1);
% df=(nvar*(nvar+1)/2 - length(a0indx));
% S=2*(logLHU-logLHR);
% SC = (nvar*(nvar+1)/2 - length(a0indx)) * log(fss);
% disp(['T -- effective sample size: ' num2str(fss)])
% disp(['Trace in the overidentified posterior: ' num2str(tra)])
% disp(['Chi2 term -- 2*(logLHU-logLHR): ' num2str(S)])
% disp(['Degrees of freedom: ' num2str(df)])
% disp(['SC -- df*log(T): ' num2str(SC)])
% disp(['Akaike -- 2*df: ' num2str(2*df)])
% disp(['Classical Asymptotic Prob at chi2 term: ' num2str(cdf('chi2',S,df))])
% %*** The following is the eigenanalysis in the difference between
% %*** unrestricted (U) and restricted (R)
% norm(A0'*SpHU*A0-diag(diag(ones(6))))/6;
% norm(SpHU-A0in'*A0in)/6;
% corU = corr(SpHU);
% corR = corr(SpHR);
% [vU,dU]=eigsort(SpHU,1);
% [vR,dR]=eigsort(SpHR,1);
% [log(diag(dU)) log(diag(dR)) log(diag(dU))-log(diag(dR))];
% sum(log(diag(dU)));
% sum(log(diag(dR)));
%else
% disp('To run SC test, turn options_.ms.contemp_reduced_form =1 first and then turn options_.ms.contemp_reduced_form =0')
%end
%***** Simply regression
%X=[phi(:,3) y(:,2)-phi(:,2) y(:,1)-phi(:,7) ones(fss,1)];
%� Y=y(:,3);
%� b=regress(Y,X)
%=== Computes the roots for the whole system.
rootsinv = fn_varoots(Bhat,nvar,options_.ms.nlags )
abs(rootsinv)
bhat =xhat;
n0const=n0; % For constant parameter models.
n0const=n0; % For constant parameter models.
npconst=np; % For constant parameter models.
save outdata_a0dp_const.mat A0hat bhat Aphat n0const