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Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand

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  • Andre Jungmittag

Abstract

This paper applies combining forecasts of air travel demand generated from the same model but over different estimation windows. The combination approach used resorts to Pesaran and Pick (2011), but the empirical application is extended in several ways. The forecasts are based on a seasonal Box-Jenkins model (SARIMA), which is adequate to forecast monthly air travel demand with distinct seasonal patterns at the largest German airport Frankfurt am Main. Furthermore, forecasts with forecast horizons from one to twelve months-ahead, which are based on different average estimation windows, expanding windows and single rolling windows, are compared with baseline forecasts based on an expanding window of the observations after a structural break. The forecast exercise shows that the average window forecasts mostly outperform the alternative single window forecasts.
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  • Andre Jungmittag, 2016. "Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(4), pages 373-380, July.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:4:p:373-380
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    1. Giraitis, Liudas & Kapetanios, George & Price, Simon, 2013. "Adaptive forecasting in the presence of recent and ongoing structural change," Journal of Econometrics, Elsevier, vol. 177(2), pages 153-170.
    2. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.
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    4. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    5. Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger, 2011. "Forecasting (aggregate) demand for US commercial air travel," International Journal of Forecasting, Elsevier, vol. 27(3), pages 923-941, July.
    6. Pesaran, M. Hashem & Pick, Andreas & Pranovich, Mikhail, 2013. "Optimal forecasts in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 134-152.
    7. Peter Schulze & Alexander Prinz, 2009. "Forecasting container transshipment in Germany," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2809-2815.
    8. Pesaran, M. Hashem & Pick, Andreas, 2011. "Forecast Combination Across Estimation Windows," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 307-318.
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    Cited by:

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    2. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.
    3. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    4. Chengyuan Zhang & Fuxin Jiang & Shouyang Wang & Shaolong Sun, 2020. "A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries," Papers 2002.09201, arXiv.org.
    5. Håvard Hungnes, 2020. "Predicting the exchange rate path. The importance of using up-to-date observations in the forecasts," Discussion Papers 934, Statistics Norway, Research Department.
    6. Zhang, Xiaomeng & Zhang, Xinyu, 2023. "Optimal model averaging based on forward-validation," Journal of Econometrics, Elsevier, vol. 237(2).
    7. Gudmundsson, S.V. & Cattaneo, M. & Redondi, R., 2021. "Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19," Journal of Air Transport Management, Elsevier, vol. 91(C).
    8. Håvard Hungnes, 2018. "Encompassing tests for evaluating multi-step system forecasts invariant to linear transformations," Discussion Papers 871, Statistics Norway, Research Department.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation

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