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Persistent and transient inefficiency in a spatial autoregressive panel stochastic frontier model

Author

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  • Hung-pin Lai

    (National Chung Cheng University and Research Center of Humanities and Social Sciences, Academia Sinica)

  • Kien C. Tran

    (Uniin finite samples. We consider the followingversity of Lethbridge)

Abstract

In this paper, we extend the four-component stochastic frontier model to allow for global spatial dependence via the endogenous spatial autoregressive variable. Our proposed model is more general than the model considered by (Glass et al., 2016) in the sense that we include a random effect as well as a permanent efficiency component. With the spatial autoregressive specification, our model is able to capture the asymmetric efficiency spillovers and also decompose the persistent/transient inefficiencies into direct and indirect efficiencies. Moreover, we also investigate the marginal effects of the exogenous variables on the persistent/transient efficiency. We suggest a maximum simulated likelihood method to estimate the frontier parameters of the model, and we predict the efficiencies using the simulated estimator. Monte Carlo simulations reveal that the suggested estimator performs well in finite samples. An empirical application is considered to illustrate the usefulness of our proposed model and method.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:jproda:v:58:y:2022:i:1:d:10.1007_s11123-022-00638-z
    DOI: 10.1007/s11123-022-00638-z
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    References listed on IDEAS

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    1. Orea, Luis & Álvarez, Inmaculada C., 2019. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Journal of Econometrics, Elsevier, vol. 213(2), pages 556-577.
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    3. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    4. Glass, Anthony & Kenjegalieva, Karligash & Paez-Farrell, Juan, 2013. "Productivity growth decomposition using a spatial autoregressive frontier model," Economics Letters, Elsevier, vol. 119(3), pages 291-295.
    5. Hung-Jen Wang, 2002. "Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model," Journal of Productivity Analysis, Springer, vol. 18(3), pages 241-253, November.
    6. Morakinyo Adetutu & Anthony Glass & Karligash Kenjegalieva & Robin Sickles, 2015. "The effects of efficiency and TFP growth on pollution in Europe: a multistage spatial analysis," Journal of Productivity Analysis, Springer, vol. 43(3), pages 307-326, June.
    7. Efthymios G. Tsionas & Subal C. Kumbhakar, 2014. "FIRM HETEROGENEITY, PERSISTENT AND TRANSIENT TECHNICAL INEFFICIENCY: A GENERALIZED TRUE RANDOM‐EFFECTS model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 110-132, January.
    8. Roberto Colombi & Subal Kumbhakar & Gianmaria Martini & Giorgio Vittadini, 2014. "Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency," Journal of Productivity Analysis, Springer, vol. 42(2), pages 123-136, October.
    9. Federico Belotti & Giuseppe Ilardi & Andrea Piano Mortari, 2019. "Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency," CEIS Research Paper 459, Tor Vergata University, CEIS, revised 30 May 2019.
    10. Kutlu, Levent & Tran, Kien C. & Tsionas, Mike G., 2020. "A spatial stochastic frontier model with endogenous frontier and environmental variables," European Journal of Operational Research, Elsevier, vol. 286(1), pages 389-399.
    11. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C., 2016. "A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers," Journal of Econometrics, Elsevier, vol. 190(2), pages 289-300.
    12. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    13. Kutlu, Levent, 2018. "Estimating efficiency in a spatial autoregressive stochastic frontier model," Economics Letters, Elsevier, vol. 163(C), pages 155-157.
    14. Glass, Anthony J. & Kenjegalieva, Karligash, 2019. "A spatial productivity index in the presence of efficiency spillovers: Evidence for U.S. banks, 1992–2015," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1165-1179.
    15. Camilla Mastromarco & Laura Serlenga & Yongcheol Shin, 2016. "Modelling Technical Efficiency in Cross Sectionally Dependent Stochastic Frontier Panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 281-297, January.
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    Cited by:

    1. Kien C. Tran & Mike G. Tsionas, 2023. "Semiparametric estimation of a spatial autoregressive nonparametric stochastic frontier model," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-28, December.
    2. 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.

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    More about this item

    Keywords

    Maximum simulated likelihood; stochastic frontier; spatial autoregressive; persistent inefficiency; transient inefficiency;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

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