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International Arrivals in the Canary Islands: Persistence, Long Memory, Seasonality and other Implicit Dynamics

Author

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  • Luis A. Gil-Alana

    (Faculty of Economics, Universidad de Navarra, Edificio Biblioteca, Entrada Este, E-31080 Pamplona, Spain)

Abstract

This study models international monthly arrivals in the Canary Islands using different time-series approaches that enable an examination of the degree of persistence of the series. The author focuses on long memory processes at the long-run or zero frequency, at the seasonal frequencies and at both simultaneously. A forecasting experiment is also conducted and the results indicate that the model with two differencing parameters seems to be the most adequate specification for this series, being non-stationary with respect to the two components and mean reverting with respect to the seasonal structure.

Suggested Citation

  • Luis A. Gil-Alana, 2010. "International Arrivals in the Canary Islands: Persistence, Long Memory, Seasonality and other Implicit Dynamics," Tourism Economics, , vol. 16(2), pages 287-302, June.
    • Handle: RePEc:sae:toueco:v:16:y:2010:i:2:p:287-302
    DOI: 10.5367/000000010791305581
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    References listed on IDEAS

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    8. 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.
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    Cited by:

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    2. Dimitrios TSIOTAS & Thomas KRABOKOUKIS & Serafeim POLYZOS, 2020. "Detecting Interregional Patterns In Tourism Seasonality Of Greece: A Principal Components Analysis Approach," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 91-112, June.
    3. Luis A Gil-Alana & James E Payne, 2022. "Persistence, seasonality, and fractional integration within a nonlinear framework: Evidence from US citizens’ overseas travel," Tourism Economics, , vol. 28(3), pages 654-660, May.
    4. James E Payne & Junsoo Lee, 2024. "Global perspective on the permanent or transitory nature of shocks to tourist arrivals: Evidence from new unit root tests with structural breaks and factors," Tourism Economics, , vol. 30(1), pages 67-103, February.
    5. Xi Wu & Adam Blake, 2023. "Does the combination of models with different explanatory variables improve tourism demand forecasting performance?," Tourism Economics, , vol. 29(8), pages 2032-2056, December.
    6. 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.
    7. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    8. José María Martín Martín & José Antonio Rodriguez Martín & Karla Aída Zermeño Mejía & José Antonio Salinas Fernández, 2018. "Effects of Vacation Rental Websites on the Concentration of Tourists—Potential Environmental Impacts. An Application to the Balearic Islands in Spain," IJERPH, MDPI, vol. 15(2), pages 1-14, February.
    9. Dimitrios Tsiotas & Thomas Krabokoukis & Serafeim Polyzos, 2021. "Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece," Tourism and Hospitality, MDPI, vol. 2(1), pages 1-27, February.
    10. James E Payne & Luis A Gil-Alana, 2018. "Data measurement and the change in persistence of tourist arrivals to the United States in the aftermath of the September 11th terrorist attacks," Tourism Economics, , vol. 24(1), pages 41-50, February.

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