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Re: st: excess risk


From   Robert A Yaffee <bob.yaffee@nyu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: excess risk
Date   Tue, 29 Jun 2010 21:30:47 -0400

Dale,
  Perhaps you should consider quantile regression or quantile autoregression
as conditional value at risk, the quantile below which the distribution of losses , would constitute such risk, following the Engle Manganelli article on the subject.   Identify the quantile below which there would 
be excess value- at-risk (VaR).  If you can forecast that, you would be estimating predicting expected shortfall also.
  Alternatively,  you could you convert this quantile to a probability and then use that is the cutpoint between the excess and non-excess value at risk in an xtmelogit model.
   Robert


Robert A. Yaffee, Ph.D.
Research Professor
Silver School of Social Work
New York University

Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf

CV:  http://homepages.nyu.edu/~ray1/vita.pdf

----- Original Message -----
From: "Hardy, Dale S" <Dale.S.Hardy@uth.tmc.edu>
Date: Tuesday, June 29, 2010 1:03 pm
Subject: st: excess risk
To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>


> Hi Statalist,
> 
> Does anyone know how to do 'excess risk' using an xtmelogit model?
> 
> 
> 
> Dale Hardy, PhD RD LD CDE CHES
> Research Associate
> UT School of Public Health
> 1200 Herman Pressler Dr., Rm 645
> Houston, Tx 77030
> Phone: (713) 500-9957
> Fax: (713) 500-9264
> Email: dale.s.hardy@uth.tmc.edu
> 
> 
> 
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