Tourism Forecasting and its Relationship with Leading Economic Indicators

By Vincent Cho. 2001

128 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f9ca78662682d340573da51a7cefa8e0ba7a1cbc

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Citations in the corpus

Tourism demand modelling and forecasting—A review of recent research (2008, methodology): “Similar findings were also obtained by Cho (2003) who concluded that the ANN model outperforms the exponential smoothing and ARIMA models in modelling and forecasting the demand for Hong Kong tourism.”
A Meta-Analysis of International Tourism Demand Elasticities (2013, background): “…(Tang, Selvanathan, and Selvanathan 2007); the educational level of tourists and their age distribution (Alegre and Pou 2004); rates of unemployment (Cho 2001); the levels of income distribution and inequality (Morley 1998); one-off events such as acts of terrorism or infectious disease scares…”
Economic Recession, Job Vulnerability, and Tourism Decision Making: A Qualitative Comparative Analysis (2017, unknown): “Additionally, even if a number of studies examine the influence of levels of unemployment and disposable tourism income in tourism (Cho, 2001; Turner, Reisinger, and Witt, 1998), the practical importance of many of the proposed indicators remains limited due to their linearity (Yap and Allen, 2011).”
Recent Developments in Econometric Modeling and Forecasting (2005, methodology): “3 UK (O) TA/P Y/P RC CS TC FS D CI/ECM ADLM Log-linear ML OLS Naive 1 DW LM-AC Chow2 HESC RESET Kulendran & Witt (2003a) Q: 82.”
Modelling and Forecasting Australian Domestic Tourism (2006, background): “Law (2000), Burger, Dohnal, Kathrada, and Law (2001), Lim and McAleer (2001b) and Cho (2001, 2003) directly implement exponential smoothing methods to forecast tourism demand.”
The impact of COVID-19 on tourism sector in India (2020, background): “Forecasting plays a crucial role in tourism planning (Cho, 2001).”
A meta-analysis of international tourism demand forecasting and implications for practice (2014, result): “and other social and psychological trends (Song & Wong, 2003). According to Song, Witt, and Li (2009a), based on their examination of tourist arrivals in the UK and US, the TVP model generated the most accurate short-run forecasts, consistent with previous studies (Song, Romilly, & Liu, 1998; Song &”