Forecasting International Tourism Regional Expenditure

Abstract

The vast majority of tourism forecasting studies have centered on tourist arrivals at an aggregated level. Little research has been done of forecasting tourist expenditure at a national level let alone at a regional level. This study uses expenditure data to assess the relative economic impact of tourism into regional areas. By comparing five time-series models (the Naïve, Holt, ARMA and Basic Structural Model (BSM) with and without intervention), and three econometric models (the Vector Autoregressive (VAR) model and the Time Varying Parameter (TVP) with and without intervention), the study sought to find the most accurate model for forecasting tourism expenditure two years ahead for each of the 31 provinces of mainland China. The results show that TVP models outperform other time series and econometric models. The research also provides practical management outcomes by providing methods for forecasting tourist expenditure as an indicator of economic growth in China’s provinces. The research concludes with the findings on the most appropriate model for regional forecasting and potential new variables suitable at the regional level.