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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 09:39:06 +0000

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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