Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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


From   Cameron McIntosh <cnm100@hotmail.com>
To   STATA LIST <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Adjustment to likehood value due to dependence of data observations
Date   Thu, 22 Sep 2011 11:25:47 -0400

Memon,
On Nick's second point, see:
Freedman, D.A. (2006). On the So-Called “Huber Sandwich Estimator” and “Robust Standard Errors”. The American Statistician, 60(4),  299-302. http://www.stat.berkeley.edu/~census/mlesan.pdf
Why bother getting robust estimates of standard errors for a potentially biased estimates in a misspecified model?
Cam
----------------------------------------
> Date: Thu, 22 Sep 2011 15:30:05 +0100
> Subject: Re: st: Adjustment to likehood value due to dependence of data observations
> From: njcoxstata@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> #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.
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
 		 	   		  
*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index