Efficiency estimation and error decomposition in the stochastic frontier model: A Monte Carlo analysis

Efficiency estimation and error decomposition in the stochastic frontier model: A Monte Carlo analysis

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Article ID: iaor20001887
Country: Netherlands
Volume: 115
Issue: 3
Start Page Number: 555
End Page Number: 563
Publication Date: Jun 1999
Journal: European Journal of Operational Research
Authors:
Keywords: measurement
Abstract:

Critics of the deterministic approach to efficiency measurement argue that no allowance is made for measurement error and other statistical noise. Without controlling for measurement error, the resulting measure of efficiency will be distorted due to the contamination of noise. The stochastic frontier models purportedly allow both inefficiency and measurement error. Some proponents argue that the stochastic frontier models should be used despite the limitations because of the superior conceptual treatment of noise. However, the ultimate value of the stochastic frontier depends on its ability to properly decompose noise and inefficiency. This paper tests the validity of the stochastic frontier cross-sectional models using a Monte Carlo analysis. The results suggest that the technique does not accurately decompose the total error into inefficiency and noise components. Further, the results suggest that at best, the stochastic frontier is only as good as the deterministic model.

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