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A leading indicator approach to predicting short-term shifts in demand for business travel by air to and from the UK

Author

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  • Nenad Njegovan

    (Civil Aviation Authority, London, UK)

Abstract

This paper uses the probit model to examine whether leading indicator information could be used for the purpose of predicting short-term shifts in demand for business travel by air to and from the UK. Leading indicators considered include measures of business expectations, availability of funds for corporate travel and some well-known macroeconomic indicators. The model performance is evaluated on in- and out-of-sample basis, as well as against a linear leading indicator model, which is used to mimic the current forecasting practice in the air transport industry. The estimated probit model is shown to provide timely predictions of the early 1980s and 1990s industry recessions and is shown to be more accurate than the benchmark linear model. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Nenad Njegovan, 2005. "A leading indicator approach to predicting short-term shifts in demand for business travel by air to and from the UK," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 421-432.
    • Handle: RePEc:jof:jforec:v:24:y:2005:i:6:p:421-432
    DOI: 10.1002/for.961
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    References listed on IDEAS

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    1. Maximo Camacho & Gabriel Perez-Quiros, 2002. "This is what the leading indicators lead," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 61-80.
    2. Brons, Martijn & Pels, Eric & Nijkamp, Peter & Rietveld, Piet, 2002. "Price elasticities of demand for passenger air travel: a meta-analysis," Journal of Air Transport Management, Elsevier, vol. 8(3), pages 165-175.
    3. Mills,Terence C., 1991. "Time Series Techniques for Economists," Cambridge Books, Cambridge University Press, number 9780521405744.
    4. Birchenhall, Chris R, et al, 1999. "Predicting U.S. Business-Cycle Regimes," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 313-323, July.
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    2. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz & Varela Repolho, Hugo Miguel, 2017. "Air transportation demand forecast through Bagging Holt Winters methods," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 116-123.
    3. Yap, Ghialy & Allen, David, 2011. "Investigating other leading indicators influencing Australian domestic tourism demand," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1365-1374.
    4. Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
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    6. Becken, Susanne & Lennox, James, 2012. "Implications of a long-term increase in oil prices for tourism," Tourism Management, Elsevier, vol. 33(1), pages 133-142.
    7. Schiff, Aaron & Becken, Susanne, 2011. "Demand elasticity estimates for New Zealand tourism," Tourism Management, Elsevier, vol. 32(3), pages 564-575.
    8. Scarpel, Rodrigo Arnaldo, 2013. "Forecasting air passengers at São Paulo International Airport using a mixture of local experts model," Journal of Air Transport Management, Elsevier, vol. 26(C), pages 35-39.

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