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Unknown latent structure and inefficiency in panel stochastic frontier models

Author

Listed:
  • Levent Kutlu

    (University of Texas Rio Grande Valley)

  • Kien C. Tran

    (University of Lethbridge)

  • Mike G. Tsionas

    (Lancaster University Management School)

Abstract

This paper extends the fixed effect panel stochastic frontier models to allow group heterogeneity in the slope coefficients. We propose the first-difference penalized maximum likelihood (FDPML) and control function penalized maximum likelihood (CFPML) methods for classification and estimation of latent group structures in the frontier as well as inefficiency. Monte Carlo simulations show that the proposed approach performs well in finite samples. An empirical application is presented to show the advantages of data-determined identification of the heterogeneous group structures in practice.

Suggested Citation

  • Levent Kutlu & Kien C. Tran & Mike G. Tsionas, 2020. "Unknown latent structure and inefficiency in panel stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 54(1), pages 75-86, August.
    • Handle: RePEc:kap:jproda:v:54:y:2020:i:1:d:10.1007_s11123-020-00584-8
    DOI: 10.1007/s11123-020-00584-8
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    References listed on IDEAS

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    1. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    2. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2016. "Endogeneity in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 190(2), pages 280-288.
    3. Michael Koetter & James W. Kolari & Laura Spierdijk, 2012. "Enjoying the Quiet Life under Deregulation? Evidence from Adjusted Lerner Indices for U.S. Banks," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 462-480, May.
    4. 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.
    5. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2017. "Endogenous environmental variables in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 199(2), pages 131-140.
    6. Chen, Yi-Yi & Schmidt, Peter & Wang, Hung-Jen, 2014. "Consistent estimation of the fixed effects stochastic frontier model," Journal of Econometrics, Elsevier, vol. 181(2), pages 65-76.
    7. Liangjun Su & Zhentao Shi & Peter C. B. Phillips, 2016. "Identifying Latent Structures in Panel Data," Econometrica, Econometric Society, vol. 84, pages 2215-2264, November.
    8. Laeven, Luc & Levine, Ross, 2009. "Bank governance, regulation and risk taking," Journal of Financial Economics, Elsevier, vol. 93(2), pages 259-275, August.
    9. Kutlu, Levent, 2010. "Battese-coelli estimator with endogenous regressors," Economics Letters, Elsevier, vol. 109(2), pages 79-81, November.
    10. 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.
    11. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    12. Kutlu, Levent & Tran, Kien C. & Tsionas, Mike G., 2019. "A time-varying true individual effects model with endogenous regressors," Journal of Econometrics, Elsevier, vol. 211(2), pages 539-559.
    13. 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.
    14. Tran, Kien C. & Tsionas, Efthymios G., 2013. "GMM estimation of stochastic frontier model with endogenous regressors," Economics Letters, Elsevier, vol. 118(1), pages 233-236.
    15. Wang, Hung-Jen & Ho, Chia-Wen, 2010. "Estimating fixed-effect panel stochastic frontier models by model transformation," Journal of Econometrics, Elsevier, vol. 157(2), pages 286-296, August.
    16. Stiroh, Kevin J & Strahan, Philip E, 2003. "Competitive Dynamics of Deregulation: Evidence from U.S. Banking," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(5), pages 801-828, October.
    17. John H. Boyd & Gianni De Nicoló, 2005. "The Theory of Bank Risk Taking and Competition Revisited," Journal of Finance, American Finance Association, vol. 60(3), pages 1329-1343, June.
    18. Mustafa U. Karakaplan & Levent Kutlu, 2017. "Handling Endogeneity in Stochastic Frontier Analysis," Economics Bulletin, AccessEcon, vol. 37(2), pages 889-901.
    19. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    20. Demsetz, Rebecca S & Strahan, Philip E, 1997. "Diversification, Size, and Risk at Bank Holding Companies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(3), pages 300-313, August.
    21. Griffiths, William E. & Hajargasht, Gholamreza, 2016. "Some models for stochastic frontiers with endogeneity," Journal of Econometrics, Elsevier, vol. 190(2), pages 341-348.
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    Citations

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    Cited by:

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    3. Levent Kutlu, 2020. "Greenhouse Gas Emission Efficiencies of World Countries," IJERPH, MDPI, vol. 17(23), pages 1-11, November.
    4. Marta Arbelo-Pérez & Yaiza Armas-Cruz & Antonio Arbelo, 2022. "Environmental strategy and firm performance: A new methodological proposal," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(8), pages 283-292.
    5. Alexander D. Stead & Phill Wheat & William H. Greene, 2023. "On hypothesis testing in latent class and finite mixture stochastic frontier models, with application to a contaminated normal-half normal model," Journal of Productivity Analysis, Springer, vol. 60(1), pages 37-48, August.

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    More about this item

    Keywords

    Classification; Fixed effect; Group heterogeneity; Panel stochastic frontier; Penalized control function maximum likelihood; Penalized first-difference maximum likelihood.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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