Mitchell, James, Poon, Aubrey, Zhu, Dan (2024) Constructing density forecasts from quantile regressions: multimodality in macro-financial dynamics. Journal of Applied Econometrics, . ISSN 0883-7252. E-ISSN 1099-1255. (doi:10.1002/jae.3049) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:104737)
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Official URL: https://doi.org/10.1002/jae.3049 |
Abstract
Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the “data speak.” Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions.
Item Type: | Article |
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DOI/Identification number: | 10.1002/jae.3049 |
Uncontrolled keywords: | density forecasts; quantile regressions; financial conditions |
Subjects: | H Social Sciences |
Divisions: | Divisions > Division of Human and Social Sciences > School of Economics |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Aubrey Poon |
Date Deposited: | 25 Jan 2024 09:54 UTC |
Last Modified: | 19 Apr 2024 13:01 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/104737 (The current URI for this page, for reference purposes) |
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