J. Datzire’s scientific contributions

Purpose.- To fill the gap due to the lack of studies on tourism demand forecasting that use non-linear models. The aim of the paper is to introduce consumer expectations in time-series models in order to analyze their usefulness to forecast tourism demand. This is the first study on tourism demand forecasting for Catalonia. Design/methodology/approach - The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the United Kingdom, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (1, 2, 3, 6 and 12 months). Findings - Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. We have found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analyzed. Research limitations/implications - This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting. Originality/value - To date, there have been no studies on tourism demand forecasting that use non-linear models such as self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fulfils this gap and analyzes their forecasting performance at a regional level.