Abstract

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of modelling and forecasting domestic tourism. During Covid-19 many countries tried to develop domestic tourism as an alternative to inbound tourism. In Armenia domestic tourism has grown recently, and in 2020 the decrease was 33% compared to last year. The main purpose of the research is to model and forecast domestic tourism growth in Armenia. Systematization of the literary sources and approaches for solving the problem indicates that many models and different variables are used to forecast tourism development. Methodological tools of the research methods were static and dynamic models, years of research were 2001-2020, quarterly data. The paper presents the results of an empirical analysis, which showed that with the static regression analysis a 1% change in GDP will lead to a change of 4.43% in the number of domestic tourists, a 1% change in the CPI will lead to a 14.55% change in the number of domestic tourists. For dynamic modelling we used 12 competing short-term forecasting models. Based on the recursive and rolling forecast simulation results we concluded that out-of-sample forecasts obtained by the small-scale models outperform forecasts obtained by the large-scale models at all forecast horizons. So, the forecasts of the domestic tourists’ growth obtained by small-scale models are more appropriate from the practical point of view. Then, in order to check whether the differences in forecasts obtained by the different models are statistically significant we applied Diebold-Mariano test. Based on the results of this test we concluded that there is not sufficient evidence to favor large-scale over small-scale models. This means that the forecast results obtained for domestic tourist growth by using the small scale models would not be statistically different from the results obtained by the large scale models. Based on the analysis, the forecasted values for domestic tourists for the future years were determined. The results of the research can be useful for state bodies, as well as private organizations, and for everybody who wants to model and forecast tourism development.

Keywords: domestic tourism, forecasting models, recursive and rolling regression, statistics, data.

JEL Classification: Z30, C53, C50.

Cite as: Poghosyan, K., Tovmasyan, G. (2021). Modelling and Forecasting Domestic Tourism. Case Study from Armenia. SocioEconomic Challenges, 5(2), 96-110. https://doi.org/10.21272/sec.5(2).96-110.2021

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Alessi L., Barigozzi M., Capasso M. (2010). Improved penalization for determining the number of factors in approximate factor models. Statistics and Probability Letters, Elsevier. Available at: [CrossRef].
2. Alvarez R., Camacho M., Perez-Quiros G. (2016). Aggregate versus disaggregate information in dynamic factor models. International Journal of Forecasting, 32(2), 680-694. Available at: [Crossref].
3. Arratibel O., Kamps C., Leiner-Killinger N. (2009). Inflation forecasting in the new EU member states. ECB Working Paper No. 1015. Available at: [Link].
4. Barhoumi K., Darné O., Ferrara L. (2014). Dynamic factor models: A review of the literature. OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, 2013(2), 73-107. Available at: [Crossref].
5. Bai J., Ng S. (2002). Determining the number of factors in approximate factor models, Econometrica, 70(1), 191-221. Available at: [Link].
6. Bento J. P. C. (2016). Tourism and economic growth in Portugal: an empirical investigation of causal links. Tourism & Management Studies, 12(1), 164-171. Available at: [Crossref].
7. Brownlee J․ (2019). How To Backtest Machine Learning Models for Time Series Forecasting. Available at: [Link].
8. Diebold F., Mariano R. (1995). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 13(3), 253-263. Available at: [Link].
9. Diebold F. (2015). Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests. Journal of Business & Economic Statistics, 33(1), 1-1, Available at: [Crossref].
10. Doz C., Giannone D., Reichlin L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205. Available at: [Crossref].
11. Doz C., Giannone D., Reichlin L. (2012). A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models. The Review of Economics and Statistics, 94(4), 1014-1024. Available at: [Link].
12. Dritsakis N., Athanasiadis S. (2000). An Econometric Model of Tourist Demand. Journal of Hospitality & Leisure Marketing, 7(2), 39-49. Available at: [Crossref].
13. Forni M., Hallin M., Lippi M., Reichlin L. (2000). The Generalized Dynamic-Factor Model: Identification And Estimation. The Review of Economics and Statistics, 82(4), 540-554. Available at: [Crossref].
14. Forni M., Hallin M., Lippi M., Reichlin L. (2005). The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting. Journal of the American Statistical Association, 100, 830-840. Available at: [Crossref].
15. Hamal, K. (1996). Modelling Domestic Holiday Tourism Demand in Australia: Problems and Solutions. Asia Pacific Journal of Tourism Research, 1, 35-46. Available at: [Crossref].
16. Ghalehkhondabi I., Ardjmand E., Young W. A., Weckman G. R. (2019). A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. Journal of Tourism Futures. Available at: [Link].
17. Hamilton J.D. (1994). Time Series Analysis, Princeton University Press, 799 p. Available at: [Link].
18. Hoffman, B. (2008). Do monetary indicators lead euro area inflation? ECB Working Paper No. 867. Available at: [Link].
19. Jansen, W. Jos., Xiaowen J., de Winter, Jasper M. (2016). Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts. International Journal of Forecasting, 32(2), 411-436. Available at: [Crossref].
20. Kim H. J., Chen M.H., and Jangc S.S. (2006). Tourism expansion and economic development: The case of Taiwan, Tourism Management, 27(5), 925–933. Available at: [Link].
21. Kočenda E., Poghosyan K., Nowcasting Real GDP Growth: Comparison between Old and New EU Countries. Eastern European Economics, 58(3), 197-220. Available at: [Crossref].
22. Li G., Song H., Witt S.F. (2006). Forecasting Tourism Demand Using Econometric Models, in Buhalis D., Costa    (eds.). Tourism management  dynamics: Trends,  Management  and Tools. Butterworth-Heinemann: Oxford, 2006, pp. 219-228, Available at: [Crossref].
23. Li G., (2009). Tourism Demand Modelling and Forecasting: A Review of Literature related to Greater China, Journal of China Tourism Research, 5, 2-40. Available at: [Crossref].
24. Pirschel I., Wolters M. (2014). Forecasting German Key Macroeconomic Variables Using Large Dataset Methods, Kiel Working Papers1925, Kiel Institute for the World Economy. Available at: [Link].
25. Poghosyan K., Poghosyan R. (2020). On the applicability of dynamic factor models for forecasting real GDP growth in Armenia, Applied Econometrics, 61, 28–46. Available at: [Crossref].
26. Rangan G., Kabundi A. (2011). A large factor model for forecasting macroeconomic variables in South Africa. International Journal of Forecasting, 27(4), 1076-1088. Available at: [Crossref].
27. Song H., Smeral E., Li G., Chen J. L. (2008). Tourism Forecasting: Accuracy of Alternative Econometric Models Revisited, WIFO Working Papers, No. 326, Austrian Institute of Economic Research (WIFO), Vienna. Available at: [Link].
28. Stock J., Watson M. (2002). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business & Economic Statistics, American Statistical Association, 20(2), 147-162. Available at: [Crossref].
29. The socio-economic situation in the RA (2021). January, p. 115. Available at: [Link].
30. Yap G. C. (2010). An econometric analysis of Australian domestic tourism demand, 311p. Edith Cowan University. Available at: [Link].