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Structural shocks and trend inflation

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  • Bowen Fu, Ivan Mendieta-Muñoz

Abstract

We propose an unobserved components model with stochastic volatility and structural shocks to explore the relevant factors that influence trend inflation in the USA. Using structural shocks that incorporate a broad set of information for the US economy, we find that four structural shocks have significant effects on trend inflation: productivity, price mark-up, government policy, and finance. During and in the aftermath of the Great Recession, trend inflation became more volatile after incorporating the structural shocks, implying that long-run inflation expectations tended to be less well-anchored in these periods.

Suggested Citation

  • Bowen Fu, Ivan Mendieta-Muñoz, 2023. "Structural shocks and trend inflation," Working Paper Series, Department of Economics, University of Utah 2023_04, University of Utah, Department of Economics.
  • Handle: RePEc:uta:papers:2023_04
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    File URL: https://economics.utah.edu/research/publications/2023-04.pdf
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    References listed on IDEAS

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    1. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2016. "A Bounded Model of Time Variation in Trend Inflation, Nairu and the Phillips Curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 551-565, April.
    2. Regis Barnichon & Geert Mesters, 2020. "Identifying Modern Macro Equations with Old Shocks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(4), pages 2255-2298.
    3. Dupor, Bill & Li, Rong, 2015. "The expected inflation channel of government spending in the postwar U.S," European Economic Review, Elsevier, vol. 74(C), pages 36-56.
    4. James H. Stock & Mark W. Watson, 2016. "Core Inflation and Trend Inflation," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 770-784, October.
    5. Kamber, Güneş & Wong, Benjamin, 2020. "Global factors and trend inflation," Journal of International Economics, Elsevier, vol. 122(C).
    6. Elmar Mertens & James M. Nason, 2020. "Inflation and professional forecast dynamics: An evaluation of stickiness, persistence, and volatility," Quantitative Economics, Econometric Society, vol. 11(4), pages 1485-1520, November.
    7. Elmar Mertens, 2016. "Measuring the Level and Uncertainty of Trend Inflation," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 950-967, December.
    8. 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.
    9. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    10. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    11. James M. Nason & Gregor W. Smith, 2021. "Measuring the slowly evolving trend in US inflation with professional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 1-17, January.
    12. 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.
    13. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    14. Shih‐Tang Hwu & Chang‐Jin Kim, 2019. "Estimating Trend Inflation Based on Unobserved Components Model: Is It Correlated with the Inflation Gap?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(8), pages 2305-2319, December.
    15. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
    16. Mr. Maxym Kryshko, 2011. "Bayesian Dynamic Factor Analysis of a Simple Monetary DSGE Model," IMF Working Papers 2011/219, International Monetary Fund.
    17. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    18. Sacha Gelfer, 2019. "Data-Rich DSGE Model Forecasts of the Great Recession and its Recovery," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 32, pages 18-41, April.
    19. 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.
    20. Cecchetti, Stephen & Feroli, Michael & Hooper, Peter & Kashyap, Anil & Schoenholtz, Kermit L., 2017. "Deflating Inflation Expectations: The Implications of Inflation’s Simple Dynamics," CEPR Discussion Papers 11925, C.E.P.R. Discussion Papers.
    21. Benabou, Roland, 1992. "Inflation and markups : Theories and evidence from the retail trade sector," European Economic Review, Elsevier, vol. 36(2-3), pages 566-574, April.
    22. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    23. 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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    More about this item

    Keywords

    trend inflation; structural shocks; dynamics of inflation expectations; unobserved components; stochastic volatility. JEL Classification: C11; C32; E31; E37;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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