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Asymmetry in the conditional distribution of euro-area inflation

Author

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  • Alex Tagliabracci

    (Bank of Italy)

Abstract

Macroeconomic conditions are among the key determinants of the inflation outlook. This paper studies how business cycles affect the conditional distribution of euro-area inflation forecasts. Using a quantile regression approach, I estimate the conditional distribution of inflation to assess the impact of business cycle conditions over time and the possible asymmetries across quantiles of inflation. Interestingly, downside risks to inflation forecasts are related to the business cycle while upside risks are instead relatively stable over time and are not affected by the state of the economy.

Suggested Citation

  • 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.
    • Handle: RePEc:bdi:wptemi:td_1270_20
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    File URL: https://www.bancaditalia.it/pubblicazioni/temi-discussione/2020/2020-1270/en_tema_1270.pdf
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    References listed on IDEAS

    as
    1. Andrade, Philippe & Fourel, Valère & Ghysels, Eric & Idier, Julien, 2014. "The financial content of inflation risks in the euro area," International Journal of Forecasting, Elsevier, vol. 30(3), pages 648-659.
    2. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    3. López-Salido, J David & Loria, Francesca, 2019. "Inflation at Risk," CEPR Discussion Papers 14074, C.E.P.R. Discussion Papers.
    4. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    5. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    6. 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.
    7. Rossi, Barbara & Sekhposyan, Tatevik, 2019. "Alternative tests for correct specification of conditional predictive densities," Journal of Econometrics, Elsevier, vol. 208(2), pages 638-657.
    8. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    9. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    10. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    11. Ciccarelli, Matteo & Osbat, Chiara, 2017. "Low inflation in the euro area: Causes and consequences," Occasional Paper Series 181, European Central Bank.
    12. Fabio Busetti & Michele Caivano & Lisa Rodano, 2015. "On the conditional distribution of euro area inflation forecast," Temi di discussione (Economic working papers) 1027, Bank of Italy, Economic Research and International Relations Area.
    13. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
    14. Tsong, Ching-Chuan & Lee, Cheng-Feng, 2011. "Asymmetric inflation dynamics: Evidence from quantile regression analysis," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 668-680.
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    Cited by:

    1. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    2. Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021. "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, vol. 101(C).
    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. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.

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    More about this item

    Keywords

    inflation; quantile regression; conditional distribution; asymmetry; downside risks;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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