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

By Carey Goh, R. Law. 2002

450 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Sociology and Political Science, Tourism, Leisure and Hospitality Management

View Paper: https://www.semanticscholar.org/paper/a04c2b06c858beebf520327523c0d1f6dbefc7bd

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Citations in the corpus

Support vector regression with genetic algorithms in forecasting tourism demand (2007, methodology): “Goh and Law (2002) applied the time series SARIMA and MARIMA with interventions in forecasting tourism demand using ten arrival series for Hong Kong. Recently, neural networks have been successfully employed for modelling time series. Unlike conventional statistical models, neural networks are data-”
Modeling and forecasting carbon dioxide emissions in China using Autoregressive Integrated Moving Average (ARIMA) models (2019, methodology): “In Thailand, Pruethsan (2017) examined CO2 emissions using the VARIMAX technique over the period 2000 - 2015 and discovered that the VARIMAX (2, 1, 2) and VARIMAX (2, 1, 3) models are optimal models for modeling CO2 emissions in Thailand.”
A meta-analysis of international tourism demand forecasting and implications for practice (2014, background): “Other studies suggest that the ARIMA and SARIMA approaches are preferred in tourism demand forecasting when the time series does not exhibit structural breaks (see for example, Gustavsson & Nordstrom, 2001; Goh & Law, 2002; Chu, 2008).”
Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, background): “Goh and Law (2002) applied a seasonal ARIMA model to inbound tourism in Hong Kong.”
Modeling Seasonality in Tourism Forecasting (2005, methodology): “To determine the order of integration and to test for a unit root at zero frequency and seasonal frequencies, unit root tests such as the Augmented-Dickey Fuller test (Goh and Law 2002) and the HEGY (Hylleberg et al. 1990) test (Kulendran and King 1997; Kulendran and Witt 2001) were used.”
What could the next 40 years hold for global tourism? (2015, background): “Tourism forecasting began in the 1960s, with most early applications in Europe and North America (Goh & Law, 2002; Montgomery, Johnson, & Gardiner, 1990).”
Tourism demand for Italy and the business cycle (2010, methodology): “Alternative error measures include: the root mean square percentage error, the mean absolute error, and Theil U statistic (see Cho, 2003; Goh & Law, 2002, among others).”
Extending the Theory of Planned Behavior: Visa Exemptions and the Traveller Decision-making Process (2011, methodology): “aSuggested values were based on Hair et al. (1998) and Bearden et al.”
Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, unknown): “More specifically, Goh and Law (2002) apply Seasonal ARIMA (SARIMA) and Multivariate ARIMA (MARIMA) models, and compare their forecasting accuracy against a number of exponential smoothing models, moving average models, as well as, a random walk model (naive model).”
Tourism demand modelling and forecasting—A review of recent research (2008, methodology): “…demand using seasonal long-memory process Gil-Alana et al (2004) Q Spain (I) ARIMA-SF No Modelling demand using seasonal long-memory process Goh & Law (2002) Tourists' characteristics and demand: genetic algorithm Hernández-López et al (2007) Survey data Tenerife (I) genetic algorithm with…”
Recent Developments in Econometric Modeling and Forecasting (2005, background): “Within the tourism context, however, the impact of interventions, as well as outlier detection, has only been assessed for timeseries models (see, for instance, Goh and Law 2002; Chu 2004).”