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Indirect inference estimation of stochastic production frontier models with skew-normal noise

Author

Listed:
  • Hung-pin Lai

    (National Chung Cheng University
    Academia Sinica)

  • Subal C. Kumbhakar

    (State University of New York at Binghamton
    Inland Norway University of Applied Sciences)

Abstract

In this paper we consider a stochastic frontier model in which both the noise and inefficiency components are asymmetric, viz., the noise term is skew normal and the inefficiency term is half-normal. This formulation avoids the criticism that skewness of the composite error term (sum of the noise and inefficiency) cannot be an indicator of inefficiency because skewness can also arise from the noise term. Our estimator of inefficiency does not depend on skewness of the one-sided error alone; it controls for skewness in the noise term as well. We further generalize the model by introducing determinants of skewness of the noise term as well as determinants of inefficiency. Additionally, we test hypotheses that the noise term is either symmetric (normal) or has a constant skewness parameter. Instead of using the standard ML method, we use the indirect inference (II) approach to estimate the parameters of the proposed model. Formulae for predicting (in)efficiency are also provided. Finally, we provide both simulation and empirical results using the II estimation approach to showcase workings of our model.

Suggested Citation

  • Hung-pin Lai & Subal C. Kumbhakar, 2023. "Indirect inference estimation of stochastic production frontier models with skew-normal noise," Empirical Economics, Springer, vol. 64(6), pages 2771-2793, June.
    • Handle: RePEc:spr:empeco:v:64:y:2023:i:6:d:10.1007_s00181-023-02412-y
    DOI: 10.1007/s00181-023-02412-y
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    References listed on IDEAS

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    1. Christopher Busch & David Domeij & Fatih Guvenen & Rocio Madera, 2022. "Skewed Idiosyncratic Income Risk over the Business Cycle: Sources and Insurance," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(2), pages 207-242, April.
    2. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
    3. Leopold Simar & Paul Wilson, 2010. "Inferences from Cross-Sectional, Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 62-98.
    4. Schmidt, Peter & Lin, Tsai-Fen, 1984. "Simple tests of alternative specifications in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 24(3), pages 349-361, March.
    5. Lai, Hung-pin & Kumbhakar, Subal C., 2018. "Panel data stochastic frontier model with determinants of persistent and transient inefficiency," European Journal of Operational Research, Elsevier, vol. 271(2), pages 746-755.
    6. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2016. "Efficiency and environmental factors in the US electricity transmission industry," Energy Economics, Elsevier, vol. 55(C), pages 234-246.
    7. Lai, Hung-pin & Kumbhakar, Subal C., 2018. "Endogeneity in panel data stochastic frontier model with determinants of persistent and transient inefficiency," Economics Letters, Elsevier, vol. 162(C), pages 5-9.
    8. Hung-pin Lai & Kien C. Tran, 2022. "Persistent and transient inefficiency in a spatial autoregressive panel stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 58(1), pages 1-13, August.
    9. Kraus, Alan & Litzenberger, Robert H, 1976. "Skewness Preference and the Valuation of Risk Assets," Journal of Finance, American Finance Association, vol. 31(4), pages 1085-1100, September.
    10. C. Adcock, 2010. "Asset pricing and portfolio selection based on the multivariate extended skew-Student-t distribution," Annals of Operations Research, Springer, vol. 176(1), pages 221-234, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Indirect inference estimation; Skew-normal error; Stochastic frontier model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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