Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model

Todd Clark, Florian Huber, Gary Koop, Massimiliano Marcellino and Michael Pfarrhofer

No 18549, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We develop a non-parametric quantile panel regression model. Within each quantile, the response function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information is captured through a conditionally heteroskedastic latent factor. The non-parametric feature enhances flexibility, while the panel feature increases the number of observations in the tails. We develop Bayesian methods for inference and apply several versions of the model to study growth-at-risk dynamics in a panel of 11 advanced economies. Our framework usually improves upon single-country quantile models in recursive growth forecast comparisons.

JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Date: 2023-10
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Related works:
Journal Article: Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model (2024)
Working Paper: Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model (2021)
Working Paper: Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model (2021)
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