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From |
"Dimitriy V. Masterov" <dvmaster@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: How to interpret an estimated model in log form in practice |

Date |
Wed, 25 Jan 2012 10:03:14 -0500 |

Talal, This is a pretty vague question, but as someone who asks many such questions, let me give it a try. In the future, it may help to provide more detail about your problem, such as what the y and x variables are and what estimation procedure you are using. For some examples of useful geometric means in economics, take a look at: http://davegiles.blogspot.com/2012/01/extracting-correct-meaning-from-data.html For a slightly more relevant example to your problem, take a look at the eform() option of regress described here: http://www.stata-journal.com/sjpdf.html?articlenum=st0054 To transform your predictions back to the original scale, you need levpredict from SSC. It's post-estimation command for use after a linear regression model with a logarithmic dependent variable has been estimated. It generates predictions of the levels of the dependent variable for the estimation sample. These predictions reduce the retransformation bias that arises when predictions of the log dependent variable are exponentiated. Finally, take a look at using glm with a log link function instead of calculating ln(y) yourself. The assumptions about the error term are different, but I've found it works very well and avoids this whole retransformation business. I think glm can adjust for serial correlation, but this getting into an area where I am not qualified to offer solid advice. HTH, DVM * * 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: How to interpret an estimated model in log form in practice***From:*"Almutairi T." <T.Almutairi@soton.ac.uk>

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