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Re: st: Adjustment to likehood value due to dependence of data observations


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Adjustment to likehood value due to dependence of data observations
Date   Thu, 22 Sep 2011 15:30:05 +0100

#1 is easier. The answer is No in general. If you have time series for
panels, you need to fit appropriate models at the outset. That is the
principle. If you ignore that, then in broad terms, your parameter
estimates may sometimes be about right, but standard errors are likely
to be wrong and P-values are likely to be very very wrong. However, if
the model really is wrong, it is best to fit another.

#2 I don't understand. If there is a trend in residuals then your
model sounds misspecified. Getting a better idea of what the standard
errors are, or should be, for a model you have fitted that is
evidently wrong doesn't sound very useful. You may be misunderstanding
what -robust- options do, which is much less than people often think.

On Thu, Sep 22, 2011 at 3:10 PM, Abdul Q Memon <a.memon@ucl.ac.uk> wrote:

> I would really appreciate your reply on this.
>
> I have run several models using glm (possion and negative binomial)
> command in STATA. Based on the log-likelihood and BIC values I have
> selected the most appropriate models (with smallest BIC values). After
> this I have run GEE with AR1 structure for only the preferred model to
> account for serial correlation in data. I have these two questions.
>
> 1. Since my model seclection is based on (log-likelihood and BIC values)
> and in this case data is not independent (time series and panel data), is
> there a way in stata to adjust the likelihood after running glm command if
> the data is not independent.
>
> 2. After running gee command still there is some trend in residuals. Do i
> need to run robust command to adjust standard errors after gee?? my
> understanding is robust command is for corrections to standard error after
> OLS.
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