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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