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Specification Testing of Production in a Stochastic Frontier Model

Author

Listed:
  • Guo, X.
  • Li, G.-R.
  • McAleer, M.J.
  • Wong, W.-K.
Abstract
Parametric production frontier functions are frequently used in stochastic frontier models, but there do not seem to be any empirical test statistics for its plausibility. To bridge the gap in the literature, we develop two test statistics based on local smoothing and an empirical process, respectively. Residual-based wild bootstrap versions of these two test statistics are also suggested. The distributions of technical inefficiency and the noise term are not specified, which allows specification testing of the production frontier function even under heteroscedasticity. Simulation studies and a real data example are presented to examine the finite sample sizes and powers of the test statistics. The theory developed in this paper is useful for production mangers in their decisions on production.

Suggested Citation

  • Guo, X. & Li, G.-R. & McAleer, M.J. & Wong, W.-K., 2017. "Specification Testing of Production in a Stochastic Frontier Model," Econometric Institute Research Papers EI 2017-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:102298
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    References listed on IDEAS

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    2. Ying Li & Yue Xia & Yang-Che Wu & Wing-Keung Wong, 2020. "The Sustainability of Energy Substitution in the Chinese Electric Power Sector," Sustainability, MDPI, vol. 12(13), pages 1-16, July.
    3. Mike G. Tsionas, 2019. "Robust Bayesian Inference in Stochastic Frontier Models," JRFM, MDPI, vol. 12(4), pages 1-9, December.
    4. Inkoom, Emmanuel Wisgtos & Dadzie, Samuel Kwesi Ndzebah & Ndebugri, Joseph, 2020. "Promoting Improved Agricultural Technologies to Increase Smallholder Farm Production Efficiency: Ghanaian Study of Cassava Farmers," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 8(3), July.
    5. William C. Horrace & Yulong Wang, 2022. "Nonparametric tests of tail behavior in stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 537-562, April.
    6. Christopher F. Parmeter & Valentin Zelenyuk, 2019. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis," Operations Research, INFORMS, vol. 67(6), pages 1628-1658, November.
    7. Kim-Hung Pho & Tuan-Kiet Tran & Thi Diem-Chinh Ho & Wing-Keung Wong, 2019. "Optimal Solution Techniques in Decision Sciences A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), pages 114-161, March.
    8. Kexin Li & Jianxu Liu & Yuting Xue & Sanzidur Rahman & Songsak Sriboonchitta, 2022. "Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand," Agriculture, MDPI, vol. 12(8), pages 1-17, July.
    9. Wenjing Xie & João Paulo Vieito & Ephraim Clark & Wing-Keung Wong, 2020. "Could Mergers Become More Sustainable? A Study of the Stock Exchange Mergers of NASDAQ and OMX," Sustainability, MDPI, vol. 12(20), pages 1-25, October.
    10. Hassan Jalil Shah & Jenho Peter Ou & Saman Attiq & Muhammad Umer & Wing-Keung Wong, 2022. "Does Inclusive Leadership Improve the Sustainability of Employee Relations? Test of Justice Theory and Employee Perceived Insider Status," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
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    More about this item

    Keywords

    Production frontier function; Stochastic frontier model; Specification testing; Wild bootstrap; Smoothing process; Empirical process; Simulations;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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