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Re: st: Regression Across Two Groups


From   Muhammad Anees <anees@aneconomist.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Regression Across Two Groups
Date   Wed, 14 Dec 2011 14:48:50 +0500

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

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*   http://www.ats.ucla.edu/stat/stata/


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