```.-
help for ^skewt^
.-

Computes Johnson's skewness adjusted t-statistic and generates bootstrapped con
> fidence intervals
-------------------------------------------------------------------------------
> ---------------------

Syntax
------

^skewt^ ^varlist^ ^[if]^ ^[in]^ ^,^ ^bs^ ^seed(#)^ ^reps(#)^ ^size(#)^ ^nowarn^
>  ^nohead^ ^saving^ ^(bs)^ ^replace^

Description
-----------

^skewtt^: This program implements the skewness adjusted bootstrapped t-statisti
> c procedure as in
Lyon, Barber, and Tsai (1999). The skewness adjustment is based on Johnson (197
> 8). The program
requires that the user has a data file which contains the appropriate abnormal
> return stored as a
variable. The skewt command is issued after this data file has been opened (use
> d).

The skewness adjusted t statistic is given by the following formula.

N*(s + (1/3*G*(S-squared)) +((1/(6*(N-squared)))*G ))

where G is skewness
S is the standard deviation
N is the square root of the number of observations

Example usage
--------------

sysuse auto

skewt mpg, bs reps(1000) size(2) seed(1234) nowarn nohead saving (c:\bs) replac
> e

Output from the program
-----------------------
The program displays the parameters (N, S, G and the sample Mean) calculated fr
> om the sample that feed into the formula to compute
the skewness adjusted t statistic

The program displays the following parameters for the example usage above.

N coefficient  = 8.602325267042627
S-coefficient  = 2.09027474443816
G-coefficient  = 1.653433511704859
Sample mean    = 6165.256756756757

after the bootstrap procedure the following table is displayed

------------------------------------------------------------------------------
|   Observed   Bootstrap                         Normal-based
|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_bs_1 |   38.72841   7.316759     5.29   0.000     24.38782    53.06899
------------------------------------------------------------------------------

Options for the bootstrap
--------------------------

reps (#): This determines the number of replications

size(#) : This determines what proportion of the sample is used for the bootstr
> ap. #=1 implies
all the data is used. #=2 implies half of the data is used.

saving () : This is used to save the bootstrapped values. For example, saving (
> C:\bs) will save the
results in c:\ . The file name will be the name of the varibale selected for th
> e test, prefixed with bs_.

replace: the option replace replaces the file in which the bootstrapped values
> are stored.

Futher Details
----------------

Further details and cutoffs for other significance levels can be calcuated from
>  the
saved file. To do that, First open the file containing the saved bootstrap resu
> lts, for example,

use "c:\bs_mpg.dta",clear

then issue the following commands

* To inspect the bootstrapped distribution

estat bootstrap, all
tabstat _bs*,s(mean median p5 p25 p75 p95 p99 max min)
histogram  _bs_1,normal

* To retreive the percentile condifence intervals

_pctile _bs_1, p(.5, 2.5, 5, 95, 97.5, 99.5)
return li

References
------------

Lyon, John D., Brad M. Barber, and Chih-Ling Tsai. 1999. Improved Methods for T
> ests of Long-Run Abnormal Stock Returns.
The Journal of Finance 54, no. 1:165-201.

Johnson, Norman J. 1978. Modified t Tests and Confidence Intervals for Asymmetr
> ical Populations.
Journal of the American Statistical Association 73, no. 363:536-544.

Author
------
Rajesh Tharyan
Xfi-Centre for Finance and Investment
University of Exeter
www.ex.ac.uk\xfi
r.tharyan@ex.ac.uk

Scott Merryman
scott.merryman@gmail.com

Also see
--------
STB:  STB-26 sg40 - for the johnson ado
Manual:  [5s] ttest
```