Forecasting Low Frequency Macroeconomic Events with High Frequency Data

Galvão, Ana B.; Owyang, Michael T.

doi:10.20955/wp.2020.028

Working Paper Revision

Forecasting Low Frequency Macroeconomic Events with High Frequency Data

Abstract: 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 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.

Keywords: mixed frequency models; recession; financial indicators; weekly activity index; event probability forecasting;

JEL Classification: C25; C53; E32;

https://doi.org/10.20955/wp.2020.028

Status: Published in Journal of Applied Econometrics