Is Big Data a Big Help? Evidence From Nowcasting Food Inflation During Covid‐19 and Wartime

ABSTRACT With Covid‐19 and Russian invasion of Ukraine triggering vast uncertainty, non‐traditional (potentially big) data sources could provide timely information on the state of the economy. We investigate the usefulness of big data during these events in a bottom‐up nowcasting exercise for Polish food inflation. We show that information embedded in roughly 250 million web‐scraped prices makes it possible to closely track official inflation and provides the most accurate nowcasts in times of elevated uncertainty and volatility, while competing model‐based frameworks skew nowcasts towards historical patterns, which leads to much lower accuracy. In turn, during normal times, time series models with web‐scraped prices provide slightly more accurate nowcasts but the difference in predictive quality is negligible in comparison to nowcasts based purely on online prices. We also extensively experiment on how to optimally aggregate daily online prices. For practitioners we formulate four guidelines. First, let big data speak, especially during uncertain times. Second, model‐based frameworks with online prices are not necessary to obtain precise nowcasts of price developments, even on long samples. Third, simplest aggregation methods of individual product prices lead to the most accurate nowcasts. Fourth, for nowcasting quality the gain from observing prices daily is marginal compared to weekly frequency of data collection.