Food inflation nowcasting with web scraped data

In this paper we evaluate the ability of web scraped data to improve nowcasts of Polish food inflation. The nowcasting performance of online price indices is compared with aggregated and disaggregated benchmark models in a pseudo realtime experiment. We also explore product selection and classification problems, their importance in constructing web price indices and other limitations of online datasets. Therefore, we experiment not only with raw indices, but also with several approaches to include them into model-based forecasts. Our findings indicate that the optimal way to incorporate web scraped data into regular forecasting is to include them in simple distributed-lag models at the lowest aggregation level, combine the forecasts and aggregate them using statistical office methodology. We find this approach superior to other benchmark models which do not take online information into account.