forecasting low‐frequency macroeconomic events with high‐frequency data (replication data)
High-frequency financial and economic indicators are usually time-aggregated before computing forecasts of macroeconomic events, such as recessions. We propose a mixed-frequency alternative that delivers high-frequency probability forecasts (including their confidence bands) for low-frequency events. The new approach is compared with single-frequency alternatives using loss functions for rare-event forecasting. We find (i) the weekly-sampled term spread improves over the monthly-sampled to predict NBER recessions, (ii) the predictive content of financial variables is supplementary to economic activity for forecasts of vulnerability events, and (iii) a weekly activity index can date the 2020 business cycle peak in real-time using a mixed-frequency filtering.