Nowcasting Business Cycles: A Bayesian Approach to Dynamic Heterogeneous Factor Models
ISBN: 978-1-78560-353-2, eISBN: 978-1-78560-352-5
Publication date: 6 January 2016
Abstract
We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend existing approaches by permitting for heterogenous lead–lag patterns of the various indicators along the business cycles. The framework is well suited for high-frequency monitoring of current economic conditions in real time – nowcasting – since inference can be conducted in the presence of mixed frequency data and irregular patterns of data availability. Our assessment of the underlying index of business cycle conditions is accurate and more timely than popular alternatives, including the Chicago Fed National Activity Index (CFNAI). A formal real-time forecasting evaluation shows that the framework produces well-calibrated probability nowcasts that resemble the consensus assessment of the Survey of Professional Forecasters.
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Acknowledgements
Acknowledgments
The views expressed are those of the authors and do not necessarily reflect those of the European Central Bank, the Eurosystem, the European Stability Mechanism, the Federal Reserve Bank of New York, the Board of Governors of Federal Reserve System. This work was partly supported by the research contracts ARC-AUWB/2010-15/ULB-11 and IAP P7/06 StUDys (DG).
Citation
D’Agostino, A., Giannone, D., Lenza, M. and Modugno, M. (2016), "Nowcasting Business Cycles: A Bayesian Approach to Dynamic Heterogeneous Factor Models", Dynamic Factor Models (Advances in Econometrics, Vol. 35), Emerald Group Publishing Limited, Leeds, pp. 569-594. https://doi.org/10.1108/S0731-905320150000035014
Publisher
:Emerald Group Publishing Limited
Copyright © 2016 Emerald Group Publishing Limited