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Re: st: Modelling extremely rare events (binary)


From   Abhimanyu Arora <abhimanyu.arora1987@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Modelling extremely rare events (binary)
Date   Tue, 14 Jun 2011 11:28:58 +0200

Hi
Perhaps you could have a look at Gary Kings's -relogit-?
Best
Abhimanyu

On Tue, Jun 14, 2011 at 10:05 AM, Markus Eberhardt
<markus.eberhardt@economics.ox.ac.uk> wrote:
> Hello everybody
>
> I have an empirical problem where for a very large dataset (panel,
> around 20,000 panel members with over 60,000 observations) I have two
> binary outcome variables A and B. The occurrence of either is
> extremely rare: only about 1.5% and 0.1% of observations for A and B
> respectively. I am for the time being treating this as a pooled panel,
> so not accounting for any fixed effects at the panel member level. My
> empirical model is made up of continuous and binary variables. In the
> logit and probit I am estimating A and B separately, for biprobit
> jointly, for mlogit I have four categories (0, A occurrs, B occurrs,
> both occurr). Ideally the analysis does account for the jointess of
> the decision as in the biprobit and mlogit approaches.
>
> Here are my questions:
> (1) DOES THIS AT ALL MAKE SENSE? Having estimated logit, probit,
> bivariate probit and multinomial logit I am concerned about the
> viability of what I am doing to this data: given the minute share of
> actual events occurring (1s, rather than 0s) is it at all possible
> that a logit-type model could tell me anything meaningful? So far I am
> getting interpretable empirical results, but it was put to me that
> these were entirely unreliable (or even spurious) given the extreme
> rarety of the event. Note that there are strong priors (from the
> descriptive analysis) that a certain characteristic (binary) drives
> the outcomes, so I imagine that a fixed effect and/or an interaction
> of this binary characteristic with other (continuous) RHS variables
> may provide an intuitive 'fit', but I am unsure whether this is
> empirically satisfied.
> (2) USEFUL DIAGNOSTICS? My diagnostics for the model(s) are hampered
> by the fact that it's difficult to get a handle on what constitutes a
> substantial deviation for the predicted from the observed outcomes.
> Apart from -fitstat- type diagnostics, are there any other things I
> could do to chose between rival models and/or to convince myself that
> what I'm doing is at all meaningful in this challenging empirical
> case?
> (3) ALTERNATIVE EMPIRICAL MODELS? Are there any other empirical
> specifications that are better suited to fit this data? I tried to
> search for extremely rare events such as earthquakes, but couldn't get
> much out of it.
> (4) PANEL ELEMENT? Possibly a bridge too far, but would there be any
> option to get the panel element of the data to have a bearing on the
> empirics.
>
> Thanks a lot in advance.
> markus
>
> Markus Eberhardt
> ESRC Post-doctoral Research Fellow, Centre for the Study of African
> Economies, Department of Economics, University of Oxford
> Stipendiary Lecturer, St Catherine's College, Oxford
>
> web: http://sites.google.com/site/medevecon/home
> email: markus.eberhardt@economics.ox.ac.uk
> twitter: http://twitter.com/sjoh2052
> mail: Centre for the Study of African Economies, Department of
> Economics, Manor Rd, Oxford OX1 3UQ, England
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