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

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  • 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.

Suggested Citation

  • Fildes, Robert & Wei, Yingqi & Ismail, Suzilah, 2011. "Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures," International Journal of Forecasting, Elsevier, vol. 27(3), pages 902-922, July.
  • Handle: RePEc:eee:intfor:v:27:y::i:3:p:902-922
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    as
    1. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    2. Anderson, James E & Kraus, Marvin, 1981. "Quality of Service and the Demand for Air Travel," The Review of Economics and Statistics, MIT Press, vol. 63(4), pages 533-540, November.
    3. Li, Gang & Song, Haiyan & Witt, Stephen F., 2006. "Time varying parameter and fixed parameter linear AIDS: An application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 22(1), pages 57-71.
    4. Carlo A. Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 755-783, December.
    5. Pagan, Adrian, 1987. "Three Econometric Methodologies: A Critical Appraisal," Journal of Economic Surveys, Wiley Blackwell, vol. 1(1), pages 3-24.
    6. 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, July.
    7. Abed, Seraj Y. & Ba-Fail, Abdullah O. & Jasimuddin, Sajjad M., 2001. "An econometric analysis of international air travel demand in Saudi Arabia," Journal of Air Transport Management, Elsevier, vol. 7(3), pages 143-148.
    8. Scott Blunk & David Clark & James McGibany, 2006. "Evaluating the long-run impacts of the 9/11 terrorist attacks on US domestic airline travel," Applied Economics, Taylor & Francis Journals, vol. 38(4), pages 363-370.
    9. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    10. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, October.
    11. Nenad Njegovan, 2006. "Are Shocks to Air Passenger Traffic Permanent or Transitory? Implications for Long-Term Air Passenger Forecasts for the UK," Journal of Transport Economics and Policy, University of Bath, vol. 40(2), pages 315-328, May.
    12. Jorge-Calderón, J.D., 1997. "A demand model for scheduled airline services on international European routes," Journal of Air Transport Management, Elsevier, vol. 3(1), pages 23-35.
    13. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    14. P. Geoffrey Allen & Robert Fildes, 2005. "Levels, Differences and ECMs – Principles for Improved Econometric Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 881-904, December.
    15. Hubbard, Raymond & Vetter, Daniel E., 1996. "An empirical comparison of published replication research in accounting, economics, finance, management, and marketing," Journal of Business Research, Elsevier, vol. 35(2), pages 153-164, February.
    16. Lai, Sue Ling & Lu, Whei-Li, 2005. "Impact analysis of September 11 on air travel demand in the USA," Journal of Air Transport Management, Elsevier, vol. 11(6), pages 455-458.
    17. Garcia-Ferrer, Antonio, et al, 1987. "Macroeconomic Forecasting Using Pooled International Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(1), pages 53-67, January.
    18. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    19. Zellner, Arnold & Hong, Chansik & Min, Chung-ki, 1991. "Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 275-304.
    20. Matsumoto, Hidenobu, 2004. "International urban systems and air passenger and cargo flows: some calculations," Journal of Air Transport Management, Elsevier, vol. 10(4), pages 239-247.
    21. Goodrich, Robert L., 2000. "The Forecast Pro methodology," International Journal of Forecasting, Elsevier, vol. 16(4), pages 533-535.
    22. Grubb, Howard & Mason, Alexina, 2001. "Long lead-time forecasting of UK air passengers by Holt-Winters methods with damped trend," International Journal of Forecasting, Elsevier, vol. 17(1), pages 71-82.
    23. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    24. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
    25. Riddington, GL, 1993. "Time varying coefficient models and their forecasting performance," Omega, Elsevier, vol. 21(5), pages 573-583, September.
    26. Fridström, Lasse & Thune-Larsen, Harald, 1989. "An econometric air travel demand model for the entire conventional domestic network: The case of Norway," Transportation Research Part B: Methodological, Elsevier, vol. 23(3), pages 213-223, June.
    27. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    28. Zellner,Arnold & Palm,Franz C. (ed.), 2004. "The Structural Econometric Time Series Analysis Approach," Cambridge Books, Cambridge University Press, number 9780521814072, October.
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