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From |
"Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it> |

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

Subject |
R: st: R: linear regression question |

Date |
Tue, 17 Mar 2009 09:24:21 +0100 |

Dear Laszlo, thanks for your remark. The potential misleading arises because the use of natural log is the reference in econometrics textbook. However, as you suggested, a thorough check of this requirement should be made, in order to avoid bewildering results. Kind Regards, Carlo -----Messaggio originale----- Da: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di dr kardos laszlo Inviato: martedì 17 marzo 2009 8.34 A: statalist@hsphsun2.harvard.edu Oggetto: Re: st: R: linear regression question unless i got something wrong, the relative change in y associated with a unit change in x in such models works out as antilog(beta) on the appropriate base. in this case, because galina explicitly mentioned natural log, and using carlo's example, it is exp(.2) = 1.2214, a 22.14% increase. try with base-10 and you will get something completely different. the approximation 100*beta% works better and better as beta approaches zero (and as the log-transformation base approaches 1, but that's not typical in practice). in the stata journal article referred to below, beta=.0741516 and exp(beta)=1.07697, arguably close to 1.07415. in other cases, the difference might be to an extent you do not want to ignore. laszlo Galina Hayes wrote: > Thanks very much everyone, very helpful. > Galina > ----- Original Message ----- > From: "Maarten buis" <maartenbuis@yahoo.co.uk> > To: statalist@hsphsun2.harvard.edu > Sent: Sunday, March 15, 2009 11:48:52 AM GMT -05:00 US/Canada Eastern > Subject: Re: st: R: linear regression question > > > --- On Sun, 15/3/09, Carlo Lazzaro wrote: > >> your thread seems to refer to a log-linear model, where >> only the dependent variable (i.e., Y) is log-transformed. >> >> In a log-linear model, a unit-change in the independent >> variable X (i.e., DeltaX=1)is associated with a 100*Beta% >> change in Y. >> > > This is one possible way of interpreting such a model. An > alternative way is discussed in: Roger Newson (2003) "Stata > Tip 1: The eform() option with regress" The Stata Journal, > 3(4): 445. > http://www.stata-journal.com/article.html?article=st0054 > > Both interpretations are correct, they are just different > ways of looking at the same model. > > Hope this helps, > Maarten > > ----------------------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > http://home.fsw.vu.nl/m.buis/ > ----------------------------------------- > > > > > * > * 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/ > * > * 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/ > > * * 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/ * * 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/

**Follow-Ups**:**Re: R: st: R: linear regression question***From:*dr kardos laszlo <l_kardos@chello.hu>

**References**:**Re: st: R: linear regression question***From:*dr kardos laszlo <l_kardos@chello.hu>

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