() and Peter Jochumzen
Rajeev Dhawan: Anderson Graduate School of Management, Postal: University of California, 110 westwood Plaza, Los Angeles CA90095, USA
Peter Jochumzen: Department of Economics, Lund University, Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund, Sweden
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. corporations.
33 pages, September 29, 1999
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