Abstract: This paper proposes a measure of real-time inflation expectations based on
metadata, i.e., data about data, constructed from internet search queries
performed on the search engine Google. The forecasting performance of the Google
Inflation Search Index (GISI) is assessed relative to 37 other indicators of
inflation expectations – 36 survey measures and the TIPS spread. For decades,
the academic literature has focused on three measures of inflation expectations:
the Livingston Survey, Survey of Professional Forecasters, and the Michigan
Survey. While useful in developing models of forecasting inflation, these low
frequency measures appear anachronistic in the modern era of higher frequency
and real-time data. I demonstrate that higher frequency measures tend to
outperform lower frequency measures in tests of accuracy, predictive power, and
rationality. Furthermore, Granger Causality tests indicate that the GISI
metadata indicator anticipates the inflation rate by 12 months, and
out-of-sample forecasts show that the GISI has the lowest forecast error of all
the inflation expectations indicators tested.
