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Forecasting tourism demand using consumer expectations

Oscar Claveria (Assistant Professor at the Research Institute of Applied Economics (IREA), Department of Econometrics, Statistics and Spanish Economy, University of Barcelona, Barcelona, Spain)
Jordi Datzira (Consultor at Datzira Development Services, SL, Barcelona, Spain)

Tourism Review

ISSN: 1660-5373

Article publication date: 11 May 2010

2838

Abstract

Purpose

There is a lack of studies on tourism demand forecasting that use non‐linear models. The aim of this paper is to introduce consumer expectations in time‐series models in order to analyse their usefulness to forecast tourism demand.

Design/methodology/approach

The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, 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 (one, two, three, six 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. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed.

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

This is the first study on tourism demand focusing specifically on Catalonia. 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 fills this gap and analyses forecasting performance at a regional level.

Keywords

Citation

Claveria, O. and Datzira, J. (2010), "Forecasting tourism demand using consumer expectations", Tourism Review, Vol. 65 No. 1, pp. 18-36. https://doi.org/10.1108/16605371011040889

Publisher

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Emerald Group Publishing Limited

Copyright © 2010, Emerald Group Publishing Limited

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