Working Paper Revision
Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics
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;
https://doi.org/10.26509/frbc-wp-202212r
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Provider: Federal Reserve Bank of Cleveland
Part of Series: Working Papers
Publication Date: 2023-04-11
Number: 22-12R
Related Works
- Working Paper Revision (2023-04-11) : You are here.
- Working Paper Original (2022-05-09) : Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics