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Specification Testing of Production in a Stochastic Frontier Model

Author

Listed:
  • Xu Guo

    (Beijing Normal University)

  • Gao-Rong Li

    (Beijing University of Technology)

  • Michael McAleer

    (Econometric Institute, Erasmus University Rotterdam)

  • Wing-Keung Wong

    (Asia University, Lingnan University)

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

  • Xu Guo & Gao-Rong Li & Michael McAleer & Wing-Keung Wong, 2017. "Specification Testing of Production in a Stochastic Frontier Model," Tinbergen Institute Discussion Papers 17-097/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20170097
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    References listed on IDEAS

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    Cited by:

    1. Nguyen Huu Hau & Tran Trung Tinh & Hoa Anh Tuong & Wing-Keung Wong, 2020. "Review of Matrix Theory with Applications in Education and Decision Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(1), pages 28-69, March.
    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.
    11. Cheng, Ming-Yen & Wang, Shouxia & Xia, Lucy & Zhang, Xibin, 2024. "Testing specification of distribution in stochastic frontier analysis," Journal of Econometrics, Elsevier, vol. 239(2).
    12. Kim-Hung Pho & Thi Diem-Chinh Ho & Tuan-Kiet Tran & Wing-Keung Wong, 2019. "Moment Generating Function, Expectation And Variance Of Ubiquitous Distributions With Applications In Decision Sciences: A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(2), pages 65-150, June.

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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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