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


From   "Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   R: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Mon, 17 Dec 2012 16:17:26 +0100

Laura replied to my previous comment:
@ 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?

The main meaning of my example is that you cannot be sure, after invoking
-robust-, that heteroskedasticity is automatically  removed. In other words,
homoskedasticity should be checked graphically even after - robust -. I
would also test whether or not your OLS model suffers from omitted variable
bias (-estat ovtest-), which is a more serious issue than
heteroskedasticity.  

Best regards,
Carlo

Dott. Carlo Lazzaro
Studio di Economia Sanitaria
Via Stefanardo da Vimercate, 19
20128 Milano
Tel/fax: 02/26000516
Portatile: 335/6786741
e-mail: carlo.lazzaro@tiscalinet.it
               carlo.lazzaro@tin.it

The main meaning of my example was that you cannot be sure, after invoking
-robust-, that heteroskedasticity is automatically  removed. In other words,
homoskedasticity should be checked graphically even after - robust -. I
would also test whether or not your OLS model suffers from omitted variable
bias (-estat ovtest-), which is a more serious issue than
heteroskedasticity.  

Best regards,
Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Laura R.
Inviato: lunedì 17 dicembre 2012 15:29
A: statalist@hsphsun2.harvard.edu
Oggetto: Re: st: OLS assumptions not met: transformation, gls, or glm as
solutions?

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.


Laura
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