What is it about?
The aim of this paper is to introduce consumer expectations in linear and non-linear time series models in order to analyse their usefulness to forecast tourism demand. The paper forecasts tourism demand to Catalonia from the main four visitor markets (France, the UK, Germany and Italy)
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Why is it important?
When comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models (AR and SETAR models). In all cases, forecasts of tourist arrivals are better than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed.
Perspectives
This is the first study on tourism demand forecasting that incorporates consumer expectations in non-linear models such as self-exciting threshold autoregressions and Markov switching regime models. This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting.
Oscar Claveria
AQR-IREA, Univeristy of Barcelona
Read the Original
This page is a summary of: Forecasting tourism demand using consumer expectations, The Tourist Review, May 2010, Emerald,
DOI: 10.1108/16605371011040889.
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