IDEAS home Printed from https://ideas.repec.org/p/cir/cirwor/2026s-06.html

Who Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure

Author

Listed:
  • Dalibor Stevanovic

Abstract

This paper studies the 2021 U.S. inflation forecasting failure. The author shows that the failure was primarily driven by sample composition rather than functional-form misspecification: estimation samples dominated by the Great Moderation underweight supply-shock regimes, and expectations anchored to that regime were slow to recognize the shift. Three historically informed adjustments, an intercept correction, a similarity re-estimation on 1970s data, and a kernel-weighted estimator, substantially close the forecast gap, and the gains extend to eight additional U.S. price indices. Household survey respondents over 60, whose lifetime includes the 1970s, reported higher inflation expectations from early 2021, consistent with experience-based learning; younger cohorts remained anchored to the prevailing regime. A controlled experiment with large language models conditioned on “experienced” and “young” professional personas confirms that experiential priors generate significant forecast differences under a common training leakage assumption. Across all three exercises, the source of the prior mattered more than the sophistication of the model. Cet article étudie l’échec des prévisions d’inflation aux États-Unis en 2021. L'auteur montre que cet échec s’explique principalement par la composition de l’échantillon d’estimation plutôt que par une mauvaise spécification de la forme fonctionnelle : des échantillons dominés par la période de la Grande Modération ont sous-pondéré les régimes marqués par des chocs d’offre, et des anticipations ancrées dans ce régime ont tardé à reconnaître le changement. Trois ajustements fondés sur l’expérience historique, une correction de constante, une ré-estimation par similarité à partir des données des années 1970, et un estimateur pondéré par noyau, réduisent substantiellement l’écart de prévision, et ces gains s’étendent à huit indices de prix américains supplémentaires. Les répondants aux enquêtes auprès des ménages âgés de plus de 60 ans, dont l’expérience de vie inclut les années 1970, ont déclaré des anticipations d’inflation plus élevées dès le début de 2021, ce qui est cohérent avec l’hypothèse d’un apprentissage fondé sur l’expérience ; les cohortes plus jeunes sont restées ancrées dans le régime dominant. Une expérience contrôlée utilisant de grands modèles de langage conditionnés par des profils professionnels « expérimentés » et « jeunes » confirme que des priors expérientiels génèrent des différences significatives de prévision sous une hypothèse commune de fuite d’information liée à l’entraînement. Dans les trois exercices, la source des croyances initiales a compté davantage que la sophistication du modèle.

Suggested Citation

  • Dalibor Stevanovic, 2026. "Who Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure," CIRANO Working Papers 2026s-06, CIRANO.
  • Handle: RePEc:cir:cirwor:2026s-06
    as

    Download full text from publisher

    File URL: https://cirano.qc.ca/files/publications/2026s-06.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben Bernanke & Olivier Blanchard, 2025. "What Caused the US Pandemic-Era Inflation?," American Economic Journal: Macroeconomics, American Economic Association, vol. 17(3), pages 1-35, July.
    2. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    3. Goulet Coulombe, Philippe & Marcellino, Massimiliano & Stevanović, Dalibor, 2021. "Can Machine Learning Catch The Covid-19 Recession?," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 71-109, May.
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    5. 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.
    6. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    7. Miguel Faria-e-Castro & Fernando Leibovici, 2024. "Artificial Intelligence and Inflation Forecasts," Review, Federal Reserve Bank of St. Louis, vol. 106(12), pages 1-14, November.
    8. Mathieu Pedemonte & Hiroshi Toma & Esteban Verdugo, 2023. "Aggregate Implications of Heterogeneous Inflation Expectations: The Role of Individual Experience," Working Papers 23-04, Federal Reserve Bank of Cleveland.
    9. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
    10. Zarifhonarvar, Ali, 2026. "Generating inflation expectations with large language models," Journal of Monetary Economics, Elsevier, vol. 157(C).
    11. Ulrike Malmendier & Stefan Nagel, 2016. "Learning from Inflation Experiences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(1), pages 53-87.
    12. Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024. "Large Language Models: An Applied Econometric Framework," Papers 2412.07031, arXiv.org, revised Dec 2025.
    13. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    14. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
    15. Tae‐Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Forecasting Under Structural Breaks Using Improved Weighted Estimation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1485-1501, December.
    16. M.Jahangir Alam & Shane Boyle & Huiyu Li & Tatevik Sekhposyan, 2026. "ChatMacro: Evaluating Inflation Forecasts of Generative AI," Working Paper Series 2026-04, Federal Reserve Bank of San Francisco.
    17. Ricardo Reis, 2021. "Losing the Inflation Anchors," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 52(2 (Fall)), pages 307-379.
    18. Tae‐Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Optimal forecast under structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 965-987, August.
    19. Diego A. Comin & Robert C. Johnson & Callum J. Jones, 2023. "Supply Chain Constraints and Inflation," NBER Working Papers 31179, National Bureau of Economic Research, Inc.
    20. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    21. Jean-Marie Dufour & Dalibor Stevanović, 2013. "Factor-Augmented VARMA Models With Macroeconomic Applications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 491-506, October.
    22. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    23. Pesaran, M. Hashem & Pick, Andreas & Pranovich, Mikhail, 2013. "Optimal forecasts in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 134-152.
    24. Tae-Hwy Lee & Ekaterina Seregina, 2022. "Combining Forecasts under Structural Breaks Using Graphical LASSO," Papers 2209.01697, arXiv.org, revised Sep 2023.
    25. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    26. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    27. Gardner, Ben & Scotti, Chiara & Vega, Clara, 2022. "Words speak as loudly as actions: Central bank communication and the response of equity prices to macroeconomic announcements," Journal of Econometrics, Elsevier, vol. 231(2), pages 387-409.
    28. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-494, Sept.-Oct.
    29. Etienne Briand & Massimiliano Marcellino & Dalibor Stevanovic, 2024. "Inflation, Attention and Expectations," Working Papers 24-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Dec 2024.
    30. 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. Dalibor Stevanovic, 2026. "Who Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure," Papers 2604.14467, arXiv.org.
    2. Barbara Rossi, 2021. "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them," Journal of Economic Literature, American Economic Association, vol. 59(4), pages 1135-1190, December.
    3. Philippe Goulet Coulombe, 2024. "The macroeconomy as a random forest," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 401-421, April.
    4. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    5. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    6. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    7. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    8. Anton Skrobotov, 2024. "Time series forecasting under structural breaks," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 76, pages 120-139.
    9. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
    10. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    11. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    12. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    13. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    14. Karanasos, Menelaos & Paraskevopoulos,Alexandros & Canepa, Alessandra, 2020. "Unified Theory for the Large Family of Time Varying Models with Arma Representations: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202008, University of Turin.
    15. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    16. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    17. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    18. Boot, Tom & Pick, Andreas, 2020. "Does modeling a structural break improve forecast accuracy?," Journal of Econometrics, Elsevier, vol. 215(1), pages 35-59.
    19. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    20. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • 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:cir:cirwor:2026s-06. 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: Webmaster (email available below). General contact details of provider: https://edirc.repec.org/data/ciranca.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.