A Semiparametric Derivative Estimator in Log Transformation Models
AbstractThis paper considers a regression model with a log-transformed dependent variable. The log transformed model is estimated by simple least squares, but computing the conditional mean of the dependent variable on the original scale given the explanatory variables analytically requires knowing the conditional distribution of the error term in the transformed model. We show how to obtain a consistent estimator for the conditional mean and its derivatives without specifying the conditional distribution of the error term. The asymptotic distribution of the estimator is derived. The proposed procedure is then illustrated with health expenditure data from the Medical Expenditure Panel Survey.
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Bibliographic InfoPaper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 06/06.
Date of creation: Jul 2006
Date of revision:
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Postal: HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/postgrad/herc/hedg/
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log transformation; conditional mean; series estimator; asymptotic distribution; derivative estimator;
Other versions of this item:
- Chunrong Ai & Edward C. Norton, 2008. "A semiparametric derivative estimator in log transformation models," Econometrics Journal, Royal Economic Society, vol. 11(3), pages 538-553, November.
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