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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26017
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| Title: | A Stochastic Frontier Model with Correction for Sample Selection |
| Authors: | Greene, William |
| Keywords: | Stochastic Frontier
sample Selection
Simulation
Efficiency |
| Issue Date: | Mar-2008 |
| Series/Report no.: | EC-08-09 |
| Abstract: | 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. |
| URI: | http://hdl.handle.net/2451/26017 |
| Appears in Collections: | Economics Working Papers
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