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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: computing elasticities after using lpoly |

Date |
Mon, 29 Oct 2012 21:26:01 -0400 |

Bert Lloyd <bert.lloyd.89@gmail.com>: There is no way to get the slope terms or SE or CI from -lpoly- and it is not easy to calculate them; but bootstrapping should be easy enough, and work well provided you have a large N relative to a small number of fixed values at which you want to compute slopes and SE or CI. On Sun, Oct 28, 2012 at 9:51 PM, Bert Lloyd <bert.lloyd.89@gmail.com> wrote: > Dear Austin, > > Sorry to be imprecise and thanks for allowing me to revise-and-resubmit. > > My question is about inference on the estimated slope, rather than the > estimated conditional mean. > > In terms of your example, are the standard errors given by _se[time] > likely to be consistent? > > If not, is there a way to obtain the analytical standard errors for > the slope term directly from lpoly? Or would a bootstrapping approach > be preferable? (If I am reading the help file for R's np package > correctly, npreg and related functions compute analytical standard > errors for the gradient but also provide bootstrap estimates -- see > citation below.) > > Thanks, > > BL > > Hayfield and Racine, "The np package," v 0.40-13, 2012; pages 9-11. > http://cran.r-project.org/web/packages/np/vignettes/np.pdf > > Li and Racine, Nonparametric Econometrics, 2007, provide a formula for > the asymptotic variance of the local polynomial estimator (Theorem > 2.10, pg. 90), although it is not obvious to me whether this is for > data assumed to be i.i.d. (homoskedastic) or i.n.i.d. > (heteroskedastic). > > On Sun, Oct 28, 2012 at 7:11 PM, Austin Nichols <austinnichols@gmail.com> wrote: >> Bert Lloyd <bert.lloyd.89@gmail.com>: >> I do not understand your question; are you asking whether -lpoly- >> computes consistent standard errors? If so, please read the manual >> entry for -lpoly- and the references therein. If you mean CIs in my >> example, there are none! >> >> On Sat, Oct 27, 2012 at 10:43 PM, Bert Lloyd <bert.lloyd.89@gmail.com> wrote: >>> 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 >>> * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: computing elasticities after using lpoly***From:*Arka Roy Chaudhuri <gabuisi@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Arka Roy Chaudhuri <gabuisi@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Arka Roy Chaudhuri <gabuisi@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Bert Lloyd <bert.lloyd.89@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: computing elasticities after using lpoly***From:*Bert Lloyd <bert.lloyd.89@gmail.com>

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