Climate Risks and Forecastability of US Inflation: Evidence from Dynamic Quantile Model Averaging

Jiawen Luo (), Shengjie Fu (), Oguzhan Cepni and Rangan Gupta
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Jiawen Luo: School of Business Administration, South China University of Technology, Guangzhou 510640, China
Shengjie Fu: School of Business Administration, South China University of Technology, Guangzhou 510640, China

No 202420, Working Papers from University of Pretoria, Department of Economics

Abstract: This study examines the impact of climate-related risks on the inflation rates of the United States, focusing on the overall Consumer Price Index (CPI) and its significant components, namely food and beverages and housing inflation. Employing quantile regression models and a comprehensive dataset spanning from January 1985 to September 2022, we analyze five specific climate risk factors alongside traditional macroeconomic predictors. Our findings indicate that models incorporating individual climate risks generally outperform those considering only macroeconomic factors. However, models combination strategies that integrate all five climate risk measures consistently deliver superior forecasting performance. Notably, the pronounced effect of climate risks on food inflation significantly contributes to the observed trends in the overall CPI, which is largely driven by this subcomponent. This research highlights the crucial role of climate factors in forecasting inflation, suggesting potential avenues for enhancing economic policy-making in light of evolving climate conditions.

Keywords: Climate risks; US inflation; Dynamic quantile moving averaging; Forecasting (search for similar items in EconPapers)
JEL-codes: C21 C22 C53 E31 Q54 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2024-05
New Economics Papers: this item is included in nep-cna, nep-env and nep-mon
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