Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models

• Lindsay W. Turner ^[1] ; Stephen F. Witt ^{[1] [2]}
  1. [1] Victoria University

     Victoria University

     Australia

     
  2. [2] University of Surrey

     University of Surrey

     Guildford District, Reino Unido

     
• Localización: Tourism economics: the business and finance of tourism and recreation, ISSN 1354-8166, Vol. 7, Nº. 2, 2001, págs. 135-147
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Tourism demand forecasting remains an important research area, as the search for more accurate forecasting methods continues. In particular, there is concern that many methods do not improve upon a simple naïve process. Structural time series models have shown significant potential as both univariate and explanatory forecasting tools. Inbound tourism to New Zealand from Australia, Japan, the UK and the USA disaggregated by purpose of visit is analysed, using both univariate and multivariate structural time series models, and their respective forecasting accuracy is compared. The naïve ‘no change’ model is used for benchmark comparison purposes. The structural time series model outperforms the naïve process, but the causal structural time series model does not generate more accurate forecasts than the univariate model.