Re: [Gretl-users] Dealing with heteroskedasticity

*That means the precondition of using "hsk" is to test the form of heteroskedasticity if it can be well approximated as a quadratic function of the regressors. Is there any way of doing that in Gretl? *

On Mon, Mar 21, 2011 at 5:20 AM, Allin Cottrell <cottrell(a)wfu.edu <mailto: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 _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu <mailto:Gretl-users@lists.wfu.edu> http://lists.wfu.edu/mailman/listinfo/gretl-users _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users