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Google data in bridge equation models for German GDP

Thomas Götz and Thomas Knetsch

International Journal of Forecasting, 2019, vol. 35, issue 1, 45-66

Abstract: Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system.

Keywords: Big data; Bridge equation models; Forecasting; Principal components analysis; Partial least squares; LASSO; Boosting (search for similar items in EconPapers)
Date: 2019
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Working Paper: Google data in bridge equation models for German GDP (2017) Downloads
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DOI: 10.1016/j.ijforecast.2018.08.001

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