--- Nuno <[email protected]> wrote:
> I'm trying to calculate the bootstrapped skewness-adjusted t-stat
> proposed by Lyon, Barber, and Tsai (1999), 'Improved Methods for
> Tests of Long-Run Abnormal Stock Returns', The Journal of Finance,
> Vol. 54, No. 1, pp. 165-201, in order to correct the skewness
> inherent to stocks returns.
If you worry about non-normality why not go for the whole range of
tried and tested tests based on order statistics (sometimes called
non-parametric statistics). Within Stata, Roger Newson has a whole lot
of packages writen in this area, and he has also writen a number of
articles on them. You can get them at:
http://www.imperial.ac.uk/nhli/r.newson/papers.htm
The approach in the paper you cite worry me a bit for two reasons
(though I did not read it very carefully) : First, it looks like you
are doing some form of bias correction using the bootstrap. The
inventor of the bootstrap warns against such bias corrections as the
estimate of the bias is measured very inacurately (Efron and Tibshirani
1993, pp. 138)). Second, the need to bootstrap some arbitrary amount
less than the number of observations in order to get the right test
indicates to me that something is not quite right with this method. The
overal feel I got was that they were pursuing a dead-end by trying to
make a t-test work for a case for which it was not designed, while
there are other tests availabel that were designed for this situation.
Again this is just a first impression, but I would reccomend taking a
good long hard look at those non-parametric tests before continuing
along this road.
Hope this helps,
Maarten
Bradley Efron and Robert J. Tibshirani (1993) An Introduction to the
Bootstrap. Chapman & Hall/CRC.
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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