Affiliation:
1. AMBERD Research Center, Armenian State University of Economics, Lecturer at Management Chair, Public Administration Academy
Abstract
ABSTRACT. Armenia has great opportunities and resources for tourism development. In 2021, the number of domestic tourists in Armenia was 1595826, an increase of 52.6% compared to 2020. In 2022, the number of domestic tourists was higher than in previous years - 1929940. The article evaluates the factors which influence domestic tourism demand with the use of ordinary and weighted least squares regression models. The main variables discussed in the models are: real GDP growth rate, consumer price index, average cost of tour packages from Armenia to other countries that can be considered as an alternative to domestic tourism, and dummy variables. The time series are based on quarterly data from 2005-2021. According to the analysis results, 1% increase in GDP will lead to a 0.22% increase in the number of domestic tourists, 1% increase in prices will lead to a decrease in the number of domestic tourists by about 0.12%, and 1% increase in foreign tour package prices will increase the number of domestic tourists by about 0.14%. The article also presents some suggestions for domestic tourism development in Armenia. The results may be useful for similar studies, as well as for state and private sectors conducting forecasts, planning, etc.
Publisher
Centre of Sociological Research, NGO
Subject
General Economics, Econometrics and Finance,Sociology and Political Science
Reference30 articles.
1. Allen, D., & Yap, G. (2009). Modelling Australian domestic tourism demand: a panel data approach. Joondalup, Australia: Edith Cowan University, Retrieved May 5, 2022, from https://ro.ecu.edu.au/cgi/viewcontent.cgi?article=7986&context=ecuworks
2. Anderson, M., Sharfi, K. & Gholston, S. (2006),. “Direct demand forecasting model for small urban communities using multiple linear regression”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 1981 No. 1, 114-117.
3. Blainey, S. & Mulley, C. (2013). “Using geographically weighted regression to forecast rail demand in the Sydney region”, presented at the 36th Australasian Transport Research Forum Conference, Brisbane, pp. 2-4.
4. Breusch-Godfrey Test, Retrieved February 5, 2023, from https://real-statistics.com/multiple-regression/autocorrelation/breusch-godfrey-test/
5. Cai, L. A., Hu, B., Feng, R. (2002). Domestic tourism demand in China’s urban centres: Empirical analyses and marketing implications, Journal of Vocational Marketing, Vol. 8, Issue 1,. 64-74, https://doi.org/10.1177/135676670200800107