Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany

We study how millions of highly granular and weekly household scanner data combined with novel machine learning techniques can help to improve the nowcast of monthly German inflation in real time. Our nowcasting exercise targets three hierarchy levels of the official consumer price index. First, we construct a large set of weekly scanner-based price indices at the lowest aggregation level underlying official German inflation, such as those of butter and coffee beans. We show that these indices track their official counterparts extremely well. Within a mixed-frequency modeling framework, we also demonstrate that these scanner-based price indices improve inflation nowcasts at this very narrow level, notably already after the first seven days of a month. Second, we apply shrinkage estimators to exploit the large set of scanner-based price indices in nowcasting product groups such as processed and unprocessed food. This yields substantial predictive gains compared to a time series benchmark model. Finally, we nowcast headline inflation. Adding high-frequency information on energy and travel services, we construct highly competitive nowcasting models that are on par with, or even outperform, survey-based inflation expectations that are notoriously difficult to beat.