Forecasting low‐frequency macroeconomic events with high‐frequency data

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‐frequenc...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2022-11, Vol.37 (7), p.1314-1333
Main Authors: Galvão, Ana Beatriz, Owyang, Michael
Format: Article
Language:English
Subjects:
Business cycles
Econometrics
Economic activity
Economic conditions
Economic indicators
event probability forecasting
financial indicators
Forecasting
mixed frequency models
recession
Recessions
Time use
weekly activity index
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Summary: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.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2931