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Univariate and multivariate forecasting of tourism demand using state-space models

Author

Listed:
  • Elisa Jorge-González

    (Universidad de La Laguna, Spain)

  • Enrique González-Dávila

    (Universidad de La Laguna, Spain)

  • Raquel Martín-Rivero

    (Universidad de La Laguna, Spain)

  • Domingo Lorenzo-Díaz

    (Universidad de La Laguna, Spain; Instituto Canario de Estadística (ISTAC), Spain)

Abstract

Tourism forecasting plays a major role in tourism planning and management and it is one of the main economic activities in many countries. For this reason, it is fundamental to provide several models that allow describing and forecasting the tourist demand. International visitants who arrive at a certain tourist destination may come from countries or regions with similar or different customs and behaviours and therefore be able to present correlated arrival patterns. Based on the state-space methods with time-varying parameters, this study develops the application and comparison of univariate and multivariate models in the applied case of German and British tourist at Canary Islands (Spain). The choice of model can be conditioned by the volume of tourists from one country with respect to the other. Structural models will be used incorporating intervention and exogenous variables, among which airline seat reservations for regular flights.

Suggested Citation

  • Elisa Jorge-González & Enrique González-Dávila & Raquel Martín-Rivero & Domingo Lorenzo-Díaz, 2020. "Univariate and multivariate forecasting of tourism demand using state-space models," Tourism Economics, , vol. 26(4), pages 598-621, June.
    • Handle: RePEc:sae:toueco:v:26:y:2020:i:4:p:598-621
    DOI: 10.1177/1354816619857641
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    References listed on IDEAS

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    1. Silva, Emmanuel Sirimal & Ghodsi, Zara & Ghodsi, Mansi & Heravi, Saeed & Hassani, Hossein, 2017. "Cross country relations in European tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 151-168.
    2. Commandeur, Jacques J.F. & Koopman, Siem Jan, 2007. "An Introduction to State Space Time Series Analysis," OUP Catalogue, Oxford University Press, number 9780199228874.
    3. Tsui, Wai Hong Kan & Ozer Balli, Hatice & Gilbey, Andrew & Gow, Hamish, 2014. "Forecasting of Hong Kong airport's passenger throughput," Tourism Management, Elsevier, vol. 42(C), pages 62-76.
    4. Andrea Saayman & Ilsé Botha, 2017. "Non-linear models for tourism demand forecasting," Tourism Economics, , vol. 23(3), pages 594-613, May.
    5. Gonzalez, Pilar & Moral, Paz, 1995. "An analysis of the international tourism demand in Spain," International Journal of Forecasting, Elsevier, vol. 11(2), pages 233-251, June.
    6. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    7. Jaume Rossello-Nadal, 2001. "Forecasting Turning Points in International Visitor Arrivals in the Balearic Islands," Tourism Economics, , vol. 7(4), pages 365-380, December.
    8. Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George, 2011. "Forecasting tourist arrivals using time-varying parameter structural time series models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 855-869, July.
    9. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
    10. George Athanasopoulos & Ashton de Silva, 2010. "Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand," Monash Econometrics and Business Statistics Working Papers 11/09, Monash University, Department of Econometrics and Business Statistics.
    11. Medeiros, Marcelo C. & McAleer, Michael & Slottje, Daniel & Ramos, Vicente & Rey-Maquieira, Javier, 2008. "An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals," Journal of Econometrics, Elsevier, vol. 147(2), pages 372-383, December.
    12. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    13. Lindsay W. Turner & Stephen F. Witt, 2001. "Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models," Tourism Economics, , vol. 7(2), pages 135-147, June.
    14. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    15. Jae H. Kim & Imad Moosa, 2001. "Seasonal Behaviour of Monthly International Tourist Flows: Specification and Implications for Forecasting Models," Tourism Economics, , vol. 7(4), pages 381-396, December.
    16. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844.
    17. Pfeffermann, D. & Allon, J., 1989. "Multivariate exponential smoothing: Method and practice," International Journal of Forecasting, Elsevier, vol. 5(1), pages 83-98.
    18. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    19. Garcia-Ferrer, Antonio & Queralt, Ricardo A., 1997. "A note on forecasting international tourism demand in Spain," International Journal of Forecasting, Elsevier, vol. 13(4), pages 539-549, December.
    20. 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.
    21. 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.
    22. Wai Hong Kan Tsui & Faruk Balli, 2017. "International arrivals forecasting for Australian airports and the impact of tourism marketing expenditure," Tourism Economics, , vol. 23(2), pages 403-428, March.
    23. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    24. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    25. Song, Haiyan & Hyndman, Rob J., 2011. "Tourism forecasting: An introduction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 817-821, July.
    26. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    27. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
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