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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Sat, 6 Oct 2012 11:21:59 +0100 |

That paper is not accessible to me at this time. You can differentiate numerically using -dydx-. But I wouldn't use -lpoly- for that purpose at all. I would use -fracpoly- or a spline approach (e.g. -rcspline- (SSC)) to get smooths before I tried to get slopes. The problem with all these approaches is that estimates of slope are very sensitive to noise. Nick On Sat, Oct 6, 2012 at 11:07 AM, Arka Roy Chaudhuri <gabuisi@gmail.com> wrote: > Thank you Nick for your advice. However my point in using lpoly is > precisely its flexibility. But at the same time I want a measure of > the slope of the curve that I get from using lpoly at different points > of the curve-so I am not actually looking for one specific > number.Rather I am trying to find the slope at different values of my > independent variable-so that I can get an idea of the elasticities at > different parts of the expenditure distribution. Basically I am trying > to implement both Fig 2 and Fig 3 in the following paper (albeit in my > context):Deaton, Angus S. and Subramanian, Shankar. "The Demand for > Food and Calories," Journal of Political Economy, Feb. 1996,104(1), > pp. 133-62 > > On Sat, Oct 6, 2012 at 2:19 AM, Nick Cox <njcoxstata@gmail.com> wrote: >> It seems to me that the whole point of -lpoly- is to be flexible about >> modelling a relationship. It has absolutely no sense of any idea of an >> overall slope. If you want a number for the slope, -lpoly- is useless. >> If you want an independent view of how far the relationship after some >> smoothing really is (e.g.) linear or monotonic, then -lpoly- can be >> useful, mostly by providing a graph. For what you want, any >> appropriate regression method will be better, such as -regress- or >> -qreg-. >> >> Nick >> >> On Sat, Oct 6, 2012 at 10:06 AM, Arka Roy Chaudhuri <gabuisi@gmail.com> wrote: >> >>> I am using Stata 11 on a Windows 7 machine. I am using lpoly to >>> estimate nonparametric regressions of the form: >>> >>> log(y)=f(log(x)) + u >>> >>> where y= per capita expenditure on food >>> x= total per capita expenditure >>> >>> Using lpoly, I can get a graph of per capita expenditure on food >>> against total per capita expenditure. However I am also interested in >>> obtaining expenditure elasticities of percapita expenditure on food at >>> different levels of total per capita expenditure i.e in my context >>> d(log(y)/d(log(x)) where x and y are as earlier defined.The problem is >>> that lpoly does not give estimates of any slope coefficients so I am >>> at a loss on how to compute these elasticities. I would really >>> appreciate if anybody could give me advice in this regard. >>> * * 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/

**Follow-Ups**:**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>

**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>

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