Shehzad Ali <[email protected]>:
Whether those models are appropriate depends on the causal model you
propose that connects these constructs (you do not even mention what
the x variables are), and how you think the data are generated. What
is the source of bias in regressing medical expenditures on insurance?
Are the errors normally distributed? Etc. etc.
Are we to understand that medical expenditure is zero when someone
does not seek medical advice? In that case, modeling expected med exp
as exp(Xb) may make sense, using e.g. ivpois, in which case you might
predict med exp of 0.03 for those folks who have zero expenditure. If
the desired med exp is low, then it is no surprise that these folks
don't seek medical advice.
I think you probably have a complicated story in mind, where people
have unobserved heterogeneity in preferences (discount rates, risk
prefs, price elasticity etc.) and are induced (possibly in unforeseen
ways) by past choices to optimize at corner solutions, but it is
impossible to comment on your use of the empirical models without
seeing a cogent theory to motivate them.
On Nov 11, 2007 2:14 PM, Shehzad Ali <[email protected]> wrote:
> Hello listers,
>
> I have a query about using biprobit, treatreg and heckman steps in stata and
> generating IMRs. I am using a three-part model for medical expenditure.
> Here is a summary:
>
> I have 1,500 in the sample who felt sick, of which 1,000 sought medical
> advice and hence had medical expenditure. Here the first selection bias is
> with
> regards to seeking treatment when ill. Then in the sample, 700 individuals
> are insured and 1,300 are not. This is the second selection bias which is
> related to insurance purchasing decision. So I need to take into account two
> selection equations for my medical expenditure (outcome) equation, i.e.
> sought medical advice when ill, and bought insurance. I have to bear in mind
> that insurance decision also affects decision to seeking care when ill and
> medical expenditure when treatment is sought.
>
> Here is what I am thinking of doing:
>
> First part of the model:
>
> biprobit (eq1: visit_when_ill = insurance x1 x2 x3) (eq 2: insurance=x1 x2
> x3 x4)
>
> Here first eq is for decision to seek care when ill and second decision is
> to buy insurance.
>
> Predict imr1, xb
>
> Second part:
>
> heckman med_expenditure x1 x2 x3 imr1 insurance, treat(insurance= x1 x2 x3
> x4)
>
> predict imr2,xb
>
> Third part:
>
> treatreg med_expenditure x1 x2 x3 insurance imr2, treat(insurance=x1 x2 x3
> x4)
>
> Is this the right approach to take? Any help would be greatly appreciated.
>
> With sincere thanks,
>
> Shehzad
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
