A Bayesian panel vector autoregression to analyze the impact of climate shocks on high-income economies

Huber, F., Krisztin, T. ORCID: https://orcid.org/0000-0002-9241-8628, & Pfarrhofer, M. (2023). A Bayesian panel vector autoregression to analyze the impact of climate shocks on high-income economies. The Annals of Applied Statistics 17 (2) 1543-1573. 10.1214/22-AOAS1681.

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Abstract

In this paper we assess the impact of climate shocks on futures markets for agricultural commodities and a set of macroeconomic quantities for multiple high-income economies. To capture relations among countries, markets, and climate shocks, this paper proposes parsimonious methods to estimate high-dimensional panel vector autoregressions. We assume that coefficients associated with domestic lagged endogenous variables arise from a Gaussian mixture model while further parsimony is achieved using suitable global-local shrinkage priors on several regions of the parameter space. Our results point toward pronounced global reactions of key macroeconomic quantities to climate shocks. Moreover, the empirical findings highlight substantial linkages between regionally located shocks and global commodity markets.

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