Forecasting low‐frequency macroeconomic events with high‐frequency data
提出一种混合频率方法，用高频金融和经济指标生成低频事件（如衰退）的高频概率预测，发现周度期限利差优于月度数据，且金融变量对脆弱性事件预测有补充作用。
Summary 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.