Abstract
The purpose of this study is to investigate tourism demand and its determinants with panel data models. This paper empirically investigates the determinants of tourism demand for a statistically significant sample of eleven European countries for the years 1996–2015. Various potential determinants are investigated, including gross domestic product, consumer price index, the average per capita tourism expenditure, and the marketing expenses to promote tourism industry. The empirical results indicate that the explanatory variables affect the tourism demand of the EU countries and play an important role in strategies that affect total cost, demand, and structure of the market. As the marketing and advertising expenses revealed a dynamically interacts with tourist demand, their implications in decision making policies were discussed.
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References
Adhikari R, Agrawal RK (2012) Forecasting strong seasonal time series with artificial neural networks. J Sci Ind Res 71:657–666
Algieri B (2006) An econometric estimation of the demand for tourism: the case of Russia. Tour Econ 12:5–20
Andrew WP, Crange DA, Lee CK (1990) Forecasting hotel occupancy rates with time series models: a empirical analysis. Hosp Res J 14(2):173–181
Assaf AG, Li G, Song H, Tsionas MG (2019) Modelling and forecasting regional tourism demand using the Bayesian global vector autoregressive (BGVAR) model. J Travel Res 58(3):383–397
Au N, Law R (2002) Categorical classification of tourism dining. Ann Tour Res 29:819–833
Baldigara T, Mamula M (2015) Modelling international tourism demand using seasonal ARIMA models. Tour Hosp Manag 21(1):19–31
Brand D (1973) Travel demand forecasting: some foundations and a review. Highway Research Board Special Report, No. 143, Williamsburg, VA
Brida J, Risso W (2009) A dynamic panel data study of the German demand for tourism in south Tryrol. Tour Hosp Res 9(4):305–314
Cankurt S, Subasi A (2015) Developing tourism demand forecasting models using machine learning techniques with trend, seasonal and cyclic components. Balk J Electr Compt Eng 3(1):42–49
Cankurt S, Subasi A (2016) Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey. Turk J Electr Eng Comput Sci 24(5):3388–3404
Carter HR, Griffiths WE, Liitkepohl H, Tsounh CL (1988) Introduction to the theory and practice of econometrics, 2nd edn. John Wiley, New York
Chan Y (1979) Review and compilation of demand forecasting experiences: an aggregation of estimation procedures, Department of Transportation, No. DOT-P-30-80-25, Washington, DC
Chan F, Lim C, McAleer M (2005) Modelling multivariate international tourism demand and volatility. Tour Manag 26:459–471
Chinnakum W, Boonyasana P (2017) Modelling Thailand tourism demand: a dual generalized maximum entropy estimator for panel data regression models. Thai J Math. Special Issue on Entropy in Econometrics, 67–78
Cho V (2003) A comparison of three different approaches to tourist arrival forecasting. Tour Manag 24:323–330
Claveria O, Torra S (2014) Forecasting tourism demand to Catalonia: neural networks vs. time series models. Econ Model 36(C):220–228
Clements MP, Hendry DF (1998) Forecasting economic time series. Cambridge University Press, Cambridge
Constantino HA, Fernandes PO, Teixeira JP (2016) Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study. Rev Appl Manag Stud 14(2):113–124
Coshall JT (2005) A selection strategy for modelling UK tourism flows by air to European destinations. Tour Econ 11:141–158
Coshall J (2006) Time series analyses of UK outbound travel by air. J Travel Res 44:335–347
Crouch GI (1994) The study of international tourism demand: a review of practice. J Travel Res 33:41–54
Deng M, Athanasopoulos G (2011) Modelling Australian domestic and international inbound travel: a spatial-temporal approach. Tour Manag 32(5):1075–1084
Dritsakis N (2004) Cointegration analysis of German and British tourism demand for Greece. Tour Manag 25:111–119
Ghalehkhondabi I, Ardjmand E, Young WA, Weckman GR (2019) A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. J Tour Futures 5(1):75–93
Goh C, Law R (2002) Modelling and forecasting tourism demand for arrivals with stochastic no stationarity seasonality and intervention. Tour Manag 23:499–510
Goh C, Law R (2011) The methodological progress of tourism demand forecasting: a review of related literature. J Travel Tour Mark 28(3):296–317
Gunter U, Onder I (2015) Forecasting international city tourism demand for Paris: accuracy of uni- and multivariate models employing monthly data. Tour Manag 46:123–135
Han Z, Dubarry R, Sinclair MT (2006) Modelling US tourism demand for European destinations. Tour Manag 27:1–10
Hanafiah MHM, Harun MFM (2010) Tourism demand in Malaysia: a cross-sectional pool time-series analysis. Int J Trade Econ Financ 1(1):80–83
Karakitsiou A, Mavrommati A (2017) Machine learning methods in tourism demand forecasting: some evidence from Greece. MIBES Transactions 11(1):92–105
Karlaftis MG (2010) Critical review and analysis of air-travel demand: forecasting models, computational models, software engineering, and advanced technologies in air transportation: Next Generation Applications, IGI Global, 71–87
Khaidi SM, Abul N, Sarah NM (2019) Tourism demand forecasting—a review on the variables and models. J Phys Conf Ser 1366:012111. https://doi.org/10.1088/1742-6596/1366/1/012111
Kirilenko AP, Stepchenkova S (2018) Tourism research from its inception to present day: subject area, geography, and gender distributions. PLoS One 13(11):e0206820. https://doi.org/10.1371/journal.pone.0206820
Kon SC, Turner WL (2005) Neural network forecasting of tourism demand. Tour Econ 11:301–328
Kulendran N, Wilson K (2000) Modelling business travel. Tour Econ 6:47–59
Lathiras P, Siriopoulos C (1998) The demand for tourism to Greece: a Cointegration approach. Tour Econ 4(2):171–185
Li G, Wong KF, Song H, Witt SF (2006) Tourism demand forecasting: a time varying parameter error correction model. J Travel Res 45:175–185
Liang YH (2014) Forecasting models for Taiwanese tourism demand after allowance for mainland China tourists visiting Taiwan. Comput Ind Eng 74:111–119
Lim C (1997a) Review of international tourism demand models. Ann Tour Res 24:835–849
Lim C (1997b) An econometric classification and review of international tourism demand models. Tour Econ 3:69–81
Lim C (1999) A meta-analysis review of international tourism demand. J Travel Res 37:273–284
Lim C, McAleer M (2001) Cointegration analysis of quarterly tourism demand by Hong Kong and Singapore for Australia. Appl Econ 33:1599–1619
Maddala GS (1987) Recent developments in the econometrics of panel data analysis. Transp Res 21:303–326
Maddala GS (1991) To Pool or not to Pool: that is the question. J Quant Econ 7:255–263
Moro S, Rita P, Cortez P (2017) A text mining approach to analysing annals literature. Ann Tour Res 66:208–210
Oigenblick L, Kirschenbaum A (2002) Tourism and immigration. Ann Tour Res 29(4):1086–1100. https://doi.org/10.1016/s0160-7383(02)00023-3
Önder I (2017) Forecasting tourism demand with Google trends: accuracy comparison of countries versus cities. Int J Tour Res 19:648–660. https://doi.org/10.1002/jtr.2137
Palmer A, Montaño JJ, Sesé A (2006) Designing an artificial neural network for forecasting tourism time-series. Tour Manag 27:781–790
Rafidah A, Shabri A, Nurulhuda A, Suhaila Y (2017) A wavelet support vector machine combination model for Singapore tourist arrival to Malaysia. IOP Conf Ser Mater Sci Eng 226(1):012077
Rosselló J (2001) Forecasting turning points in international visitor arrivals in the Balearic Islands. Tour Econ 7:365–380
Sheldon PJ, Var T (1985) Tourism forecasting: a review of empirical research. J Forecast 4(2):183–195
Song H, Jiang Y (2019) Dynamic pricing decisions by potential tourists under uncertainty: the effects of tourism advertising. Tour Econ 25(2):213–234
Song H, Li G (2008) Tourism demand modelling and forecasting. Tour Res 29(2):203–220
Song H, Witt SF (2000) Tourism demand modelling and forecasting: modern econometric approaches. Pergamon, Cambridge
Song H, Wong KF (2003) Tourism demand modelling: a time-varying parameter approach. J Travel Res 42:57–64
Song H, Witt SF, Li G (2003) Modelling and forecasting the demand for Thai tourism. Tour Econ 9:363–387
Song H, Li G, Witt SF, Athanasopoulos G (2011) Forecasting tourist arrivals using time-varying parameter structural time series models. Int J Forecast 27(3):855–869
Surugiu C, Leitão NC, Surugiu MR (2011) A panel data modelling of international tourism demand: evidences for Romania. Ekonomska istraživanja/Econ Res 24(1):134–145
Tang J, Sriboonchitta S, Yuan X (2015) Forecasting inbound tourism demand to China using time series models and belief functions. In: Econometrics of risk. Springer, Cham, pp 329–341
Tsiakali K (2018) User-generated-content versus marketing-generated-content: personality and contentinfluence on traveler’s behavior. J Hosp Market Manag 27(8):946–972
Turner LW, Witt SF (2001) Forecasting tourism using univariate and multivariate structural time series models. Tour Econ 7:135–147
Vanhove N (1980) Forecasting in tourism. Tour Rev 35(3):2–7
Witt SF, Witt CA (1995) Forecasting tourism demand: a review of empirical research. Int J Forecast 11:447–475
Yang X, Pan P, Evans JA, Lv B (2015) Forecasting Chinese tourist volume with search engine data. Tour Manag 46:386–397
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Mavrommati, A., Pendaraki, K., Kontogeorgos, A. (2021). Tourism Demand Modelling and Forecasting: Evidence from EU Countries. In: Karanovic, G., Polychronidou, P., Karasavvoglou, A., Maskarin Ribaric, H. (eds) Tourism Management and Sustainable Development. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-74632-2_3
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