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Modeling multivariate tourism expenditure using vine copula: empirical findings from of Fribourg-Switzerland

Author

Listed:
  • Brida Juan Gabriel

    (Universidad de la República)

  • Moreno Leonardo

    (Universidad de la República
    Universidad de la República)

  • Scaglione Miriam

    (HES-SO Wallis/Valais)

Abstract

The measurement of economic contribution of tourism in a given geographical area needs, besides non-monetary measures such frequentation ones, a monetary one namely tourism expenditure. In addition to these macroeconomic related purposes, the whole statistical tourism measures shed light on tourist behavior, which are of great interest for marketing analysis. This study proposes to model the joint distribution of tourism expenditure disaggregated by category simultaneously with a set of covariates associated with the tourist’s trip (including destination, days of stay, season of the year, among others). The dependence structure, among all variables, is established by a family of pairwise copulas (regular vines), which allows fitting a high-dimensional multivariate statistical model. As an application, the model is calibrated with a database for the Fribourg region (Switzerland). The good fit of the model to the data is observed both in the marginal distributions and in the dependence structure. The parameters representing dependence of the vine copula show strong relationship between the different categories of spending, both for tourists and excursionists. The empirical results show that the key variables to understand the associations between them are the destination in the region and the place of origin of the visitor. In general terms, the empirical results show that a visitor that expends more on one item is likely to expend more on a complementary items of spending.

Suggested Citation

  • Brida Juan Gabriel & Moreno Leonardo & Scaglione Miriam, 2024. "Modeling multivariate tourism expenditure using vine copula: empirical findings from of Fribourg-Switzerland," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4093-4116, October.
  • Handle: RePEc:spr:qualqt:v:58:y:2024:i:5:d:10.1007_s11135-024-01839-4
    DOI: 10.1007/s11135-024-01839-4
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    References listed on IDEAS

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    1. Christine Lim, 2006. "A Survey of Tourism Demand Modelling Practice: Issues and Implications," Chapters, in: Larry Dwyer & Peter Forsyth (ed.), International Handbook on the Economics of Tourism, chapter 1, Edward Elgar Publishing.
    2. Anastasios Panagiotelis & Claudia Czado & Harry Joe, 2012. "Pair Copula Constructions for Multivariate Discrete Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1063-1072, September.
    3. Panagiotelis, Anastasios & Czado, Claudia & Joe, Harry & Stöber, Jakob, 2017. "Model selection for discrete regular vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 138-152.
    4. Grace Yoon & Raymond J Carroll & Irina Gaynanova, 2020. "Sparse semiparametric canonical correlation analysis for data of mixed types," Biometrika, Biometrika Trust, vol. 107(3), pages 609-625.
    5. Frees, Edward W., 1995. "Assessing cross-sectional correlation in panel data," Journal of Econometrics, Elsevier, vol. 69(2), pages 393-414, October.
    6. Marta Disegna & Linda Osti, 2016. "Tourists' Expenditure Behaviour: The Influence of Satisfaction and the Dependence of Spending Categories," Tourism Economics, , vol. 22(1), pages 5-30, February.
    7. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    8. Stöber, Jakob & Hong, Hyokyoung Grace & Czado, Claudia & Ghosh, Pulak, 2015. "Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 28-39.
    9. Pierpaolo D’Urso & Marta Disegna & Riccardo Massari, 2020. "Satisfaction and Tourism Expenditure Behaviour," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 1081-1106, June.
    10. José Francisco Baños-Pino & David Boto-García & Eduardo Del Valle & Emma Zapico, 2023. "Is visitors’ expenditure at destination influenced by weather conditions?," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(10), pages 1554-1572, May.
    11. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    12. Olya, Hossein GT & Mehran, Javaneh, 2017. "Modelling tourism expenditure using complexity theory," Journal of Business Research, Elsevier, vol. 75(C), pages 147-158.
    13. Liang Zhu & Christine Lim & Wenjun Xie & Yuan Wu, 2017. "Analysis of tourism demand serial dependence structure for forecasting," Tourism Economics, , vol. 23(7), pages 1419-1436, November.
    14. Jaume Rosselló Nadal & María Santana Gallego, 2022. "Gravity models for tourism demand modeling: Empirical review and outlook," Journal of Economic Surveys, Wiley Blackwell, vol. 36(5), pages 1358-1409, December.
    15. Vera Shanshan Lin & Haiyan Song, 2015. "A review of Delphi forecasting research in tourism," Current Issues in Tourism, Taylor & Francis Journals, vol. 18(12), pages 1099-1131, December.
    16. Embrechts, Paul & Hofert, Marius, 2013. "Statistical Inference For Copulas In High Dimensions: A Simulation Study," ASTIN Bulletin, Cambridge University Press, vol. 43(2), pages 81-95, May.
    17. Weiying Cai & Hui Di & Xingpeng Liu, 2019. "Estimation of the Spatial Suitability of Winter Tourism Destinations Based on Copula Functions," IJERPH, MDPI, vol. 16(2), pages 1-18, January.
    18. Juan Gabriel Brida & Isabel Cortes-Jimenez & Manuela Pulina, 2016. "Has the tourism-led growth hypothesis been validated? A literature review," Current Issues in Tourism, Taylor & Francis Journals, vol. 19(5), pages 394-430, April.
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    More about this item

    Keywords

    Copula; Fribourg (Switzerland); Regular vines; Tourist spending categories;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • Z30 - Other Special Topics - - Tourism Economics - - - General
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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