Supply Constraints and Conditional Distribution Predictability of Inflation and its Volatility: A Non-parametric Mixed-Frequency Causality-in-Quantiles Approach

Massimiliano Caporin (), Rangan Gupta (), Sowmya Subramaniam () and Hudson Torrent ()
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Massimiliano Caporin: Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241, 35121 Padova, Italy
Rangan Gupta: Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
Sowmya Subramaniam: Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India
Hudson Torrent: Department of Statistics, Universidade Federal do Rio Grande do Sul Porto Alegre, 91509-900, Brazil

No 202526, Working Papers from University of Pretoria, Department of Economics

Abstract: We use a mixed-frequency non-parametric causality-in-quantiles test to detect predictability from newspapers articles-based daily indexes of supply bottlenecks to the conditional distributions of monthly inflation rate and its volatility of China, the European Monetary Union (EMU), the United Kingdom (UK) and the United States (US). Based on a sample period of January 2010 to December 2024, we find that the causal impact of supply bottlenecks on inflation volatility is consistently ob-served across the four economies, while the same is particularly strong for the inflation rates of the EMU and the UK. The second-moment impact is further emphasized in a forecasting set-up, as we detect statistically significant impact of these supply chain constraints in the prediction of the lower quantiles of inflation volatility. Our findings have important implications for monetary policy decisions.

Keywords: Inflation; Inflation Volatility; Supply Bottlenecks; Mixed-Frequency; Nonparametric Causality-in-Quantiles Test (search for similar items in EconPapers)
JEL-codes: C22 C53 E23 E31 (search for similar items in EconPapers)
Pages: 12 pages
Date: 2025-08
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