In some instances, the model for healthcare expenditures does have true
zeros that are identifiable. In one study I consulted on the data came
from a health insurer, and zeros were people who had not gone to
hospital.
The use of a continuous model for data in which there is a clump of
zeros seems incorrect. There is no transformation that can remove this
clump. The severity of the problem depends a bit on the size of the
clump. In the hospital insurance data (wanting to estimate
hospitalization costs in the policy holders) 95% of the population had
no costs. Pretending that these were continuous would lead to some
nonsense results. At the present time, I have a data set that has 32
out of 145 people with zeros. However, these are not necessarily
identifiable since they could be slightly greater than zero. I'm
gritting my teeth on this and pretending all is well. However, a
histogram shows enormous skewness. I'll probably try a square root.
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 Austin
Nichols
Sent: Saturday, August 16, 2008 8:50 AM
To: [email protected]
Subject: Re: st: stata code for two-part model
Shehzad Ali et al. --
See also
http://www.nber.org/papers/t0228
The two part models of health expenditures have always struck me as a
bad idea; think about how you would get predictions for each indiv in
your sample. The "stage 1" probit classifies people as having
expenditures or not (some correctly, some not) and then the "stage 2"
ols model gives predicted expenditures only for those people who
actually have positive expenditures (not those who are classified by
the probit as likely to have positive expenditures) unless you predict
out of sample. At least one preferred approach of calculating
marginal effects by comparing predictions over the whole sample turns
out to be practically and analytically difficult in that setting.
However, a -glm- with a log link (or equivalently a -poisson-
regression) has no trouble: those people with extremely low predicted
expenditures would round to zero predicted expenditures if you thought
about a survey with expenditures measured discretely in dollars, say.
Everyone has E(y)=exp(Xb) and there is no real issue with calculating
marginal effects. Once you are in the -glm- framework it is also easy
to think about model fit and alternative links...
On Sat, Aug 16, 2008 at 3:41 AM, Eva Poen <[email protected]> wrote:
> Shehzad,
>
> this looks like a hurdle model. Have you search the ssc archives to
> see if someone else has programmed it for you? Have a look at
> -hplogit-, for example.
>
> If you end up doing it yourself, I think you need to do a bit of
> programming. In order for -mfx- to work after your estimation, you
> need a way of telling it what you want the marginal effects to be
> calculated for. In your case, this would be the overall expected cost
> of care from your model. The way to feed this to -mfx- is via the
> predict(predict_option), but for this to work you need to write a
> -predict- command and an estimation command for your model.
>
> See for example this post:
> http://www.stata.com/statalist/archive/2005-10/msg00091.html
>
> Hope this helps,
> Eva
>
>
>
> 2008/8/16 Shehzad Ali <[email protected]>:
>> Hi,
>>
>> I was wondering if someone can help with stata code for calculating
marginal
>> effects after two-part models for say, cost of care. Here, first part
is a
>> probit model for seeking care or not, and the second part is an OLS
model of
>> cost of care, conditional on decision to seek care. Here is the
simplified
>> code:
>>
>> probit care $xvar
>>
>> reg cost $zvar if care==1
>>
>> mfx
>>
>> I understand that mfx after the second part gives us the marginal
effects
>> for the OLS part only, and not the conditional marginal effects.
>>
>> Any help would be appreciated.
>>
>> Thanks,
>>
>> Shehzad
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