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ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry

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  • Nieto, María Rosa
  • Carmona-Benítez, Rafael Bernardo

Abstract

•The ARIMA + GARCH + Bootstrap method is proposed to forecast pax in the air transport industry.•Pax is analyzed and represented by conditional mean, variance, and the distribution of the error.•The performance of the proposed method is compared to 4 of the most used forecasting methods in the air transport industry.•The performance of the ARIMA + GARCH + Bootstrap method is validated by using two tests of superior ability.

Suggested Citation

  • Nieto, María Rosa & Carmona-Benítez, Rafael Bernardo, 2018. "ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 1-8.
    • Handle: RePEc:eee:jaitra:v:71:y:2018:i:c:p:1-8
    DOI: 10.1016/j.jairtraman.2018.05.007
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    4. Wang, Zhanwei & Song, Woon-Kyung, 2020. "Sustainable airport development with performance evaluation forecasts: A case study of 12 Asian airports," Journal of Air Transport Management, Elsevier, vol. 89(C).
    5. Maria Rosa Nieto & Rafael Bernardo Carmona-Benítez, 2021. "An Approach to Measure the Performance and the Efficiency of Future Airport Infrastructure," Mathematics, MDPI, vol. 9(16), pages 1-28, August.
    6. Carmona-Benítez, Rafael Bernardo & Nieto, María Rosa, 2020. "SARIMA damp trend grey forecasting model for airline industry," Journal of Air Transport Management, Elsevier, vol. 82(C).
    7. Emami Javanmard, Majid & Tang, Yili & Martínez-Hernández, J. Adrián, 2024. "Forecasting air transportation demand and its impacts on energy consumption and emission," Applied Energy, Elsevier, vol. 364(C).
    8. Wang, Sen & Gao, Yi, 2021. "A literature review and citation analyses of air travel demand studies published between 2010 and 2020," Journal of Air Transport Management, Elsevier, vol. 97(C).

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