Forecasting the Demand for International Business Tourism

By N. Kulendran, Stephen F. Witt. 2003

154 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/42cee289fb44db246ab5d61d1bea1b4de82b4ab9

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Citations in the corpus

Forecasting tourist arrivals using time-varying parameter structural time series models (2011, methodology): “This is consistent with most of the tourism demand literature using CSMs, such as the studies of González and Moral (1995) and Kulendran and Witt (2003).”
Structural equation modelling and regression analysis in tourism research (2012, background): “…(2004) Current Issues in Tourism Cortés-Jiménez and Blake (2011) Journal of Travel Research SEM analysis is used to model tourism demand and/or travel expenditure of travellers from different countries Kulendran and Witt (2003) Journal of Travel Research Seiler, Hsieh, Seiler, and Hsieh (2002)”
Modeling tourism: A fully identified VECM approach (2009, methodology): “When system approaches are used (for example Dritsakis, 2004; Gangnes & Bonham, 1998; Kulendran & King, 1997; Kulendran & Witt, 2001, 2003; Lim & McAleer, 2001; Song & Witt, 2003), identification is obtained exclusively using Johansen’s reduced rank regression technique, and identified…”
Modelling and Forecasting Australian Domestic Tourism (2006, background): “González and Moral (1995), Garcı́a-Ferrer and Queralt (1997), Greenidge (2001), Turner and Witt (2001) and Kulendran and Witt (2003) also consider causal structural time series models.”
Modeling Seasonality in Tourism Forecasting (2005, methodology): “Within the multiplicative seasonal ARIMA modeling context, to model stochastic nonstationary seasonal variation and deterministic seasonal variation, two variants of seasonal ARIMA models were considered (Kulendran and King 1997; Kulendran and Witt 2001).”
Tourism demand for Italy and the business cycle (2010, background): “Furthermore, as also emphasized by Kulendran and Witt (2003), forecasting performances should be evaluated on both medium and long time horizons.”
The Impact of 9/11 and Other Terrible Global Events on Tourism in the United States and Hawaii (2006, background): “Examples of applications in tourism-demand forecasting include Dritsakis (2004), Kulendran and Witt (2003), and Song, Witt, and Li (2003).”
Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, unknown): “Furthermore, a number of authors compare ARIMA-type models with other time-series or econometric models (see, inter alia Goh and Law, 2002; Kulendran and Witt, 2003; Chu, 2004; Kim and Moosa, 2005; Vu and Turner, 2006; Wong et al., 2007; Chu, 2008; Brida and Risso, 2011; Wan et al., 2013).”
The impact of COVID-19 on tourism sector in India (2020, methodology): “…like regression model, exponential smoothing (Athanasopoulos & Hyndman, 2008), Basic structural time series model (BSM) and causal structural time series model (STSM) (Kulendran & Witt, 2003; Turner & Witt, 2001), Autoregressive Moving Average with External Variables (ARMAX) (Yang et al., 2015).”
Forecasting international tourist flows to Macau (2006, background): “Such studies include De Mello et al (2002), Dritsakis (2003), Huybers (2003), Kulendran and Witt (2001, 2003), Lim and McAleer (2001, 2002), Morley (2000), Papatheodorou (2001), Song et al (2000, 2003a, b), and Webber (2001).”
A meta-analysis of international tourism demand forecasting and implications for practice (2014, background): “However, according to Turner and Witt (2001) and Kulendran and Witt (2003b), no evidence has yet emerged to suggest that the inclusion of explanatory variables improves the forecasting accuracy of the BSM model.”
Tourism demand modelling and forecasting—A review of recent research (2008, methodology): “For example, ECM was used in the studies of Kulendran, and Wilson (2000), Kulendran and Witt (2003b), Lim and McAleer (2001), Song and Witt (2000).”