Working Papers, Department of Economics, Lund University
Stochastic Frontier Production Function With Errors-In-Variables
() and Peter Jochumzen
Abstract: This paper develops a procedure for estimating parameters
of a cross-sectional stochastic frontier production function when the
factors of production suffer from measurement errors. Specifically, we use
Fuller's (1987) reliability ratio concept to develop an estimator for the
model in Aigner et al (1977). Our Monte-Carlo simulation exercise
illustrates the direction and the severity of bias in the estimates of the
elasticity parameters and the returns to scale feature of the production
function when using the traditional maximum-likelihood estimator (MLE) in
presence of measurement errors. In contrast the reliability ratio based
estimator consistently estimates these parameters even under extreme degree
of measurement errors. Additionally, estimates of firm level technical
efficiency are severely biased for traditional MLE compared to reliability
ratio estimator, rendering inter-firm efficiency comparisons infeasible.
The seriousness of measurement errors in a practical setting is
demonstrated by using data for a cross-section of publicly traded U.S.
Keywords: Errors-In-Variables; Stochastic Frontier; Technical Efficiency; Reliability Ratio; (follow links to similar papers)
JEL-Codes: C15; C21; D24; (follow links to similar papers)
33 pages, September 29, 1999
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