Pseudolikelihood estimation of the stochastic frontier model
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Date
2016
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Abstract
Stochastic frontier analysis is a popular tool to assess firm performance. Almost
universally it has been applied using maximum likelihood estimation. An alternative
approach, pseudolikelihood estimation, which decouples estimation of the error component
structure and the production frontier, has been adopted in several advanced settings. To
date, no formal comparison has yet to be conducted comparing these methods in a standard,
parametric cross sectional framework. We seek to produce a comparison of these two competing
methods using Monte Carlo simulations. Our results indicate that pseudolikelihood
estimation enjoys almost identical performance to maximum likelihood estimation across a
range of scenarios, and out performs maximum likelihood estimation in settings where the
distribution of inefficiency is incorrectly specified.