An alternative corrected ordinary least squares estimator for the stochastic frontier model

Christopher F. Parmeter () and Shirong Zhao ()
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Christopher F. Parmeter: University of Miami
Shirong Zhao: Dongbei University of Finance and Economics

Empirical Economics, 2023, vol. 64, issue 6, No 15, 2857 pages

Abstract: Abstract The corrected ordinary least squares (COLS) estimator of the stochastic frontier model exploits the higher order moments of the OLS residuals to estimate the parameters of the composed error. However, both “Type I” and “Type II” failures in COLS can result from finite sample bias that arises in the estimation of these higher order moments, especially in small samples. We propose a novel modification to COLS by using the first moment of the absolute value of the composite error term in place of the third moment for both the Normal-Half Normal and Normal-Exponential specifications. We demonstrate via simulations that this switch considerably reduces the occurrence of both Type I and Type II failures. These Monte Carlo simulations also reveal that our alternative COLS approach, in general, performs better than standard COLS.

Keywords: Production; Efficiency; Type I failure; Type II failure; Absolute value (search for similar items in EconPapers)
JEL-codes: C1 C3 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00181-023-02401-1

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