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Frontier production functions are important for the prediction of technical efficiencies of individual firms in an industry. A stochastic frontier production function model for panel data is presented, for which the firm effects are an exponential function of time. The best predictor for the technical efficiency of an individual firm at a particular time period is presented for this time-varying model. An empirical example is presented using agricultural data for paddy farmers in a village in India.This article is a revision of the Invited Paper presented by the senior author in the Productivity and Efficiency Analysis sessions at the ORSA/TIMS 30th Joint National Meeting, Philadelphia, Pennsylvania, 29–31 October 1990. We have appreciated comments from Martin Beck, Phil Dawson, Knox Lovell and three anonymous referees. We gratefully acknowledge the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) for making available to us the data obtained from the Village Level Studies in India.  相似文献
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A Stochastic Frontier Production Function with Flexible Risk Properties   总被引:2,自引:1,他引:1  
This paper considers a stochastic frontier production function which has additive, heteroscedastic error structure. The model allows for negative or positive marginal production risks of inputs, as originally proposed by Just and Pope (1978). The technical efficiencies of individual firms in the sample are a function of the levels of the input variables in the stochastic frontier, in addition to the technical inefficiency effects. These are two features of the model which are not exhibited by the commonly used stochastic frontiers with multiplicative error structures.An empirical application is presented using cross-sectional data on Ethiopian peasant farmers. The null hypothesis of no technical inefficiencies of production among these farmers is accepted. Further, the flexible risk models do not fit the data on peasant farmers as well as the traditional stochastic frontier model with multiplicative error structure.  相似文献
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