IDEAS home Printed from https://ideas.repec.org/p/uta/papers/2023_04.html
   My bibliography  Save this paper

Structural shocks and trend inflation

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://economics.utah.edu/research/publications/2023-04.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yunjong Eo & Luis Uzeda & Benjamin Wong, 2023. "Understanding trend inflation through the lens of the goods and services sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 751-766, August.
    2. McNeil, James, 2023. "Monetary policy and the term structure of inflation expectations with information frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    3. 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.
    4. Guido Ascari & Luca Fosso, 2021. "The Inflation Rate Disconnect Puzzle: On the International Component of Trend Inflation and the Flattening of the Phillips Curve," Working Paper 2021/17, Norges Bank.
    5. 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.
    6. Mengheng Li & Siem Jan Koopman, 2021. "Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 614-627, August.
    7. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    8. Ascari, Guido & Fosso, Luca, 2024. "The international dimension of trend inflation," Journal of International Economics, Elsevier, vol. 148(C).
    9. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.
    10. Juan Angel Garcia & Aubrey Poon, 2022. "Inflation trends in Asia: implications for central banks [Are Phillips curves useful for forecasting inflation?]," Oxford Economic Papers, Oxford University Press, vol. 74(3), pages 671-700.
    11. Guido Ascari & Paolo Bonomolo & Qazi Haque, 2023. "The Long-Run Phillips Curve is ... a Curve," Working Papers 789, DNB.
    12. Roberto Duncan & Enrique Martínez‐García, 2023. "Forecasting inflation in open economies: What can a NOEM model do?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 481-513, April.
    13. Lukmanova, Elizaveta & Rabitsch, Katrin, 2023. "Evidence on monetary transmission and the role of imperfect information: Interest rate versus inflation target shocks," European Economic Review, Elsevier, vol. 158(C).
    14. Ha, Jongrim & Kose, M. Ayhan & Ohnsorge, Franziska, 2023. "One-stop source: A global database of inflation," Journal of International Money and Finance, Elsevier, vol. 137(C).
    15. Florian Huber & Daniel Kaufmann, 2020. "Trend Fundamentals and Exchange Rate Dynamics," Economica, London School of Economics and Political Science, vol. 87(348), pages 1016-1036, October.
    16. Hervé Le Bihan & Danilo Leiva-León & Matías Pacce, 2023. "Underlying inflation and asymetric risks," Working Papers 2319, Banco de España.
    17. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    18. Kristin Forbes & Lewis Kirkham & Konstantinos Theodoridis, 2021. "A Trendy Approach to UK Inflation Dynamics," Manchester School, University of Manchester, vol. 89(S1), pages 23-75, September.
    19. Barbara Rossi & Atsushi Inoue & Yiru Wang, 2024. "Has the Phillips curve flattened?," French Stata Users' Group Meetings 2024 22, Stata Users Group.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:uta:papers:2023_04. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/deuutus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.