Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics

James Mitchell, Aubrey Poon and Dan Zhu ()
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Dan Zhu: https://research.monash.edu/en/persons/dan-zhu?msclkid=53234f6ccfd911ecbd56fcd55f20a96d

No 22-12R, Working Papers from Federal Reserve Bank of Cleveland

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.

Keywords: Density Forecasts; Quantile Regressions; Financial Conditions (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 E44 (search for similar items in EconPapers)
Pages: 68
Date: 2022-05-09, Revised 2023-04-11
New Economics Papers: this item is included in nep-ecm, nep-fdg, nep-for and nep-mac
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Citations: View citations in EconPapers (2)

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Journal Article: Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedcwq:94160

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DOI: 10.26509/frbc-wp-202212r

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