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

Author

Listed:
  • 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 S. Torrent

    (Department of Statistics, Universidade Federal do Rio Grande do Sul Porto Alegre, 91509-900, Brazil)

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.

Suggested Citation

  • Massimiliano Caporin & Rangan Gupta & Sowmya Subramaniam & Hudson S. Torrent, 2025. "Supply Constraints and Conditional Distribution Predictability of Inflation and its Volatility: A Non-parametric Mixed-Frequency Causality-in-Quantiles Approach," Working Papers 202526, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202526
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    References listed on IDEAS

    as
    1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    2. Ascari, Guido & Bonam, Dennis & Smadu, Andra, 2024. "Global supply chain pressures, inflation, and implications for monetary policy," Journal of International Money and Finance, Elsevier, vol. 142(C).
    3. Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022. "High-frequency monitoring of growth at risk," International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
    4. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    5. Pablo Burriel & Iván Kataryniuk & Carlos Moreno Pérez & Francesca Viani, 2024. "A New Supply Bottlenecks Index Based on Newspaper Data," International Journal of Central Banking, International Journal of Central Banking, vol. 20(2), pages 17-67, April.
    6. Kevin L. Kliesen & Devin Werner, 2022. "Using Beige Book Text Analysis to Measure Supply Chain Disruptions," Economic Synopses, Federal Reserve Bank of St. Louis, issue 18, pages 1-2, June.
    7. Binder, Carola C., 2017. "Measuring uncertainty based on rounding: New method and application to inflation expectations," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 1-12.
    8. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    9. Lin Chen & Stephanie Houle, 2023. "Turning Words into Numbers: Measuring News Media Coverage of Shortages," Discussion Papers 2023-8, Bank of Canada.
    10. Polat, Onur & Somani, Dhanashree & Gupta, Rangan & Karmakar, Sayar, 2025. "Shortages and machine-learning forecasting of oil returns volatility: 1900–2024," Finance Research Letters, Elsevier, vol. 79(C).
    11. Cui, Lianbiao & Yue, Suyun & Nghiem, Xuan-Hoa & Duan, Mei, 2023. "Exploring the risk and economic vulnerability of global energy supply chain interruption in the context of Russo-Ukrainian war," Resources Policy, Elsevier, vol. 81(C).
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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