An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals

Marcelo C. Medeiros, Michael McAleer, Daniel Slottje, Vicente Ramos, Javier Rey-Maquieira

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)372-383
Number of pages12
JournalJournal of Econometrics
Volume147
Issue number2
DOIs
StatePublished - Dec 2008
Externally publishedYes

Keywords

  • Neural networks
  • Nonlinear models
  • Passenger arrivals
  • Semi-parametric models
  • Smooth transition
  • Time series
  • Tourism demand

ASJC Scopus subject areas

  • Economics and Econometrics

Cite this