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Re: st: -robust- of time series data


From   Robert A Yaffee <[email protected]>
To   [email protected]
Subject   Re: st: -robust- of time series data
Date   Wed, 01 Oct 2003 22:40:37 -0400

Mark and Eddy,
  Consider the Newey-West estimators if you want something robust to both autocorrelation and heteroskedasticity. You merely have to specify the appropriate lag length in the newey regression procedure.
  Regards,
 - Bob Yaffee


Robert A. Yaffee, Ph.D.
Senior Research/Statistical Consultant
Statistics and Social Science Group
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New York University
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----- Original Message -----
From: Mark Schaffer <[email protected]>
Date: Wednesday, October 1, 2003 1:12 pm
Subject: Re: st: -robust- of time series data

> Eddy,
> 
> Date sent:      	Wed, 1 Oct 2003 09:24:12 -0700 (PDT)
> From:           	Eddy <[email protected]>
> Subject:        	st: -robust- of time series data
> To:             	[email protected]
> Send reply to:  	[email protected]
> 
> > A perhaps naive question: Does it make sense to use the -robust-
> > option (sandwitch variance estimate) in a ML estimation of a time
> > series model? What is the variance estimates robust to in this case?
> > Serial correlations? Thanks.
> 
> They're still robust to heteroskedasticity but that's it.  If you 
> want robustness to serial correlation as well, you need something 
> like -newey-.
> 
> --Mark
> 
> > Eddy
> > 
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> 
> Prof. Mark E. Schaffer
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