Modelling and Forecasting Domestic Tourism. Case Study from Armenia

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.