A meta-analysis of international tourism demand forecasting and implications for practice
By Bo Peng, Haiyan Song, G. Crouch. 2014
242 citations recorded by the source corpus.
Numerous studies on tourism forecasting have now been published over the past five decades. However, no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980–2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modeling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context. The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings.
Subfields: Sociology and Political Science, Tourism, Leisure and Hospitality Management
View Paper: https://www.semanticscholar.org/paper/5812eda9e08614c9adebeef4c6b5f456f0879553
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In Sociology and Political Science
- D. Wu
- Shujie Shen
- Haiyan Song
- M. Meo
- Mubbshar Ali
- Salman Masood Sheikh
- M. A. H. Chowdhury
- Ghulam Mustafa Shaikh
In Tourism, Leisure and Hospitality Management
Citations in the corpus
- New developments in tourism and hotel demand modeling and forecasting (2017, result): “Employing the mega-regression analysis, Kim and Schwartz (2013) and Peng et al. (2014) empirically verified this finding by examining 32 and 262 studies respectively.”
- Asymmetric impact of oil prices, exchange rate, and inflation on tourism demand in Pakistan: new evidence from nonlinear ARDL (2018, background): “Like other essential factors, Peng, Song, and Crouch (2014) found that econometric modeling methods are a vital factor for estimating the tourism demand.”