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Nowcasting Euro Area GDP Growth Using Bayesian Quantile Regression

James Mitchell (Federal Reserve Bank of Cleveland, Ohio, U.S.A.)
Aubrey Poon (University of Strathclyde, Glasgow, U.K.)
Gian Luigi Mazzi (Eurostat, Luxembourg City, Luxembourg (retired))

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling

ISBN: 978-1-80262-062-7, eISBN: 978-1-80262-061-0

Publication date: 18 January 2022

Abstract

This chapter uses an application to explore the utility of Bayesian quantile regression (BQR) methods in producing density nowcasts. Our quantile regression modeling strategy is designed to reflect important nowcasting features, namely the use of mixed-frequency data, the ragged-edge, and large numbers of indicators (big data). An unrestricted mixed data sampling strategy within a BQR is used to accommodate a large mixed-frequency data set when nowcasting; the authors consider various shrinkage priors to avoid parameter proliferation. In an application to euro area GDP growth, using over 100 mixed-frequency indicators, the authors find that the quantile regression approach produces accurate density nowcasts including over recessionary periods when global-local shrinkage priors are used.

Keywords

Acknowledgements

Acknowledgments

We thank an anonymous referee, Todd Clark, Dan Zhu, and participants at the OECD’s Workshop on Time Series Methods for Official Statistics (Paris, September 2019) for helpful comments and conversations. This chapter replaces an earlier 2019 Eurostat working paper by Mazzi and Mitchell entitled “Nowcasting Euro Area GDP Growth Using Quantile Regression.” The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Cleveland or the Federal Reserve System.

Citation

Mitchell, J., Poon, A. and Mazzi, G.L. (2022), "Nowcasting Euro Area GDP Growth Using Bayesian Quantile Regression", Chudik, A., Hsiao, C. and Timmermann, A. (Ed.) Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling (Advances in Econometrics, Vol. 43A), Emerald Publishing Limited, Leeds, pp. 51-72. https://doi.org/10.1108/S0731-90532021000043A004

Publisher

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Emerald Publishing Limited

Copyright © 2022 James Mitchell, Aubrey Poon and Gian Luigi Mazzi