Thanks to all those who provided response on this. Yes I agree with Tony on the use of two-part (hurdle) model - a logit and a zero-truncated negative binomial (or zero-truncated poisson) model. My worry was with issues around unobserved heterogeneity. I guess I will explore the use of finite mixture models (fmm) to handle this.
Regards
Jon
________________________________
From: "Lachenbruch, Peter" <[email protected]>
To: [email protected]
Sent: Tuesday, 2 June, 2009 18:22:16
Subject: st: RE: AW: Sample selection models under zero-truncated negative binomial models
This could also be handled by a two-part or hurdle model. The 0 vs. non-zero model is given by a probit or logit (my preference) model. The non-zeros are modeled by the count data or OLS or what have you. The results can be combined since the likelihood separates (the zero values are identifiable - no visits vs number of visits).
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Martin Weiss
Sent: Tuesday, June 02, 2009 7:02 AM
To: [email protected]
Subject: st: AW: Sample selection models under zero-truncated negative binomial models
<>
Try
*************
ssc d cmp
*************
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von John Ataguba
Gesendet: Dienstag, 2. Juni 2009 16:00
An: Statalist statalist mailing
Betreff: st: Sample selection models under zero-truncated negative binomial
models
Dear colleagues,
I want to enquire if it is possible to perform a ztnb (zero-truncated
negative binomial) model on a dataset that has the zeros observed in a
fashion similar to the heckman sample selection model.
Specifically, I have a binary variable on use/non use of outpatient health
services and I fitted a standard probit/logit model to observe the factors
that predict the probaility of use. Subsequently, I want to explain the
factors the influence the amount of visits to the health facililities. Since
this is a count data, I cannot fit the standard Heckman model using the
standard two-part procedure in stata command -heckman-.
My fear now is that my sample of users will be biased if I fit a ztnb model
on only the users given that i have information on the non-users which I
used to run the initial probit/logit estimation.
Is it possible to generate the inverse of mills' ratio from the probit model
and include this in the ztnb model? will this be consistent? etc...
Are there any smarter suggestions? Any reference that has used the similar
sample selection form will be appreciated.
Regards
Jon
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