Simone,
I used to model a twopart model in marketing (Buy/NoBuy as cloglog, and
quantity as truncated Poisson)
The likehood for each observation
P(y=0)=1-p(y>0)
P(y)=P(y>0).P(y|y>0)
I think you'll have to take into account all the observations (even those
who never incur an expense)..
Please pay attention to the percentage of 'patients' (if less than 5% you'll
get rare events biases..)
Normally, as the 'non-patients' show no variance for the expenses, the two
models show no significat correlations
You can also try a two step modelling (one estimation for the decision -all
observations-, an other for the expenses -patients-..)
Hope this helps
Naji
-----Message d'origine-----
De : [email protected]
[mailto:[email protected]]De la part de Simone Boyce
Envoye : vendredi 25 juin 2004 17:01
A : Stata
Objet : st: Two Part Model
Hi,
I'm interested in modeling a two part model, which
seems to go by many names in different fields:
two-tiered and hurdle models. Essentially, I'm trying
to model medical cost expenditure, but the data
consist of many individuals who never incur an
expense. Hence, there are many zeroes in the data.
As a result, I'm separating the decision into two
parts:
1) the decision to see a doctor and 2) the amount of
the expense:
P(y>0) =X_1B_1+e_1 (1)
E(y|y>0) = log(y)|y>0= X_2B_2+e_2 (2)
where the error terms between equations (1) and (2)
are independent.
I know that this is usually estimated as a probit (eqn
1) and then OLS (eqn 2) to compute
E(y)= P(y>0)E(y|y>0). However, how do I determine the
marginal effects and the associated standard errors?
Especially, if one of the variables that I'm
interested in is in both equations. Has anyone
programmed the two part model?
Thanks,
Simone
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