Forecasting Low Frequency Macroeconomic Events with High Frequency Data

41 Pages Posted: 15 Sep 2020

See all articles by Ana Galvão

Ana Galvão

affiliation not provided to SSRN

Michael Owyang

Federal Reserve Bank of St. Louis - Research Division

Date Written: September, 2020

Abstract

High-frequency financial and economic activity indicators are usually time aggregated before forecasts of low-frequency macroeconomic events, such as recessions, are computed. We propose a mixed-frequency modelling alternative that delivers high-frequency probability forecasts (including their confidence bands) for these low-frequency events. The new approach is compared with single-frequency alternatives using loss functions adequate to rare event forecasting. We provide evidence that: (i) weekly-sampled spread improves over monthly-sampled to predict NBER recessions, (ii) the predictive content of the spread and the Chicago Fed Financial Condition Index (NFCI) is supplementary to economic activity for one-year-ahead forecasts of contractions, and (iii) a weekly activity index can date the 2020 business cycle peak two months in advance using a mixed-frequency filtering.

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

JEL Classification: C25, C53, E32

Suggested Citation

Galvão, Ana and Owyang, Michael T., Forecasting Low Frequency Macroeconomic Events with High Frequency Data (September, 2020). FRB St. Louis Working Paper No. 2020-028, Available at SSRN: https://ssrn.com/abstract=3690568 or http://dx.doi.org/10.20955/wp.2020.028

Ana Galvão (Contact Author)

affiliation not provided to SSRN

Michael T. Owyang

Federal Reserve Bank of St. Louis - Research Division ( email )

411 Locust St
Saint Louis, MO 63011
United States

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