When does Heckmanís two-step procedure for censored data work and when does it not?
Abstract: Heckmanís 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; (follow links to similar papers)
JEL-Codes: C10; (follow links to similar papers)
22 pages, February 22, 2008
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