Stas gives good advice, except that the implication of his last
throw-away remark is incorrect. Far from being exotic, standard errors
for skewness are often used, even if tacitly. Many people use tests for
(non-zero) skewness, (non-Gaussian) kurtosis, or both, based on
estimates of skewness and/or kurtosis and their standard errors. See
e.g. -varnorm-. These tests are often attributed to Jarque and Bera by
economists, on the usual grounds
1. they wrote about them
2. they are economists
3. econometricians seem to have a bias to naming tests after people,
rather than using names to describe what they do (I owe this observation
to Stas himself).
These criteria do not include the more convincing grounds that Jarque
and Bera invented or discovered them.
More importantly, these tests are based on asymptotic ses when there is
plenty of evidence that they are a lousy approximation for small
samples.
My own biases are that you want to examine for normality you are best
off with -qnorm- and that if you want a test regardless you are better
off with -omninorm- from SSC. But neither is adapted to the survey case.
Nick
[email protected]
Stas Kolenikov
On 11/3/08, Richard Palmer-Jones <[email protected]> wrote:
> I hope this is not a stupid question, but how can I get skewness
> estimates from svyset data? If it is, please enlighten me.
If you want to eye-ball it, you can run just the regular -summarize
[fw=], d-, and keep your fingers crossed that the algebra is the same
with -fw- and -pw-. A better way would be to create the necessary
moments of your variables, and make that nonlinear skewness formula
out of them:
g x2 = x*x
g x3 = x*x
svy: means x x2 x3
nlcom (x3-3*x2*x+2*x)/(x2-x*x)^3/2
or whatever the proper -nlcom- syntax is. I bet you've never seen
standard errors on skewness coefficient in your life :))
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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