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Forecasting British Tourist Arrivals in the Balearic Islands Using Meteorological Variables

Author

Listed:
  • Marcos à lvarez-Díaz

    (Department of Economics, University of Vigo, Lagoas-Marcosende s/n, 36200 Vigo, Spain)

  • Jaume Rosselló-Nadal

    (Departament d'Economia Aplicada, Centre de Recerca Econòmica (UIB·Sa Nostra), Carretera Valldemossa km 7.5, 07122 Palma de Mallorca, Spain)

Abstract

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.

Suggested Citation

  • Marcos à lvarez-Díaz & Jaume Rosselló-Nadal, 2010. "Forecasting British Tourist Arrivals in the Balearic Islands Using Meteorological Variables," Tourism Economics, , vol. 16(1), pages 153-168, March.
    • Handle: RePEc:sae:toueco:v:16:y:2010:i:1:p:153-168
    DOI: 10.5367/000000010790872079
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    References listed on IDEAS

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    3. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
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