IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/202526.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.up.ac.za/media/shared/61/WP/wp_2025_26.zp269313.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. Lin Chen & Stephanie Houle, 2023. "Turning Words into Numbers: Measuring News Media Coverage of Shortages," Discussion Papers 2023-8, Bank of Canada.
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bouri, Elie & Cepni, Oguzhan & Gupta, Rangan & Liu, Ruipeng, 2025. "Supply chain constraints and the predictability of the conditional distribution of international stock market returns and volatility," Economics Letters, Elsevier, vol. 247(C).
    2. Dhanashree Somani & Rangan Gupta & Sayar Karmakar & Vasilios Plakandaras, 2025. "Supply Bottlenecks and Machine Learning Forecasting of International Stock Market Volatility," Working Papers 202521, University of Pretoria, Department of Economics.
    3. Sara Boni & Massimiliano Caporin & Francesco Ravazzolo, 2024. "Nowcasting Inflation at Quantiles: Causality from Commodities," BEMPS - Bozen Economics & Management Paper Series BEMPS102, Faculty of Economics and Management at the Free University of Bozen.
    4. Zhangying Li & O-Chia Chuang & Rangan Gupta & Elie Bouri, 2025. "The Roles of Global Supply Chain Pressure and Economic Conditions in Forecasting the VaR of Commodity Markets: A Quantile GARCH-MIDAS Approach," Working Papers 202528, University of Pretoria, Department of Economics.
    5. Matteo Bonato & Rangan Gupta & Christian Pierdzioch, 2024. "Do Shortages Forecast Aggregate and Sectoral U.S. Stock Market Realized Variance? Evidence from a Century of Data," Working Papers 202450, University of Pretoria, Department of Economics.
    6. Dario Caldara & Matteo Iacoviello & David Yu, 2025. "Measuring Shortages since 1900," International Finance Discussion Papers 1407, Board of Governors of the Federal Reserve System (U.S.).
    7. Coibion, Olivier & Gorodnichenko, Yuriy, 2025. "Inflation, Expectations and Monetary Policy: What Have We Learned and to What End?," IZA Discussion Papers 17919, Institute of Labor Economics (IZA).
    8. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    9. Nickel, Christiane & Kilponen, Juha & Moral-Benito, Enrique & Koester, Gerrit & Ciccarelli, Matteo & Enders, Almira & Holton, Sarah & Landau, Bettina & Venditti, Fabrizio & Bobeica, Elena & Brand, Cla, 2025. "A strategic view on the economic and inflation environment in the euro area," Occasional Paper Series 371, European Central Bank.
    10. Labonne, Paul, 2025. "Asymmetric uncertainty: Nowcasting using skewness in real-time data," International Journal of Forecasting, Elsevier, vol. 41(1), pages 229-250.
    11. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Sowmya Subramaniam, 2020. "High-Frequency Movements of the Term Structure of Interest Rates of the United States: The Role of Oil Market Uncertainty," Working Papers 202085, University of Pretoria, Department of Economics.
    12. Dufrénot, Gilles & Rhouzlane, Meryem & Vaccaro-Grange, Etienne, 2022. "Potential growth and natural yield curve in Japan," Journal of International Money and Finance, Elsevier, vol. 124(C).
    13. Abeeb Olaniran & David Gabauer & Rangan Gupta & Onur Polat, 2025. "Predicting the Conditional Distribution of Risk Aversion The Role of Climate Risks in a Cross-Quantilogram Framework," Working Papers 202524, University of Pretoria, Department of Economics.
    14. Hie Joo Ahn & Leland E. Farmer, 2024. "Disagreement About the Term Structure of Inflation Expectations," Finance and Economics Discussion Series 2024-084, Board of Governors of the Federal Reserve System (U.S.).
    15. Yang, Shaopeng & Fu, Yuxi, 2025. "Interconnectedness among supply chain disruptions, energy crisis, and oil market volatility on economic resilience," Energy Economics, Elsevier, vol. 143(C).
    16. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    17. Balcilar, Mehmet & Gupta, Rangan & Wang, Shixuan & Wohar, Mark E., 2020. "Oil price uncertainty and movements in the US government bond risk premia," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    18. Laumer, Sebastian & Schaffer, Matthew, 2025. "Monetary policy transmission under supply chain pressure," European Economic Review, Elsevier, vol. 172(C).
    19. Quinlan Lee, Stephen Snudden, 2025. "Exact Mixed-Frequency Data Sampling (eMIDAS)," LCERPA Working Papers jc0157, Laurier Centre for Economic Research and Policy Analysis, revised Jun 2025.
    20. Puch González, Luis Antonio & Guinea, Laurentiu & Ruiz, Jesús, 2024. "The asymmetry puzzle: the supply chain disruptions news shocks effects on oil prices and inflation," UC3M Working papers. Economics 43758, Universidad Carlos III de Madrid. Departamento de Economía.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pre:wpaper:202526. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.