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Tourism in the Canary Islands: forecasting using several seasonal time series models

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Author Info

  • Luis A. Gil-Alana

    (University of Navarra, Pamplona, Spain)

  • Juncal Cunado

    (University of Navarra, Pamplona, Spain)

  • Fernando Perez de Gracia

    (University of Navarra, Pamplona, Spain)

Abstract

This paper deals with the analysis of the number of tourists travelling to the Canary Islands by means of using different seasonal statistical models. Deterministic and stochastic seasonality is considered. For the latter case, we employ seasonal unit roots and seasonally fractionally integrated models. As a final approach, we also employ a model with possibly different orders of integration at zero and the seasonal frequencies. All these models are compared in terms of their forecasting ability in an out-of-sample experiment. The results in the paper show that a simple deterministic model with seasonal dummy variables and AR(1) disturbances produce better results than other approaches based on seasonal fractional and integer differentiation over short horizons. However, increasing the time horizon, the results cannot distinguish between the model based on seasonal dummies and another using fractional integration at zero and the seasonal frequencies. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1077
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 27 (2008)
Issue (Month): 7 ()
Pages: 621-636

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Handle: RePEc:jof:jforec:v:27:y:2008:i:7:p:621-636

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Hyllerberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal Integration And Cointegration," Papers 0-88-2, Pennsylvania State - Department of Economics.
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  6. Gil-Alana, Luis A., 2002. "Seasonal long memory in the aggregate output," Economics Letters, Elsevier, vol. 74(3), pages 333-337, February.
  7. Christine Lim & Michael McAleer, 2001. "Time Series Forecasts of International Tourism Demand for Australia," ISER Discussion Paper 0533, Institute of Social and Economic Research, Osaka University.
  8. Franses, Philip Hans, 1991. "Seasonality, non-stationarity and the forecasting of monthly time series," International Journal of Forecasting, Elsevier, vol. 7(2), pages 199-208, August.
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  13. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
  14. William R. Parke, 1999. "What Is Fractional Integration?," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 632-638, November.
  15. Gustavsson, Patrik & Nordström, Jonas, 1999. "The Impact of Seasonal Unit Roots and Vector ARMA Modeling on Forecasting Monthly Tourism Flows," Working Paper Series 150, Trade Union Institute for Economic Research, revised 01 Jul 2000.
  16. Ashley, Richard, 1998. "A new technique for postsample model selection and validation," Journal of Economic Dynamics and Control, Elsevier, vol. 22(5), pages 647-665, May.
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  19. Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
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Cited by:
  1. Arias Martín, Pedro, 2013. "La situación del empleo en turismo rural en España/The Employment Situation in Rural Tourism in Spain," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 31, pages 257 (22pags, Enero.
  2. Juncal Cuñado & Alberiko Gil-Alana, Luis & Perez De Gracia, Fernando, 2011. "Modelling International Monthly Tourist in Spain/Modelización de llegadas mensuales de turistas a España," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 29, pages 723-736, Diciembre.
  3. 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, July.

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