olspanelkeanej.m

时间序列分析中常用到的matlab代码

function results = olsPanelKeaneJ(p,t,y,x);

% PURPOSE: Computes regression with Keane and Runkle (JPE 1998)
%          covariance matrix for cross-sectional, fully robust (see
%          Woolridge (2002) eqn (7.26)) for within-panel variable
%          NOTE:  Automatically adds constant
%          NOTE:  Allows for arbitrary within-panel serial correlation
%                 and heteroskedasticity; assumes zero cross-sectional
%                 serial corrlation and constant cross-sectional
%                 correlation within time period
%---------------------------------------------------
% USAGE:     results = olsPanelKeaneJ(p,t,y,x)
% where:     p = NTx1 vector of panel variables
%            t = NTx1 vector of time variables
%            y = NTx1 vector of independent variables
%            x = NTxK matrix of regressors (excluding constant)
%---------------------------------------------------
% RETURNS: a structure
%        results.meth  = 'ols'
%        results.beta  = bhat     (nvar x 1)
%        results.tstat = t-stats  (nvar x 1)
%        results.yhat  = yhat     (nobs x 1)
%        results.resid = residuals (nobs x 1)
%        results.sige  = e'*e/(n-k)   scalar
%        results.rsqr  = rsquared     scalar
%        results.rbar  = rbar-squared scalar
%        results.dw    = Durbin-Watson Statistic
%        results.nobs  = nobs
%        results.nvar  = nvars
%        results.y     = y data vector (nobs x 1)
%        results.bint  = (nvar x2 ) vector with 95% confidence intervals on
%---------------------------------------------------
% Requires ols() function from LeSage Econometrics toolbox
%---------------------------------------------------
% Judson Caskey
% University of Michigan
% [e-mail]
%---------------------------------------------------

if size(p,1)~=size(y,1) | size(p,1)~=size(x,1)...
   | size(y,1)~=size(x,1) | size(t,1)~=size(y,1) | size(t,1)~=size(x,1)
    error('p,t,x and y must have same number of rows');
elseif size(p,2)~=1 | size(y,2)~=1 | size(t,2)~=1
    error('p, t and y must be column vectors');
end;

panelitems = unique(p);
timeitems = unique(t);
m = size(panelitems,1);
n = size(x,1);

% Check for duplicate times for a given panel item

ptmp=sortrows([p,t],[1,2]);
if sum(and(ptmp(2:end,1)==ptmp(1:end-1,1),ptmp(2:end,2)==ptmp(1:end-1,2))) > 0
    error('Cannot have duplicate panel/time indices');
end;
    
clear ptmp;

x=[x,ones(size(x,1),1)];
resultstmp = ols(y,x);

x = sortrows([p,t,resultstmp.resid,x],[2,1]);

% Lower triangle (without diagonal) elements give cross-products for t
b=0;
for k=1:1:size(timeitems,1);
    etmp = x(x(:,2)==timeitems(k),3);
    b = b + 2*sum(sum(tril(etmp*etmp',-1)));
end;


clear etmp;

b = b/size(x,1)/(size(panelitems,1)-1);

% Construct variance matrix with blocks for each time for cross-sectional

varmat=zeros(size(x(:,4:end),2),size(x(:,4:end),2));
for k=1:1:size(timeitems,1);
    xtmp=x(x(:,2)==timeitems(k),4:end);
    varmat = varmat + xtmp'*(b*ones(size(xtmp,1),size(xtmp,1))...
                -b*eye(size(xtmp,1)))*xtmp;
end;

clear xtmp;

% Add variance for within-panel unit

for k=1:1:size(panelitems,1);
    xtmp=x(x(:,1)==panelitems(k),4:end);
    utmp=x(x(:,1)==panelitems(k),3);
    gtmp=utmp'*xtmp;
    varmat=varmat+gtmp'*gtmp;
end;

% Remove panel, time and residual variables from x
x = x(:,4:end);

% Add robust variance and replace t-statistics
results=resultstmp;
results = rmfield(results,'bint');
results.varmat=m*n/(m-1)/(n-1)*inv(x'*x)*varmat*inv(x'*x);
results.tstat=results.beta./sqrt(diag(results.varmat));