[R] heteroskedasticityrobust standard errors
Ott Toomet
siim at obs.ee
Fri Mar 22 10:02:38 CET 2002
Hi,
I attach a version of small program for White stderrors. Basically, it
calculates a new covariance matrix and prints the results with new stderrors
(without any tests and additional information). It should be rewritten to
use the objectoriented architecture, but I have not had need so long.
you should call it as:
summaryw(lm(y~x))
Regards,
Ott Toomet
P.S. If you get/write a better version, let me know.

### ols with White' heteroscedasticity consistent stderrors
summaryw < function( model) {
s < summary( model)
X < model.matrix( model)
u2 < residuals( model)^2
XDX < 0
## here one needs essentially to calculate X'DX. But due to the fact that D
## is huge (NxN), it is better to do it with a cycle.
for( i in 1:nrow( X)) {
XDX < XDX + u2[i]*X[i,]%*%t( X[i,])
}
XX1 < solve( t( X)%*%X)
varcovar < XX1 %*% XDX %*% XX1
stdh < sqrt( diag( varcovar))
t < model$coefficients/stdh
p < 2*pnorm( abs( t))
results < cbind( model$coefficients, stdh, t, p)
dimnames(results) < dimnames( s$coefficients)
results
}
On Thu, 21 Mar 2002, Grant Farnsworth wrote:
I am trying to compute the white heteroskedasticityrobust standard errors
(also called the Huber standard errors) in a linear model, but I can't seem
to find a function to do it. I know that the design library in S+ has
something like this (robcov?), but I have not yet seen this library ported
to R.

Anyone know if there is already a function built into R to do this
relatively simple job?

Thanks,
Grant
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