Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany - Günter W. Beck; Kai Carstensen; Jan-Oliver Menz; Richard Schnorrenberger; Elisabeth Wieland (European Central Bank (ECB), 2024)
We study how millions of granular and weekly household scanner data combined with machine learning can help to improve the real-time nowcast of German inflation. Our nowcasting exercise targets three hierarchy levels of inflation: individual products, product groups, and headline inflation. At the individual product level, we construct a large set of weekly scanner-based price indices that closely match their official counterparts, such as butter and coffee beans. Within a mixed-frequency setup, these indices significantly improve inflation nowcasts already after the first seven days of a month. For nowcasting product groups such as processed and unprocessed food, we apply shrinkage estimators to exploit the large set of scanner-based price indices, resulting in substantial predictive gains over autoregressive time series models. Finally, by adding high-frequency information on energy and travel services, we construct competitive nowcasting models for headline inflation that are on par with, or even outperform, survey-based inflation expectations.
Keywords: E31; C55; E37; C53; 330; Inflation nowcasting; machine learning methods; scanner price data; mixed-frequency modeling