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

Johannes Bleher and Thomas Dimpfl

Econometrics and Statistics, 2022, vol. 24, issue C, 1-26

Abstract: A regression-based algorithm is proposed 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. The algorithm is used to construct an index of prices searched online (IPSO). The IPSO improves monthly inflation forecasts for the United States and the Euro Area and helps to predict consumption loan growth in the Euro Area. The IPSO is also contemporaneously correlated with consumption loan growth in the Euro Area.

Keywords: Inflation; Consumption; Forecast; Prediction; Google Search Volume; Google Trends (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:24:y:2022:i:c:p:1-26

DOI: 10.1016/j.ecosta.2021.10.006

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