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From |
"Christer Thrane" <ch-thran@online.no> |

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

Subject |
st: Re: transforming predictions from loglinear models |

Date |
Fri, 26 Aug 2005 18:47:34 +0200 |

Wooldridge shows you how to do this--se example 6.7 at:

http://fmwww.bc.edu/gstat/examples/wooldridge/wooldridge6.html

Best,

Christer

--

Professor Christer Thrane

Department of Social Science

Lillehammer University College

2626 Lillehammer, Norway

+ 47 61 28 81 70 (fax)

+ 47 61 28 82 47 (phone, work)

+ 47 61 25 53 04 (phone, home)

E-mail, work: christer.thrane@hil.no

E-mail, home: ch-thran@online.no

----- Original Message ----- From: <Colin.Vance@dlr.de>

To: <statalist@hsphsun2.harvard.edu>

Sent: Friday, August 26, 2005 10:49 AM

Subject: st: transforming predictions from loglinear models

Hello,

I want to estimate a simple log-linear OLS regression in Stata and then

use the model to generate predictions. Say the model looks like this:

regress ln_y ln_x1 ln_x2

where all the variables are in logs. After running the model, I'd like

to predict values of y over different values of x1, holding x2 fixed at

the mean. The problem is that my predicted y is in log form, which I

want to transform to y. One solution is to simply take exp(prediction of

ln_y), but this has been shown to result in a biased predictor. The

following article discusses various techniques for dealing with this,

focusing specifically on a Laplace conversion:

van Garderen, Kees Jan, 2001.

"Optimal prediction in loglinear models," Journal of Econometrics,

Elsevier, vol. 104(1), pages 119-140

Does anyone know if any such techniques have been implemented in Stata?

Would predictnl do the trick, as in:

predictnl yhat = exp(_b[cons] + _b[ln_x1]*ln_x1 + _b[ln_x2]*ln_x2],

se(yhat_se)

Many thanks for any input,

Colin

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**References**:**st: transforming predictions from loglinear models***From:*<Colin.Vance@dlr.de>

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