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RE: st: estimating distribution functions from Kernel densities


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
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