Improving disaggregated short-term food inflation forecasts with webscraped data (Christian Beer, Robert Ferstl, Bernhard Graf)

This study examines the effectiveness of using webscraped data to predict price developments in the Austrian food retail sector. We calculate monthly nowcasts of price changes based on daily price data collected by the OeNB since mid-2020, using Eurostat methodology for price index calculation, along with further details provided by the national statistics office. We assess the quality of our nowcasts by comparing them with various baseline models and more advanced time series methods also covering machine learning approaches. Our findings indicate that webscraped data are a useful way to obtain more accurate nowcasts with a time advantage, amounting to several weeks, over traditional data sources. In addition, we are the first, to our knowledge, to explore the possibility of using the improved accuracy of the nowcasts as a basis for disaggregated short-term forecasts that extend up to one quarter. While direct forecasts at higher levels of aggregation produce slightly more accurate overall metrics, indirect forecasts derived from disaggregated data provide superior insights into the underlying dynamics of specific sub-components. Our results show that more advanced time series models have trade-offs in terms of computational efficiency while performing very similarly to more traditional methods. These findings have implications for policymakers who aim to develop an effective system for real-time monitoring of inflation dynamics at a very granular level.