Abstract
We used Monte Carlo simulations to compare the finite sample properties of three types of stochastic frontier models (KGMHLBC, RSCFG, and FE) that were designed to examine how observable characteristics of a company may influence its technical efficiency. RSCFG has a scaling property that gauges the effect of environmental factors on technical inefficiency but not on the inefficiency distribution. KGMHLBC does not have this scaling property. However, both RSCFG and KGMHLBC assume a specific distribution of technical inefficiency and are estimated using maximum likelihood analysis. On the other hand, FE does not impose any such distributional assumption of inefficiency and is estimated by the fixed effect treatment. Our simulation results reveal that FE is robust and insensitive to various specifications for the estimation of production technology and the marginal effect of environmental factors on efficiency, whereas RSCFG and KGMHLBC are likely sensitive to the a priori distribution of technical inefficiency. However, based on the rank correlations between inefficiency estimates and true inefficiency, FE produced the worst estimate of inefficiency.
The earlier draft of this paper was based on the second author’s thesis. The first author acknowledges that this work is supported by the National Research Foundation of Korea Grant funded by Korean Government (NRF-2013S1A3A2053312).
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Notes
- 1.
The two-step approach estimates a standard stochastic production function first and estimates the inefficiency equation second, whereas the one-step approach substitutes the inefficiency equation for the inefficiency term in the production function and then estimates the production function and the inefficiency equation simultaneously.
- 2.
In Battese and Coelli’s model (1992), s(z it ,δ) = exp[−δ(t − T)]. Thus, z is assumed to be individual-invariant.
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Lee, Y.H., Shin, J. (2014). Comparison of Stochastic Frontier “Effect” Models Using Monte Carlo Simulation. In: Sickles, R., Horrace, W. (eds) Festschrift in Honor of Peter Schmidt. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-8008-3_8
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