Using Monte Carlo Simulation Approach, Estimation of Stochastic Frontier Production Function and Test of Parameters under Restricted Alternatives

Md. Sifat Ar Salan

Abstract

The stochastic frontier production function can be used to estimate the technical efficiencies among the firms when all the firms are not technically efficient. In most of the economic problems, prior information about the parameters is known in advance. That is, the sign of the parameters are known, which is also true for the Stochastic Frontier Production. We notice that the existing techniques of estimating the Stochastic Frontier Production Function do not consider the above restriction or prior information about the parameters. Ignorance of this information may lead to inefficient estimate as well as low power of the test of parameters under restricted alternatives. Moreover, King (1986) pointed out that the usual tests are not totally suited in this type of case. In this article, we will consider a method for estimating the parameters of the stochastic frontier production function when some of the parameters are restricted and testing the parameters of the model with restricted alternatives. Also, this method will be compared with the existing method by using Monte Carlo Simulation approach. We will apply this method for real life data, in which variety of study inputs such as allocation of student time into formal study (lectures & classes), self-study, private tuition, reading newspapers, watching TV, in mobile phone, leisure and sleeping are used to produce output (scores). Keywords: Stochastic frontier production function, Monte Carlo Simulation, Maximum Likelihood Estimation, Cobb–Douglas production functionCite this ArticleMd. Sifat Ar Salan, Using Monte Carlo Simulation Approach, Estimation of Stochastic Frontier Production Function and Test of Parameters under Restricted Alternatives. Research & Reviews: Journal of Statistics. 2019; 8(1): 26–31p.