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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: automatically adjusting for bias in antilog transformation? |

Date |
Tue, 28 Sep 2010 16:22:44 +0000 (GMT) |

--- On Tue, 28/9/10, Tatyana Deryugina wrote: > I'm estimating a model of the following form: ln y = a + bx > + cz + e, which I assume to be normally distributed with > heteroskedastic errors. I would like to compute the average > change in Y due to X, > e.g. > E[Y|X=x] - E[Y|X=0]. I know that simply computing > exp(a_hat+b_hat*x+c_hat*z) - exp(a_hat+c_hat*z) will result > in biased estimates. > > I found how to do the correction for heteroskedasticity in > SAS and I'm wondering if there's a way to do the corresponding > calculation in Stata without a lot of manual manipulation of > the variables It is best to prevent a problem before trying to fix it. In your case you are modeling E(log(y)) which gives you problems as exp(E(log(y))) != E(y). The simplest solution is to model log(E(y)) instead, because exp(log(E(y))) = E(y). In Stata you do this by using the -glm- command in combination with the -link(log)- option. A nice discussion of this can be found in this article: Nicholas J. Cox, Jeff Warburton, Alona Armstrong, Victoria J. Holliday (2007) "Fitting concentration and load rating curves with generalized linear models" Earth Surface Processes and Landforms, 33(1):25--39. <http://www3.interscience.wiley.com/journal/114281617/abstract> Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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: st: automatically adjusting for bias in antilog transformation?***From:*Ronan Conroy <rconroy@rcsi.ie>

**References**:**st: automatically adjusting for bias in antilog transformation?***From:*Tatyana Deryugina <tatyanad@mit.edu>

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