An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals
In this paper we provide an alternative approach to analyze the demand for international tourism in the Balearic Islands, Spain, by using a neural network model that incorporates time-varying conditional volatility. We consider daily air passenger arrivals to Palma de Mallorca, Ibiza and Mahon, which are located in the islands of Mallorca, Ibiza and Menorca, respectively, as a proxy for international tourism demand for the Balearic Islands. Spain is a world leader in terms of total international tourist arrivals and receipts, and Mallorca is one of the most popular destinations in Spain. For tourism management and marketing, it is essential to forecast high frequency international tourist demand accurately. As it is important to provide sensible international tourism demand forecast intervals, it is also necessary to model their variances accurately. Moreover, time-varying variances provide useful information regarding the risks associated with variations in international tourist arrivals.
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- van Dijk, D.J.C. & Terasvirta, T. & Franses, Ph.H.B.F., 2000.
"Smooth transition autoregressive models - A survey of recent developments,"
Econometric Institute Research Papers
EI 2000-23/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
- van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," SSE/EFI Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001.
- Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002.
"Building neural network models for time series: A statistical approach,"
SSE/EFI Working Paper Series in Economics and Finance
508, Stockholm School of Economics.
- Timo Teräsvirta & Marcelo C. Medeiros & Gianluigi Rech, 2006. "Building neural network models for time series: a statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 49-75.
- Marcelo C. Medeiros & Timo Terasvirta & Gianluigi Rech, 2002. "Building Neural Network Models for Time Series: A Statistical Approach," Textos para discussão 461, Department of Economics PUC-Rio (Brazil).
- repec:cup:cbooks:9780521252805 is not listed on IDEAS
- repec:cup:cbooks:9780521574464 is not listed on IDEAS
- Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
- Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005.
"Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination,"
International Journal of Forecasting,
Elsevier, vol. 21(4), pages 755-774.
- Timo Teräsvirta & Dick van Dijk & Marcelo Cunha Medeiros, 2004. "Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination," Textos para discussão 485, Department of Economics PUC-Rio (Brazil).
- Teräsvirta, Timo & van Dijk, Dick & Medeiros, Marcelo, 2004. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," SSE/EFI Working Paper Series in Economics and Finance 561, Stockholm School of Economics, revised 04 Nov 2004.
- Xiaohong Chen & Xiaotong Shen, 1998. "Sieve Extremum Estimates for Weakly Dependent Data," Econometrica, Econometric Society, vol. 66(2), pages 289-314, March.
- Ana Bartolome & Michael McAleer & Vicente Ramos & Javier Rey-Maquieira, 2009. "Risk Management for International Tourist Arrivals: An Application to the Balearic Islands, Spain," CIRJE F-Series CIRJE-F-665, CIRJE, Faculty of Economics, University of Tokyo.
- Mayte Suarez -Farinas & Carlos E. Pedreira & Marcelo C. Medeiros, 2004.
"Local Global Neural Networks: A New Approach for Nonlinear Time Series Modeling,"
Journal of the American Statistical Association,
American Statistical Association, vol. 99, pages 1092-1107, December.
- Mayte Suarez Farinãs & Carlos Eduardo Pedreira & Marcelo C. Medeiros, 2003. "Local-global neural networks: a new approach for nonlinear time series modelling," Textos para discussão 470, Department of Economics PUC-Rio (Brazil).
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
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