Nick Cox replied to Viktor Slavtchev:
> -mlbeta- is, or was, a user-written program by Sean (Jack) Buckley.
> As your report implies, his personal website on which
> his files sat seems to have disappeared.
>
> I have a version of his program on my machine from an
> earlier downloading. 0s and 1s are ignored by the program
> as a direct or indirect consequence of its use of maximum
> likelihood and the fact that ln 0 is indeterminate.
>
> I won't post the program here for three reasons. It is not
> mine to do so; as I know no location for the author, that
> would be posting a program with no known means of support;
> and another program exists, -betafit- on SSC, that offers
> an alternative. -betafit- resembles -mlbeta- in not
> supporting 0s or 1s.
>
> When people have 0s and 1s that often means that they have
> one or more such spikes in their distributions, so that
> it can be moot whether a beta model is applicable in any case.
It's true that you can no longer download -mlbeta- from Jack Buckley's
old university URL into Stata (I lost my copy of it in a machine
move), but if Viktor desperately wants to get hold of -mlbeta-, you
could always contact Jack at his current address at the National (US)
Institute of Education Sciences ([email protected]) and I'm sure
he'd be more than happy to supply a copy to anyone who wanted one
(he's normally quite good with email reponses). However, I might be
able to snag a copy of the full code as helpfully amended, and
displayed, by Richard Williams and Scott Merryman in response to my
request to run -mlbeta- with a -nocons- option. I'll see if I can find
it for you from the Statalist archives, but it might take a while.
However, you might not need it, for there is an alternative that
Viktor can use to fit his proportional models. It doesn't involve a
user-written program and it might turn out to be better. Typing
. glm y x1 x2, family(binomial) link(logit) robust
fits a 'fractional logit' (FL) model as conceived by Papke and
Wooldridge (1996) (obviously, where -y x1 x2- are your variables). An
advantage that FL models have over beta-distributed (BD) models is
that they can include 0s and 1s in the response variable.
In fitting some FL models to some British opinion-poll data recently,
I found that they comfortably outperformed the same models using the
BD parameterization (via -betafit-) in terms of goodness of fit, in
producing the correct signs on _all_ of the coefficients (although the
BD models didn't do too badly here, either) and in producing a greater
efficiency of those coefficients. Perhaps somebody out there has run a
rather more formal, Monte Carlo analysis on the properties of FL
versus BD models that I'm not aware of.
However, Maarten Buis (from whom I picked up this routine as well as
the above paper!) strikes a cautionary note on FL models, which you
can read at
http://www.stata.com/statalist/archive/2007-07/msg00786.html
I hope this helps.
--
Clive Nicholas
[Please DO NOT mail me personally here, but at
<[email protected]>. Thanks!]
LE Papke and JM Wooldridge (1996) "Econometric Methods For Fractional
Response Variables With An Application to 401(K) Plan Participation
Rates", Journal of Applied Econometrics 11(6), 619-32.
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