This paper uses support vector regressions (SVRs) and Google search data to test whether observing Internet habits can provide insights into trends in tourist arrivals in Barbados. The empirical evidence suggests that Google Trends data may be used to pick up changing patterns and trends in tourist arrivals from the UK and Canada. In the case of the USA, the authors find no evidence to suggest that Google data add any significant information to what can be ‘learned’ from an autoregressive SVR.
ArtolaC.GalánE. (2012), ‘Tracking the future on the web: Construction of leading indicators using Internet searches’, Banco de Espana Occasional Paper 1203.
2.
CastleJ.L.FawcettN.W.HendryD.F. (2009), ‘Nowcasting is not just contemporaneous forecasting’, National Institute Economic Review, Vol 210, No 1, pp 71–89.
3.
ChenK.-Y.WangC.-H. (2007), ‘Support vector regression with genetic algorithms in forecasting tourism demand’, Tourism Management, Vol 28, No 1, pp 215–226.
4.
ChoiH.VarianH. (2012), ‘Predicting the present with google trends’, Economic Record, Vol 88, Supplement 1, pp 2–9.
5.
ClarkT.E.WestK.D. (2007), ‘Approximately normal tests for equal predictive accuracy in nested models’, Journal of Econometrics, Vol 138, No 1, pp 291–311.
6.
FondeurY.KarameF. (2013), ‘Can Google data help predict French youth unemployment?’, Economic Modelling, Vol 30, pp 117–125.
7.
GoelS.HofmanJ.M.LahaieS.PennockD.M.WattsD.J. (2010a), ‘Predicting consumer behavior with Web search’, Proceedings of the National Academy of Sciences, Vol 107, No 41, pp 17486–17490.
8.
GoelS.HofmanJ.M.LahaieS.PennockD.M.WattsD.J. (2010b), ‘What can search predict’, 19th International World Wide Web Conference, Raleigh, NC, 26–30 April.
9.
JackmanM. (2012), ‘Revisiting the tourism-led growth hypothesis for Barbados: A disaggregated market approach’, Regional and Sectoral Economic Studies, Vol 12, No 2, pp 15–26.
10.
MincerJ.A.ZarnowitzV. (1969), ‘The evaluation of economic forecasts’, in Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, NBER, 1–46.
11.
PanB.WuD.C.SongH. (2012), ‘Forecasting hotel room demand using search engine data’, Journal of Hospitality and Tourism Technology, Vol 3, No 3, pp 196–210.
12.
SmithE.WhiteS. (2011), ‘What insights can google trends provide about tourism in specific destinations?’, 2nd International Conference on the Measurement and Economic Analysis of Regional Tourism, Biboa, Spain,
13.
SmolaA.J.SchölkopfB. (2004), ‘A tutorial on support vector regression’, Statistics and Computing, Vol 14, No 3, pp 199–222.
14.
VapnikV.GolowichS.E.SmolaA. (1997), ‘Support vector method for function approximation, regression estimation, and signal processing’, in MozerE.JordanM.PetscheT., eds, Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, pp 281–287.
15.
VosenS.SchmidtT. (2011), ‘Forecasting private consumption: Survey based indicators vs. Google trends’, Journal of Forecasting, Vol 30, No 6, pp 565–578.
16.
WorrellD.BelgraveA.GrosvenorT.LescottA. (2011), ‘An analysis of the tourism sector in Barbados’, Central Bank of Barbados Economic Review, Vol 37, Nos 1 & 2, pp 49–75.