Forecasting tourism demand cycles: A Markov switching approach

Forecasting tourism demand taking cyclical patterns into account has gained popularity. The focus has traditionally been on univariate time-series models, which does not consider the influence of varying elasticities in upswings and downswings. This article aims to fill this void by forecasting tourism demand to five destinations, using a Markov-switching approach. Rolling forecasts using two variants of the Markov-Switching model are compared to traditional models, including autoregressive distributed lag, autoregressive integrated moving average and naïve forecasts. The findings show that accounting for asymmetric behaviour in the tourism cycle itself or in price and income elasticities improves forecasts, especially for long-haul destinations.