THE RELATIONSHIP BETWEEN TOURIST ARRIVALS AND ACCOMODATION IN ROMANIAN REGIONS. A PANEL DATA APPROACH

Mihaela Simionescu

Abstract


This research is a novelty for the literature regarding tourism demand modeling in Romania. Panel data approach has been applied to analyze the relationship between tourist arrivals and the establishments of touristsreception with functions of tourists' accommodation in the eight Romanian regions (Nord-West region, Central region, Nord-East region, South-East region, South-Muntenia region, Bucharest-Ilfov region, South-West Oltenia and West regions). According to panel VAR Granger causality test, the establishments of touristsreception with functions of tourists' accommodation are a cause for tourist arrivals, but the relationship is not reciprocal. A valid fixed effects model was built and an increase in the number of establishments of touristsreception with functions of tourists' accommodation with one establishment increased in average the number of tourist arrivals with around 293 people in Romanian regions over the period 1990-2015. According to panel vector-autoregressive model, the tourist arrivals in the current period were positively influenced by the establishments of touristsreception with functions of tourists' accommodation and tourist arrivals in the previous period.


Keywords


tourist arrivals, accommodation, panel data, Granger causality

References


Akın, M. (2015) A novel approach to model selection in tourism demand modeling, Tourism Management, no. 48, pp. 64-72.

Cankurt, S., Subasi, A. (2015) Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components, Balkan Journal of Electrical and Computer Engineering, 3(1).

Cárdenas-García, P. J., Sánchez-Rivero, M., Pulido-Fernández, J. I. (2015) Does Tourism Growth Influence Economic Development?, Journal of Travel Research, 54(2), pp. 206-221.

Chan, F., Lim, C., McAleer, M. (2005) Modelling multivariate international tourism demand and volatility, Tourism Management, 26(3), pp. 459-471.

Chen, J. S., Hsu, C. H. (2001) Developing and validating a riverboat gaming impact scale, Annals of Tourism Research, 28(2), pp. 459-476.

Chu, F. L. (1998) Forecasting tourism: a combined approach, Tourism Management, 19(6), pp. 515-520.

Chu, F. L. (2004) Forecasting tourism demand: a cubic polynomial approach, Tourism Management, 25(2), pp. 209-218.

De Mello, M. M., Fortuna, N. (2005) Testing alternative dynamic systems for modelling tourism demand, Tourism Economics, 11, pp. 517-537.

Divisekera, S. (2003) A model of demand for international tourism, Annals of tourism research, 30(1), pp. 31-49.

DR Vaughan, D., Farr, H., Slee, D. R. (2000) Estimating and interpreting the local economic benefits of visitor spending: An explanation, Leisure studies, 19(2), pp. 95-118.

Eilat, Y., Einav, L. (2003) The Determinants of International Tourism: A Three-Dimensional Panel Data Analysis, Retrieved January 22, 2007.

Frechtling, D. (2012) Forecasting tourism demand, Routledge.

Gasmi, A., Sassi, S. (2015) International tourism demand in Tunisia: Evidence from dynamic panel data model, Economics Bulletin, 35(1), pp. 507-518.

Gelan, A. (2003) Local economic impacts: The British open, Annals of tourism research, 30(2), pp. 406-425.

Hernández-López, M., Cáceres-Hernández, J. J. (2007) Forecasting tourists' characteristics by a genetic algorithm with a transition matrix, Tourism Management, 28, pp. 290-297.

Kulendran, N., Witt, S. F. (2001) Cointegration versus least squares regression, Annals of Tourism Research, 28(2), pp. 291-311.

Kulendran, N., Wong K. K. F. (2005) Modeling Seasonality in Tourism Forecasting, Journal of Travel Research, 44, pp. 163-170.

Leitão, N. C. (2015) Modelling portuguese tourism demand: a panel data approach, International Journal of Engineering and Industrial Management, (1), pp. 45-58.

Li, G., Wong, K. F., Song, H., Witt, S. F. (2006) Tourism demand forecasting: A time varying parameter error correction model, Journal of Travel Research, 45, pp. 175-185.

Lim, C., McAleer, M. (2001) Monthly seasonal variations: Asian tourism to Australia, Annals of Tourism Research, 28(1), pp. 68-82.

Mangion, M. L., Durbarry, R., Sinclair, M. T. (2005) Tourism competitiveness: price and quality Tourism competitiveness: price and quality, Tourism Economics, 11, pp. 45-68.

Naudé, W. A., Saayman, A. (2005) Determinants of tourist arrivals in Africa: a panel data regression analysis. Tourism Economics, 11(3), pp. 365-391.

Pai, P. F., Hong, W. C., Chang, P. T., Chen, C. T. (2006), The application of support vector machines to forecast tourist arrivals in Barbados: An empirical study, International Journal of Management, 23, pp. 375-385.

Pratt, S. (2015) The economic impact of tourism in SIDS, Annals of Tourism Research, 52, pp. 148-160.

Roget, F. M., Gonzalez, X. A. R. (2006) Rural tourism demand in Galicia, Spain, Tourism Economics, 12, pp. 21-31.

Saayman, A., Saayman, M., Naudé, W. A. (2000) The impact of tourist spending in South Africa: Spatial implications, South African journal of economic and management sciences, 3(3), pp. 369-386.

Saayman, M., Saayman, A., Rhodes, J. A. (2001) Domestic tourist spending and economic development: the case of the North West Province, Development Southern Africa, 18(4), pp. 443-455.

Song, H., Li, G. (2008) Tourism demand modelling and forecasting—A review of recent research, Tourism Management, 29(2), pp. 203-220.

Song, H., Turner, L. (2006) Tourism demand forecasting. International handbook on the economics of tourism, 89.

Tang, C. F., Tan, E. C. (2015) The determinants of inbound tourism demand in Malaysia: another visit with non-stationary panel data approach, Anatolia, 1-12.

Wong, K. K. F., Song, H., Chon, K. S. (2006) Bayesian models for tourism demand forecasting, Tourism Management, 27, pp. 773-780.


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