Forescasting British tourist arrivals in the Balearic Island using meterological variables

Marcos Alvarez Díaz ^[1] ; Jaume Rosselló Nadal ^[2]
1. [1] Universidade de Vigo
  
  Universidade de Vigo
  
  Vigo, España
2. [2] Universitat de les Illes Balears
  
  Universitat de les Illes Balears
  
  Palma de Mallorca, España
Localización: Tourism economics: the business and finance of tourism and recreation, ISSN 1354-8166, Vol. 16, Nº. Extra 1, 2010 (Ejemplar dedicado a: Special Focus: The Economics of Tourism – New Directions), págs. 153-168
Idioma: inglés
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Resumen
- This paper investigates the possibility of improving the predictive ability of a tourism demand model with meteorological explanatory variables. The authors use as a case study the monthly British tourism demand for the Balearic Islands (Spain). For this purpose, a transfer function model and causal artificial neural network are fitted. The results are compared with those obtained by non-causal methods: an ARIMA model and an autoregressive neural network.
  
  The results indicate that incorporating meteorological variables can increase predictive power, although the most accurate prediction is obtained using a non-causal model - specifically, an autoregressive neural network.