On Thu, 4 Sep 2008, Carrasco-Torrecilla, Roman R wrote:
Dear all,
Sorry if this is too obvious.
I am trying to fit my multiple regression model using lm()
Before starting model simplification using step() I checked whether the
model presented heteroscedasticity with ncv.test() from the CAR package.
It presents it.
I want to correct for it, I used hccm() from the CAR package as well and
got the Heteroscedasticity-Corrected Covariance Matrix.
I am not sure what am I supposed to do with the matrix. I guess I should
run my model again telling it to use that matrix but I don't really find
the parameter in lm() to tell R so. I guess it should be somewhere in
weights?
If you have a reasonable approximation of the pattern of
heteroskedasticity, you can supply it in the "weights" argument to lm()
and perform WLS.
hccm() on the other hand does not assume a particular pattern of
heteroskedasticity (with the obvious advantages and disadvantages). You
can easily employ it for inference based on Wald statistics. The
"car" package provides linear.hypothesis() for this and the package
"lmtest" provides functions coeftest() and waldtest().
I would really appracite if you could show me how I would do it or
recommend a text on how to correct heteroscedasticity with R.
The "sandwich" package which provides more flexible implementations of
the estimators underlying hccm() as well as other estimators has
vignette("sandwich", package = "sandwich")
with some background information and hands-on examples.
hth,
Z
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