IDEAS home Printed from https://ideas.repec.org/a/eee/moneco/v145y2024iss0304393224000230.html
   My bibliography  Save this article

Inflation at risk

Author

Listed:
  • López-Salido, David
  • Loria, Francesca

Abstract

Inflation at risk (IaR) refers to the tails of the distribution of inflation over a forecast horizon. We study IaR using quantile regressions in a panel of OECD countries for a sample that includes the Global Financial Crisis and the rise in inflation during the Covid-19 pandemic. First, we find that even though recently the conditional mean of inflation has been low and stable, there was ample variability in the tails. Second, financial conditions have a nonlinear effect on the predictive inflation distribution. Third, the role of economic drivers of IaR has changed over time. Our approach to measure tails complements others using financial market quotes and survey data.

Suggested Citation

  • López-Salido, David & Loria, Francesca, 2024. "Inflation at risk," Journal of Monetary Economics, Elsevier, vol. 145(S).
    • Handle: RePEc:eee:moneco:v:145:y:2024:i:s:s0304393224000230
    DOI: 10.1016/j.jmoneco.2024.103570
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304393224000230
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmoneco.2024.103570?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Simon Gilchrist & Raphael Schoenle & Jae Sim & Egon Zakrajšek, 2017. "Inflation Dynamics during the Financial Crisis," American Economic Review, American Economic Association, vol. 107(3), pages 785-823, March.
    2. Carlos Montes-Galdón & Eva Ortega, 2022. "Skewed SVARs: Tracking the Structural Sources of Macroeconomic Tail Risks," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 177-210, Emerald Group Publishing Limited.
    3. Stephen G. Cecchetti, 2008. "Measuring the Macroeconomic Risks Posed by Asset Price Booms," NBER Chapters, in: Asset Prices and Monetary Policy, pages 9-43, National Bureau of Economic Research, Inc.
    4. Lutz Kilian & Simone Manganelli, 2007. "Quantifying the Risk of Deflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2-3), pages 561-590, March.
    5. Ana Beatriz Galvão & Michael T. Owyang, 2018. "Financial Stress Regimes and the Macroeconomy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(7), pages 1479-1505, October.
    6. Olesya Grishchenko & Sarah Mouabbi & Jean‐Paul Renne, 2019. "Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(5), pages 1053-1096, August.
    7. Marco Del Negro & Marc P. Giannoni & Frank Schorfheide, 2015. "Inflation in the Great Recession and New Keynesian Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 168-196, January.
    8. N. Gregory Mankiw & Ricardo Reis & Justin Wolfers, 2004. "Disagreement about Inflation Expectations," NBER Chapters, in: NBER Macroeconomics Annual 2003, Volume 18, pages 209-270, National Bureau of Economic Research, Inc.
    9. Andrade, P. & Ghysels, E. & Idier, J., 2012. "Tails of Inflation Forecasts and Tales of Monetary Policy," Working papers 407, Banque de France.
    10. Andrade, Philippe & Crump, Richard K. & Eusepi, Stefano & Moench, Emanuel, 2016. "Fundamental disagreement," Journal of Monetary Economics, Elsevier, vol. 83(C), pages 106-128.
    11. Machado, José A.F. & Santos Silva, J.M.C., 2019. "Quantiles via moments," Journal of Econometrics, Elsevier, vol. 213(1), pages 145-173.
    12. Manzan, Sebastiano & Zerom, Dawit, 2013. "Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?," International Journal of Forecasting, Elsevier, vol. 29(3), pages 469-478.
    13. Rossi, Barbara & Sekhposyan, Tatevik, 2019. "Alternative tests for correct specification of conditional predictive densities," Journal of Econometrics, Elsevier, vol. 208(2), pages 638-657.
    14. Lawrence J. Christiano & Martin S. Eichenbaum & Mathias Trabandt, 2015. "Understanding the Great Recession," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 110-167, January.
    15. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    16. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    17. Lutz Kilian & Simone Manganelli, 2008. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1103-1129, September.
    18. Wolters Maik H. & Tillmann Peter, 2015. "The changing dynamics of US inflation persistence: a quantile regression approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 161-182, April.
    19. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    20. Giovanni Favara & Simon Gilchrist & Kurt F. Lewis & Egon Zakrajšek, 2016. "Updating the Recession Risk and the Excess Bond Premium," FEDS Notes 2016-10-06, Board of Governors of the Federal Reserve System (U.S.).
    21. Chortareas, Georgios & Magonis, George & Panagiotidis, Theodore, 2012. "The asymmetry of the New Keynesian Phillips Curve in the euro-area," Economics Letters, Elsevier, vol. 114(2), pages 161-163.
    22. Ricardo Reis, 2021. "Losing the Inflation Anchors," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 52(2 (Fall)), pages 307-379.
    23. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    24. Yoshibumi Makabe & Yoshihiko Norimasa, 2022. "The Term Structure of Inflation at Risk: A Panel Quantile Regression Approach," Bank of Japan Working Paper Series 22-E-4, Bank of Japan.
    25. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    26. Blanchard, Oliver & Cerutti, Eugenio & SUmmers, Lawrence, 2015. "Inflation and Activity - Two Explorations and Their Monetary Policy Implications," Working Paper Series 15-070, Harvard University, John F. Kennedy School of Government.
    27. O. Grishchenko & S. Mouabbi & J.-P. Renne, 2017. "The Joint Dynamics of U.S. and Euro-area Inflation Rates: Expectations and Time-varying Uncertainty," Working papers 622, Banque de France.
    28. Ryan Niladri Banerjee & Juan Contreras & Aaron Mehrotra & Fabrizio Zampolli, 2020. "Inflation at risk in advanced and emerging economies," BIS Working Papers 883, Bank for International Settlements.
    29. Xu, Qifa & Niu, Xufeng & Jiang, Cuixia & Huang, Xue, 2015. "The Phillips curve in the US: A nonlinear quantile regression approach," Economic Modelling, Elsevier, vol. 49(C), pages 186-197.
    30. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    31. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.
    32. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    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. J. David López-Salido & Francesca Loria, 2020. "Inflation at Risk," Finance and Economics Discussion Series 2020-013, Board of Governors of the Federal Reserve System (U.S.).
    2. López-Salido, J David & Loria, Francesca, 2019. "Inflation at Risk," CEPR Discussion Papers 14074, C.E.P.R. Discussion Papers.
    3. Banerjee, Ryan & Contreras, Juan & Mehrotra, Aaron & Zampolli, Fabrizio, 2024. "Inflation at risk in advanced and emerging market economies," Journal of International Money and Finance, Elsevier, vol. 142(C).
    4. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    5. Conti, Antonio M., 2021. "Resurrecting the Phillips Curve in Low-Inflation Times," Economic Modelling, Elsevier, vol. 96(C), pages 172-195.
    6. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    8. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    9. Yoshibumi Makabe & Yoshihiko Norimasa, 2022. "The Term Structure of Inflation at Risk: A Panel Quantile Regression Approach," Bank of Japan Working Paper Series 22-E-4, Bank of Japan.
    10. Ryan Niladri Banerjee & Juan Contreras & Aaron Mehrotra & Fabrizio Zampolli, 2020. "Inflation at risk in advanced and emerging economies," BIS Working Papers 883, Bank for International Settlements.
    11. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    12. James Mitchell & Aubrey Poon & Dan Zhu, 2024. "Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 790-812, August.
    13. Adams, Patrick A. & Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico, 2021. "Forecasting macroeconomic risks," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1173-1191.
    14. Antonio M. Conti & Concetta Gigante, 2018. "Weakness in Italy�s core inflation and the Phillips curve: the role of labour and financial indicators," Questioni di Economia e Finanza (Occasional Papers) 466, Bank of Italy, Economic Research and International Relations Area.
    15. Stéphane Lhuissier & Aymeric Ortmans & Fabien Tripier, 2024. "The Risk of Inflation Dispersion in the Euro Area," Working papers 954, Banque de France.
    16. Ascari, Guido & Fosso, Luca, 2024. "The international dimension of trend inflation," Journal of International Economics, Elsevier, vol. 148(C).
    17. Caldara, Dario & Ferrante, Francesco & Iacoviello, Matteo & Prestipino, Andrea & Queralto, Albert, 2024. "The international spillovers of synchronous monetary tightening," Journal of Monetary Economics, Elsevier, vol. 141(C), pages 127-152.
    18. Anthony Garratt & Ivan Petrella, 2022. "Commodity prices and inflation risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 392-414, March.
    19. Richard K. Crump & Stefano Eusepi & Emanuel Moench & Bruce Preston, 2021. "The Term Structure of Expectations," Staff Reports 992, Federal Reserve Bank of New York.

    More about this item

    Keywords

    Inflation risks; Quantile regression;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

    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:eee:moneco:v:145:y:2024:i:s:s0304393224000230. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505566 .

    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.