Specification Choices in Quantile Regression for Empirical Macroeconomics

Andrea Carriero, Todd Clark and Massimiliano Marcellino

No 18901, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks to macroeconomic indicators. In this paper we examine various choices in the specification of quantile regressions for macro applications, for example, choices related to how and to what extent to include shrinkage, and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, using for evaluation both quantile scores and quantile-weighted continuous ranked probability scores at a range of quantiles spanning from the left to right tail. Across a range of applications, we find that shrinkage is generally helpful to quantile forecast accuracy, with Bayesian quantile regression dominating frequentist quantile regression.

Keywords: Quantile regression; Bayesian methods (search for similar items in EconPapers)
JEL-codes: C53 E17 E37 F47 (search for similar items in EconPapers)
Date: 2024-03
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP18901 (application/pdf)

Related works:
Journal Article: Specification Choices in Quantile Regression for Empirical Macroeconomics (2025)
Working Paper: Specification Choices in Quantile Regression for Empirical Macroeconomics (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:18901

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP18901

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().