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Group-specific stochastic production frontier models with parametric specifications

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  • Lee, Young Hoon

Abstract

This paper develops a stochastic frontier model that not only focuses more on group-specific temporal variations in technical efficiency rather than individual temporal variations, but also allows for a parametric function of the time-varying coefficient of the efficiency factor. We derived the concentrated least squares estimator and its asymptotic properties. When applied to the Penn World data set, the group-specific models yield much more variation in the temporal patterns of efficiency across countries. This application demonstrates the feasibility of applying a group-specific stochastic frontier model with a parametric function of temporal pattern to a real empirical analysis.

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  • Lee, Young Hoon, 2010. "Group-specific stochastic production frontier models with parametric specifications," European Journal of Operational Research, Elsevier, vol. 200(2), pages 508-517, January.
    • Handle: RePEc:eee:ejores:v:200:y:2010:i:2:p:508-517
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    Cited by:

    1. Young H. Lee, 2014. "Stochastic Frontier Models Using GAUSS," Working Papers 1403, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    2. Badunenko, Oleg & Kumbhakar, Subal C., 2016. "When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models," European Journal of Operational Research, Elsevier, vol. 255(1), pages 272-287.
    3. William Horrace & Seth Richards-Shubik & Ian Wright, 2015. "Expected efficiency ranks from parametric stochastic frontier models," Empirical Economics, Springer, vol. 48(2), pages 829-848, March.
    4. Yip, Tsz Leung & Sun, Xin Yu & Liu, John J., 2011. "Group and individual heterogeneity in a stochastic frontier model: Container terminal operators," European Journal of Operational Research, Elsevier, vol. 213(3), pages 517-525, September.
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    6. Wheat, Phill & Smith, Andrew, 2012. "Is the choice of (t−T) in Battese and Coelli (1992) type stochastic frontier models innocuous? Observations and generalisations," Economics Letters, Elsevier, vol. 116(3), pages 291-294.
    7. Lin, Winston T. & Chuang, Chia-Hung, 2013. "Investigating and comparing the dynamic patterns of the business value of information technology over time," European Journal of Operational Research, Elsevier, vol. 228(1), pages 249-261.
    8. Zhiguang ZHANG & Haiqing HU & Winston T. LIN, 2019. "Analyzing the Impacts of Unobserved National Characteristics on Economic Performance of Information Technology based on a Partial Adjustment Approach With Dynamic and Variable Speed of Adjustment," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 128-142, March.
    9. Tsionas, Mike G., 2017. "Microfoundations for stochastic frontiers," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1165-1170.
    10. Lin, Winston T. & Kao, Ta-Wei (Daniel), 2014. "The partial adjustment valuation approach with dynamic and variable speeds of adjustment to evaluating and measuring the business value of information technology," European Journal of Operational Research, Elsevier, vol. 238(1), pages 208-220.
    11. Anaya, Karim L. & Pollitt, Michael G., 2017. "Using stochastic frontier analysis to measure the impact of weather on the efficiency of electricity distribution businesses in developing economies," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1078-1094.

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