st: Re: transforming predictions from loglinear models

["Christer Thrane" <ch-thran@online.no>]

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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