On Mon, Mar 21, 2011 at 5:20 AM, Allin
Cottrell
<cottrell@wfu.edu>
wrote:
On Mon, 21 Mar 2011, Md. Mohan Uddin wrote:
> (1) In Gretl there is an option "Robust standard error"
for correcting for
> heteroskedasticity.
>
> (2) I can see that there is another option from:
Model>other linear
> model>heteroskedasticity corrected... in GUI.
>
> My question is when can I use (1) ** "Robust standard
error" and when** (2)
> **Model>other linear model>heteroskedasticity
corrected... for correcting
> for heteroskedasticity.*
Robust standard errors give you a means of inference that is
robust with respect to heteroskedasticity, but the point
estimates
are not altered: if the model is estimated via OLS you still
get
the OLS coefficients.
The "heteroskedasticity corrected" ("hsk") routine not only
revises the standard errors, but also the point estimates
(coefficients); it does so via weighted least squares.
IF the model is correctly specified and the heteroskedasticity
is
of a form that can be well approximated as a quadratic
function of
the regressors, the hsk estimator is more efficient than OLS.
Allin Cottrell
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