Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models

By L. Turner, Stephen F. Witt. 2001

129 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5eb6757a2534c746a6b1250494b78ac3e84ac77b

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Citations in the corpus

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 tourist arrivals using time-varying parameter structural time series models (2011, background): “…have been applied in the tourism demand forecasting context, and several studies have demonstrated their superior forecasting performance relative to other time series alternatives (see for example González & Moral, 1995, 1996; Kulendran & King, 1997; Kulendran & Witt, 2001; Turner & Witt, 2001).”
Tourism Development and Economic Growth in Seven Mediterranean Countries: A Panel Data Approach (2012, unknown): “The economists tend to rely on quantitative forecasts based on econometric modelling, which is often the most popular tool for projecting scenarios of tourism demand and tourism development (Turner and Witt, 2001; Witt et al, 2003; Wong et al, 2006; Song et al, 2009; Smeral, 2010).”
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.”
Tourism demand modelling and forecasting—A review of recent research (2008, background): “…be decomposed further into holiday tourist arrivals, business tourist arrivals, tourist arrivals for visiting friends and relatives (VFR) purposes (e.g., Turner and Witt, 2001a, 2001b, and Kulendran and Wong, 2005, respectively), and tourist arrivals by air (Coshall, 2005; Rosselló-Nadal, 2001).”
Tourism demand for Italy and the business cycle (2010, background): “…through Harvey’s structural time series approach (STS, see Harvey, 1989), which has progressively gained in popularity and exemplified by its application in González and Moral (1995, 1996), Greenidge (2001), Turner and Witt (2001), Kim and Moosa (2005) and Vu and Turner (2006) among others.”
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.”