Research Reports,
University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law

Abstract: Heckman’s two-step procedure (Heckit) for estimating the parameters in linear models from censored data is frequently used by econometricians, despite of the fact that earlier studies cast doubt on the procedure. In this paper it is shown that estimates of the hazard h for approaching the censoring limit, the latter being used as an explanatory variable in the second step of the Heckit, can induce multicollinearity. The influence of the censoring proportion and sample size upon bias and variance in three types of random linear models are studied by simulations. From these results a simple relation is established that describes how absolute bias depends on the censoring proportion and the sample size. It is also shown that the Heckit may work with non-normal (Laplace) distributions, but it collapses if h deviates too much from that of the normal distribution. Data from a study of work resumption after sick-listing are used to demonstrate that the Heckit can be very risky.

Keywords: Censoring; Cross-sectional and panel data; Hazard; Multicollinearity

JEL-codes: C10

22 pages, February 22, 2008

Full text files

9593 HTML file 

Download statistics

• University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law
• Home page for this series

Questions (including download problems) about the papers in this series should be directed to Linus Schiöler ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:hhs:gunsru:2008_002This page generated on 2024-09-13 22:14:37.