The problem was about hospitalization costs. These can be true zeros.
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: Monday, August 18, 2008 9:38 AM
To: [email protected]
Subject: Re: st: stata code for two-part model
Peter <[email protected]>:
I think this claim is a bit of a red herring: "use of a continuous
model for data in which there is a clump of zeros seems incorrect."
Note that the -glm- approach assumes the mean of y given observables X
is nonzero, and E(y|X)=exp(Xb), not that observed y is nonzero!
Including the observations where y=0 is the whole point of the -glm-
approach--otherwise we would run ols regression of ln(y) on X. And if
you are claiming that the "true" model for (expected) healthcare
expenditures does have true zeros that are identifiable, then I
disagree. Some of your obs may spend nothing on health care (though
annual spending, including myriad items such as aspirin, is unlikely
to truly be zero for anyone) but that does not mean their conditional
mean should be zero. Maybe people who are dead have a conditional
mean of zero, but they should probably be excluded from the
analysis...
When spending is measured in discrete dollars, a big clump of people
who have predicted spending less than 50 cents may have a conditional
mean of zero measured in the same units as the data. But that does
not mean their "true" conditional mean is zero.
That said, a demand/expenditure model will have more and more "true"
(or rounded off) zeros as the category of demand/expenditure gets
narrower and narrower and the time window over which it is measured
gets narrower... think aspirin expenditures by week or day... but it
is not clear to me that a two-part model is the right approach even in
those cases.
On Mon, Aug 18, 2008 at 11:33 AM, Lachenbruch, Peter
<[email protected]> wrote:
> 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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