A Stochastic Frontier Model with Correction for Sample Selection

17 Pages Posted: 17 Apr 2008

See all articles by William H. Greene

William H. Greene

New York University Stern School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: March 2008


Heckman's (1979) sample selection model has been employed in three decades of applications of linear regression studies. The formal extension of the method to nonlinear models, however, is of more recent vintage. A generic solution for nonlinear models is proposed in Terza (1998). We have developed simulation based approach in Greene (2006). This paper builds on this framework to obtain a sample selection correction for the stochastic frontier model. We first show a surprisingly simple way to estimate the familiar normal-half normal stochastic frontier model (which has a closed form log likelihood) using maximum simulated likelihood. The next step is to extend the technique to a stochastic frontier model with sample selection. Here, the log likelihood does not exist in closed form, and has not previously been analyzed. We develop a simulation based estimation method for the stochastic frontier model. In an application that seems superficially obvious, the method is used to revisit the World Health Organization data [WHO (2000), Tandon et al. (2000)] where the sample partitioning is based on OECD membership. The original study pooled all 191 countries. The OECD members appear to be discretely different from the rest of the sample. We examine the difference in a sample selection framework.

Keywords: Stochastic Frontier, sample Selection, Simulation, Efficiency

JEL Classification: C14, C23, C25

Suggested Citation

Greene, William H., A Stochastic Frontier Model with Correction for Sample Selection (March 2008). Available at SSRN: https://ssrn.com/abstract=1120973 or http://dx.doi.org/10.2139/ssrn.1120973

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