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Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Mon, 17 Dec 2012 14:43:38 +0000

On the question directed at me: Let's turn this round. Why do you
think that adding an indicator variable to the model will capture
heteroskedasticity?

It sounds as if you need a different model. David gave several very
useful pointers.

Nick

On Mon, Dec 17, 2012 at 2:29 PM, Laura R. <laura.roh@googlemail.com> wrote:
> Thank you very much for your help so far.
>
> Please let me reply one by one.
>
> @ Carlo: I conducted your example and with my data it seems the same,
> the -robust- option does not seem to change the graphical pictures or
> the tests (-estat hettest-,  -iqr-) much. So the robust option has to
> be visible in the graphics and the tests, that it induced
> homoskedasticity?
>
>
> @ Nick:
> As to the equality of variances between the cases from the 2 surveys,
> a referee seems concerned about inferences one can make from the
> descriptive statistics. Therefore, I would like to use -sdtest- to see
> whether variances are the same in the two samples.
> And for the regression, I think that adding the year-dummy would be
> enough to account for it?
>
> The variances of the regression residuals are another thing, this is
> for model validation. Yes, there I plotted the residuals, and the
> variances seem to become larger as the dep. var. becomes larger,
> especially the lower bound (with negative values) changes.
>
>
> @ Maarten:
> So you would not worry about heteroskedasticity or the distribution of
> errors. What would you write in the paper then? "There is
> heteroskedasticity and non-normal error distribution, but I still use
> OLS because ...?" I am very curious, because I would like to keep the
> OLS
>
> @ Maarten & David:
> About linearity: as independent variables, I mainly have categorical
> variables. So - scatter y x- or -graph matrix y x x- does not help
> much, because the cases are only on the lines for 0 and 1. How can I
> see whether I have a linear relationship between y and x, if x is
> categorical?
>
> @ David:
> Yes, I think about transformation, and will read again about
> interpretation. Still, just having minutes to interpret would be
> easier, also for readers which are not so familiar with
> transformation. Also, I am not sure whether OLS with transformed
> dependent variable, or -glm- without transformed variable would be
> better.
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