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Re: st: xtgee robust standard errors
At 09:27 16/08/02 -0500, Roberto G. Gutierrez wrote:
Bo Cutter <email@example.com> asks:
Strictly speaking, it depends what you mean by "misspecified". According to
-[R] xtgee-, the "robust" variances provided by -xtgee- are "semi-robust",
or "semi-Huber" in the terminology of Newson (2000) and Hardin and Hilbe
(2001). This means that, in general, they are asymptotically robust to
mis-specification of the covariance structure, but not asymptotically
robust to mis-specification of the conditional mean Y given X. However, if
the link function is the canonical link for the variance function (or
family), then the semi-Huber variance is also the full Huber variance,
which is robust to mis-specification both of the covariance and of the
conditional mean. The identity link is canonical for the Gaussian family,
the inverse link is canonical for the gamma family, the inverse square link
is canonical for the inverse Gaussian family, the logit link is canonical
for the binomial family, and the log link is canonical for the Poisson family.
> I am using the xtgee command to get robust standard error estimates for a
> random-effects panel data model. (i.e. xtgee..., family(gauss) link(ident)
> robust). p.446 of manual S-Z (last sentence, first paragaph) has a
> that implies these robust standard errors are correct even if the
> panel-specific effect is correlated with the regressors. Can anyone
> if I am interpreting this statement correctly ? And do you know what the
> reference behind this statement is ?
Yes, this statement is true. In general, the robust standard errors are a
true indicator of the sample-to-sample variability of your parameter estimates
even in mis-specified models.
As for a reference, try
Domowitz & White (1982). Misspecified models with dependent observations.
Journal of Econometrics, 20, 35-58.
I hope this helps.
Hardin J, Hilbe J. Generalized linear models and extensions. College
Station, TX: Stata Press; 2001.
Newson R. rglm: Robust variance estimates for generalized linear models. In
Stata Technical Bulletin Reprints 2000; 181-190. College Station, TX: Stata
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