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Estimation of Dynamic Stochastic Frontier Model using Likelihood-based Approaches

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  • Lai, Hung-pin
  • Kumbhakar, Subal C.

Abstract

Almost all the existing panel stochastic frontier models treat technical efficiency as static. Consequently there is no mechanism by which an inefficient producer can improve its efficiency over time. The main objective of this paper is to propose a panel stochastic frontier model that allows the dynamic adjustment of persistent technical inefficiency. The model also includes transient inefficiency which is assumed to be heteroscedastic. We consider three likelihood-based approaches to estimate the model: the full maximum likelihood (FML), pairwise composite likelihood (PCL) and quasi-maximum likelihood (QML) approaches. Moreover, we provide Monte Carlo simulation results to examine and compare the finite sample performances of the three above-mentioned likelihood-based estimators. Finally, we provide an empirical application to the dynamic model.

Suggested Citation

  • Lai, Hung-pin & Kumbhakar, Subal C., 2018. "Estimation of Dynamic Stochastic Frontier Model using Likelihood-based Approaches," MPRA Paper 87830, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:87830
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    References listed on IDEAS

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    Cited by:

    1. Tsionas, Mike G. & Kumbhakar, Subal C., 2021. "Stochastic frontier models with time-varying conditional variances," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1115-1132.
    2. 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.
    3. Md Harun Or Rosid & Zhao Xuefeng & Sk Alamgir Hossain & Mohammad Raihanul Hasan & Md Reza Sultanuzzaman, 2021. "The Impact of GDP on Cross-Country Efficiency in Wealth Maximization: a Joint Analysis Through the Stochastic Frontier and Generalized Method of Moments," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(1), pages 1-6.
    4. Cave, Joshua & Chaudhuri, Kausik & Kumbhakar, Subal C., 2023. "Dynamic firm performance and estimator choice: A comparison of dynamic panel data estimators," European Journal of Operational Research, Elsevier, vol. 307(1), pages 447-467.
    5. Valentin Zelenyuk & Zhichao Wang, 2023. "Random vs. Explained Inefficiency in Stochastic Frontier Analysis: The Case of Queensland Hospitals," CEPA Working Papers Series WP052023, School of Economics, University of Queensland, Australia.

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    More about this item

    Keywords

    Technical inefficiency; panel data; copula; full maximum likelihood estimation; pairwise composite likelihood estimation; quasi-maximum likelihood estimation;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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