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

Author

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  • 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.

Suggested Citation

  • Luis A. Gil-Alana & Juncal Cunado & Fernando Perez de Gracia, 2008. "Tourism in the Canary Islands: forecasting using several seasonal time series models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 621-636.
  • Handle: RePEc:jof:jforec:v:27:y:2008:i:7:p:621-636
    DOI: 10.1002/for.1077
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    References listed on IDEAS

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    Cited by:

    1. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    2. repec:eee:touman:v:31:y:2010:i:6:p:846-854 is not listed on IDEAS
    3. Jose Angelo Divino & Michael McAleer, 2009. "Modelling the Growth and Volatility in Daily International Mass Tourism to Peru," Documentos de Trabajo del ICAE 2009-15, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    4. 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(22)-257, Enero.
    5. Divino, Jose Angelo & McAleer, Michael, 2010. "Modelling and forecasting daily international mass tourism to Peru," Tourism Management, Elsevier, vol. 31(6), pages 846-854.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    7. 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.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting"," IREA Working Papers 201701, University of Barcelona, Research Institute of Applied Economics, revised Jan 2017.
    9. 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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