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Statistical Methods & Applications

, Volume 21, Issue 2, pp 121–137 | Cite as

A partially adaptive estimator for the censored regression model based on a mixture of normal distributions

  • Steven B. CaudillEmail author
Article

Abstract

The goal of this paper is to introduce a partially adaptive estimator for the censored regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using an EM algorithm adapted from the work of Bartolucci and Scaccia (Comput Stat Data Anal 48:821–834, 2005). A Monte Carlo study is conducted to compare the small sample properties of this estimator to the performance of some common alternative estimators of censored regression models including the usual tobit model, the CLAD estimator of Powell (J Econom 25:303–325, 1984), and the STLS estimator of Powell (Econometrica 54:1435–1460, 1986). In terms of RMSE, our partially adaptive estimator performed well. The partially adaptive estimator is applied to data on wife’s hours worked from Mroz (1987). In this application we find support for the partially adaptive estimator over the usual tobit model.

Keywords

Partially adaptive estimator Censored regression model Tobit model 

JEL Classification

C240 

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Economics and BusinessRhodes CollegeMemphisUSA

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