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Merge remote-tracking branch 'houtanb/master'

time-shift
Sébastien Villemot 2012-02-03 08:51:06 +01:00
parent c6849a0194 dd1a48c7c7
commit b8e1ecbcec
8 changed files with 1407 additions and 1402 deletions
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3
matlab/ms-sbvar/cstz/fn_foregraph.m
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@ -39,8 +39,7 @@ hornum = cell(length(vyrs),1); % horizontal year (number)
count=0; count=0;
for k=vyrs' for k=vyrs'
count=count+1; count=count+1;
jnk=num2str(k); hornum{count}=num2str(k);
hornum{count}=jnk(3:4); % e.g., with '1990', we have '90'
end end
count=0; count=0;

2
matlab/ms-sbvar/cstz/fn_rnrprior_covres_dobs.m
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@ -141,6 +141,8 @@ for i = 1:lags
sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
elseif (q_m==4) elseif (q_m==4)
sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
elseif (q_m==1)
sgpbid((i-1)*nvar+j) = (1/(i*4)^mu(4))^2/sgsh(j); % ith equation
else else
error('Incompatibility with lags, check the possible errors!!!') error('Incompatibility with lags, check the possible errors!!!')
%warning('Incompatibility with lags, check the possible errors!!!') %warning('Incompatibility with lags, check the possible errors!!!')

2
matlab/ms-sbvar/cstz/fn_rnrprior_covres_dobs_tv2.m
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@ -163,6 +163,8 @@ for i = 1:lags
sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation sgpbid((i-1)*nvar+j) = lagdecay^2/sgsh(j); % ith equation
elseif (q_m==4) elseif (q_m==4)
sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation sgpbid((i-1)*nvar+j) = (1/i^mu(4))^2/sgsh(j); % ith equation
elseif (q_m==1)
sgpbid((i-1)*nvar+j) = (1/(i*4)^mu(4))^2/sgsh(j); % ith equation
else else
error('Incompatibility with lags, check the possible errors!!!') error('Incompatibility with lags, check the possible errors!!!')
%warning('Incompatibility with lags, check the possible errors!!!') %warning('Incompatibility with lags, check the possible errors!!!')

394
matlab/ms-sbvar/ms_mardd.m
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@ -1,197 +1,197 @@
function ms_mardd(options_) function ms_mardd(options_)
% Applies to both linear and exclusion restrictions. % Applies to both linear and exclusion restrictions.
% (1) Marginal likelihood function p(Y) for constant structural VAR models, using Chib (1995)'s ``Marginal Likelihood from the Gibbs Output'' in JASA. % (1) Marginal likelihood function p(Y) for constant structural VAR models, using Chib (1995)'s ``Marginal Likelihood from the Gibbs Output'' in JASA.
% (2) Conditional likelihood function f(Y|A0, A+) on the ML estimate for constant exclusion-identified models. % (2) Conditional likelihood function f(Y|A0, A+) on the ML estimate for constant exclusion-identified models.
% See Forecast (II) pp.67-80. % See Forecast (II) pp.67-80.
% %
% Tao Zha, September 1999. Quick revisions, May 2003. Final revision, September 2004. % Tao Zha, September 1999. Quick revisions, May 2003. Final revision, September 2004.
% Copyright (C) 2011 Dynare Team % Copyright (C) 2011 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
% Dynare is free software: you can redistribute it and/or modify % Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by % it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or % the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version. % (at your option) any later version.
% %
% Dynare is distributed in the hope that it will be useful, % Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of % but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details. % GNU General Public License for more details.
% %
% You should have received a copy of the GNU General Public License % You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
msstart2 % start the program in which everyhting is initialized through msstart2.m msstart2 % start the program in which everyhting is initialized through msstart2.m
if ~options_.ms.indxestima if ~options_.ms.indxestima
warning(' ') warning(' ')
disp('You must set IxEstima=1 in msstart to run this program') disp('You must set IxEstima=1 in msstart to run this program')
disp('Press ctrl-c to abort now') disp('Press ctrl-c to abort now')
pause pause
end end
A0xhat = zeros(size(A0hat)); A0xhat = zeros(size(A0hat));
Apxhat = zeros(size(Aphat)); Apxhat = zeros(size(Aphat));
if (0) if (0)
%Robustness check to see if the same result is obtained with the purterbation of the parameters. %Robustness check to see if the same result is obtained with the purterbation of the parameters.
for k=1:nvar for k=1:nvar
bk = Uiconst{k}'*A0hat(:,k); bk = Uiconst{k}'*A0hat(:,k);
gk = Viconst{k}'*Aphat(:,k); gk = Viconst{k}'*Aphat(:,k);
A0xhat(:,k) = Uiconst{k}*(bk + 5.2*randn(size(bk))); % Perturbing the posterior estimate. A0xhat(:,k) = Uiconst{k}*(bk + 5.2*randn(size(bk))); % Perturbing the posterior estimate.
Apxhat(:,k) = Viconst{k}*(gk + 5.2*randn(size(gk))); % Perturbing the posterior estimate. Apxhat(:,k) = Viconst{k}*(gk + 5.2*randn(size(gk))); % Perturbing the posterior estimate.
end end
else else
%At the posterior estimate. %At the posterior estimate.
A0xhat = A0hat; % ML estimate of A0 A0xhat = A0hat; % ML estimate of A0
Apxhat = Aphat; % ML estimate of A+ Apxhat = Aphat; % ML estimate of A+
end end
%--- Rename variables. %--- Rename variables.
YatYa = yty; YatYa = yty;
XatYa = xty; XatYa = xty;
ytx = xty'; ytx = xty';
YatXa = ytx; YatXa = ytx;
XatXa = xtx; XatXa = xtx;
%--------- The log value of p(A0,A+) at some point such as the peak ---------- %--------- The log value of p(A0,A+) at some point such as the peak ----------
vlog_a0p = 0; vlog_a0p = 0;
Yexpt=0; % exponential term for Y in p(Y|A0,A+) at some point such as the peak Yexpt=0; % exponential term for Y in p(Y|A0,A+) at some point such as the peak
Apexpt=0.0; % 0.0 because we have chosen posterior estimate of A+ as A+*. Exponential term for A+ conditional on A0 and Y Apexpt=0.0; % 0.0 because we have chosen posterior estimate of A+ as A+*. Exponential term for A+ conditional on A0 and Y
%======= Computing the log prior pdf of a0a+ and the exponential term for Y in p(Y|A0,A+). %======= Computing the log prior pdf of a0a+ and the exponential term for Y in p(Y|A0,A+).
for k=1:nvar for k=1:nvar
a0k = A0xhat(:,k); % meaningful parameters in the kth equation. a0k = A0xhat(:,k); % meaningful parameters in the kth equation.
apk = Apxhat(:,k); % meaningful parameters in the kth equation. apk = Apxhat(:,k); % meaningful parameters in the kth equation.
%--- Prior settings. %--- Prior settings.
S0bar = H0invtld{k}; %See Claim 2 on p.69b. S0bar = H0invtld{k}; %See Claim 2 on p.69b.
Spbar = Hpinvtld{k}; Spbar = Hpinvtld{k};
bk = Uiconst{k}'*a0k; % free parameters in the kth equation. bk = Uiconst{k}'*a0k; % free parameters in the kth equation.
gk = Viconst{k}'*apk; % free parameters in the kth equation. gk = Viconst{k}'*apk; % free parameters in the kth equation.
gbark = Ptld{k}*bk; % bar: prior gbark = Ptld{k}*bk; % bar: prior
%--- The exponential term for Y in p(Y|A0,A+) %--- The exponential term for Y in p(Y|A0,A+)
Yexpt = Yexpt - 0.5*(a0k'*YatYa*a0k - 2*apk'*XatYa*a0k + apk'*XatXa*apk); Yexpt = Yexpt - 0.5*(a0k'*YatYa*a0k - 2*apk'*XatYa*a0k + apk'*XatXa*apk);
%--- The log prior pdf. %--- The log prior pdf.
vlog_a0p = vlog_a0p - 0.5*(size(Uiconst{k},2)+size(Viconst{k},2))*log(2*pi) + 0.5*log(abs(det(S0bar))) + ... vlog_a0p = vlog_a0p - 0.5*(size(Uiconst{k},2)+size(Viconst{k},2))*log(2*pi) + 0.5*log(abs(det(S0bar))) + ...
0.5*log(abs(det(Spbar))) - 0.5*(bk'*S0bar*bk+(gk-gbark)'*Spbar*(gk-gbark)); 0.5*log(abs(det(Spbar))) - 0.5*(bk'*S0bar*bk+(gk-gbark)'*Spbar*(gk-gbark));
%--- For p(A+|Y,a0) only. %--- For p(A+|Y,a0) only.
tmpd = gk - Pmat{k}*bk; tmpd = gk - Pmat{k}*bk;
Apexpt = Apexpt - 0.5*tmpd'*(Hpinv{k}*tmpd); Apexpt = Apexpt - 0.5*tmpd'*(Hpinv{k}*tmpd);
end end
vlog_a0p vlog_a0p
%--------- The log value of p(Y|A0,A+) at some point such as the peak. ---------- %--------- The log value of p(Y|A0,A+) at some point such as the peak. ----------
%--------- Note that logMarLHres is the same as vlog_Y_a, just to double check. ---------- %--------- Note that logMarLHres is the same as vlog_Y_a, just to double check. ----------
vlog_Y_a = -0.5*nvar*fss*log(2*pi) + fss*log(abs(det(A0xhat))) + Yexpt vlog_Y_a = -0.5*nvar*fss*log(2*pi) + fss*log(abs(det(A0xhat))) + Yexpt
% a: given a0 and a+ % a: given a0 and a+
logMarLHres = 0; % Initialize log of the marginal likelihood (restricted or constant parameters). logMarLHres = 0; % Initialize log of the marginal likelihood (restricted or constant parameters).
for ki=1:fss %ndobs+1:fss % Forward recursion to get the marginal likelihood. See F on p.19 and pp. 48-49. for ki=1:fss %ndobs+1:fss % Forward recursion to get the marginal likelihood. See F on p.19 and pp. 48-49.
%---- Restricted log marginal likelihood function (constant parameters). %---- Restricted log marginal likelihood function (constant parameters).
[A0l,A0u] = lu(A0xhat); [A0l,A0u] = lu(A0xhat);
ada = sum(log(abs(diag(A0u)))); % log|A0| ada = sum(log(abs(diag(A0u)))); % log|A0|
termexp = y(ki,:)*A0xhat - phi(ki,:)*Apxhat; % 1-by-nvar termexp = y(ki,:)*A0xhat - phi(ki,:)*Apxhat; % 1-by-nvar
logMarLHres = logMarLHres - (0.5*nvar)*log(2*pi) + ada - 0.5*termexp*termexp'; % log value logMarLHres = logMarLHres - (0.5*nvar)*log(2*pi) + ada - 0.5*termexp*termexp'; % log value
end end
logMarLHres logMarLHres
%--------- The log value of p(A+|Y,A0) at some point such as the peak ---------- %--------- The log value of p(A+|Y,A0) at some point such as the peak ----------
totparsp = 0.0; totparsp = 0.0;
tmpd = 0.0; tmpd = 0.0;
for k=1:nvar for k=1:nvar
totparsp = totparsp + size(Viconst{k},2); totparsp = totparsp + size(Viconst{k},2);
tmpd = tmpd + 0.5*log(abs(det(Hpinv{k}))); tmpd = tmpd + 0.5*log(abs(det(Hpinv{k})));
end end
vlog_ap_Ya0 = -0.5*totparsp*log(2*pi) + tmpd + Apexpt; vlog_ap_Ya0 = -0.5*totparsp*log(2*pi) + tmpd + Apexpt;
%=================================== %===================================
% Compute p(a0,k|Y,ao) at some point such as the peak (in this situation, we simply % Compute p(a0,k|Y,ao) at some point such as the peak (in this situation, we simply
% generate results from the original Gibbs sampler). See FORECAST (2) pp.70-71 % generate results from the original Gibbs sampler). See FORECAST (2) pp.70-71
%=================================== %===================================
%--- Global set up for Gibbs. %--- Global set up for Gibbs.
[Tinv,UT] = fn_gibbsrvar_setup(H0inv, Uiconst, Hpinv, Pmat, Viconst, nvar, fss); [Tinv,UT] = fn_gibbsrvar_setup(H0inv, Uiconst, Hpinv, Pmat, Viconst, nvar, fss);
% %
vlog_a0_Yao = zeros(nvar,1); vlog_a0_Yao = zeros(nvar,1);
% the log value of p(a0k|Y,ao) where ao: other a's at some point such as the peak of ONLY some a0's % the log value of p(a0k|Y,ao) where ao: other a's at some point such as the peak of ONLY some a0's
vlog=zeros(ndraws2,1); vlog=zeros(ndraws2,1);
for k=1:nvar for k=1:nvar
bk = Uiconst{k}'*A0xhat(:,k); bk = Uiconst{k}'*A0xhat(:,k);
indx_ks=[k:nvar]; % the columns that exclude 1-(k-1)th columns indx_ks=[k:nvar]; % the columns that exclude 1-(k-1)th columns
A0gbs0 = A0hat; % starting at some point such as the peak A0gbs0 = A0hat; % starting at some point such as the peak
nk = n0(k); nk = n0(k);
if k<nvar if k<nvar
%--------- The 1st set of draws to be tossed away. ------------------ %--------- The 1st set of draws to be tossed away. ------------------
for draws = 1:ndraws1 for draws = 1:ndraws1
if ~mod(draws,nbuffer) if ~mod(draws,nbuffer)
disp(' ') disp(' ')
disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws)) disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws))
end end
A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks); A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
A0gbs0=A0gbs1; % repeat the Gibbs sampling A0gbs0=A0gbs1; % repeat the Gibbs sampling
end end
%--------- The 2nd set of draws to be used. ------------------ %--------- The 2nd set of draws to be used. ------------------
for draws = 1:ndraws2 for draws = 1:ndraws2
if ~mod(draws,nbuffer) if ~mod(draws,nbuffer)
disp(' ') disp(' ')
disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws)) disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws))
end end
[A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks); [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
%------ See p.71, Forecast (II). %------ See p.71, Forecast (II).
%------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks. %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II). Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation. gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
[Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II). [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
% %
vlog(draws) = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-... vlog(draws) = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-... 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
0.5*fss*bk'*(Vtr\(Vtr'\bk)); 0.5*fss*bk'*(Vtr\(Vtr'\bk));
A0gbs0=A0gbs1; % repeat the Gibbs sampling A0gbs0=A0gbs1; % repeat the Gibbs sampling
end end
vlogm=max(vlog); vlogm=max(vlog);
qlog=vlog-vlogm; qlog=vlog-vlogm;
vlogxhat=vlogm-log(ndraws2)+log(sum(exp(qlog))); vlogxhat=vlogm-log(ndraws2)+log(sum(exp(qlog)));
vlog_a0_Yao(k) = vlogxhat; vlog_a0_Yao(k) = vlogxhat;
% The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's % The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's
else else
disp(' ') disp(' ')
disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws')) disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws'))
[A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks) [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks)
%------ See p.71, Forecast (II). %------ See p.71, Forecast (II).
%------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks. %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.
Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II). Vk = Tinv{k}\Wcell{k}; %V_k on p.71 of Forecast (II).
gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation. gbeta = Vk\bk; % inv(V_k)*b_k on p.71 of Forecast (II) where alpha_k = b_k in our notation.
[Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II). [Vtq,Vtr]=qr(Vk',0); %To get inv(V_k)'*inv(V_k) in (*) on p.71 of Forecast (II).
% %
vloglast = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-... vloglast = 0.5*(fss+nk)*log(fss)-log(abs(det(Vk)))-0.5*(nk-1)*log(2*pi)-...
0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-... 0.5*(fss+1)*log(2)-gammaln(0.5*(fss+1))+fss*log(abs(gbeta(1)))-...
0.5*fss*bk'*(Vtr\(Vtr'\bk)); 0.5*fss*bk'*(Vtr\(Vtr'\bk));
vlog_a0_Yao(k) = vloglast; vlog_a0_Yao(k) = vloglast;
end end
end end
ndraws2 ndraws2
disp('Prior pdf -- log(p(a0hat, a+hat)):'); disp('Prior pdf -- log(p(a0hat, a+hat)):');
vlog_a0p vlog_a0p
disp('LH pdf -- log(p(Y|a0hat, a+hat)):'); disp('LH pdf -- log(p(Y|a0hat, a+hat)):');
vlog_Y_a vlog_Y_a
disp('Posterior Kernal -- logp(ahat) + logp(Y|ahat):'); disp('Posterior Kernal -- logp(ahat) + logp(Y|ahat):');
vlog_Y_a + vlog_a0p vlog_Y_a + vlog_a0p
disp('Posterior pdf -- log(p(a0_i_hat|a0_other_hat, Y)):'); disp('Posterior pdf -- log(p(a0_i_hat|a0_other_hat, Y)):');
vlog_a0_Yao vlog_a0_Yao
disp('Posterior pdf -- log(p(aphat|a0hat, Y)):'); disp('Posterior pdf -- log(p(aphat|a0hat, Y)):');
vlog_ap_Ya0 vlog_ap_Ya0
%--------- The value of marginal density p(Y) ---------- %--------- The value of marginal density p(Y) ----------
disp(' '); disp(' ');
disp(' '); disp(' ');
disp('************ Marginal Likelihood of Y or Marginal Data Density: ************'); disp('************ Marginal Likelihood of Y or Marginal Data Density: ************');
vlogY = vlog_a0p+vlog_Y_a-sum(vlog_a0_Yao)-vlog_ap_Ya0 vlogY = vlog_a0p+vlog_Y_a-sum(vlog_a0_Yao)-vlog_ap_Ya0

324
matlab/ms-sbvar/ms_write_markov_file.m
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@ -1,162 +1,162 @@
function ms_write_markov_file(fname, options) function ms_write_markov_file(fname, options)
% function ms_write_markov_file(fname, options) % function ms_write_markov_file(fname, options)
% %
% INPUTS % INPUTS
% fname: (string) name of markov file % fname: (string) name of markov file
% options: (struct) options % options: (struct) options
% %
% OUTPUTS % OUTPUTS
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% none % none
% Copyright (C) 2011 Dynare Team % Copyright (C) 2011 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
% Dynare is free software: you can redistribute it and/or modify % Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by % it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or % the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version. % (at your option) any later version.
% %
% Dynare is distributed in the hope that it will be useful, % Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of % but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details. % GNU General Public License for more details.
% %
% You should have received a copy of the GNU General Public License % You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
n_chains = length(options.ms.ms_chain); n_chains = length(options.ms.ms_chain);
nvars = size(options.varobs,1); nvars = size(options.varobs,1);
fh = fopen(fname,'w'); fh = fopen(fname,'w');
%/******************************************************************************/ %/******************************************************************************/
%/********************* Markov State Variable Information **********************/ %/********************* Markov State Variable Information **********************/
%/******************************************************************************/ %/******************************************************************************/
fprintf(fh,'//== Markov State Variables with Simple Restrictions ==//\n\n'); fprintf(fh,'//== Markov State Variables with Simple Restrictions ==//\n\n');
%//This number is NOT used but read in. %//This number is NOT used but read in.
fprintf(fh,'//== Number Observations ==//\n'); fprintf(fh,'//== Number Observations ==//\n');
fprintf(fh,'0\n\n'); fprintf(fh,'0\n\n');
fprintf(fh,'//== Number independent state variables ==//\n'); fprintf(fh,'//== Number independent state variables ==//\n');
fprintf(fh,'%d\n\n',n_chains); fprintf(fh,'%d\n\n',n_chains);
for i_chain = 1:n_chains for i_chain = 1:n_chains
%//=====================================================// %//=====================================================//
%//== state_variable[i] (1 <= i <= n_state_variables) ==// %//== state_variable[i] (1 <= i <= n_state_variables) ==//
%//=====================================================// %//=====================================================//
fprintf(fh,'//== Number of states for state_variable[%d] ==//\n', ... fprintf(fh,'//== Number of states for state_variable[%d] ==//\n', ...
i_chain); i_chain);
n_states = length(options.ms.ms_chain(i_chain).regime); n_states = length(options.ms.ms_chain(i_chain).regime);
fprintf(fh,'%d\n\n',n_states); fprintf(fh,'%d\n\n',n_states);
%//== 03/15/06: DW TVBVAR code reads the data below and overwrite the prior data read somewhere else if any. %//== 03/15/06: DW TVBVAR code reads the data below and overwrite the prior data read somewhere else if any.
%//== Each column contains the parameters for a Dirichlet prior on the corresponding %//== Each column contains the parameters for a Dirichlet prior on the corresponding
%//== column of the transition matrix. Each element must be positive. For each column, %//== column of the transition matrix. Each element must be positive. For each column,
%//== the relative size of the prior elements determine the relative size of the elements %//== the relative size of the prior elements determine the relative size of the elements
%//== of the transition matrix and overall larger sizes implies a tighter prior. %//== of the transition matrix and overall larger sizes implies a tighter prior.
fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ... fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ...
i_chain); i_chain);
Alpha = ones(n_states,n_states); Alpha = ones(n_states,n_states);
for i_state = 1:n_states for i_state = 1:n_states
p = 1-1/options.ms.ms_chain(i_chain).regime(i_state).duration; p = 1-1/options.ms.ms_chain(i_chain).regime(i_state).duration;
Alpha(i_state,i_state) = p*(n_states-1)/(1-p); Alpha(i_state,i_state) = p*(n_states-1)/(1-p);
fprintf(fh,'%22.16f',Alpha(i_state,:)); fprintf(fh,'%22.16f',Alpha(i_state,:));
fprintf(fh,'\n'); fprintf(fh,'\n');
end end
fprintf(fh,['\n//== Dirichlet dimensions for state_variable[%d] ' ... fprintf(fh,['\n//== Dirichlet dimensions for state_variable[%d] ' ...
'==//\n'],i_chain); '==//\n'],i_chain);
% fprintf(fh,'%d ',repmat(n_states,1,n_states)); % fprintf(fh,'%d ',repmat(n_states,1,n_states));
fprintf(fh,'%d ',repmat(2,1,n_states)); fprintf(fh,'%d ',repmat(2,1,n_states));
fprintf(fh,'\n\n'); fprintf(fh,'\n\n');
%//== The jth restriction matrix is n_states-by-free[j]. Each row of the restriction %//== The jth restriction matrix is n_states-by-free[j]. Each row of the restriction
%//== matrix has exactly one non-zero entry and the sum of each column must be one. %//== matrix has exactly one non-zero entry and the sum of each column must be one.
fprintf(fh,['//== Column restrictions for state_variable[%d] ' ... fprintf(fh,['//== Column restrictions for state_variable[%d] ' ...
'==//\n'],i_chain); '==//\n'],i_chain);
for i_state = 1:n_states for i_state = 1:n_states
if i_state == 1 if i_state == 1
M = eye(n_states,2); M = eye(n_states,2);
elseif i_state == n_states elseif i_state == n_states
M = [zeros(n_states-2,2); eye(2)]; M = [zeros(n_states-2,2); eye(2)];
else else
M = zeros(n_states,2); M = zeros(n_states,2);
M(i_state+[-1 1],1) = ones(2,1)/2; M(i_state+[-1 1],1) = ones(2,1)/2;
M(i_state,2) = 1; M(i_state,2) = 1;
disp(M) disp(M)
end end
for j_state = 1:n_states for j_state = 1:n_states
fprintf(fh,'%f ',M(j_state,:)); fprintf(fh,'%f ',M(j_state,:));
fprintf(fh,'\n'); fprintf(fh,'\n');
end end
fprintf(fh,'\n'); fprintf(fh,'\n');
end end
end end
%/******************************************************************************/ %/******************************************************************************/
%/******************************* VAR Parameters *******************************/ %/******************************* VAR Parameters *******************************/
%/******************************************************************************/ %/******************************************************************************/
%//NOT read %//NOT read
fprintf(fh,'//== Number Variables ==//\n'); fprintf(fh,'//== Number Variables ==//\n');
fprintf(fh,'%d\n\n',nvars); fprintf(fh,'%d\n\n',nvars);
%//NOT read %//NOT read
fprintf(fh,'//== Number Lags ==//\n'); fprintf(fh,'//== Number Lags ==//\n');
fprintf(fh,'%d\n\n',options.ms.nlags); fprintf(fh,'%d\n\n',options.ms.nlags);
%//NOT read %//NOT read
fprintf(fh,'//== Exogenous Variables ==//\n'); fprintf(fh,'//== Exogenous Variables ==//\n');
fprintf(fh,'1\n\n'); fprintf(fh,'1\n\n');
%//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that %//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
%this state variable controls the jth column of A0 and Aplus %this state variable controls the jth column of A0 and Aplus
fprintf(fh,['//== Controlling states variables for coefficients ==//\' ... fprintf(fh,['//== Controlling states variables for coefficients ==//\' ...
'n']); 'n']);
for i_var = 1:nvars for i_var = 1:nvars
for i_chain = 1:n_chains for i_chain = 1:n_chains
if ~isfield(options.ms.ms_chain(i_chain),'svar_coefficients') ... if ~isfield(options.ms.ms_chain(i_chain),'svar_coefficients') ...
|| isempty(options.ms.ms_chain(i_chain).svar_coefficients) || isempty(options.ms.ms_chain(i_chain).svar_coefficients)
i_equations = 0; i_equations = 0;
else else
i_equations = ... i_equations = ...
options.ms.ms_chain(i_chain).svar_coefficients.equations; options.ms.ms_chain(i_chain).svar_coefficients.equations;
end end
if strcmp(i_equations,'ALL') || any(i_equations == i_var) if strcmp(i_equations,'ALL') || any(i_equations == i_var)
fprintf(fh,'%d ',1); fprintf(fh,'%d ',1);
else else
fprintf(fh,'%d ',0); fprintf(fh,'%d ',0);
end end
end end
fprintf(fh,'\n'); fprintf(fh,'\n');
end end
%//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that %//== nvar x n_state_variables matrix. In the jth row, a non-zero value implies that
%this state variable controls the jth diagonal element of Xi %this state variable controls the jth diagonal element of Xi
fprintf(fh,'\n//== Controlling states variables for variance ==//\n'); fprintf(fh,'\n//== Controlling states variables for variance ==//\n');
for i_var = 1:nvars for i_var = 1:nvars
for i_chain = 1:n_chains for i_chain = 1:n_chains
if ~isfield(options.ms.ms_chain(i_chain),'svar_variances') ... if ~isfield(options.ms.ms_chain(i_chain),'svar_variances') ...
|| isempty(options.ms.ms_chain(i_chain).svar_variances) || isempty(options.ms.ms_chain(i_chain).svar_variances)
i_equations = 0; i_equations = 0;
else else
i_equations = ... i_equations = ...
options.ms.ms_chain(i_chain).svar_variances.equations; options.ms.ms_chain(i_chain).svar_variances.equations;
end end
if strcmp(i_equations,'ALL') || any(i_equations == i_var) if strcmp(i_equations,'ALL') || any(i_equations == i_var)
fprintf(fh,'%d ',1); fprintf(fh,'%d ',1);
else else
fprintf(fh,'%d ',0); fprintf(fh,'%d ',0);
end end
end end
fprintf(fh,'\n'); fprintf(fh,'\n');
end end
fclose(fh); fclose(fh);

134
matlab/ms-sbvar/ms_write_mhm_input.m
View File

@ -1,68 +1,68 @@
function ms_write_mhm_input(fname, options_ms) function ms_write_mhm_input(fname, options_ms)
% function ms_write_mhm_input(fname, options_ms) % function ms_write_mhm_input(fname, options_ms)
% %
% INPUTS % INPUTS
% fname: (string) filename % fname: (string) filename
% options_ms: (struct) options % options_ms: (struct) options
% %
% OUTPUTS % OUTPUTS
% none % none
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% none % none
% Copyright (C) 2011 Dynare Team % Copyright (C) 2011 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
% Dynare is free software: you can redistribute it and/or modify % Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by % it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or % the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version. % (at your option) any later version.
% %
% Dynare is distributed in the hope that it will be useful, % Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of % but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details. % GNU General Public License for more details.
% %
% You should have received a copy of the GNU General Public License % You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
fh = fopen(fname,'w'); fh = fopen(fname,'w');
fprintf(fh,'/**********************************************************\n'); fprintf(fh,'/**********************************************************\n');
fprintf(fh,' *** This input file is read by swzmsbvar_mhm_1 and swzmsbvar_mhm_1.exe only, NOT by swzmsbvar_printdraws.exe.\n'); fprintf(fh,' *** This input file is read by swzmsbvar_mhm_1 and swzmsbvar_mhm_1.exe only, NOT by swzmsbvar_printdraws.exe.\n');
fprintf(fh,' ***\n'); fprintf(fh,' ***\n');
fprintf(fh,' **********************************************************/\n'); fprintf(fh,' **********************************************************/\n');
fprintf(fh,'\n\n//------------- 1st set of posterior draws to find optimal scales for Metropolis (30000). ---------------\n'); fprintf(fh,'\n\n//------------- 1st set of posterior draws to find optimal scales for Metropolis (30000). ---------------\n');
fprintf(fh,'//== number draws for first burn-in ==// //For determining the Metropolis scales only.\n'); fprintf(fh,'//== number draws for first burn-in ==// //For determining the Metropolis scales only.\n');
fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_1); fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_1);
fprintf(fh,'//------------- MCMC burn-in draws once the Metropolis scales (previous stage) are fixed. --------------\n'); fprintf(fh,'//------------- MCMC burn-in draws once the Metropolis scales (previous stage) are fixed. --------------\n');
fprintf(fh,'//------------- 2nd set of standard burn-in posterior draws to throw away the initial draws (10000). ---------------\n'); fprintf(fh,'//------------- 2nd set of standard burn-in posterior draws to throw away the initial draws (10000). ---------------\n');
fprintf(fh,'//== number draws for second burn-in ==//\n'); fprintf(fh,'//== number draws for second burn-in ==//\n');
fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_2); fprintf(fh,'%d\n\n',options_ms.draws_nbr_burn_in_2);
fprintf(fh,'//--------------- 1st set of posterior draws to compute the mean and variance for the weighting function in the MHM (200000) ----------------\n'); fprintf(fh,'//--------------- 1st set of posterior draws to compute the mean and variance for the weighting function in the MHM (200000) ----------------\n');
fprintf(fh,'//== number draws to estimate mean and variance ==//\n'); fprintf(fh,'//== number draws to estimate mean and variance ==//\n');
fprintf(fh,'%d\n\n',options_ms.draws_nbr_mean_var_estimate); fprintf(fh,'%d\n\n',options_ms.draws_nbr_mean_var_estimate);
fprintf(fh,'//--------------- Only applied to mhm_2 process: total number of MCMC draws = thinning factor * 2nd set of saved posterior draws ----------------\n'); fprintf(fh,'//--------------- Only applied to mhm_2 process: total number of MCMC draws = thinning factor * 2nd set of saved posterior draws ----------------\n');
fprintf(fh,'//== thinning factor for modified harmonic mean process ==//\n'); fprintf(fh,'//== thinning factor for modified harmonic mean process ==//\n');
fprintf(fh,'%d\n\n',options_ms.thinning_factor); fprintf(fh,'%d\n\n',options_ms.thinning_factor);
fprintf(fh,'//--------------- 2nd set of saved posterior draws from MHM_2 (second stage): saved draws AFTER thinning (1000000) ----------------\n'); fprintf(fh,'//--------------- 2nd set of saved posterior draws from MHM_2 (second stage): saved draws AFTER thinning (1000000) ----------------\n');
fprintf(fh,'//== number draws for modified harmonic mean process ==//\n'); fprintf(fh,'//== number draws for modified harmonic mean process ==//\n');
fprintf(fh,'%d\n\n',options_ms.draws_nbr_modified_harmonic_mean); fprintf(fh,'%d\n\n',options_ms.draws_nbr_modified_harmonic_mean);
fprintf(fh,'//------- 1st stage: computing all three tightness factors for Dirichlet. ---------\n'); fprintf(fh,'//------- 1st stage: computing all three tightness factors for Dirichlet. ---------\n');
fprintf(fh,'//------- 2nd stage: hard-code the second scale factor (in principle, we can do all three). ---------\n'); fprintf(fh,'//------- 2nd stage: hard-code the second scale factor (in principle, we can do all three). ---------\n');
fprintf(fh,'//------- It seems that Dan''s code only use the first element of the following scales. The scale applies to the Dirichlet''s hyperparameter alpha for the diagonal of the transition matrix in the weighting function. Note that the weighting function for the transition matrix parameters is Dirichlet. ---------\n'); fprintf(fh,'//------- It seems that Dan''s code only use the first element of the following scales. The scale applies to the Dirichlet''s hyperparameter alpha for the diagonal of the transition matrix in the weighting function. Note that the weighting function for the transition matrix parameters is Dirichlet. ---------\n');
fprintf(fh,'//== scale values for Dirichlet distribution ==//\n'); fprintf(fh,'//== scale values for Dirichlet distribution ==//\n');
fprintf(fh,'3\n\n'); fprintf(fh,'3\n\n');
fprintf(fh,'%f ',options_ms.dirichlet_scale); fprintf(fh,'%f ',options_ms.dirichlet_scale);
fprintf(fh,'\n'); fprintf(fh,'\n');
fclose(fh); fclose(fh);

1619
matlab/ms-sbvar/msstart2.m
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File diff suppressed because it is too large Load Diff

331
matlab/ms-sbvar/msstart_setup.m
View File

@ -1,166 +1,165 @@
%function []= msstart_setup(options_) %function []= msstart_setup(options_)
% Copyright (C) 2011 Dynare Team % Copyright (C) 2011 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
% %
% Dynare is free software: you can redistribute it and/or modify % Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by % it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or % the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version. % (at your option) any later version.
% %
% Dynare is distributed in the hope that it will be useful, % Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of % but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details. % GNU General Public License for more details.
% %
% You should have received a copy of the GNU General Public License % You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% ** ONLY UNDER UNIX SYSTEM % ** ONLY UNDER UNIX SYSTEM
%path(path,'/usr2/f1taz14/mymatlab') %path(path,'/usr2/f1taz14/mymatlab')
%=========================================== %===========================================
% Exordium I % Exordium I
%=========================================== %===========================================
format short g % format format short g % format
% %
%options_.ms.freq = 4; % quarters or months %options_.ms.freq = 4; % quarters or months
%options_.ms.initial_year=1959; % beginning of the year %options_.ms.initial_year=1959; % beginning of the year
%options_.ms.initial_subperiod=1; % begining of the quarter or month %options_.ms.initial_subperiod=1; % begining of the quarter or month
%options_.ms.final_year=2005; % final year %options_.ms.final_year=2005; % final year
%options_.ms.final_subperiod=4; % final month or quarter %options_.ms.final_subperiod=4; % final month or quarter
nData=(options_.ms.final_year-options_.ms.initial_year)*options_.ms.freq + (options_.ms.final_subperiod-options_.ms.initial_subperiod+1); nData=(options_.ms.final_year-options_.ms.initial_year)*options_.ms.freq + (options_.ms.final_subperiod-options_.ms.initial_subperiod+1);
% total number of the available data -- this is all you have % total number of the available data -- this is all you have
%*** Load data and series %*** Load data and series
%load datainf_argen.prn % the default name for the variable is "options_.ms.data". %load datainf_argen.prn % the default name for the variable is "options_.ms.data".
%load datacbogdpffr.prn %load datacbogdpffr.prn
%options_.ms.data = datacbogdpffr; %options_.ms.data = datacbogdpffr;
%clear datacbogdpffr; %clear datacbogdpffr;
[nt,ndv]=size(options_.data); [nt,ndv]=size(options_.data);
if nt~=nData if nt~=nData
disp(' ') disp(' ')
warning(sprintf('nt=%d, Caution: not equal to the length in the data',nt)); warning(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
%disp(sprintf('nt=%d, Caution: not equal to the length in the data',nt)); %disp(sprintf('nt=%d, Caution: not equal to the length in the data',nt));
disp('Press ctrl-c to abort') disp('Press ctrl-c to abort')
return return
end end
%-------- %--------
%1 CBO output gap -- log(x_t)-log(x_t potential) %1 CBO output gap -- log(x_t)-log(x_t potential)
%2 GDP deflator -- (P_t/P_{t-1})^4-1.0 %2 GDP deflator -- (P_t/P_{t-1})^4-1.0
%2 FFR/100. %2 FFR/100.
options_.ms.vlist = [1:size(options_.varobs,1)]; % 1: U; 4: PCE inflation. options_.ms.vlist = [1:size(options_.varobs,1)]; % 1: U; 4: PCE inflation.
options_.ms.varlist=cellstr(options_.varobs); options_.ms.varlist=cellstr(options_.varobs);
options_.ms.log_var = sort(varlist_indices(options_.ms.vlistlog,options_.varobs)); % subset of "options_.ms.vlist. Variables in log level so that differences are in **monthly** growth, unlike R and U which are in annual percent (divided by 100 already). options_.ms.log_var = sort(varlist_indices(options_.ms.vlistlog,options_.varobs)); % subset of "options_.ms.vlist. Variables in log level so that differences are in **monthly** growth, unlike R and U which are in annual percent (divided by 100 already).
options_.ms.percent_var =setdiff(options_.ms.vlist,options_.ms.log_var); options_.ms.percent_var =setdiff(options_.ms.vlist,options_.ms.log_var);
%options_.ms.restriction_fname='ftd_upperchol3v'; %Only used by msstart2.m. %options_.ms.restriction_fname='ftd_upperchol3v'; %Only used by msstart2.m.
ylab = options_.ms.varlist; ylab = options_.ms.varlist;
xlab = options_.ms.varlist; xlab = options_.ms.varlist;
%---------------- %----------------
nvar = size(options_.varobs,1); % number of endogenous variables nvar = size(options_.varobs,1); % number of endogenous variables
nlogeno = length(options_.ms.log_var); % number of endogenous variables in options_.ms.log_var nlogeno = length(options_.ms.log_var); % number of endogenous variables in options_.ms.log_var
npereno = length(options_.ms.percent_var); % number of endogenous variables in options_.ms.percent_var npereno = length(options_.ms.percent_var); % number of endogenous variables in options_.ms.percent_var
if (nvar~=(nlogeno+npereno)) if (nvar~=(nlogeno+npereno))
disp(' ') disp(' ')
warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist') warning('Check xlab, nlogeno or npereno to make sure of endogenous variables in options_.ms.vlist')
disp('Press ctrl-c to abort') disp('Press ctrl-c to abort')
return return
elseif (nvar==length(options_.ms.vlist)) elseif (nvar==length(options_.ms.vlist))
nexo=1; % only constants as an exogenous variable. The default setting. nexo=1; % only constants as an exogenous variable. The default setting.
elseif (nvar<length(options_.ms.vlist)) elseif (nvar<length(options_.ms.vlist))
nexo=length(options_.ms.vlist)-nvar+1; nexo=length(options_.ms.vlist)-nvar+1;
else else
disp(' ') disp(' ')
warning('Make sure there are only nvar endogenous variables in options_.ms.vlist') warning('Make sure there are only nvar endogenous variables in options_.ms.vlist')
disp('Press ctrl-c to abort') disp('Press ctrl-c to abort')
return return
end end
%------- A specific sample is considered for estimation ------- %------- A specific sample is considered for estimation -------
yrStart=options_.ms.initial_year; yrStart=options_.ms.initial_year;
qmStart=options_.ms.initial_subperiod; qmStart=options_.ms.initial_subperiod;
yrEnd=options_.ms.final_year; yrEnd=options_.ms.final_year;
qmEnd=options_.ms.final_subperiod; qmEnd=options_.ms.final_subperiod;
%options_.forecast = 4; % number of years for forecasting %options_.forecast = 4; % number of years for forecasting
if options_.forecast<1 if options_.forecast<1
error('To be safe, the number of forecast years should be at least 1') error('To be safe, the number of forecast years should be at least 1')
end end
ystr=num2str(yrEnd); forelabel = [num2str(yrEnd) ':' num2str(qmEnd) ' Forecast'];
forelabel = [ ystr(3:4) ':' num2str(qmEnd) ' Forecast'];
nSample=(yrEnd-yrStart)*options_.ms.freq + (qmEnd-qmStart+1);
nSample=(yrEnd-yrStart)*options_.ms.freq + (qmEnd-qmStart+1); if qmEnd==options_.ms.freq
if qmEnd==options_.ms.freq E1yrqm = [yrEnd+1 1]; % first year and quarter (month) after the sample
E1yrqm = [yrEnd+1 1]; % first year and quarter (month) after the sample else
else E1yrqm = [yrEnd qmEnd+1]; % first year and quarter (month) after the sample
E1yrqm = [yrEnd qmEnd+1]; % first year and quarter (month) after the sample end
end E2yrqm = [yrEnd+options_.forecast qmEnd]; % end at the last month (quarter) of a calendar year after the sample
E2yrqm = [yrEnd+options_.forecast qmEnd]; % end at the last month (quarter) of a calendar year after the sample [fdates,nfqm]=fn_calyrqm(options_.ms.freq,E1yrqm,E2yrqm); % forecast dates and number of forecast dates
[fdates,nfqm]=fn_calyrqm(options_.ms.freq,E1yrqm,E2yrqm); % forecast dates and number of forecast dates [sdates,nsqm] = fn_calyrqm(options_.ms.freq,[yrStart qmStart],[yrEnd qmEnd]);
[sdates,nsqm] = fn_calyrqm(options_.ms.freq,[yrStart qmStart],[yrEnd qmEnd]); % sdates: dates for the whole sample (including options_.ms.nlags)
% sdates: dates for the whole sample (including options_.ms.nlags) if nSample~=nsqm
if nSample~=nsqm warning('Make sure that nSample is consistent with the size of sdates')
warning('Make sure that nSample is consistent with the size of sdates') disp('Hit any key to continue, or ctrl-c to abort')
disp('Hit any key to continue, or ctrl-c to abort') pause
pause end
end imstp = 4*options_.ms.freq; % <<>> impulse responses (4 years)
imstp = 4*options_.ms.freq; % <<>> impulse responses (4 years) nayr = 4; %options_.forecast; % number of years before forecasting for plotting.
nayr = 4; %options_.forecast; % number of years before forecasting for plotting.
%------- Prior, etc. -------
%------- Prior, etc. ------- %options_.ms.nlags = 4; % number of options_.ms.nlags
%options_.ms.nlags = 4; % number of options_.ms.nlags %options_.ms.cross_restrictions = 0; % 1: cross-A0-and-A+ restrictions; 0: options_.ms.restriction_fname is all we have
%options_.ms.cross_restrictions = 0; % 1: cross-A0-and-A+ restrictions; 0: options_.ms.restriction_fname is all we have % Example for indxOres==1: restrictions of the form P(t) = P(t-1).
% Example for indxOres==1: restrictions of the form P(t) = P(t-1). %options_.ms.contemp_reduced_form = 0; % 1: contemporaneous recursive reduced form; 0: restricted (non-recursive) form
%options_.ms.contemp_reduced_form = 0; % 1: contemporaneous recursive reduced form; 0: restricted (non-recursive) form %options_.ms.real_pseudo_forecast = 0; % 1: options_.ms.real_pseudo_forecast forecasts; 0: real time forecasts
%options_.ms.real_pseudo_forecast = 0; % 1: options_.ms.real_pseudo_forecast forecasts; 0: real time forecasts %options_.ms.bayesian_prior = 1; % 1: Bayesian prior; 0: no prior
%options_.ms.bayesian_prior = 1; % 1: Bayesian prior; 0: no prior indxDummy = options_.ms.bayesian_prior; % 1: add dummy observations to the data; 0: no dummy added.
indxDummy = options_.ms.bayesian_prior; % 1: add dummy observations to the data; 0: no dummy added. %options_.ms.dummy_obs = 0; % No dummy observations for xtx, phi, fss, xdatae, etc. Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m.
%options_.ms.dummy_obs = 0; % No dummy observations for xtx, phi, fss, xdatae, etc. Dummy observations are used as an explicit prior in fn_rnrprior_covres_dobs.m. %if indxDummy
%if indxDummy % options_.ms.dummy_obs=nvar+1; % number of dummy observations
% options_.ms.dummy_obs=nvar+1; % number of dummy observations %else
%else % options_.ms.dummy_obs=0; % no dummy observations
% options_.ms.dummy_obs=0; % no dummy observations %end
%end %=== The following mu is effective only if options_.ms.bayesian_prior==1.
%=== The following mu is effective only if options_.ms.bayesian_prior==1.
mu = options_.ms.coefficients_prior_hyperparameters;
mu = options_.ms.coefficients_prior_hyperparameters;
% mu(1): overall tightness and also for A0;
% mu(1): overall tightness and also for A0; % mu(2): relative tightness for A+;
% mu(2): relative tightness for A+; % mu(3): relative tightness for the constant term;
% mu(3): relative tightness for the constant term; % mu(4): tightness on lag decay; (1)
% mu(4): tightness on lag decay; (1) % mu(5): weight on nvar sums of coeffs dummy observations (unit roots);
% mu(5): weight on nvar sums of coeffs dummy observations (unit roots); % mu(6): weight on single dummy initial observation including constant
% mu(6): weight on single dummy initial observation including constant % (cointegration, unit roots, and stationarity);
% (cointegration, unit roots, and stationarity); %
% %
% hpmsmd = [0.0; 0.0];
hpmsmd = [0.0; 0.0]; indxmsmdeqn = [0; 0; 0; 0]; %This option disenable using this in fn_rnrprior_covres_dobs.m
indxmsmdeqn = [0; 0; 0; 0]; %This option disenable using this in fn_rnrprior_covres_dobs.m
tdf = 3; % degrees of freedom for t-dist for initial draw of the MC loop
tdf = 3; % degrees of freedom for t-dist for initial draw of the MC loop nbuffer = 1000; % a block or buffer of draws (buffer) that is saved to the disk (not memory)
nbuffer = 1000; % a block or buffer of draws (buffer) that is saved to the disk (not memory) ndraws1=1*nbuffer; % 1st part of Monte Carlo draws
ndraws1=1*nbuffer; % 1st part of Monte Carlo draws ndraws2=10*ndraws1; % 2nd part of Monte Carlo draws
ndraws2=10*ndraws1; % 2nd part of Monte Carlo draws % seednumber = options_.DynareRandomStreams.seed; %7910; %472534; % if 0, random state at each clock time
% seednumber = options_.DynareRandomStreams.seed; %7910; %472534; % if 0, random state at each clock time % % good one 420 for [29 45], [29 54]
% % good one 420 for [29 45], [29 54] % if seednumber
% if seednumber % randn('state',seednumber);
% randn('state',seednumber); % rand('state',seednumber);
% rand('state',seednumber); % else
% else % randn('state',fix(100*sum(clock)));
% randn('state',fix(100*sum(clock))); % rand('state',fix(100*sum(clock)));
% rand('state',fix(100*sum(clock))); % end
% end % nstarts=1 % number of starting points
% nstarts=1 % number of starting points % imndraws = nstarts*ndraws2; % total draws for impulse responses or forecasts
% imndraws = nstarts*ndraws2; % total draws for impulse responses or forecasts %<<<<<<<<<<<<<<<<<<<
%<<<<<<<<<<<<<<<<<<<