Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32421
Title: Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention
Authors: Goh, C
Law, R 
Keywords: Intervention
Stochastic nonstationary seasonality
Time series
Tourism demand forecasting
Issue Date: 2002
Publisher: Pergamon Press
Source: Tourism management, 2002, v. 23, no. 5, p. 499-510 How to cite?
Journal: Tourism management 
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.
URI: http://hdl.handle.net/10397/32421
ISSN: 0261-5177
EISSN: 1879-3193
DOI: 10.1016/S0261-5177(02)00009-2
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