<>
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Concerning your question:
Others will know more about this (especially how appropriate this
approach is, and whether or not you could reliably interpret results).
Conditioning on x is pretty much like splitting your sample in two.
Therefore you could estimate it in two parts: one for F() with - ...
if x > 0- and one for G() with - ... if x < 0-.
If you want to do it in one step, and you can write down F() and G()
such that they evaluate to zero whenever x is equal to zero, the
problem is fairly easily solved by splitting x and adding the
functions. Here is a nonsensical example.
***
sysuse auto
qui sum price
scalar mean = r(mean)
gen priceneg = ((price-mean)<0)*(price-mean)
gen pricepos = ((price-mean)>0)*(price-mean)
nl (weight = {beta}*( -sqrt(-priceneg)) + {gamma}*log(pricepos))
***
Also, I believe you could write a function evaluator program for -nl-
which implements this kind of condition; see -help nl-. But, again, I
cannot comment on the properties of the estimator under these
conditions.
Eva
2009/4/14 Quang Nguyen <[email protected]>:
> Dear All:
>
> I have the model as follows: y = F(x; lamda) for x>0 and y= G(x:
> lamda) for x<0. Both F and G are nonlinear. Do you think we can use
> _nl_ to estimate this model? Is there a way we can write like this:
>
> nl {( y = F(x; lamda) if x>0 ) (y= G(x: lamda) if x<0)}
>
> Many thanks!
>
> On Tue, Apr 14, 2009 at 6:54 AM, Martin Weiss <[email protected]> wrote:
>> <>
>>
>> Rudy may also want to take a cue from the MUS book datasets at
>> http://www.stata-press.com/data/mus.html
>> File mus04p1sim.do shows how the pros use -simulate- with the -seed- set...
>>
>>
>> HTH
>> Martin
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