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From |
philippe van kerm <philippe.vankerm@ceps.lu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: estimating distribution functions from Kernel densities |

Date |
Fri, 10 Oct 2008 12:12:22 +0200 |

For an example of what Austin suggests -- directly estimating inequality measures from reweighted data instead of integrating counterfactual PDFs, see Biewen M. (2001), Measuring the effects of socio-economic variables on the income distribution : an application to the East German transition process, The Review of economics and statistics, 83(1), 185-190. Philippe > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner- > statalist@hsphsun2.harvard.edu] On Behalf Of Henrique Neder > Sent: Friday, October 10, 2008 2:30 AM > To: statalist@hsphsun2.harvard.edu > Subject: RES: st: estimating distribution functions from Kernel > densities > > I think that programs svylorenz, inequal, calculate inequality measures > directly from the data, for example, a sequence of observations of > income. I > want to use the DFL method to decompose the effects of the minimum wage > and > the characteristics of workers. For this, obtained through this method > is a > counterfactual Kernel density function. After obtaining this function, > I > desire from the same function (and not directly from my observed income > data) to obtain some measures of inequality, as the difference between > the > ninth decile and the first decile, the Theil index and the Gini index, > as is > done in the Dinardo, J., NM Fortin, and T. Lemieux (1996) article. For > the > first measure (difference between deciles) and I think also for the > other > two, is necessary to obtain the cumulative distribution function > related to > the Kernel density function. For this I need a procedure in Stata to > integrate the kernel density function. I discovered that in the set of > commands named moremata there is a code (mm_kint()) that apparently > integrates Kernel density functions. This is the right path? > > Regards > > Henrique Neder > > -----Mensagem original----- > De: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] Em nome de Austin Nichols > Enviada em: quinta-feira, 9 de outubro de 2008 18:05 > Para: statalist@hsphsun2.harvard.edu > Assunto: Re: st: estimating distribution functions from Kernel > densities > > Henrique Neder <hdneder@ufu.br> : > I think you need to be more clear about what you mean to do. The DFL > approach reweights the data to obtain the counterfactual density. If > you are interested in an inequality measure for the reweighted > distribution, can't you just compute the measure using your new > weights, using e.g. a program by Stephen Jenkins et al. (findit > svygei, findit ineqdeco)? > > DiNardo, J., N.M. Fortin, and T.Lemieux (1996) "Labour Market > Insitutions and the Distribution of Wages, 1973-1992: A Semiparametric > Approach," Econometrica, 64(5): 1001-1044. > > > On Thu, Oct 9, 2008 at 4:52 PM, Henrique Neder <hdneder@ufu.br> wrote: > > Dear Stata listers > > > > > > I am studying the methodology of decomposition of Dinardo, Fortin and > > Lemieux, known as Dfl. I need to estimate the inter-quantile > deviation, > the > > Gini index and the Theil index for Kernel density functions. There > would > be > > some method (for example, numerical integration) to do this using > Stata? > > > > Henrique Neder > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RES: st: estimating distribution functions from Kernel densities***From:*"Henrique Neder" <hdneder@ufu.br>

**References**:**st: estimating distribution functions from Kernel densities***From:*"Henrique Neder" <hdneder@ufu.br>

**Re: st: estimating distribution functions from Kernel densities***From:*"Austin Nichols" <austinnichols@gmail.com>

**RES: st: estimating distribution functions from Kernel densities***From:*"Henrique Neder" <hdneder@ufu.br>

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