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Forecasting tourist arrivals using origin country macroeconomics

Author

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  • Ioannis Chatziantoniou
  • Stavros Degiannakis
  • Bruno Eeckels
  • George Filis

Abstract

This study utilizes both disaggregated data and macroeconomic indicators in order to examine the importance of the macroeconomic environment of origin countries for analysing destinations’ tourist arrivals. In particular, it is the first study to present strong empirical evidence that both of these features in tandem provide statistically significant information of tourist arrivals in Greece. The forecasting exercises presented in our analysis show that macroeconomic indicators conducive to better forecasts are mainly origin country-specific, thus highlighting the importance of considering the apparent sharp national contrasts among origin countries when investigating domestic tourist arrivals. Given the extent of the dependency of the Greek economy on tourism income and also the perishable nature of the tourist product itself, results have important implications for policymakers in Greece.

Suggested Citation

  • Ioannis Chatziantoniou & Stavros Degiannakis & Bruno Eeckels & George Filis, 2016. "Forecasting tourist arrivals using origin country macroeconomics," Applied Economics, Taylor & Francis Journals, vol. 48(27), pages 2571-2585, June.
  • Handle: RePEc:taf:applec:v:48:y:2016:i:27:p:2571-2585
    DOI: 10.1080/00036846.2015.1125434
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    Cited by:

    1. 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.
    2. Uju Violet Alola & Darya Baeva & Andrew Adewale Alola, 2023. "Determining the (A)symmetric Role of Business–Consumer Confidence in Outward–Inward Tourism in Russia: A Competitiveness Perspective," International Journal of Global Business and Competitiveness, Springer, vol. 18(1), pages 22-34, June.
    3. Giovanni De Luca & Monica Rosciano, 2020. "Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    6. Wai Hong Kan Tsui & Faruk Balli & David Tat Wei Tan & Oscar Lau & Mudassar Hasan, 2018. "New Zealand business tourism," Tourism Economics, , vol. 24(4), pages 386-417, June.
    7. Conrado Diego García-Gómez & Ender Demir & Ming-Hsiang Chen & José María Díez-Esteban, 2022. "Understanding the effects of economic policy uncertainty on US tourism firms’ performance," Tourism Economics, , vol. 28(5), pages 1174-1192, August.
    8. Sudeshna Ghosh, 2021. "Business Confidence and Business Tourism in Japan," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 1-23, February.
    9. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    10. Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).
    11. Gizem Uzuner & Sudeshna Ghosh, 2021. "Do pandemics have an asymmetric effect on tourism in Italy?," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1561-1579, October.
    12. 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.
    13. Anastasiou, Dimitris & Drakos, Konstantinos & Kapopoulos, Panayotis, 2022. "Predicting international tourist arrivals in Greece with a novel sector-specific business leading indicator," MPRA Paper 113860, University Library of Munich, Germany.
    14. Huang-Ping Yen & Po-Chi Chen & Kung-Cheng Ho, 2021. "Analyzing Destination Accessibility From the Perspective of Efficiency Among Tourism Origin Countries," SAGE Open, , vol. 11(2), pages 21582440211, April.
    15. Chi-Wei Su & Xian-Li Meng & Ran Tao & Muhammad Umar, 2023. "Chinese consumer confidence: A catalyst for the outbound tourism expenditure?," Tourism Economics, , vol. 29(3), pages 696-717, May.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F19 - International Economics - - Trade - - - Other
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General

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