Forecasting accuracy evaluation of tourist arrivals

This paper evaluates the use of several parametric and nonparametric forecasting techniques for predicting tourism demand in selected European countries. We find that no single model can provide the best forecasts for any of the countries in the short-, medium- and long-run. The results, which are tested for statistical significance, enable forecasters to choose the most suitable model (from those evaluated here) based on the country and horizon for forecasting tourism demand. Should a single model be of interest, then, across all selected countries and horizons the Recurrent Singular Spectrum Analysis model is found to be the most efficient based on lowest overall forecasting error. Neural Networks and ARFIMA are found to be the worst performing models.     

Dynamic relationships between tourist arrivals,immigrants, and crimes in the United States

This paper investigates the dynamic relationships between tourist arrivals, immigrants, and crimes in the United States (U.S.) from 1984 to 2013. Our findings affirm the social structural perspectives (i.e. Merton's Strain theory and Social Disorganization theory), which contain a popular perception about the immigration and crime that they both go hand in hand. Results of bivariate analysis revealed that immigrants admitted by Europe, Mexico, and North America to U.S. are positively correlated with key crimes. Tourist arrivals positively influence crime rate only in short-run, which affirms the Opportunity Structural perspectives (i.e. Routine Activity and Hot Spot theories). Furthermore, immigrants positively influence tourist arrivals, which supports the Visiting Friends and Relatives (VFR) empirical perspective. Therefore, concerned authorities can focus on environmental design initiative in concerned areas (i.e. immigrants' communities and tourists' cities). Furthermore, future research and implications are discussed.     

Dynamic relationships among tourist arrivals,crime rate,and macroeconomic variables in Taiwan

This article uses Granger causality test and multivariate threshold model’s causality to test the existence of dynamic nonlinear relationship among tourist arrival, the crime rate, and macroeconomic variables 2001–2016 in Taiwan. The empirical result shows that tourist arrival has a negative effect on the crime rate and there is two-way causality between the exchange rate and tourist arrival. Where the CPI or the exchange rate as the threshold variables in the multivariate threshold model, the Regime 1 and linear results are same, but the Regime 2 results are different, which shows that not all variables have the same dynamic relation.