Univariate versus multivariate time series forecasting: an application to international tourism demand

By J. D. Preez, Stephen F. Witt. 2003

235 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/6bb4b5ae2b89b0e6ece9070807849a890589fcc8

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Citations in the corpus

Forecasting tourist arrivals with machine learning and internet search index (2017, methodology): “…model has also been widely applied to tourism forecasting and performed well (Athanasopoulos, Hyndman, Song, & Wu, 2011; Brida & Risso, 2011; Chang & Liao, 2010; Chen et al., 2012; Du Preez & Witt, 2003; Jungmittag, 2016; Li & Sheng, 2016; Liang, 2014; Lim & McAleer, 2002; Shahrabi et al., 2013).”
Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures (2011, methodology): “…consideration of interactions between contemporaneous air traffic flows, through both the inclusion of a “world trade” variable and estimation using a Seemingly Unrelated Regression (SUR) approach, offers the prospect of improved accuracy, but, as Du Preez and Witt (2003) show, this is far from in-”
A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis (2015, methodology): “A large spectrum of models have been developed and successfully applied in MTS modeling and forecasting (Du Preez and Witt 2003; Ljung and Glad 1994).”
Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, background): “Song and Witt (2004) considered vector autoregressive (VAR) modeling of inbound tourism to Macau, where the multivariate process contained the exploratory variables, and found that the VAR model produces superior results for medium-and long-term horizons.”
Tourism demand modelling and forecasting—A review of recent research (2008, background): “Moreover, Du Preez and Witt (2003) investigated the intervention effects of the time series models on forecasting performance within a state space framework.”
Inbound international tourism to Canary Islands: a dynamic panel data model (2006, result): “However, several studies (Chan, Hui, & Yuen, (1999); Du Preez & Witt, 2003; Kulendran & Witt, 2001; Turner, Kulendran, & Pergat, 1995; Witt, Song, & Louvieris, 2003) compared the forecasting ability of the BoxJenkins approach to other modelling techniques and found that they generally outperform…”
Modelling and Forecasting Australian Domestic Tourism (2006, background): “Finally, du Preez and Witt (2003) consider a multivariate basic structural model.”
Modeling Seasonality in Tourism Forecasting (2005, background): “…(2003b) compared the forecasting performance of the leading indicator approach with the econometric model and univariate time-series models; and Preez and Witt (2003) compared the forecasting performance of the multivariate model and univariate ARIMA model within the state space modeling…”
Determinants of Tourist Arrivals in Africa: A Panel Data Regression Analysis (2005, methodology): “Greater stability coincides with more tourist arrivals and this result confirms the finding of Eilat and Einav (2003).”
A meta-analysis of international tourism demand forecasting and implications for practice (2014, background): “Preez and Witt (2003) show that the ARIMA approach performs best in terms of forecasting accuracy and goodness of fit.”
25 years of time series forecasting (2006, methodology): “The population characteristics of VARMA processes appear to have been first derived by Quenouille (1957, 1968), although software to implement them only became available in the 1980s and 1990s. Since VARIMA models can accommodate assumptions on exogeneity and on contemporaneous relationships, they o”