Time series forecasts of international travel demand for Australia

Time series forecasts of international travel demand for Australia,10.1016/S0261-5177(01)00098-X,Tourism Management,Christine Lim,Michael McAleer

Time series forecasts of international travel demand for Australia   (Citations: 47)
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This paper analyses stationary and non-stationary international tourism time series data by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting. Various Box–Jenkins Autoregressive Integrated Moving Average (ARIMA) models are estimated over the period 1975(1)–1989(4) for tourist arrivals to Australia from Hong Kong, Malaysia and Singapore. The mean absolute percentage error and root mean squared error (RMSE) are used as measures of forecast accuracy. As the best fitting ARIMA model is found to have the lowest RMSE, this model is used to obtain post-sample forecasts. Tourist arrivals data for 1990(1)–1996(4) are compared with the forecast performance of the ARIMA model for each origin market. The fitted ARIMA model forecasts tourist arrivals from Singapore for the period 1990(1)–1996(4) very well. Although the ARIMA model outperforms the seasonal ARIMA models for Hong Kong and Malaysia, the forecasts of tourist arrivals are not as accurate as in the case of Singapore.
Journal: Tourism Management - TOURISM MANAGE , vol. 23, no. 4, pp. 389-396, 2002
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    • ...More recent works include those of Lim and McAleer (2001), Balogh, Kovacs, Chaiboonsri, and Chaitip (2009), and Chaovanapoonphol, Lim, McAleer, and Wiboonpongse (2010)...

    Kirill Furmanovet al. Tourism flows from the Russian Federation to the European Union

    • ...In these research works, each of the time series under investigation is first tested for stationarity by employing the Augmented Dickey Fuller (ADF) unit root tests (Lathiras & Siriopoulos, 1998; Seddighi & Shearing, 1997; Vogt & Wittayakorn, 1998; Webber, 2001; Lim & McAleer, 2002; Song et al, 2003) or the Phillips-Perron (PP) test (Song et al, 2000; Algieri, 2006)...

    Carey Gohet al. The Methodological Progress of Tourism Demand Forecasting: A Review of...

    • ...There are studies that have examined the determinants of demand for tourists visiting Australia (see, inter alia, Kulendran, 1996; Lim and McAleer, 1999; 2001; 2002a; Lim, 2004); there are studies that have attempted to forecast visitor arrivals to Australia (see, inter alia, Kulendran and King, 1997; Lim and McAleer, 2002b); and there are studies that examine the competitiveness of Australia's tourism industry (see, inter alia, Dwyer et al, 2001, 2002; Kim and Dwyer, 2003)...

    Paresh Kumar Narayan. Are shocks to tourism transitory at business cycle horizons?

    • ...In conclusion, e-commerce not only improves the accessibility of information transmission which are so interactive and multimedia that traditional media do not have, but also makes sure that real-time transmission of voice, images, text and other information can directly set up bridge from the information release to receiving to improve the efficiency of market transactions [6]...

    Xiuhua Liu. E-commerce development model of tourist attractions

    • ...In tourism demand forecasting, the majority of researcher have focused on econometrics, and the time series models play import roles[1, 2]. However, there are two drawbacks in ARIMA models: (1) There are very rigorous restriction in variables and materials in ARIMA models, (2) ARIMA models show with form of equations, that more difficult to understand for general users...

    Hung-Lieh Chouet al. Forecasting Tourism Demand Based on Improved Fuzzy Time Series Model

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