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Maximum simulated likelihood estimation of the seemingly unrelated stochastic frontier regressions

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  • Hung-pin Lai

    (National Chung Cheng University)

Abstract

In this paper, we use the maximum simulated likelihood (MSL) approach to estimate multiple stochastic frontier (SF) models with random effects and correlated composite errors. We show that the separate estimation of the single equation ignores the correlation between the composite errors and causes significant efficiency loss in estimation. In addition to using Monte Carlo simulation to investigate the finite sample performance of the simulated estimator, we demonstrate the usefulness of our approach in estimating the technical efficiency of Taiwan’s international hotels based on their accommodation and restaurant divisions.

Suggested Citation

  • Hung-pin Lai, 2021. "Maximum simulated likelihood estimation of the seemingly unrelated stochastic frontier regressions," Empirical Economics, Springer, vol. 60(6), pages 2943-2968, June.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:6:d:10.1007_s00181-020-01962-9
    DOI: 10.1007/s00181-020-01962-9
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    References listed on IDEAS

    as
    1. William Greene, 2003. "Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function," Journal of Productivity Analysis, Springer, vol. 19(2), pages 179-190, April.
    2. Hung-pin Lai & Cliff Huang, 2013. "Maximum likelihood estimation of seemingly unrelated stochastic frontier regressions," Journal of Productivity Analysis, Springer, vol. 40(1), pages 1-14, August.
    3. 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.
    4. Murray D. Smith, 2008. "Stochastic frontier models with dependent error components," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 172-192, March.
    5. Christopher F. Parmeter & Robin C. Sickles (ed.), 2021. "Advances in Efficiency and Productivity Analysis," Springer Proceedings in Business and Economics, Springer, number 978-3-030-47106-4, March.
    6. Christine Amsler & Artem Prokhorov & Peter Schmidt, 2014. "Using Copulas to Model Time Dependence in Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 497-522, August.
    7. Genius, Margarita & Stefanou, Spiro E. & Tzouvelekas, Vangelis, 2012. "Measuring productivity growth under factor non-substitution: An application to US steam-electric power generation utilities," European Journal of Operational Research, Elsevier, vol. 220(3), pages 844-852.
    8. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    9. 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.
    10. Christine Amsler & Peter Schmidt, 2021. "A Survey of the Use of Copulas in Stochastic Frontier Models," Springer Proceedings in Business and Economics, in: Christopher F. Parmeter & Robin C. Sickles (ed.), Advances in Efficiency and Productivity Analysis, pages 125-138, Springer.
    11. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    More about this item

    Keywords

    Maximum likelihood estimation; Copula; Seemingly unrelated stochastic frontier regressions; Random effects;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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