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A structural vector autoregressive model of technical efficiency and delays with an application to Chinese airlines

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  • Tsionas, Mike G.
  • Chen, Zhongfei
  • Wanke, Peter

Abstract

This study reports on the performance assessment of Chinese airlines from 2006 to 2014 using a stochastic distance function where technical efficiency and a measure of flight delays follow a joint structural autoregressive process. This model is used to investigate whether technical efficiency causes flight punctuality or the other way around. The model, however, yields a non-trivial likelihood function and is not amenable to estimation using least squares or standard maximum likelihood techniques. To estimate the model therefore, we propose and implement maximum simulated likelihood with importance sampling. The results suggest a mutual dependence (feedback) between technical efficiency and delays. Policy implications are derived.

Suggested Citation

  • Tsionas, Mike G. & Chen, Zhongfei & Wanke, Peter, 2017. "A structural vector autoregressive model of technical efficiency and delays with an application to Chinese airlines," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 1-10.
  • Handle: RePEc:eee:transa:v:101:y:2017:i:c:p:1-10
    DOI: 10.1016/j.tra.2017.05.003
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    References listed on IDEAS

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    Cited by:

    1. Lin, Boqiang & Xu, Bin, 2018. "How to promote the growth of new energy industry at different stages?," Energy Policy, Elsevier, vol. 118(C), pages 390-403.
    2. Chen, Zhongfei & Tzeremes, Panayiotis & Tzeremes, Nickolaos G., 2018. "Convergence in the Chinese airline industry: A Malmquist productivity analysis," Journal of Air Transport Management, Elsevier, vol. 73(C), pages 77-86.
    3. Yu, Hang & Zhang, Yahua & Zhang, Anming & Wang, Kun & Cui, Qiang, 2019. "A comparative study of airline efficiency in China and India: A dynamic network DEA approach," Research in Transportation Economics, Elsevier, vol. 76(C).
    4. Yu, Bin & Guo, Zhen & Asian, Sobhan & Wang, Huaizhu & Chen, Gang, 2019. "Flight delay prediction for commercial air transport: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 203-221.
    5. Chen, Zhongfei & Wanke, Peter & Antunes, Jorge Junio Moreira & Zhang, Ning, 2017. "Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model," Energy Economics, Elsevier, vol. 68(C), pages 89-108.

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