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Forecasting (aggregate) demand for US commercial air travel

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  • Carson, Richard T.
  • Cenesizoglu, Tolga
  • Parker, Roger

Abstract

We analyze whether it is better to forecast air travel demand using aggregate data at (say) a national level, or to aggregate the forecasts derived for individual airports using airport-specific data. We compare the US Federal Aviation Administration’s (FAA) practice of predicting the total number of passengers using macroeconomic variables with an equivalently specified AIM (aggregating individual markets) approach. The AIM approach outperforms the aggregate forecasting approach in terms of its out-of-sample air travel demand predictions for different forecast horizons. Variants of AIM, where we restrict the coefficient estimates of some explanatory variables to be the same across individual airports, generally dominate both the aggregate and AIM approaches. The superior out-of-sample performances of these so-called quasi-AIM approaches depend on the trade-off between heterogeneity and estimation uncertainty. We argue that the quasi-AIM approaches exploit the heterogeneity across individual airports efficiently, without suffering from as much estimation uncertainty as the AIM approach.

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Bibliographic Info

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

Volume (Year): 27 (2011)
Issue (Month): 3 ()
Pages: 923-941

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Handle: RePEc:eee:intfor:v:27:y:2011:i:3:p:923-941

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

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Keywords: Demand forecasting; Disaggregation; Transportation forecasting; Panel data;

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References

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Cited by:
  1. Ralf Brueggemann & Helmut Luetkepohl, 2011. "Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights," Economics Working Papers ECO2011/17, European University Institute.
  2. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2014. "Golden Rule of Forecasting: Be conservative," MPRA Paper 53579, University Library of Munich, Germany.
  3. Jungmittag, Andre, 2014. "Combination of forecasts across estimation windows: An application to air travel demand," Working Paper Series: Business and Law 05, Frankfurt University of Applied Sciences, Faculty of Business and Law.

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