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Specification Testing of Production in a Stochastic Frontier Model

Author

Listed:
  • Xu Guo

    (School of Statistics, Beijing Normal University, Beijing.)

  • Gao-Rong Li

    ( Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing.)

  • Wing-Keung Wong

    ( Department of Finance and Big Data Research Center, Asia University Department of Economics and Finance, Hang Seng Management College Department of Economics, Lingnan University.)

  • Michael McAleer

    ( Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)

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 & Wing-Keung Wong & Michael McAleer, 2017. "Specification Testing of Production in a Stochastic Frontier Model," Documentos de Trabajo del ICAE 2017-23, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    • Handle: RePEc:ucm:doicae:1723
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    2. 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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    5. 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).
    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 & 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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    9. 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.
    10. 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.
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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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