IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2024-63.html

Inflation Expectations with Finite Horizon Planning

Author

Abstract

Under finite horizon planning, households and firms evaluate a full set of state-contingent paths along which the economy might evolve out to a finite horizon but have limited ability to process events beyond that horizon. We show--analytically and empirically--that such a model accounts for an initial underreaction and subsequent overreaction of inflation forecasts. A planning horizon of four quarters can account for the evidence on the predictability of inflation forecast errors and macroeconomic data. Our identification and estimation strategies combine full-information methods based on aggregate data with regression-based estimates that directly use inflation expectations data.

Suggested Citation

  • Christopher J. Gust & Edward P. Herbst & J. David López-Salido, 2024. "Inflation Expectations with Finite Horizon Planning," Finance and Economics Discussion Series 2024-063, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2024-63
    DOI: 10.17016/FEDS.2024.063
    as

    Download full text from publisher

    File URL: https://www.federalreserve.gov/econres/feds/files/2024063pap.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.17016/FEDS.2024.063?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1295-1328.
    2. Sergey Slobodyan & Raf Wouters, 2012. "Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(2), pages 65-101, April.
    3. Michael Woodford, 2019. "Monetary Policy Analysis When Planning Horizons Are Finite," NBER Macroeconomics Annual, University of Chicago Press, vol. 33(1), pages 1-50.
    4. Carlos Carvalho & Stefano Eusepi & Emanuel Moench & Bruce Preston, 2023. "Anchored Inflation Expectations," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(1), pages 1-47, January.
    5. Arturo Ormeño & Krisztina Molnár, 2015. "Using Survey Data of Inflation Expectations in the Estimation of Learning and Rational Expectations Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(4), pages 673-699, June.
    6. Woodford, Michael & Xie, Yinxi, 2022. "Fiscal and monetary stabilization policy at the zero lower bound: Consequences of limited foresight," Journal of Monetary Economics, Elsevier, vol. 125(C), pages 18-35.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gust, Christopher & Herbst, Edward & López-Salido, David, 2025. "Optimal monetary policy with uncertain private sector foresight," Journal of Monetary Economics, Elsevier, vol. 155(C).
    2. Haochun Ma & Jordan Roulleau-Pasdeloup, 2025. "Clearing Up the Effective Lower Bound Morass," Papers 2511.04782, arXiv.org.

    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. Kuang, Pei & Mitra, Kaushik, 2016. "Long-run growth uncertainty," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 67-80.
    2. Na, Seunghoon & Xie, Yinxi, 2025. "Expectations and the UIP puzzles when foresight is limited," Journal of International Economics, Elsevier, vol. 158(C).
    3. Karel Musil & Stanislav Tvrz & Jan Vlcek, 2021. "News versus Surprise in Structural Forecasting Models: Central Bankers' Practical Perspective," Research and Policy Notes 2021/02, Czech National Bank, Research and Statistics Department.
    4. Rychalovska, Yuliya & Slobodyan, Sergey & Wouters, Raf, 2025. "Survey expectations, learning and inflation dynamics," European Economic Review, Elsevier, vol. 180(C).
    5. Christopher J. Gust & Arsenios Skaperdas, 2024. "Government Debt, Limited Foresight, and Longer-term Interest Rates," Finance and Economics Discussion Series 2024-027, Board of Governors of the Federal Reserve System (U.S.).
    6. Sylvain Leduc & Kevin Moran & Robert J. Vigfusson, 2023. "Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 392-407, March.
    7. Stefano Gnocchi & Fanny McKellips & Rodrigo Sekkel & Laure Simon & Yinxi Xie & Yang Zhang, 2024. "The Output-Inflation Trade-off in Canada," Discussion Papers 2024-07, Bank of Canada.
    8. Hagenhoff, Tim & Lustenhouwer, Joep, 2020. "The role of stickiness, extrapolation and past consensus forecasts in macroeconomic expectations," Working Papers 0686, University of Heidelberg, Department of Economics.
    9. Andrade, Philippe & Gautier, Erwan & Mengus, Eric, 2023. "What matters in households’ inflation expectations?," Journal of Monetary Economics, Elsevier, vol. 138(C), pages 50-68.
    10. Iliopulos, Eleni & Perego, Erica & Sopraseuth, Thepthida, 2021. "International business cycles: Information matters," Journal of Monetary Economics, Elsevier, vol. 123(C), pages 19-34.
    11. Ambrocio, Gene & Haavio, Markus & McClung, Nigel, 2024. "Monetary policy announcements and sacrifice ratios," Bank of Finland Research Discussion Papers 12/2024, Bank of Finland.
    12. Stéphane Dupraz & Hervé Le Bihan & Julien Matheron, 2022. "Make-up Strategies with Finite Planning Horizons but Forward-Looking Asset Prices," Working papers 862, Banque de France.
    13. Lustenhouwer, Joep, 2020. "Fiscal Stimulus In Expectations-Driven Liquidity Traps," Working Papers 0683, University of Heidelberg, Department of Economics.
    14. Cole, Stephen J. & Milani, Fabio, 2021. "Heterogeneity in individual expectations, sentiment, and constant-gain learning," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 627-650.
    15. Moučka, Jakub & Němec, Daniel, 2025. "Improving macroeconomic model credibility: Reducing reliance on frictions through observed inflation expectations," Economic Modelling, Elsevier, vol. 153(C).
    16. Sarantis Tsiaplias, 2024. "Inflation as a 'bad', heuristics and aggregate shocks: New evidence on expectation formation," Melbourne Institute Working Paper Series wp2024n03, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    17. Meggiorini, Greta, 2023. "Behavioral New Keynesian Models: An empirical assessment," Journal of Macroeconomics, Elsevier, vol. 77(C).
    18. F. Di Pace & K. Mitra & S. Zhang, 2021. "Adaptive Learning and Labor Market Dynamics," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(2-3), pages 441-475, March.
    19. Fabio Milani, 2012. "The Modeling of Expectations in Empirical DSGE Models: A Survey," Advances in Econometrics, in: DSGE Models in Macroeconomics: Estimation, Evaluation, and New Developments, pages 3-38, Emerald Group Publishing Limited.
    20. Alberto Ciancio & Camilo Garcia-Jimeno, 2026. "Anticipating State Action: Risk Perceptions and Consumption under Immigration Enforcement," Working Papers 2026_02, Business School - Economics, University of Glasgow.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E70 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - General

    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:fip:fedgfe:2024-63. 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: Ryan Wolfslayer ; Keisha Fournillier (email available below). General contact details of provider: https://edirc.repec.org/data/frbgvus.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.