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Soft-Noncrossing Bayesian Panel Quantile Regression for Measuring Climate Tail Risk

Florian Huber, Aubrey Poon, Dan Zhu

arXiv 5 Aug 2026 · Econometrics

arXiv:2608.04664 · PDF · Extracted main text

Abstract

We develop a hierarchical Bayesian panel quantile regression model in which unit-specific coefficient paths are smoothed across quantiles by Gaussian processes, while a common time effect absorbs aggregate shocks. Componentwise-monotone Bernstein polynomials, perturbed by unit-specific deviations, deliver soft noncrossing, and we provide identification conditions together with a bound on the crossing probability. Applying the model to 33 countries over 1979--2023, we find that global temperature shocks generate a systemic, non-diversifiable downside risk to output growth. This risk is concentrated in the lower tail and disproportionately affects emerging markets. Finally, we apply our framework to risk analysis and show that the model reduces out-of-sample tail-risk forecast loss by roughly one-third relative to country-specific quantile regressions.

Citation extraction

34
references
57
in-text mentions
34
distinct cited
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self-citations
15,856
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Mohaddes, Kamiar and Raissi, Mehdi (2024) Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2023Q30.87462100%
2Bilal, Adrien and Känzig, Diego R (2026) The Macroeconomic Impact of Climate Change: Global Versus Local Temperature0.87452100%
3Adrian, Tobias and Boyarchenko, Nina and Giannone, Domenico (2019) Vulnerable growth0.84333100%
4Angrist, Joshua and Chernozhukov, Victor and Fernández-Val, Iván (2006) Quantile regression under misspecification, with an application to the U.S. wage structure0.73732100%
5Li, Ta-Hsin and Megiddo, Nimrod (2025) Spline Quantile Regression0.73732100%
6Santos, Bruno and Kneib, Thomas (2020) Noncrossing structured additive multiple-output Bayesian quantile regression models0.73732100%
7Berg, Kimberly A and Curtis, Chadwick C and Mark, Nelson C (2024) GDP and temperature: Evidence on cross-country response heterogeneity0.64422100%
8Bondell, Howard D and Reich, Brian J and Wang, Huixia (2010) Noncrossing quantile regression curve estimation0.64422100%
9Kahn, Matthew E and Mohaddes, Kamiar and Ng, Ryan NC and Pesaran, M… (2021) Long-term macroeconomic effects of climate change: A cross-country analysis0.64422100%
10Koenker, Roger (2005) Quantile Regression0.64422100%

Showing the top 10 of 34 scored citations.