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Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures

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Author Info

  • Fildes, Robert
  • Wei, Yingqi
  • Ismail, Suzilah

Abstract

Airline traffic forecasting is important to airlines and regulatory authorities. This paper examines a number of approaches to forecasting short- to medium-term air traffic flows. It contributes as a rare replication, testing a variety of alternative modelling approaches. The econometric models employed include autoregressive distributed lag (ADL) models, time-varying parameter (TVP) models and an automatic method for econometric model specification. A vector autoregressive (VAR) model and various univariate alternatives are also included to deliver unconditional forecast comparisons. Various approaches for taking into account interactions between contemporaneous air traffic flows are examined, including pooled ADL models and the enhanced models with the addition of a "world trade" variable. Based on the analysis of a number of forecasting error measures, it is concluded that pooled ADL models that include the "world trade" variable outperform the alternatives, and in particular univariate methods; and, second, that automatic modelling procedures are enhanced through judgmental intervention. In contrast to earlier results, the TVP models do not improve accuracy. Depending on the preferred error measure, the difference in accuracy may be substantial.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 3 (July)
Pages: 902-922

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Handle: RePEc:eee:intfor:v:27:y::i:3:p:902-922

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Airline traffic Comparative forecasting accuracy Econometric model building Time-varying parameter Pooled cross-section time series Replication;

References

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Cited by:
  1. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844.

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