Drop redundant rfvar3.m
This function is already present as a private function of bvar_toolbox.m. It is not needed anywhere else.time-shift
Sébastien Villemot
parent
71a68919bf
commit
aa6456b156
122
matlab/rfvar3.m
122
matlab/rfvar3.m
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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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% Original file downloaded from:
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% http://sims.princeton.edu/yftp/VARtools/matlab/rfvar3.m
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% Copyright (C) 2003-2007 Christopher Sims
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% Copyright (C) 2007-2012 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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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;
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nx = 0;
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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, error('Mismatch of x and y data lengths'),end
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if nargin < 4
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nbreaks = 0;
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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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% Add 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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% Compute OLS regression and residuals
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[vl,d,vr] = svd(X,0);
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di = 1./diag(d);
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B = (vr.*repmat(di',nvar*lags+nx,1))*vl'*y;
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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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var.B = B;
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var.u = u;
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var.xxi = xxi;
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end
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