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Determinants of Argentinean tourism demand in Uruguay

Author

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  • Gabriela Mordecki

    (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economí­a)

Abstract

In Uruguay total yearly tourists represent about 90% of its population of which historically 60% or more have come from Argentina. Tourist activities have a great impact on Uruguayan economy. They represent about 4% of Uruguayan GDP and generate near 6% of total employment and 14% of total exports. For this reason it is important to analyze the determinants behind tourism demand. In this paper we study the relationship between the number of Argentinean tourists in Uruguay, their real expenditure, Argentinean GDP and the real exchange rate (RER) between Uruguay and Argentina trying to find long-run relationships between variables, following Johansen methodology. We found two cointegration relationships, through Vector error correction models (VECM). In the first one we include real tourism expenditure, Argentinean GDP and the RER between Argentina and Uruguay. In the second one, we try to estimate the number of Argentinean tourists, using monthly dada of tourists, a monthly indicator of Argentinean activity and the RER between Uruguay and Argentina. The model's forecast indicates a slight decrease of Argentinean tourism expenditure in 2014 and a recovering for 2015.

Suggested Citation

  • Gabriela Mordecki, 2014. "Determinants of Argentinean tourism demand in Uruguay," Documentos de Trabajo (working papers) 14-17, Instituto de Economía - IECON.
    • Handle: RePEc:ulr:wpaper:dt-17-14
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    File URL: https://hdl.handle.net/20.500.12008/4257
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    References listed on IDEAS

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    1. Johansen, Soren, 1992. "Cointegration in partial systems and the efficiency of single-equation analysis," Journal of Econometrics, Elsevier, vol. 52(3), pages 389-402, June.
    2. 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.
    3. Robert Engle & Clive Granger, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    4. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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    Cited by:

    1. Abang Zainoren Abang Abdurahman & Wan Fairos Wan Yaacob & Syerina Azlin Md Nasir & Serah Jaya & Suhaili Mokhtar, 2022. "Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    2. Juan Gabriel Brida & Martín Olivera & Manuela Pulina, 2026. "Determinants of visitors’ flow in Uruguay: A SARIMAX approach," Tourism Economics, , vol. 32(1), pages 105-128, February.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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