IDEAS home Printed from https://ideas.repec.org/p/lan/wpaper/423478673.html
   My bibliography  Save this paper

Belief Distortions and Disagreement about Inflation

Author

Listed:
  • Stefano Fasani
  • Valeria Patella
  • Giuseppe Pagano Giorgianni
  • Lorenza Rossi

Abstract

This paper investigates the macroeconomic effects of a belief distortion shock—an unexpected increase in the wedge between household and professional forecaster inflation expectations. Using survey and macro data alongside machine-learning techniques, we identify this shock and examine its effects within and outside the ZLB, while conditioning on the degree of inflation disagreement. The shock increases unemployment during normal times, whereas it reduces it in the ZLB, when the monetary stance is accommodative. Inflation disagreement instead dampens the expansionary effects of the shock. A New Keynesian model with belief distortion shocks replicates these dynamics and reproduces the inflation disagreement empirical patterns.

Suggested Citation

  • Stefano Fasani & Valeria Patella & Giuseppe Pagano Giorgianni & Lorenza Rossi, 2025. "Belief Distortions and Disagreement about Inflation," Working Papers 423478673, Lancaster University Management School, Economics Department.
  • Handle: RePEc:lan:wpaper:423478673
    as

    Download full text from publisher

    File URL: http://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/economics/working-papers/LancasterWP2025_007.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    2. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    3. 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.
    4. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2014. "A Flexible Finite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 638-647, October.
    5. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    6. Diegel, Max & Nautz, Dieter, 2021. "Long-term inflation expectations and the transmission of monetary policy shocks: Evidence from a SVAR analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
    7. Guido Lorenzoni, 2009. "A Theory of Demand Shocks," American Economic Review, American Economic Association, vol. 99(5), pages 2050-2084, December.
    8. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.
    9. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    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. Brianti, Marco & Cormun, Vito, 2024. "Expectation-driven boom-bust cycles," Journal of Monetary Economics, Elsevier, vol. 146(C).
    2. José-Elías Gallegos, 2023. "Inflation persistence, noisy information and the Phillips curve," Working Papers 2309, Banco de España.
    3. Elstner, Steffen & Grimme, Christian & Kecht, Valentin & Lehmann, Robert, 2022. "The diffusion of technological progress in ICT," European Economic Review, Elsevier, vol. 149(C).
    4. George‐Marios Angeletos & Fabrice Collard & Harris Dellas, 2018. "Quantifying Confidence," Econometrica, Econometric Society, vol. 86(5), pages 1689-1726, September.
    5. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    6. Zeno Enders & Michael Kleemann & Gernot J. Muller, 2021. "Growth Expectations, Undue Optimism, and Short-Run Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 103(5), pages 905-921, December.
    7. Benhima, Kenza, 2019. "Booms and busts with dispersed information," Journal of Monetary Economics, Elsevier, vol. 107(C), pages 32-47.
    8. Jean-Paul L’Huillier & Sanjay R Singh & Donghoon Yoo, 2024. "Incorporating Diagnostic Expectations into the New Keynesian Framework," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(5), pages 3013-3046.
    9. Geiger, Martin & Gründler, Daniel & Scharler, Johann, 2023. "Monetary policy shocks and consumer expectations in the euro area," Journal of International Economics, Elsevier, vol. 140(C).
    10. Cai, Yifei & Mignon, Valérie & Saadaoui, Jamel, 2022. "Not all political relation shocks are alike: Assessing the impacts of US–China tensions on the oil market," Energy Economics, Elsevier, vol. 114(C).
    11. George-Marios Angeletos, 2018. "Frictional Coordination," Journal of the European Economic Association, European Economic Association, vol. 16(3), pages 563-603.
    12. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2024. "Averaging impulse responses using prediction pools," Journal of Monetary Economics, Elsevier, vol. 146(C).
    13. Ferreira, Leonardo N., 2022. "Forward guidance matters: Disentangling monetary policy shocks," Journal of Macroeconomics, Elsevier, vol. 73(C).
    14. Leonardo Melosi, 2017. "Signalling Effects of Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(2), pages 853-884.
    15. Aruoba, S. Borağan & Drechsel, Thomas, 2024. "The long and variable lags of monetary policy: Evidence from disaggregated price indices," Journal of Monetary Economics, Elsevier, vol. 148(S).
    16. Falck, Elisabeth & Hoffmann, Mathias & Hürtgen, Patrick, 2017. "Disagreement and monetary policy," Discussion Papers 29/2017, Deutsche Bundesbank.
    17. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    18. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    19. Johan Brannlund & Geoffrey R. Dunbar & Reinhard Ellwanger, 2022. "Are Temporary Oil Supply Shocks Real?," Staff Working Papers 22-52, Bank of Canada.
    20. Bampinas, Georgios & Panagiotidis, Theodore & Papapanagiotou, Georgios, 2023. "Oil shocks and investor attention," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 68-81.

    More about this item

    Keywords

    Inflation; Belief Distortion Shock; Inflation Disagreement; Households Expectation; Machine Learning; Local Projections; New Keynesian model; Monetary Policy; ZLB;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • 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

    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:lan:wpaper:423478673. 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: Giorgio Motta (email available below). General contact details of provider: https://edirc.repec.org/data/delanuk.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.