Re: [Gretl-users] RE Probit --robust
Sven,
thank you for your prompt answer.
Meanwhile, I found the note in the user guide in chapter 18:
Robust standard errors
For most estimators, gretl offers the option of computing an estimate of the covariance
matrix
that is robust with respect to heteroskedasticity and/or autocorrelation (and hence also
robust
standard errors). In the case of panel data, robust covariance matrix estimators are
available for
the pooled and fixed effects model but not currently for random effects. Please see
section 17.4
for details.
Jan
-----Ursprüngliche Nachricht-----
Von: Plus.line MailSystem [mailto:cyrus@mailer.plusline.de] Im Auftrag von Sven Schreiber
Gesendet: Freitag, 22. April 2016 12:54
An: gretl-users(a)lists.wfu.edu
Betreff: Re: [Gretl-users] RE Probit --robust
Am 22.04.2016 um 12:41 schrieb Jan Tille:
Dear all,
I tried to estimate a random effects probit model using the --robust
option. After this, I re-estimated the model without it.
I got the same results (i.e. standard errors) in both cases and
looking at the output it became clear why, because Gretl said that
estimation was ".based on inverse Hessian".
However, I found this post from 2013, where Sven Schreiber already
commented on this issue.
http://lists.wfu.edu/pipermail/gretl-devel/2013-December/004835.html
As time goes by....
However, I did not find an answer to this post, which brings me to my
question, is it possible use robust standard errors when one estimates
a random effects probit model in Gretl.
I don't think so. In the meantime I would repeat my Dec 2013 statement that gretl
should issue a warning, so that users don't think they have actually estimated with
robust standard errors.
Thanks for reminding.
By "robust" I mean robust to some form of unspecified
misspecification, like one checks the robust option, when using ols.
That is neither true for probit (with or without panel structure) nor for OLS. If
you're relying on robust standard errors as a cure for unspecified misspec then
you're cheating yourself (well, and your readers).
thanks,
sven
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