107 lines
4.3 KiB
Matlab
107 lines
4.3 KiB
Matlab
function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
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%function var=rfvar3(ydata,lags,xdata,breaks,lambda,mu)
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% This algorithm goes for accuracy without worrying about memory requirements.
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% ydata: dependent variable data matrix
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% xdata: exogenous variable data matrix
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% lags: number of lags
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% breaks: rows in ydata and xdata after which there is a break. This allows for
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% discontinuities in the data (e.g. war years) and for the possibility of
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% adding dummy observations to implement a prior. This must be a column vector.
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% Note that a single dummy observation becomes lags+1 rows of the data matrix,
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% with a break separating it from the rest of the data. The function treats the
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% first lags observations at the top and after each "break" in ydata and xdata as
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% initial conditions.
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% lambda: weight on "co-persistence" prior dummy observations. This expresses
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% belief that when data on *all* y's are stable at their initial levels, they will
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% tend to persist at that level. lambda=5 is a reasonable first try. With lambda<0,
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% constant term is not included in the dummy observation, so that stationary models
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% with means equal to initial ybar do not fit the prior mean. With lambda>0, the prior
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% implies that large constants are unlikely if unit roots are present.
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% mu: weight on "own persistence" prior dummy observation. Expresses belief
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% that when y_i has been stable at its initial level, it will tend to persist
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% at that level, regardless of the values of other variables. There is
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% one of these for each variable. A reasonable first guess is mu=2.
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% The program assumes that the first lags rows of ydata and xdata are real data, not dummies.
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% Dummy observations should go at the end, if any. If pre-sample x's are not available,
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% repeating the initial xdata(lags+1,:) row or copying xdata(lags+1:2*lags,:) into
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% xdata(1:lags,:) are reasonable subsititutes. These values are used in forming the
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% persistence priors.
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% Code written by Christopher Sims. This version 6/15/03.
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[T,nvar]=size(ydata);
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nox=isempty(xdata);
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if ~nox
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[T2,nx]=size(xdata);
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else
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T2=T;nx=0;xdata=zeros(T2,0);
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end
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% note that x must be same length as y, even though first part of x will not be used.
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% This is so that the lags parameter can be changed without reshaping the xdata matrix.
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if T2 ~= T, disp('Mismatch of x and y data lengths'),end
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if nargin<4
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nbreaks=0;breaks=[];
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else
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nbreaks=length(breaks);
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end
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breaks=[0;breaks;T];
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smpl=[];
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for nb=1:nbreaks+1
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smpl=[smpl;[breaks(nb)+lags+1:breaks(nb+1)]'];
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end
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Tsmpl=size(smpl,1);
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X=zeros(Tsmpl,nvar,lags);
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for is=1:length(smpl)
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X(is,:,:)=ydata(smpl(is)-(1:lags),:)';
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end
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X=[X(:,:) xdata(smpl,:)];
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y=ydata(smpl,:);
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% Everything now set up with input data for y=Xb+e
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% ------------------Form persistence dummies-------------------
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if lambda~=0 | mu>0
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ybar=mean(ydata(1:lags,:),1);
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if ~nox
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xbar=mean(xdata(1:lags,:),1);
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else
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xbar=[];
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end
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if lambda~=0
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if lambda>0
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xdum=lambda*[repmat(ybar,1,lags) xbar];
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else
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lambda=-lambda;
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xdum=lambda*[repmat(ybar,1,lags) zeros(size(xbar))];
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end
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ydum=zeros(1,nvar);
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ydum(1,:)=lambda*ybar;
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y=[y;ydum];
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X=[X(:,:);xdum];
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end
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if mu>0
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xdum=[repmat(diag(ybar),1,lags) zeros(nvar,nx)]*mu;
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ydum=mu*diag(ybar);
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X=[X;xdum];
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y=[y;ydum];
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end
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end
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[vl,d,vr]=svd(X(:,:),0);
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di=1../diag(d);
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B=vl'*y;
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B=(vr.*repmat(di',nvar*lags+nx,1))*B;
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u=y-X(:,:)*B;
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xxi=vr.*repmat(di',nvar*lags+nx,1);
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xxi=xxi*xxi';
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B=reshape(B,[nvar*lags+nx,nvar]); % rhs variables, equations
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By=B(1:nvar*lags,:);
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By=reshape(By,nvar,lags,nvar);% variables, lags, equations
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By=permute(By,[3,1,2]); %equations, variables, lags to match impulsdt.m
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if nox
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Bx=[];
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else
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Bx=B(nvar*lags+(1:nx),:)';
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end
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%logintlh=matrictint(u'*u,xxi,size(X,1)-nvar-1)-.5*nvar*(nvar+1)*log(2*pi);
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var.By=By;var.Bx=Bx;var.u=u;var.xxi=xxi;%var.logintlh=logintlh;
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% Desired features: 1) automatic dummies for vcv prior
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% 2) automatic calculation of integrated pdf, accounting
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% for the dummy variables as a prior
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% 3) automatic dummies for "Minnesota prior"
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