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Nowcasting Euro Area GDP Growth Using Bayesian Quantile Regression

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

Author

Listed:
  • James Mitchell
  • Aubrey Poon
  • Gian Luigi Mazzi

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.

Suggested Citation

  • James Mitchell & Aubrey Poon & Gian Luigi Mazzi, 2022. "Nowcasting Euro Area GDP Growth Using Bayesian Quantile Regression," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 51-72, Emerald Group Publishing Limited.
  • Handle: RePEc:eme:aecozz:s0731-90532021000043a004
    DOI: 10.1108/S0731-90532021000043A004
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