Actually, if the error has a normal distribution and there is no
heteroskedasticity the exponentiation is exp(yhat + 0.5*s^2), where s^2
is the variance of the error.
However, if normality is violated or heteroskedasticity is present or
both, other corrections have to be followed.
Have a look at Manning and Mullahy, 2001, Estimating log models: to
transform or not to transform?, Journal of Health Economics, 20,
461-494.
Cheers
Manos
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Shehzad Ali
Sent: 02 June 2008 13:24
To: [email protected]
Subject: st: prediction after y-logged regression
Hi Listers,
I am running a regression in which y variable is logged
(semi-logarithmic).
The literature suggests that in order to get an anti-logged prediction
of yhat, one should proceed like this:
1. Run the regression and predict yhat,
2. Then exponentiate the predicted yhat, and finally
3. Mutiply the exponentiated predicted yhat by the mean value of
residuals from the regression (otherwise known as smearing).
I was wondering if there is a direct way to do it in stata or if there
is any other method that experts would suggest to get anti-logged
prediction.
Thank you,
Shehzad
--
Shehzad I Ali
Department of Social Policy & Social Work University of York YO10 5NG
+44 (0) 773-813-0094
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
The University of Aberdeen is a charity registered in Scotland, No SC013683.
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
