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Forward Guidance with Bayesian Learning and Estimation

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Abstract

Considerable attention has been devoted to evaluating the macroeconomic effectiveness of the Federal Reserve's communications about future policy rates (forward guidance) in light of the U.S. economy's long spell at the zero lower bound (ZLB). In this paper, we study whether forward guidance represented a shift in the systematic description of monetary policy by estimating a New Keynesian model using Bayesian techniques. In doing so, we take into account the uncertainty that agents have about policy regimes using an incomplete information setup in which they update their beliefs using Bayes rule (Bayesian learning). We document a systematic change in U.S. policymakers' reaction function during the ZLB episode (2009-2016) that called for a persistently lower policy rate than in other regimes (we call this the forward guidance regime). Our estimates suggest that private sector agents were slow to learn about this change in real time, which limited the effectiveness of t he forward guidance regime in stimulating economic activity and curbing disinflationary pressure. We also show that the incomplete information specification of the model fits economic outcomes over the economy's long spell at the ZLB better than the full information specification.

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  • Christopher J. Gust & Edward P. Herbst & J. David López-Salido, 2018. "Forward Guidance with Bayesian Learning and Estimation," Finance and Economics Discussion Series 2018-072, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2018-72
    DOI: 10.17016/FEDS.2018.072
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    1. > Macroeconomics > Monetary Theory

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    Cited by:

    1. Krane, Spencer David & Melosi, Leonardo & Rottner, Matthias, 2023. "Learning monetary policy strategies at the effective lower bound with sudden surprises," Discussion Papers 22/2023, Deutsche Bundesbank.
    2. Tolga Özden, 2021. "Heterogeneous Expectations and the Business Cycle at the Effective Lower Bound," Working Papers 714, DNB.
    3. Spencer D. Krane & Leonardo Melosi & Matthias Rottner, 2023. "Learning Monetary Policy Strategies at the Effective Lower Bound with Sudden Surprises," Working Paper Series WP 2023-22, Federal Reserve Bank of Chicago.
    4. Martin Bodenstein & James Hebden & Fabian Winkler, 2019. "Learning and Misperception: Implications for Price-Level Targeting," Finance and Economics Discussion Series 2019-078, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    Keywords

    Bayesian estimation; Bayesian learning; forward guidance;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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