A comparative study on tourism demand in region Western Greece and its contiguous regions

Part of : Αρχείον οικονομικής ιστορίας ; Vol.XXVII, No.2, 2015, pages 19-35

Issue:
Pages:
19-35
Author:
Abstract:
In this work, a comparative study, based on tourism behavior of Western Greece region and its contiguous areas, is investigated. In particular, the inherent seasonality, as well as the future of tourism demand of region of Western Greece is explored, in comparison with its adjacent regions, which are the region of Epirus, Ionian Islands, Peloponnese and Central Greece. The study of seasonality utilizes the concentration ratio, while the forecasting models of each region are constructed using the well-known ARIMA models. The development of these models is based on a mechanistic methodology and the goodness of fit of the proposed models is confirmed using some appropriate statistical tests. The models are evaluated using a data set collected over a period of 8 years (2005-2012), describing overnight stays of the hotels of the corresponding regions. The main objective of the presented comparison is to study the region of Western Greece along with each of its contiguous regions, in respect of seasonality and future trend.
Subject:
Subject (LC):
Keywords:
Western Greece tourism, contiguous regions, overnight stays, tourism demand forecasting, forecasting model, time-series
Notes:
JEL Classification: C22, C53, This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: ARCHIMEDES III. Investing in knowledge society through the European Social Fund. The data that involves the monthly occupancy of all tourist accommodations of both foreign and domestic tourists came from the official records of the Hellenic Statistical Authority (EL. STAT., www.statistics.gr).
References (1):
  1. Allcock, J. (1994). Seasonality. In Tourism Marketing and Managenent Handbook, S. Witt and L. Moutinho, eds., pp. 86–92. New York: Prentice HallBox, G. & Jenkins, G. (1976). Time Series Analysis: Forecasting and Control(2nd ed.). San Francisco: Holden-Day.Burger, C.J.S.C., Dohnal, M., Kathrada, M., & Law, R. (2001). A practitionersguide to time-series methods for tourism demand forecasting - a case study of Durban, South Africa. Tourism Management, 22, 403–409.Chu, F. (2004) Forecasting tourism demand: a cubic polynomial approach.Tourism Management, 25, 209-218.Franses, P. H. (2004). Time series models for business and economic forecasting.Cambridge, University Press. Frees, E. W. (1996). Data Analysis Using Regression Models - The Business Perspective. New York, Prentice Hall.Hyndman R.J. (2015). Forecast: Forecasting functions for time series and linearmodels. R package version 6.1, Obtained through the Internet: http://github.com/robjhyndman/forecast, [accessed 03/08/2015].Hyndman, R.J. & Khandakar, Y. (2008). Automatic time series forecasting:the forecast package for R. Journal of Statistical Software, 27(3): 1–22.Hyndman R.J., & Koehler A.B. (2006). Another Look at Measures of ForecastAccuracy. International Journal of Forecasting, 22, 679–688.Makridakis, S., & Hibon, M. (1979). Accuracy of forecasting: An empiricalinvestigation. Journal of the Royal Statistical Society A, 142, 97-145.Panagopoulos, An. & Panagopoulos, Al. (2005). A time series method foroccupancy forecasting - A case study of West Greece. Archives of EconomicHistory, 17(1), 67–78.Psillakis, Z., Panagopoulos, Al. & Kanellopoulos, D. (2009). Low cost inferentialforecasting and tourism demand in accommodation industry. Tourismos, 4(2), 47–68.R Core Team (2015). R: A language and environment for statistical computing.R Foundation for Statistical Computing, Vienna, Austria.Smith, S. L. J. (1995). Tourism analysis: A handbook. London, Longman.Song, H.& Li, G. (2008). Tourism demand modelling and forecasting – A review of recent research. Tourism Management 29, 203–220.Tay, F.E.H., & Cao, L., (2001). Application of support vector machines in financial time series forecasting. Omega, 29, 309–317.