The Effects of Selected Macroeconomic Variables on Tourism Demand for the South Moravian Region of the Czech Republic from Germany, Poland, Austria, and Slovakia

• Authors: Tomáš Jeřábek

Title variants

Wpływ wybranych zmiennych makroekonomicznych na popyt na usługi turystyczne w regionie południowych Moraw (Czechy) wśród turystów z Niemiec, Polski, Austrii i Słowacji

• Languages of publication: EN

Abstracts

EN

International tourism is one of the most important sectors of the open economy. The aim of this paper is to investigate the effects that income as gross domestic product, tourism price as the real exchange rate, and travel cost as the price of Brent crude oil have on inbound tourism demand (tourist arrivals) from Poland, Slovakia, Germany, and Austria in the South Moravian Region of the Czech Republic over the period 2002:M1–2018:M5. The number of Polish, German, Slovak and Austrian tourists accommodated in collective accommodation establishments within the South Moravian Region as a dependent variable are considered. To achieve this aim, cointegration analysis under the VECM approach is applied. The results show that Slovak, Polish, Austrian and German tourists respond positively to their income changes. Austrian and Slovak tourists respond negatively to changes in tourism prices in the Czech Republic. Tourists from Germany and Poland do not respond to changes in the Czech price level since their elasticity coefficients are non‑significant. German, Austrian and Slovak tourists respond negatively to transportation cost changes. Polish tourists do not respond to transport cost changes since their elasticity coefficient is non‑significant.

PL

Turystyka międzynarodowa jest jednym z najważniejszych sektorów gospodarki otwartej. Celem tego artykułu jest zbadanie wpływu dochodu wyrażonego produktem krajowym brutto, kosztów usług turystycznych wyrażonych realnym kursem walutowym i kosztów podróży wyrażonych ceną ropy naftowej Brent, na przyjazdy turystów z Polski, Słowacji, Niemiec i Austrii do regionu południowych Moraw w Republice Czeskiej w okresie 2002:M1–2018:M5. Liczbę turystów polskich, niemieckich, słowackich i austriackich zakwaterowanych w obiektach zbiorowego zakwaterowania w regionie południowomorawskim przyjęto jako zmienną zależną. Aby osiągnąć cel badania, dokonano analizy kointegracji przy pomocy modelu VECM. Wyniki analizy wskazują, że słowaccy, polscy, austriaccy i niemieccy turyści pozytywnie reagują na zmiany poziomu ich dochodów. Austriaccy i słowaccy turyści reagują negatywnie na zmiany cen usług turystycznych w Czechach. Turyści z Niemiec i Polski nie reagują na zmiany cen w Czechach, gdyż w ich przypadku współczynniki elastyczności są nieistotne. Turyści z Niemiec, Austrii i Słowacji negatywnie reagują na zmiany kosztów transportu. Polscy turyści nie reagują na zmiany kosztów transportu gdyż w ich przypadku współczynnik elastyczności jest również nieistotny.

Keywords

EN

tourism demand; real exchange rate; industrial production index; crude oil price; VECM; cointegration

PL

popyt na usługi turystyczne; realny kurs walutowy; indeks produkcji przemysłowej; cena ropy naftowej; VECM; kointegracja; popyt na usługi turystyczne; realny kurs walutowy; indeks produkcji przemysłowej; cena ropy naftowej; VECM; kointegracja

• Publisher: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
• Journal: Comparative Economic Research. Central and Eastern Europe
• Year: 2019
• Volume: 22
• Issue: 3
• Pages: 25-43

Dates

published

2019-08-19

Contributors

author

Tomáš Jeřábek

• College of Business and Hotel Management, Department of Economy and Management, Brno, Czech Republic

References

• Asemota, O.J., Bala, D.A. (2012), Modelling Tourism Demand in Japan using Cointegration and Error Correction Model, “International Review of Business Research Papers”, Vol. 8, No. 2.
• Babecka, O. (2013), Competitiveness and determinants of tourism, [in:] L. Komarek (ed.), Global economic outlook‑July, https://www.cnb.cz/miranda2/export/sites/www.cnb.cz/en/monetary_policy/geo/geo_2013/gev_2013_07_en.pdf (accessed: 28.09.2018).
• Barros, C.P., Machado, L.P. (2010), The length of stay in tourism, “Annals of Tourism Research”, Vol. 37, No. 3, DOI: 10.1016/j.annals.2009.12.005.
• Brida, J.G., Scuderi, R. (2012), Determinants of tourist expenditure: a review of microeconometric models, Working paper Munich Personal RePEc Archive, https://mpra.ub.uni‑muenchen.de/38468/1/MPRA_paper_38468.pdf (accessed: 17.09.2018).
• Dickey, D., Fuller, W. (1979), Distribution of the estimators for autoregressive time series with a Unit Root, “Journal of the American Statistical Association”, Vol. 74, No. 366.
• Dritsakis, N. (2004), Tourism as a Long‑run Economic Growth Factor: An Empirical Investigation for Greece using Causality Analysis, “Tourism Economics”, Vol. 10, No. 3, DOI: 10.5367/0000000041895094.
• Enders, W. (2010), Applied econometric time series, Wiley, Hoboken.
• Falk, M. (2015), The sensitivity of tourism demand to exchange rate changes: an application to Swiss overnight stays in Austrian mountain villages during the winter season, “Current Issues in Tourism”, Vol. 18, No. 1, DOI: 10.1080/13683500.2013.810610.
• Garin‑Munoz, T. (2006), Inbound International Tourism to Canary Islands: A Dynamic Panel Data Model, “Tourism Management”, Vol. 27, No. 2, DOI: 10.1016/j.tourman.2004.10.002.
• Garratt, A., Lee, K., Pesaran, M.H., Shin, Y. (2012), Global and national macroeconometric modelling: A long‑run structural approach, Oxford University Press, London.
• Gautam, B.P. (2014), Economic Dynamics of Tourism in Nepal: A VECM Approach, “Munich Personal RePEc Archive”, https://mpra.ub.uni‑muenchen.de/58102/8/MPRA_paper_58102.pdf (accessed: 21.07.2018).
• Gerhard, R., Sedillot, F. (2003), Short‑Term Estimates of Euro Area Real GDP by Means of Monthly Data, “European Central Bank Working Paper Series’ https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp276.pdf?ef693dab796b365f5625a07d2dfcc152 (accessed: 16.09.2018).
• Gokovali, U., Bahar, O., Kozak, M. (2007), Determinants of Length of Stay: A Practical Use of Survival Analysis, “Tourism Management”, Vol. 28, No. 3, DOI: 10.1016/j.tourman.2006.05.004.
• Hor, C. (2015), Modelling International Tourism Demand in Cambodia: ARDL Model, “Review of Integrative Business &Economics Research”, Vol. 4, No. 4.
• Johansen, S., Juselius, K. (1990), Maximum Likelihood Estimation and Inference on Cointegration with Application to the Demand for Money, “Oxford Bulletin of Economics and Statistics”, Vol. 52, No. 2.
• Lin, H., Liu, L.‑M., Tseng, Y.‑H., Su. Y.‑W. (2011), Taiwan’s International tourism: a Time Series Analysis with Calendar Effects and Joint Outlier Adjustments, “International Journal of Tourism Research”, Vol. 13, No. 1, DOI: 10.1002/jtr.779. Martinez‑Garcia, E., Raya, J.M. (2008), Length of Stay for Low‑Cost Tourism, “Tourism
• Managemen”, Vol. 29, No. 6, DOI: 10.1016/j.tourman.2008.02.011.
• Massidda, C., Etzo, I. (2012), The Determinants of Italian Domestic Tourism: A Panel Data Analysis, “Tourism Management”, Vol. 33, No. 3, DOI: 10.1016/j.tourman. 2011.06.017.
• Muchapondwa, E., Pimhidzai, O. (2011), Modelling International Tourism Demand for Zimbabwe, “International Journal of Business and Social Science”, Vol. 2, No. 2.
• Ongan, S., Isik, C., Ozdemir, D. (2017), The Effects of Real Exchange Rates and Income on International Tourism Demand for the USA from Some European Union Countries, “Economies”, Vol. 5, No. 4, DOI: 10.3390/economies5040051.
• OSN (2010), International Recommendations for Tourism Statistics 2008, http://unstats.un.org/unsd/publication/Seriesm/SeriesM_83rev1e.pdf (accessed: 14.08.2018).
• Seetaram, N. (2010), Use of dynamic panel cointegration approach to model international arrivals to Australia, “Journal of Travel Research”, Vol. 49, No. 1, DOI: 10.1177/0047287509346992.
• Seo, J.H., Park, S.Y., Yu, L. (2009), The Analysis of the Relationships of Korean Outbound Tourism Demand, “Tourism Management”, Vol. 30, No. 1, DOI: 10.1016/j.tourman.2008.10.013.
• Sims, C.A. (1980), Macroeconomics and Reality, “Econometrica”, Vol. 48, No. 1.
• Song, H., Li, G. (2008), Tourism Demand Modelling and Forecasting: a Review of Recent Research, “Tourism Management”, Vol. 29, No. 2, DOI: 10.1016/j.tourman. 2007.07.016.
• Song, H., Kim, J.H., Yang, S. (2010), Confidence intervals for tourism demand elasticity, “Annals of Tourism Research”, Vol. 37, No. 2, DOI: 10.1016/j.annals.2009.10.002.
• Song, H., Witt, S., Li, G. (2008), The Advanced Econometrics of Tourism Demand, Taylor & Francis, New York.
• Surugiu, C., Leitao, N.C., Surugiu, M.R. (2011), A Panel Data Modelling of International Tourism Demand: Evidences for Romania, “Economic Research‑Ekonomska Istraživanja”, Vol. 24, No. 1, DOI: 10.1080/1331677X.2011.11517450.
• United Nations (2010), International Recommendations for Tourism Statistics 2008, http://unstats.un.org/unsd/publication/Seriesm/SeriesM_83rev1e.pdf (accessed: 19.06.2018).
• UNWTO (2018), UNWTO Tourism Highlights, https://www.eunwto.org/doi/pdf/10.18111/9789284419876 (accessed: 29.07.2018).
• Wong, K., Song, H., Chon, K.K. (2006), Bayesian Models for Tourism Demand Forecasting, “Tourism Management”, Vol. 27, No. 5, DOI: 10.1016/j.tourman.2005.05.017.

Identifiers

DOI

10.2478/cer-2019-0021