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Inflation at Risk

Author

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  • López-Salido, J David
  • Loria, Francesca

Abstract

We find that the recent muted response of the conditional mean of inflation to economic conditions does not convey an adequate representation of the overall pattern of inflation dynamics. Analyzing data from the 1970s reveals ample variability in the entire conditional distribution of inflation. Focusing on the period from 2000 onward bolsters this evidence. Using time-series data for the United States and the Euro Area, we document that looking at the entire conditional distribution of inflation uncovers – after controlling for the state of the labor market and inflation expectations – that heightened financial conditions carry substantial and persistent low-inflation risks, a feature overlooked by much of the literature. Our paper offers a new empirical perspective to existing macroeconomic models, showing that changes in credit conditions are also key to understand the dynamics of the inflation tails.

Suggested Citation

  • López-Salido, J David & Loria, Francesca, 2019. "Inflation at Risk," CEPR Discussion Papers 14074, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:14074
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    References listed on IDEAS

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    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. Laurence Ball & Sandeep Mazumder, 2019. "A Phillips Curve with Anchored Expectations and Short‐Term Unemployment," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(1), pages 111-137, February.
    3. Michael McLeay & Silvana Tenreyro, 2020. "Optimal Inflation and the Identification of the Phillips Curve," NBER Macroeconomics Annual, University of Chicago Press, vol. 34(1), pages 199-255.
    4. 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.
    5. Berkowitz, J. & Birgean, I. & Kilian, L., 1999. "On the Finite-Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series," Papers 99-01, Michigan - Center for Research on Economic & Social Theory.
    6. Rossi, Barbara & Sekhposyan, Tatevik, 2019. "Alternative tests for correct specification of conditional predictive densities," Journal of Econometrics, Elsevier, vol. 208(2), pages 638-657.
    7. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    8. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    9. 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.
    10. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    11. 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.
    12. repec:hal:wpspec:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    13. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575.
    14. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    15. 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.
    16. James H. Stock & Mark W. Watson, 2019. "Slack and Cyclically Sensitive Inflation," NBER Working Papers 25987, National Bureau of Economic Research, Inc.
    17. repec:hal:spmain:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    18. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
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    Cited by:

    1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Danilo Cascaldi-Garcia & Cisil Sarisoy & Juan M. Londono & Bo Sun & Deepa D. Datta & Thiago Ferreira & Olesya Grishchenko & Mohammad R. Jahan-Parvar & Francesca Loria & Sai Ma & Marius Rodriguez & Ilk, 2023. "What Is Certain about Uncertainty?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 624-654, June.
    3. Alex Tagliabracci, 2020. "Asymmetry in the conditional distribution of euro-area inflation," Temi di discussione (Economic working papers) 1270, Bank of Italy, Economic Research and International Relations Area.
    4. Rottner, Matthias, 2022. "Financial crises and shadow banks: A quantitative analysis," Discussion Papers 15/2022, Deutsche Bundesbank.

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    More about this item

    Keywords

    Quantile regression; Inflation risk;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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