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

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

Subject |
RE: st: logistic tranformation, proportion variables |

Date |
Thu, 13 Dec 2007 14:17:45 -0000 |

"Little" is not the adjective that springs to mind for that help file. More important, I don't think that help file answers much of the question here. As 0 and 1 are attainable, logit in the strict sense is out of the question. It seems to me that the main issue with a predictor that is a proportion is what is the shape of the function relating response | other predictors to proportional predictor | other predictors and, setting aside the instrumental variable aspect here, one handle on that might be given by added variable plots after a plain multiple regression -- or graphical near equivalents such as -mrunning- or -mlowess-. Use -findit- to locate these user-written programs. My first stab at this would be to consider some power of the predictor, say root or square. That way 0 and 1 stay as they are but you can bend the scale in the middle. Nick n.j.cox@durham.ac.uk David Airey Nick Cox has a little Stata help file on transformations. ssc install transint Marck Bulter > I have a question that is not entirely related to Stata. Do hope > that you forgive me. > > Assume the following model, > > *ivreg* pstrmon price maturity age coupon pstrmonprev pstrprev > intrest ivol compl (precmon = precmonprev) > > Where pstrmon, pstrmonprev, precmon and precmonprev are all > proportions. In this case, value bond A / total value bonds, etc. > Therefore, it can take any value between 0 and 1, 0 and 1 included. > These last 4 variables are heavily left skewed. Post estimations, > resid is heteroskedastic, and resid is not normal distributed. > On the Statalist server I have found several references to logistic > transformations, ln(y/1-y): > - http://www.stata.com/statalist/archive/2003-07/msg00285.html > - home.fsw.vu.nl/m.buis/presentations/UKsug06.pdf > - http://www.stata.com/statalist/archive/2006-02/msg00150.html > > If I transform the 4 variables using logistic transformation, the 4 > variables or no longer skewed, resid is almost homoskedastic, and > resid is almost normal distributed. > But my question is, is this transformation allowed, as I have mostly > seen only references of transformation of the dependent variable. > In addition, the transformation makes the interpretation of the > coefficients hard, any comment on this? * * 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/

**Follow-Ups**:**Re: st: logistic tranformation, proportion variables***From:*Marck Bulter <177316mb@student.eur.nl>

**References**:**st: logistic tranformation, proportion variables***From:*Marck Bulter <177316mb@student.eur.nl>

**Re: st: logistic tranformation, proportion variables***From:*David Airey <david.airey@Vanderbilt.Edu>

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