A MONTE-CARLO COMPARISON OF 2 PRODUCTION FRONTIER ESTIMATION METHODS - CORRECTED ORDINARY LEAST-SQUARES AND DATA ENVELOPMENT ANALYSIS

This paper reports the results of an experiment with simulated data that compares the estimation accuracy of two simple and very different production frontier methods: corrected ordinary least squares and data envelopment analysis. The experimental design extends a previously published paper by introducing measurement errors, a factor we show to be critical for comparative analysis of the frontier methods. Both low and high measurement error distributions are used, resulting in 95% error intervals of roughly +/- 10% and 40%, respectively, of outputs. Other variations include four inefficiency distributions covering a wide range of behavior; four sample sizes, from 25 to 200, and two piecewise Cobb-Douglas technologies with two inputs and one output each. Results include: 1) selection of the proper estimation method for a case can result in substantial gains in estimation accuracy for technical efficiencies, from 15 to 40% in mean absolute deviations; 2) COLS performs better for the classical distribution case with sample sizes of 50 or over; 3) DEA performs better for all nonclassical inefficiency distributions, even with relatively high measurement errors; 4) DEA provides surprisingly accurate estimates for the small sample size of 25, for all cases in the experiment; 5) COLS fails to decompose deviations into efficiency and measurement error components (it assumes that deviations from the frontier are either totally due to measurement errors or technical inefficiencies); and 6) neither method performs satisfactorally for high measurement errors.