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Re: st: RE: Income distribution


From   Sridhar Telidevara <[email protected]>
To   [email protected]
Subject   Re: st: RE: Income distribution
Date   Wed, 3 Mar 2010 21:41:35 -0500

I did write my own program in MATLAB but different starting values
are giving me different estimates for the parameters for Singh-Maddala
distribution. However, the estimates for the lognormal distribution
are the same for various starting values. I have used minimum
chisquare estimates as initial values for the lognormal distribution.

BTW, there are 14 income categories in the data.

Sridhar

On Wed, Mar 3, 2010 at 4:46 PM, Stas Kolenikov <[email protected]> wrote:
> 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 <[email protected]> wrote:
>
>>
>> <>
>>
>> *******
>> findit Singh-Maddala
>> *******
>>
>> throws up promising links...
>>
>> HTH
>> Martin
>>
>>
>> -----Original Message-----
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of Sridhar
>> Telidevara
>> Sent: Mittwoch, 3. März 2010 21:59
>> To: [email protected]
>> Subject: st: Income distribution
>>
>> 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.
>>
>> Thank you,
>>
>> Sridhar
>> *
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>>
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>
>
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
>
> *
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