Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function
AbstractThe normal-gamma stochastic frontier model was proposed in Greene (1990) and Beckers and Hammond (1987) as an extension of the normal-exponential proposed in the original derivations of the stochastic frontier by Aigner, Lovell and Schmidt (1977). The normal-gamma model has the virtue of providing a richer and more flexible parameterization of the inefficiency distribution 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 maximum simulated likelihood estimation as opposed to the received attempts which have approached the problem by direct maximization. Copyright Kluwer Academic Publishers 2003
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Bibliographic InfoArticle provided by Springer in its journal Journal of Productivity Analysis.
Volume (Year): 19 (2003)
Issue (Month): 2 (April)
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frontier; gamma; simulated likelihood;
Other versions of this item:
- Greene, W.H., 2000. "Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function," New York University, Leonard N. Stern School Finance Department Working Paper Seires 00-05, New York University, Leonard N. Stern School of Business-.
- William H. Greene, 2000. "Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function," Working Papers 00-05, New York University, Leonard N. Stern School of Business, Department of Economics.
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
- D33 - Microeconomics - - Distribution - - - Factor Income Distribution
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