Knitting Multi-Annual High-Frequency Google Trends to Predict Inflation and Consumption

54 Pages Posted: 10 Apr 2019 Last revised: 8 Dec 2019

See all articles by Johannes Bleher

Johannes Bleher

University of Tuebingen - Department of Statistics and Econometrics

Thomas Dimpfl

University of Tuebingen - Department of Statistics and Econometrics

Date Written: March 21, 2019

Abstract

We propose a regression-based algorithm that allows to construct arbitrarily many comparable, multi-annual, consistent time series on monthly, weekly, daily, hourly and minute-by-minute search volume indices based on the scattered data obtained from Google Trends. The accuracy of the algorithm is illustrated using old datasets from Google that have been used previously in the literature. We use our algorithm to construct an index of prices searched online (IPSO). Out-of-sample, the IPSO improves monthly inflation and consumption forecasts for the US and the Euro Area. In-sample it is contemporaneously correlated with US consumption, when controlling for seasonality, and Granger causes US inflation on a monthly frequency.

Keywords: Google Trends, Search Volume Index, Consistent Time Series; Inflation, Consumption

JEL Classification: Y1, E27, E17

Suggested Citation

Bleher, Johannes and Dimpfl, Thomas, Knitting Multi-Annual High-Frequency Google Trends to Predict Inflation and Consumption (March 21, 2019). Available at SSRN: https://ssrn.com/abstract=3357424 or http://dx.doi.org/10.2139/ssrn.3357424

Johannes Bleher (Contact Author)

University of Tuebingen - Department of Statistics and Econometrics ( email )

Germany

Thomas Dimpfl

University of Tuebingen - Department of Statistics and Econometrics ( email )

Germany

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