[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Log Normality of Dependentvar |

Date |
Mon, 8 Jun 2009 18:34:47 +0100 |

In addition, -transint- on SSC is a package containing a longish document which introduces transformations in Stata help file form. In my view, transformations are often very poorly explained, even in many textbooks. I was reckless enough to try to do better, for at least some audiences. Nick n.j.cox@durham.ac.uk sjsamuels@gmail.com Look up references to the Box-Cox transformation. That is what you did when you ran -bcskew0- . "bc"= "Box-Cox". But I have to correct my correction (You can see where this is heading!) The original Box-Cox transformation, implemented in -boxcox-, is the one that tries to transform to normality. -bcskew-, as its name implies, finds the transformation of the same form which produces zero skewness. On Mon, Jun 8, 2009 at 1:08 PM, Christian Weiss<christian.weiss@nightberry.de> wrote: > Hi Steven, > > thanks a lot for your explanation! > > Unforunately, it seems that something of oyur last message got cut off? > Where can I find information on the "power transformation"? (google > does not offer to much in that respect) > > Chris > > > On Mon, Jun 8, 2009 at 6:55 PM, <sjsamuels@gmail.com> wrote: >> the best fitting power transform to normality. But it is not relevant >> to -swilk- with the lnnormal option, because the power transform may >> not be a log (power =0) and the command does not subtract off a shift >> parameter. >> >> -Steve >> >> On Mon, Jun 8, 2009 at 12:38 PM, <sjsamuels@gmail.com> wrote: >>> -Chris-- >>> >>> -lnskew0-- finds by iteration a value of k for which y= ln(x - k) has >>> skewness zero. The manual implies that with the "lnnormal" option, >>> -swilk- , estimates "k" by the method of -lnskew0-. In fact, the ado >>> file for -swilk- does not call -lnskew0-, but instead computes an >>> approximation.. This probably accounts for the discrepancy that you >>> observed. >>> >>> Analyses of ln(var) and of the transformation -bcskew0- are >>> irrelevant to -swilk-, because the 'lnnormal" option considers the >>> hypothesis of a three-parameter lognormal distribution. I presume >>> that by "skskew0" you meant "lnskew0 >>> >>> -Steve >>> >>> On Mon, Jun 8, 2009 at 6:18 AM, Maarten buis<maartenbuis@yahoo.co.uk> wrote: >>>> >>>> --- On Mon, 8/6/09, Christian Weiss wrote: >>>>> testing my dependent var via swilk or sfrancia rejects the >>>>> Null Hypothesis of Normality. >>>> >>>> This is problematic for a number of reasons: >>>> >>>> 1) Regression never assumes that the dependent variable is >>>> normally distributed, except when you have no explanatory >>>> variables. It only assumes that the residuals are normally >>>> distributed. >>>> >>>> 2) Testing for the normality of the residuals should only >>>> be done once you are confinced that the other assumptions >>>> have been met, as violations of the other assumptions are >>>> likely to lead to residuals that look non-normal >>>> >>>> 3) The normality of the residuals is probably the least >>>> important of the regression assumptions, as regression >>>> is reasonably robust to violations of it. >>>> >>>> 4) Tests are probably not the best way to assess whether >>>> the errors are normaly distributed. Graphical inspection >>>> is usually more informative and powerful, see: >>>> -help diagnostic plots- and -ssc d hangroot- for tools >>>> to help with that. >>>> >>>> For a more general set of tools to perform post-estimation >>>> checks of regression assumptions see: >>>> -help regress postestimation-. >>>> >>>> >>> >>> On Mon, Jun 8, 2009 at 5:38 AM, Christian >>> Weiss<christian.weiss@nightberry.de> wrote: >>>> >>>> testing my dependent var via swilk or sfrancia rejects the Null >>>> Hypothesis of Normality. >>>> However, using the "lnnormal" option of swilk accepts the nully >>>> hypothesis - it seems that the dependent variable is lognormal >>>> distributed. >>>> >>>> >>>> Suprisingly,after transformim my dependent variable by ln(var) or by >>>> skskew0 / bcskew0, swilk still rejects the null hypothesis of >>>> normality. >>>> >>>> How can that be explained? >>>> >>>> ..puzzled...Chris * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Log Normality of Dependentvar***From:*Christian Weiss <christian.weiss@nightberry.de>

**Re: st: Log Normality of Dependentvar***From:*sjsamuels@gmail.com

**Re: st: Log Normality of Dependentvar***From:*sjsamuels@gmail.com

**Re: st: Log Normality of Dependentvar***From:*Christian Weiss <christian.weiss@nightberry.de>

**Re: st: Log Normality of Dependentvar***From:*sjsamuels@gmail.com

- Prev by Date:
**RE: st: Log Normality of Dependentvar** - Next by Date:
**st: RE: Forward orthogonal deviations with unbalanced panel data** - Previous by thread:
**Re: st: Log Normality of Dependentvar** - Next by thread:
**Re: st: Log Normality of Dependentvar** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |