Forecasting low‐frequency macroeconomic events with high‐frequency data

Bibliographic Data

ID21653335
AuthorsAna Beatriz Galvão (0000-0003-3263-9450, Warwick Business School University of Warwick Coventry UK, corresponding author), Michael T Owyang (0000-0002-2109-3432, Research Division Federal Reserve Bank of St. Louis St. Louis Missouri USA)
Year2022
Volume37
Issue7
Pages1314-1333
Publication date2022-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.2931
OpenAlexW3124522241
LanguageEN
Citations received2
References cited41

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

Business cycle · Econometrics · Economic indicator · Economics · Geography · Macroeconomics · Nowcasting · Recession · Statistics · Computer Science · Financial Risk and Volatility Modeling · Forecasting Techniques and Applications · Mathematics · Monetary Policy and Economic Impact

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