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From |
"M. Haider Hussain" <[email protected]> |

To |
[email protected] |

Subject |
Re: st: RE: sktest interpretation |

Date |
Thu, 8 Dec 2005 09:33:00 +0500 |

Thank you very much Dr. Cox for such an elaborate answer. It certainly helps me. Haider Hussain Social Policy and Development Center (SPDC) Karachi, Pakistan. On 12/7/05, Nick Cox <[email protected]> wrote: > This is a fairly common question on Statalist. > > Missings are irrelevant to -sktest-, and > are just ignored, so that is no problem. However, > the fact that you got missings may or may not > indicate some much deeper problem, but that's > for you to consider. > > -sktest- is here rejecting a null hypothesis > of normality. With your sample sizes, this is > totally unsurprising. You are being told that > your sample is large enough to distinguish > between "genuine" non-normality and "apparent" > non-normality that is just the sampling > fluctuation that would occur if the underlying distribution > really were normal. However, with your > sample sizes, the kind of non-normality at > which -sktest- squawks would not necessarily > trouble any data analyst with experience. > > It is salutary to cycle through the numeric > variables in Stata's auto data and look at -sktest- > results. Here n is much smaller than yours at n = 74 > but -sktest- often reports rejection on what > graphical analysis will reveal as an unproblematic > distribution. For example, -sktest- may reject if a > variable is shorter-tailed than normal. > It may reject if a variable is somewhat > irregular in distribution, but otherwise > not problematic. In a word, it is typically > over-sensitive for the practical problem. > > Any test in this area still leaves the question > of measuring, or more generally assessing, > the kind of non-normality you have and of > deciding whether non-normality is really a > problem for what you are doing. A direct > calculation of moments (or alternative > measures such as L-moments) is sometimes > helpful here. > > The issue of -sktest- versus a Jarque-Bera > test is also secondary. Jarque-Bera typically > seems to mean using asymptotic sampling distributions > for skewness and kurtosis for a problem > in which they are often a poor approximation. > (Also, Jarque and Bera just reinvented a very old > test. Why they got credit for that is mysterious, > except on the hypothesis that people have no > time for proper reading.) -sktest- is, more or less, > Jarque-Bera done better with adjustments for sample size. > My guess would be that it would make no difference > in your case. > > Graphical examination of your residuals > with -qnorm- will teach you far more about > their (non-)normality than a -sktest-. The > only practical reason for using -sktest- > is whenever that you are obliged to use it > by instruction from someone in power over you, > namely an advisor, boss, reviewer or journal editor. > > Another detail is that -sktest- does not know > that your variable is a residual and makes no > adjustment for that fact. A wild guess is that > this is just a purist issue in your case. > > Nick > [email protected] > > M. Haider Hussain > > > Sorry for such a novice-level question. > > > > I ran an ols regression with 15 estimators and 14831 observations. In > > this process, 437 missing values were generated. Then I tested > > normality of the residual using sktest and it returned following > > output. > > > > Variable | Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 > > -------------------------------------------------------------- > > ------------------------------- > > ewhe | 0.000 0.000 . > > . > > > > whereas, sktest with noadjust option returned the following output > > > > Variable | Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 > > -------------------------------------------------------------- > > ------------------------------- > > ewhe | 0.000 0.000 3693.33 > > 0.0000 > > > > > > Where're the statistics of chi2 in the first instance? Does it mean > > that sktest (without no adjust) is sensitive to the missing values? > > Can I use jb test with 14000+ observations? If not than what other > > "quantitative" tests are available? > > (Or am I misinterpreting something?) > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: RE: sktest interpretation***From:*"Nick Cox" <[email protected]>

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