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Modelling international tourism demand using seasonal ARIMA models

Author

Listed:
  • Tea Baldigara

    (University of Rijeka, Faculty of Tourism and Hospitality Management, Opatija)

  • Maja Mamula

    (University of Rijeka, Faculty of Tourism and Hospitality Management, Opatija)

Abstract

Purpose – The purpose of this study is to establish a seasonal autoregressive integrated moving average model able to capture and explain the patterns and the determinants of German tourism demand in Croatia. Design – The present study is based on the Box-Jenkins approach in building a seasonal autoregressive integrated moving average model intend to describe the behaviour of the German tourists’ flows to Croatia. Approach – The proposed model is a seasonal ARIMA(0,0,0)(1,1,3) model. Findings – The diagnostic checking and the performed tests showed that the estimated seasonal ARIMA(0,0,0)(1,1,3) model is adequate in modelling and analysing the number of German tourists ‘arrivals to Croatia. Originality of the paper – This study provides a seasonal ARIMA model helpful to analyse, understand and forecast German tourists’ flows to Croatia. Such, more detailed and systematic studies should be considered as starting points of future macroeconomic development strategies, pricing strategies and tourism sector routing strategies in Croatia, as a predominantly tourism oriented country. Classification-JEL: L83

Suggested Citation

  • Tea Baldigara & Maja Mamula, 2015. "Modelling international tourism demand using seasonal ARIMA models," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 21(1), pages 19-31, May.
    • Handle: RePEc:tho:journl:v:21:y:2015:n:1:p:19-31
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    Citations

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    Cited by:

    1. El houssin Ouassou & Hafsa Taya, 2022. "Forecasting Regional Tourism Demand in Morocco from Traditional and AI-Based Methods to Ensemble Modeling," Forecasting, MDPI, vol. 4(2), pages 1-18, April.
    2. Bahodirhon Safarov & Hisham Mohammad Al-Smadi & Makhina Buzrukova & Bekzot Janzakov & Alexandru Ilieş & Vasile Grama & Dorina Camelia Ilieș & Katalin Csobán Vargáné & Lóránt Dénes Dávid, 2022. "Forecasting the Volume of Tourism Services in Uzbekistan," Sustainability, MDPI, vol. 14(13), pages 1-18, June.
    3. Maja Uran Maravić, 2017. "Accomodation classification system in Slovenia," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 23(2), pages 235-249, November.
    4. Mladenović Jelena & Lepojević Vinko & Janković-Milić Vesna, 2016. "Modelling and Prognosis of the Export of the Republic of Serbia by Using Seasonal Holt-Winters and Arima Method," Economic Themes, Sciendo, vol. 54(2), pages 233-260, June.
    5. Thao Nguyen-Da & Yi-Min Li & Chi-Lu Peng & Ming-Yuan Cho & Phuong Nguyen-Thanh, 2023. "Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    6. Adriana Jelušić, 2017. "Modelling tourist consumption to achieve economic growth and external balance: case of Croatia," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 23(1), pages 87-104, May.
    7. José Antonio Cava Jiménez & María Genoveva Millán Vázquez de la Torre & Ricardo Hernández Rojas, 2019. "Analysis of the Tourism Demand for Iberian Ham Routes in Andalusia (Southern Spain): Tourist Profile," Sustainability, MDPI, vol. 11(16), pages 1-21, August.
    8. Balli, Hatice Ozer & Tsui, Wai Hong Kan & Balli, Faruk, 2019. "Modelling the volatility of international visitor arrivals to New Zealand," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 204-214.

    More about this item

    Keywords

    international tourism demand; econometric modelling; seasonal ARIMA models; forecasting; forecasting accuracy;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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