Nowcasting Euro Area GDP Growth Using Bayesian Quantile Regression

James Mitchell, Aubrey Poon and Gian Luigi Mazzi

A chapter in Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, 2022, vol. 43A, pp 51-72 from Emerald Group Publishing Limited

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: Quantile regression; Bayesian methods; nowcasting; big data; mixed-frequency data; density forecasting (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532021000043a004

DOI: 10.1108/S0731-90532021000043A004

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