The fact that you were smoothing residuals
was not clear to me earlier. I am not
sure that it changes the issue very much.
I'll say that the issue was not what assumptions
you are consciously making, but of what
circumstances kernel regression works well in. But in
practice, looking at the graph of smooth
and residuals would be the best guide to
whether it was producing appropriate summaries.
I can comment on how far -locpoly- is visible.
-locpoly- was written by StataCorp developers,
but is not part of official Stata 8. It
was written up in Stata Journal 2003; thus
if your Stata has been -update-d since then
it should be visible to you, as all material
in the Stata Journal is automatically indexed
in the .key files searched by -search-.
In an up-to-date Stata,
. search kernel regression
points to -locpoly-.
Nick
[email protected]
Eik Leong Swee
> Thanks for the reply. I am adopting a semi-parametric
> strategy here. First, the dependent variable
> y is binary (0,1). There are a few regressors x- one of them
> x1, of particular interest, is
> suspiciously non-linear (theorectically and empirically).
>
> The strategy is then to run a parametric probit model of y on
> x (except for x1), then regress the
> fitted residuals on x1 using kernel regression. This way, the
> fitted residuals are not necessarily
> binary like y, but they make up the component of y that is
> not explained by x , and could
> potentially be explained by x1. This is where the kernel
> regression comes in. I'm not sure if it
> is convincing enough for you. I am not assuming anything
> about the errors in the kernel
> regression, as I plan to do a bootstrap of the errors. No
> need for asymptotic theory or CLT here.
>
> One huge problem I had was finding the program locpoly. It is
> not on my Stata 8, and I could not
> find it on the web until couple of days ago. That is why I
> could not compare it to kernreg nor
> find out if CV was included in locpoly.
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