Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach
By Prosper F. Bangwayo-Skeete, Ryan W. Skeete. 2015
318 citations recorded by the source corpus.
Subfields: Epidemiology, Marketing, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/49b0dee537662f6c6835d6a1b8dbd04dc5ddef45
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Epidemiology
In Marketing
- Nikolaos Kourentzes
- Fotios Petropoulos
- M. Babai
- Stephan Kolassa
- Shujie Shen
- Konstantinos Nikolopoulos
- D. Wu
- A. Syntetos
In Sociology and Political Science
- D. Wu
- Shujie Shen
- Haiyan Song
- Ulrich Gunter
- Konstantinos Nikolopoulos
- Colin Cannonier
- M. Burke
- Nigel Harvey
Citations in the corpus
- Forecasting: theory and practice (2020, background): “Examples of those web-based leading indicators include Google Trends indices (Bangwayo-Skeete and Skeete, 2015), Google Analytics indicators (Gunter and Önder, 2016), as well as Facebook ‘likes’ (Gunter et al., 2019).”
- The economic growth impact of tourism in Small Island Developing States—evidence from the Caribbean (2018, background): “For example, the literature on the impact of tourism influencing growth includes Vanegas and Croes (2003), Ridderstaat et al. (2014), and Banwayo-Skeete and Skeete (2015).”
- New developments in tourism and hotel demand modeling and forecasting (2017, methodology): “MIDAS models estimate using the parsimonious distributed lag polynomials or nonlinear least squares method (Bangwayo-Skeete and Skeete, 2015).”