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Re: st: Regression Across Two Groups


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Regression Across Two Groups
Date   Wed, 14 Dec 2011 10:03:42 +0000

Thanks for the example.

I see earnings as a coarsely categorised variable, fit for -intreg- or
-ologit-. In what sense is earnings dichotomous (means, has two
categories)? Why did you say you were interested in logit models?

Nick

On Wed, Dec 14, 2011 at 9:48 AM, Muhammad Anees <anees@aneconomist.com> wrote:
> Thanks Nick for your suggestion.
>
> Sure, the data looks like which contains two different sample but
> related. Foreign qualified and no foreign qualification are two
> different dataset earch with the following sample data, only a sample,
> actually the data consist two different samples in two different
> files, which I can deal how to combine in stata for my purpose.
>
>
>
> ear     exper   gender  subject area    language
> 0-5000  20      m       IT      rural   Urdu
> 5000-10000      22      m       ENGINEER        urban   English
> 10001-15000     15      f       ECONOMICS       rural   Urdu
> 5000-10000      10      m       HR      urban   Urdu
> 5000-10000      5       f       STRT MGT        urban   English
> 10001-15000     8       f       MARK    urbna   English
> 0-5000  9       m       SOCIOLOGY       rural   Urdu
> 0-5000  17      m       IT      urban   Urdu
>
>
> On Wed, Dec 14, 2011 at 2:39 PM, Nick Cox <njcoxstata@gmail.com> wrote:
>> It's categorical/dichotomous, yet the example is [pro]portion of
>> earnings from outside main job. Sounds like a fractional response from
>> the latter. Muhammad: Give us an example of what observations look
>> like before this gets any more obscure, please!
>>
>> On Wed, Dec 14, 2011 at 9:18 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
>>> On Wed, Dec 14, 2011 at 6:25 AM, Muhammad Anees wrote:
>>>> Sorry for not clarifying the story about the types of variables, like
>>>> earnings which I have at hand as a categorical/dichotomous variable.
>>>> For example if an individual has a portion of earnings from doing
>>>> consultancies or involved in any R&D organizations beside their normal
>>>> routine jobs. In this case, I was interested in comparing the
>>>> regression models (across foreign qualified and not foreign qualified)
>>>> of earnings on other predictors say experience, research training, job
>>>> nature, industry, region (rural and urban) using logit/probit in case
>>>> of categorical variables and similarly using OLS for continuous
>>>> dependent variable which at least I do not have at this stage.
>>>
>>> This is still not clear. The independent/explanatory/right-hand-side/x
>>> variables aren't relevant here, they can be of any type, it is the
>>> type of  the dependent/explained/left-hand-side/y variable that
>>> matters. Earnings is typically collected as either a continuous
>>> variable (how much do you earn?) or as a choice from a set of
>>> intervals (did you earn less than x$, between x$ and y$, etc.?). None
>>> of these are correctly modeled as a logit/probit. In the former case I
>>> would use a -glm- with the -link(log)- option, in the latter case I
>>> would start with assigning each category with a reasonable
>>> representative number and than use -glm- with the -link(log)- option.
>>> There are other solutions for the latter problem, e.g. -intreg-, but
>>> if the underlying distribution is non-normal, which is likely to be
>>> the case with earnings, then it is unclear whether these alternatives
>>> are any better. The comparison is than just a matter of adding the
>>> appropriate dummies and/or interactions.
>>
>> *
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>
>
>
> --
>
> Regards
> ---------------------------
> Muhammad Anees
> Assistant Professor
> COMSATS Institute of Information Technology
> Attock 43600, Pakistan
> www.aneconomist.com
>
> *
> *   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/

*
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