I don't know why I said that because it isn't true. In fact, I was
overlooking my own work...
In terms of the original example,
webuse wpi1
g returns = D.ln_wpi
and given a download of -qplot- from the Stata Journal site, you can go
qplot returns, trscale(invttail(6, 1 - @)) xli(0) yli(0)
Alternatively, you can do it from first principles
count if !missing(returns)
local N = r(N)
sort returns
gen quantilet = invttail(6, 1 - (_n - 0.5) / `N')
scatter returns quantilet , xli(0) yli(0)
For (_n - 0.5) / `N', substitute any other plotting position formula.
For "6" substitute any other desired df.
In this example, the fit is lousy: the mean is a long way from zero and
the distribution is not even symmetric.
See also for a meant-to-be-encouraging note
SJ-7-2 gr0027 . . Stata tip 47: Quantile-quantile plots without
programming
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N.
J. Cox
Q2/07 SJ 7(2):275--279 (no
commands)
tip on producing various quantile-quantile (Q-Q) plots
That and 75 other tips have just been reprinted in
Seventy-six Stata Tips, 2nd Edition
Publisher: Stata Press
Copyright: 2009
ISBN-10: 1-59718-071-8
ISBN-13: 978-1-59718-071-9
Pages: 177; paperback
Price: $29.00
<http://www.stata.com/bookstore/tips2.html>
Nick
[email protected]
Nick Cox
========
You'd need to clone one or more existing programs, e.g. -qnorm-, -pnorm-
and replace code there with code specific to the t-distribution.
I am not familiar with the etiquette on fitting t-distributions. Isn't
the df in effect a parameter to be estimated? Otherwise, there would
need to be some justification for using a particular df.
tzygmund mcfarlane
==================
Thanks for your replies Martin & Nick.
Martin: My question was actually simpler - was my procedure correct?
That is, should the data be standardised by an estimate of the scale
before using the Kolmogorov-Smirnov procedure or is that not
necessary?
Also, from the help file for chi2fit by Stas Kolenikov I could not
figure out how to implement it for a t-distribution. Any help will be
appreciated.
Nick: I am particularly interested in deviation from a t-distribution.
My data is almost certainly non-normal. I agree about the merits of
plotting it, but am not aware of any tools for my particular case. Any
ideas?
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