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Re: st: excess risk
From
Robert A Yaffee <[email protected]>
To
[email protected]
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" <[email protected]>
Date: Tuesday, June 29, 2010 1:03 pm
Subject: st: excess risk
To: "[email protected]" <[email protected]>
> 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: [email protected]
>
>
>
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