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

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.