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Combination of long term and short term forecasts, with application to tourism demand forecasting

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  • Andrawis, Robert R.
  • Atiya, Amir F.
  • El-Shishiny, Hisham

Abstract

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.

Suggested Citation

  • Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886.
  • Handle: RePEc:eee:intfor:v:27:y:2011:i:3:p:870-886 DOI: 10.1016/j.ijforecast.2010.05.019
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    References listed on IDEAS

    as
    1. Granger, C. W. J., 1987. "Implications of Aggregation with Common Factors," Econometric Theory, Cambridge University Press, vol. 3(02), pages 208-222, April.
    2. Ito, Harumi & Lee, Darin, 2005. "Assessing the impact of the September 11 terrorist attacks on U.S. airline demand," Journal of Economics and Business, Elsevier, pages 75-95.
    3. Kohn, Robert, 1982. "When is an aggregate of a time series efficiently forecast by its past?," Journal of Econometrics, Elsevier, pages 337-349.
    4. David F. Hendry & Kirstin Hubrich, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, Taylor & Francis Journals, pages 216-227.
    5. Cline, Richard C. & Ruhl, Terry A. & Gosling, Geoffrey D. & Gillen, David W., 1998. "Air transportation demand forecasts in emerging market economies: a case study of the Kyrgyz Republic in the former Soviet Union," Journal of Air Transport Management, Elsevier, pages 11-23.
    6. Òscar Jordà & Massimiliano Marcellino, 2008. "Path Forecast Evaluation," Economics Working Papers ECO2008/34, European University Institute.
    7. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, pages 375-389.
    8. Profillidis, V.A, 2000. "Econometric and fuzzy models for the forecast of demand in the airport of Rhodes," Journal of Air Transport Management, Elsevier, pages 95-100.
    9. Aigner, Dennis J & Goldfeld, Stephen M, 1974. "Estimation and Prediction from Aggregate Data when Aggregates are Measured More Accurately than Their Components," Econometrica, Econometric Society, vol. 42(1), pages 113-134, January.
    10. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, pages 227-238.
    11. van Garderen, Kees Jan & Lee, Kevin & Pesaran, M. Hashem, 2000. "Cross-sectional aggregation of non-linear models," Journal of Econometrics, Elsevier, pages 285-331.
    12. James Heckman & Justin L. Tobias & Edward Vytlacil, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Southern Economic Journal, Southern Economic Association, vol. 68(2), pages 210-223, October.
    13. Wang, P.T. & Pitfield, David, 1999. "The derivation and analysis of the passenger peak hour: an empirical application to Brazil," ERSA conference papers ersa99pa239, European Regional Science Association.
    14. A. Espasa & E. Senra & R. Albacete, 2002. "Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors," The European Journal of Finance, Taylor & Francis Journals, pages 402-421.
    15. Òscar Jordà & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.
    16. Ito, Harumi & Lee, Darin, 2005. "Assessing the impact of the September 11 terrorist attacks on U.S. airline demand," Journal of Economics and Business, Elsevier, pages 75-95.
    17. Wang, P.T & Pitfield, D.E, 1999. "The derivation and analysis of the passenger peak hour: an empirical application to Brazil," Journal of Air Transport Management, Elsevier, pages 135-141.
    18. Zellner, Arnold & Tobias, Justin, 2004. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers Archive 12371, Iowa State University, Department of Economics.
    19. 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, pages 143-148.
    20. Saab, Samer S. & Zouein, Pierrette P., 2001. "Forecasting passenger load for a fixed planning horizon," Journal of Air Transport Management, Elsevier, pages 361-372.
    21. M. H. Pesaran & R. G. Pierse & M. S. Kumar, 1988. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," UCLA Economics Working Papers 485, UCLA Department of Economics.
    22. Roma, Moreno & Skudelny, Frauke & Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series 374, European Central Bank.
    23. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    24. Pesaran, M Hashem & Pierse, Richard G & Kumar, Mohan S, 1989. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," Econometrica, Econometric Society, vol. 57(4), pages 861-888, July.
    25. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, pages 7-26.
    26. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-214, July.
    27. van Garderen, Kees Jan & Lee, Kevin & Pesaran, M. Hashem, 2000. "Cross-sectional aggregation of non-linear models," Journal of Econometrics, Elsevier, pages 285-331.
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    Cited by:

    1. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, pages 395-412.
    2. Barrow, Devon K. & Kourentzes, Nikolaos, 2016. "Distributions of forecasting errors of forecast combinations: Implications for inventory management," International Journal of Production Economics, Elsevier, vol. 177(C), pages 24-33.
    3. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, pages 60-74.
    4. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    5. Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, pages 224-235.
    6. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    7. Kourentzes, Nikolaos & Petropoulos, Fotios, 2016. "Forecasting with multivariate temporal aggregation: The case of promotional modelling," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 145-153.
    8. repec:eee:touman:v:45:y:2014:i:c:p:181-193 is not listed on IDEAS
    9. repec:eee:touman:v:46:y:2015:i:c:p:322-335 is not listed on IDEAS
    10. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, pages 291-302.
    11. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    12. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, pages 415-444.
    13. Weron, R & Bierbrauer, M & Trück, S, 2004. "Modeling electricity prices: jump diffusion and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, pages 39-48.

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