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Re: st: computing elasticities after using lpoly
From
Bert Lloyd <[email protected]>
To
[email protected]
Subject
Re: st: computing elasticities after using lpoly
Date
Sat, 27 Oct 2012 22:43:25 -0400
Dear Austin,
This looks like a very clever solution. Do you have a sense of whether
the confidence intervals are likely to be consistent? If not, would
you recommend a bootstrapping approach?
Thanks,
BL
On Tue, Oct 9, 2012 at 11:53 AM, Austin Nichols <[email protected]> wrote:
> Arka Roy Chaudhuri <[email protected]>:
> For a worked example, try:
>
> webuse motorcycle, clear
> lpoly accel time, deg(1) k(tri) bw(3) gen(x s)
> qui levelsof x, loc(xs)
> g double schk=.
> g double b=.
> loc i 1
> qui foreach l of loc xs {
> gen double w=max(0,3-abs(time-`l'))
> reg accel time [aw=w]
> replace schk=_b[_cons]+_b[time]*`l' in `i'
> replace b=_b[time] in `i'
> drop w
> loc i=`i'+1
> }
> assert float(s)==float(schk)
> la var b "Slope"
> la var s "Conditional mean"
> sc s b x, msize(small small) c(l) name(slopes)
>
>
> On Tue, Oct 9, 2012 at 10:56 AM, Austin Nichols <[email protected]> wrote:
>> Arka Roy Chaudhuri <[email protected]>:
>> You can also get the coefs from an -lpoly- graph by noting the kernel
>> type, bandwidth, and degree, then estimating the weighted regressions
>> yourself, saving the coefs each time. If you specify the -generate(x
>> s)- option you can also get all the X points at which regressions are
>> estimated.
>>
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