A stochastic frontier model based on Rayleigh distribution


Abstract


In this paper, we present a closed formula for calculating the density of the composed error in a stochastic frontier model, having supposed that technical inefficiency components follow a Rayleigh probability distribution. Moreover, by using a Monte Carlo procedure, we analyze the properties of Maximum Likelihood and Method of Moments estimators of the disturbance terms.

DOI Code: 10.1285/i20705948v7n2p218

Keywords: Stochastic frontier analysis, Rayleigh distribution, Monte Carlo methods

References


. Aigner, D., Lovell, C.A.K. and Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21–37.

. Behr, A. and Tente, S. (2008). Stochastic frontier analysis by means of maximum likelihood and the method of moments. Discussion Paper Series 2: Banking and Financial Studies No19, Deutsche Bundesbank.

. Farrell, M.J. (1957). The Measurement of Productive Efficiency. Journal of Royal Stat. Society, series A (General), 120(3), 253–281.

. Greene, W.H. (1990). A gamma-distributed stochastic frontier model. Journal of Econometrics, 46, 141–163.

. Jondrow, J., Knox Lowell, C.A., Materov, I.S. and Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19, 233–238.

. Mishra, S.K. (2007). A Brief History of Production Functions. Working Paper Series, Social Science Research Network (SSRN), 2007.

. Papoulis, A. (1984). Probability, Random Variables, and Stochastic Processes (2th ed.). New York: McGraw-Hill.

. Stevenson, R.E. (1980). Likelihood functions for generalized stochastic frontier estimation. Journal of Econometrics, 13, 57–66.


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