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The Dynamic Persistence of Economic Shocks

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  • Jozef Barunik
  • Lukas Vacha

Abstract

This paper presents a model for smoothly varying heterogeneous persistence of economic data. We argue that such dynamics arise naturally from the dynamic nature of economic shocks with various degree of persistence. The identification of such dynamics from data is done using localised regressions. Empirically, we identify rich persistence structures that change smoothly over time in two important data sets: inflation, which plays a key role in policy formulation, and stock volatility, which is crucial for risk and market analysis.

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  • Jozef Barunik & Lukas Vacha, 2023. "The Dynamic Persistence of Economic Shocks," Papers 2306.01511, arXiv.org.
    • Handle: RePEc:arx:papers:2306.01511
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    1. HÄRDLE, Wolfgang & VIEU, Philippe, 1992. "Kernel regression smoothing of time series," LIDAM Reprints CORE 981, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Bekierman, Jeremias & Manner, Hans, 2018. "Forecasting realized variance measures using time-varying coefficient models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 276-287.
    3. Ian Dew-Becker & Stefano Giglio, 2016. "Asset Pricing in the Frequency Domain: Theory and Empirics," The Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2029-2068.
    4. Alejandro Justiniano & Giorgio E. Primiceri, 2008. "The Time-Varying Volatility of Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 98(3), pages 604-641, June.
    5. James H. Stock & Mark W. Watson, 2017. "Twenty Years of Time Series Econometrics in Ten Pictures," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 59-86, Spring.
    6. Perron, Pierre, 1991. "A Continuous Time Approximation to the Unstable First-Order Autoregressive Process: The Case without an Intercept," Econometrica, Econometric Society, vol. 59(1), pages 211-236, January.
    7. Nathan S. Balke & Mark E. Wohar, 2002. "Low-Frequency Movements in Stock Prices: A State-Space Decomposition," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 649-667, November.
    8. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    9. 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.
    10. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    11. Neuhierl, Andreas & Varneskov, Rasmus T., 2021. "Frequency dependent risk," Journal of Financial Economics, Elsevier, vol. 140(2), pages 644-675.
    12. Bandi, Federico M. & Tamoni, Andrea, 2022. "Spectral Financial Econometrics," Econometric Theory, Cambridge University Press, vol. 38(6), pages 1175-1220, December.
    13. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    14. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
    15. Holger Dette & Weichi Wu, 2022. "Prediction in Locally Stationary Time Series," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 370-381, January.
    16. Cochrane, John H, 1988. "How Big Is the Random Walk in GNP?," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 893-920, October.
    17. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    18. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    19. Dahlhaus, R., 1996. "On the Kullback-Leibler information divergence of locally stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 62(1), pages 139-168, March.
    20. James H. Stock & Mark W. Watson, 2007. "Erratum to “Why Has U.S. Inflation Become Harder to Forecast?”," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    21. Kevin Lansing, 2009. "Time Varying U.S. Inflation Dynamics and the New Keynesian Phillips Curve," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(2), pages 304-326, April.
    22. Xiangjin B. Chen & Jiti Gao & Degui Li & Param Silvapulle, 2018. "Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 88-100, January.
    23. Wolfgang Härdle & Philippe Vieu, 1992. "Kernel Regression Smoothing Of Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(3), pages 209-232, May.
    24. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
    25. Lima, Luiz Renato & Xiao, Zhijie, 2007. "Do shocks last forever? Local persistency in economic time series," Journal of Macroeconomics, Elsevier, vol. 29(1), pages 103-122, March.
    26. Fulvio Ortu & Federico Severino & Andrea Tamoni & Claudio Tebaldi, 2020. "A persistence‐based Wold‐type decomposition for stationary time series," Quantitative Economics, Econometric Society, vol. 11(1), pages 203-230, January.
    27. Evans, G B A & Savin, N E, 1981. "Testing for Unit Roots: 1," Econometrica, Econometric Society, vol. 49(3), pages 753-779, May.
    28. 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.
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    Cited by:

    1. Jozef Barunik & Lukas Vacha, 2024. "Predicting the volatility of major energy commodity prices: the dynamic persistence model," Papers 2402.01354, arXiv.org, revised Jul 2024.

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