Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention

Carey Goh, Chun Hung Roberts Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

245 Citations (Scopus)

Abstract

This paper presents the use of time series SARIMA and MARIMA with interventions in forecasting tourism demand using ten arrival series for Hong Kong. Augmented Dickey-Fuller tests indicated that all the series were seasonal nonstationary. Significant interventions such as relaxation of the issuance of out-bound visitors visas, the Asian financial crisis, the handover, and the bird flu epidemic were all empirically identified with significant test results and expected signs. The forecasts obtained using models that capture stochastic nonstationary seasonality and interventions, SARIMA and MARIMA with intervention analysis, are compared with other eight time series models and were found to have the highest accuracy.
Original languageEnglish
Pages (from-to)499-510
Number of pages12
JournalTourism Management
Volume23
Issue number5
DOIs
Publication statusPublished - 1 Oct 2002

Keywords

  • Intervention
  • Stochastic nonstationary seasonality
  • Time series
  • Tourism demand forecasting

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

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