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Forecasting the Volume of Tourism Services in Uzbekistan

Author

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  • Bahodirhon Safarov

    (Department of Digital Economy, Faculty of Human Resources Management, Samarkand State University, Samarkand 140105, Uzbekistan)

  • Hisham Mohammad Al-Smadi

    (Department of Financial and Administrative Sciences, Ajloun College, AL-Balqa Applied University, Ajloun 26816, Jordan)

  • Makhina Buzrukova

    (Department of Digital Economy, Faculty of Human Resources Management, Samarkand State University, Samarkand 140105, Uzbekistan)

  • Bekzot Janzakov

    (Department of Food and Agricultural Economics, Faculty of Economics, Samarkand Branch of Tashkent State University of Economics, Samarkand 140103, Uzbekistan)

  • Alexandru Ilieş

    (Department of Geography, Tourism and Territorial Planning, Faculty of Geography, Tourism and Sport, University of Oradea, 1 Universitatii Street, 410087 Oradea, Romania)

  • Vasile Grama

    (Department of Geography, Tourism and Territorial Planning, Faculty of Geography, Tourism and Sport, University of Oradea, 1 Universitatii Street, 410087 Oradea, Romania)

  • Dorina Camelia Ilieș

    (Department of Geography, Tourism and Territorial Planning, Faculty of Geography, Tourism and Sport, University of Oradea, 1 Universitatii Street, 410087 Oradea, Romania)

  • Katalin Csobán Vargáné

    (Department of Tourism and Hospitality Management, Faculty of Economics and Business, University of Debrecen, H-4032 Debrecen, Hungary)

  • Lóránt Dénes Dávid

    (Institute of Rural Development and Sustainable Economy, The Hungarian University of Agriculture and Life Sciences (MATE), H-2100 Godollo, Hungary)

Abstract

The aim of the present research is to assess the impact of factors such as welfare, infrastructure, security, and the environment on inbound tourism as well as to develop its forecast. Six proxy indicators of the above-mentioned factors were selected as variables, namely, welfare (real GDP per capita, life expectancy, consumer price index), infrastructure (passenger transportation volume), security (total recorded crimes), and the environment (CO 2 emissions). We used a time series-univariate ARIMA model to forecast the inbound tourism in the Republic of Uzbekistan, and applied the ARDL model to assess the impact of lagged real GDP per capita on inbound tourism in both the short and long terms. The results of our research show that security and welfare significantly affect the inflow of foreign tourists in the country, along with the impact of the COVID-19 pandemic crisis, the effects of which are expected to persist beyond 2026.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7762-:d:847867
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    References listed on IDEAS

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    1. Edwin J. Elton & Martin J. Gruber & Jonathan Spitzer, 2006. "Improved Estimates of Correlation Coefficients and their Impact on Optimum Portfolios," European Financial Management, European Financial Management Association, vol. 12(3), pages 303-318, June.
    2. Biagi, Bianca & Brandono, Maria Giovanna & Detotto, Claudio, 2012. "The effect of tourism on crime in Italy: A dynamic panel approach," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 6, pages 1-24.
    3. 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.
    4. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    5. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
    6. 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.
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    Cited by:

    1. Husanjon Juraturgunov & Murodjon Raimkulov & Young-joo Ahn & Eunice Minjoo Kang, 2023. "World Heritage Site Tourism and Destination Loyalty along the Silk Road: A Study of U.S. Travelers in Uzbekistan," Sustainability, MDPI, vol. 15(13), pages 1-21, June.

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