Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down at the end of May, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Abdel Rahmen El Lahga <rahmen.lahga@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Income distribution |

Date |
Thu, 4 Mar 2010 18:24:28 +0100 |

Hmm, Unfortunately, it seems that most of the fitted functions, using grouped data, are unreliable , i.e., they often produce summary statistics which are very different from those obtained from individual micro data. I suggest the procedure proposed recently by Shorrocks and Wan (2008) ( Ungrouping Income Distributions: Synthesising Samples for Inequality and Poverty Analysis: wider discussion paper no 16 ) which can be downloded at http://www.wider.unu.edu/publications/working-papers/research-papers/2008/en_GB/rp2008-16/ roughly the approach of Shorrocks and Wan fits any chosen distribution to grouped data to produce a sample of N observation then they propose a second stage of adjustement until that characteristics of the generated sample exactly match the reported grouped values. The simulated data can then be used to estimate various inequality and poverty measure. The DASP package of Araar and Duclos contains a ready Stata routile that apply such procedure DASP can be downloeded at http://dasp.ecn.ulaval.ca/ HTH AbdelRahmen 2010/3/4 Nick Cox <n.j.cox@durham.ac.uk>: > All the distributions named in these emails have computable CDFs > ("analytic" is for theorists or calculus teachers). -qpfit- from SSC > contains Q-Q and P-P plotting routines for several distributions used in > income work. > > I agree with Stas' main point that discrimination between candidate > distributions is tricky if the data arrive coarsely binned. It can be > pretty difficult even with thousands of records! > > Nick > n.j.cox@durham.ac.uk > > Stas Kolenikov > > No, this is the stuff to work with individual records. With grouped > data, I > don't think you'd be able to detect differences from a gamma or a > log-normal > distribution unless you have a couple dozen income categories... which > in my > experience does not happen; you are lucky if you have 10 or more; and I > don't think it is fair to say that income data "typically" come in this > form, many surveys have questions or modules on incomes and > expenditures. > > I'd say, "write your own -ml- estimator", but most of these flexible > distributions have only density functions available, while the cdfs do > not > have analytic expressions. > > On Wed, Mar 3, 2010 at 3:03 PM, Martin Weiss <martin.weiss1@gmx.de> > wrote: > >> findit Singh-Maddala > > Sridhar Telidevara > >> Does anybody know whether stata has programs to estimate the >> parameters of various distributions (like Singh-Maddala, beta-2, >> lognormal etc.) if the income data are available in the grouped form. >> Unit wise income data are not available. This is the case for >> household income data, where typically only frequencies within >> specified classes are available. The underlying distribution of the >> continuous data is modeled by the above mentioned parametric models. > > * > * 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/ > -- AbdelRahmen El Lahga * * 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**:**Re: st: RE: Income distribution***From:*Austin Nichols <austinnichols@gmail.com>

**References**:**st: Income distribution***From:*Sridhar Telidevara <sridhar.telidevara@gmail.com>

**Re: st: RE: Income distribution***From:*Stas Kolenikov <skolenik@gmail.com>

**RE: st: RE: Income distribution***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

- Prev by Date:
**st: literal single quotes** - Next by Date:
**st: failure estimating bootstrap on logistic model** - Previous by thread:
**RE: st: RE: Income distribution** - Next by thread:
**Re: st: RE: Income distribution** - Index(es):