Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function
18 Pages Posted: 31 Oct 2008
Date Written: September 2000
The normal-gamma stochastic frontier model was proposed in Greene (1990) and Beckers and Hammond (1987) as an extension of the normalexponential proposed in the original derivations of the stochastic frontier byAigner, Lovell, and Schmidt (1977). The normal-gamma model has the virtue ofproviding a richer and more flexible parameterization of the inefficiencydistribution in the stochastic frontier model than either of the canonical forms,normal-half normal and normal-exponential. However, several attempts to operationalize the normal-gamma model have met with very limited success, as the log likelihood is possesed of a significant degree of complexity. This note will propose an alternative approach to estimation of this model based on the method of simulated maximum likelihood estimation as opposed to the received attempts which have approached the problem by direct maximization.
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