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Asymmetric Quantile Persistence and Predictability: the Case of US Inflation

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  • Sebastiano Manzan
  • Dawit Zerom

Abstract

type="main" xml:id="obes12065-abs-0001"> This article investigates the evidence of time-variation and asymmetry in the persistence of US inflation. We compare the out-of-sample performance of different forecasting models and find that quantile forecasts from an Auto-Regressive (AR) model with level-dependent volatility are at least as accurate as the forecasts of the Quantile Auto-Regressive model, in particular for the core inflation measures. Our results indicate that the persistence of core inflation has been relatively constant and high, but it declined for the headline inflation measures. We also find that the asymmetric persistence of inflation shocks can be mostly attributed to the positive relation between inflation level and its volatility.

Suggested Citation

  • Sebastiano Manzan & Dawit Zerom, 2015. "Asymmetric Quantile Persistence and Predictability: the Case of US Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(2), pages 297-318, April.
    • Handle: RePEc:bla:obuest:v:77:y:2015:i:2:p:297-318
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    Cited by:

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    3. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2024. "Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1099-1127, August.
    5. Nicholas Apergis, 2022. "Evaluating tail risks for the U.S. economic policy uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3971-3989, October.
    6. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    7. Nicholas Apergis, 2023. "Forecasting energy prices: Quantile‐based risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 17-33, January.
    8. Massimiliano Caporin & Rangan Gupta, 2017. "Time-varying persistence in US inflation," Empirical Economics, Springer, vol. 53(2), pages 423-439, September.
    9. Inês da Cunha Cabral & João Nicolau, 2022. "Inflation in the G7 and the expected time to reach the reference rate: A nonparametric approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1608-1620, April.
    10. Phiri, Andrew, 2017. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) model," MPRA Paper 79956, University Library of Munich, Germany.
    11. Andrew Phiri, 2018. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) approach," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(1), pages 97-104, January.
    12. Rangan Gupta & Charl Jooste & Omid Ranjbar, 2015. "The Changing Dynamics of South Africa's Inflation Persistence: Evidence from a Quantile Regression Framework," Working Papers 201563, University of Pretoria, Department of Economics.
    13. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2024. "Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1302-1317, October.
    14. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    15. Christian Bauer & Sebastian Weber, 2016. "The Efficiency of Monetary Policy when Guiding Inflation Expectations," Research Papers in Economics 2016-14, University of Trier, Department of Economics.
    16. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2024. "Climate Risks and Forecastability of US Inflation: Evidence from Dynamic Quantile Model Averaging," Working Papers 202420, University of Pretoria, Department of Economics.
    17. Rangan Gupta & Charl Jooste & Omid Ranjbar, 2017. "South Africa’s inflation persistence: a quantile regression framework," Economic Change and Restructuring, Springer, vol. 50(4), pages 367-386, November.
    18. Wagner Piazza Gaglianone & Osmani Teixeira de Carvalho Guillén & Francisco Marcos Rodrigues Figueiredo, 2015. "Local Unit Root and Inflationary Inertia in Brazil," Working Papers Series 406, Central Bank of Brazil, Research Department.
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