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

68 Pages Posted: 10 May 2022 Last revised: 12 Apr 2023

See all articles by James Mitchell

James Mitchell

Federal Reserve Bank of Cleveland

Aubrey Poon

University of Kent - School of Economics

Dan Zhu

Monash University - Department of Econometrics & Business Statistics

Date Written: April 11, 2023

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

JEL Classification: C53, E32, E37, E44

Suggested Citation

Mitchell, James and Poon, Aubrey and Zhu, Dan, Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics (April 11, 2023). FRB of Cleveland Working Paper No. 22-12, 2022R, Available at SSRN: https://ssrn.com/abstract=4104578 or http://dx.doi.org/10.2139/ssrn.4104578

James Mitchell (Contact Author)

Federal Reserve Bank of Cleveland ( email )

East 6th & Superior
Cleveland, OH 44101-1387
United States

HOME PAGE: http://https://www.clevelandfed.org/en/our-research/economists/james-mitchell.aspx

Aubrey Poon

University of Kent - School of Economics ( email )

CT2 7NP
United Kingdom

Dan Zhu

Monash University - Department of Econometrics & Business Statistics ( email )

Wellington Road
Clayton, Victoria 3168
Australia

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