Forecasting city arrivals with Google Analytics

Ulrich Gunter and Irem Önder

Annals of Tourism Research, 2016, vol. 61, issue C, 199-212

Abstract: The ability of 10 Google Analytics website traffic indicators from the Viennese DMO website to predict actual tourist arrivals to Vienna is investigated within the VAR model class. To prevent overparameterization, big data shrinkage methods are applied: Bayesian estimation of the VAR, reduction to a factor-augmented VAR, and application of Bayesian estimation to the FAVAR, the novel Bayesian FAVAR. Forecast accuracy results show that for shorter horizons (h=1, 2months ahead) a univariate benchmark performs best, while for longer horizons (h=3, 6, 12) forecast combination methods that include the predictive information of Google Analytics perform best, notably combined forecasts based on Bates–Granger weights, on forecast encompassing tests, and on a novel fusion of these two.

Keywords: Bayesian analysis; Big data; City tourism; Factor analysis; Forecast combination; Vector autoregression (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (46)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:61:y:2016:i:c:p:199-212

DOI: 10.1016/j.annals.2016.10.007

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