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DISTRIBUTION-FREE ESTIMATION OF THE BOX–COX REGRESSION MODEL WITH CENSORING

Published online by Cambridge University Press:  25 November 2011

Songnian Chen
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Abstract

The Box–Cox regression model has been widely used in applied economics. However, there has been very limited discussion when data are censored. The focus has been on parametric estimation in the cross-sectional case, and there has been no discussion at all for the panel data model with fixed effects. This paper fills these important gaps by proposing distribution-free estimators for the Box–Cox model with censoring in both the cross-sectional and panel data settings. The proposed methods are easy to implement by combining a convex minimization problem with a one-dimensional search. The procedures are applicable to other transformation models.

Type
Brief Report
Information
Econometric Theory , Volume 28 , Issue 3 , June 2012 , pp. 680 - 695
Copyright
Copyright © Cambridge University Press 2011

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Footnotes

I thank the co-editor and two referees for their insightful comments, which have greatly improved the presentation of the paper. I also thank Ken Chay, Jerry Hausman, James Heckman, Bo Honoré, Cheng Hsiao, Lung-fei Lee, Jim Powell, Paul Ruud, Jeff Wooldridge, Zhiliang Ying, and workshop participants at the University of California at Berkeley, University of Chicago, Duke University, Johns Hopkins University, University of Michigan, Michigan State University, Northwestern University, Ohio State University, and Vanderbilt University for their helpful comments. Part of the work was carried out while I was at the National University of Singapore.

References

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