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Modeling the Evolution of Expectations and Uncertainty in General Equilibrium

Author

Listed:
  • Francesco Bianchi
  • Leonardo Melosi
Abstract
This paper develops methods to study the evolution of agents' expectations and uncertainty in general equilibrium models. A central insight consists of recognizing that the evolution of agents' beliefs can be captured by defining a set of regimes that are characterized by the degree of agents' pessimism, optimism, and uncertainty about future equilibrium outcomes. Once this kind of structure is imposed, it is possible to create a mapping between the evolution of agents' beliefs and observable outcomes. Agents in the model are fully rational, conduct Bayesian learning, and they know that they do not know. Therefore, agents form expectations taking into account that their beliefs will evolve according to what they observe in the future. The new modeling framework accommodates both gradual and abrupt changes in agents' beliefs and allows an analytical characterization of uncertainty. Shocks to beliefs are shown to have both first-order and second-order effects. To illustrate how to apply the methods, we use a prototypical Real Business Cycle model in which households form beliefs about the likely duration of high-growth and low-growth regimes.

Suggested Citation

  • Francesco Bianchi & Leonardo Melosi, 2013. "Modeling the Evolution of Expectations and Uncertainty in General Equilibrium," Working Papers 13-14, Duke University, Department of Economics.
    • Handle: RePEc:duk:dukeec:13-14
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    References listed on IDEAS

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    1. Nimark, Kristoffer, 2008. "Dynamic pricing and imperfect common knowledge," Journal of Monetary Economics, Elsevier, vol. 55(2), pages 365-382, March.
    2. Francesco Bianchi & Cosmin L. Ilut & Martin Schneider, 2018. "Uncertainty Shocks, Asset Supply and Pricing over the Business Cycle," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 810-854.
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    8. Francesco Bianchi & Leonardo Melosi, 2018. "Constrained Discretion and Central Bank Transparency," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 187-202, March.
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    15. Francesco Bianchi & Leonardo Melosi, 2014. "Dormant Shocks and Fiscal Virtue," NBER Macroeconomics Annual, University of Chicago Press, vol. 28(1), pages 1-46.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Stéphane Lhuissier & Fabien Tripier, 2021. "Regime‐dependent effects of uncertainty shocks: A structural interpretation," Quantitative Economics, Econometric Society, vol. 12(4), pages 1139-1170, November.
    2. 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.
    3. Tao Zha & Juan F. Rubio-Ramirez & Daniel F. Waggoner & Andrew T. Foerster, 2010. "Perturbation Methods for Markov-Switching Models," 2010 Meeting Papers 239, Society for Economic Dynamics.
    4. Francesco Bianchi & Leonardo Melosi, 2018. "Constrained Discretion and Central Bank Transparency," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 187-202, March.
    5. Andrew Foerster & Juan F. Rubio‐Ramírez & Daniel F. Waggoner & Tao Zha, 2016. "Perturbation methods for Markov‐switching dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 7(2), pages 637-669, July.
    6. Harrison, Ricahrd, 2014. "Estimating the effects of forward guidance in rational expectations models," LSE Research Online Documents on Economics 86327, London School of Economics and Political Science, LSE Library.
    7. Harrison, Richard, 2015. "Estimating the effects of forward guidance in rational expectations models," European Economic Review, Elsevier, vol. 79(C), pages 196-213.
    8. Bianchi, Francesco, 2016. "Methods for measuring expectations and uncertainty in Markov-switching models," Journal of Econometrics, Elsevier, vol. 190(1), pages 79-99.
    9. Borovicka, J. & Hansen, L.P., 2016. "Term Structure of Uncertainty in the Macroeconomy," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1641-1696, Elsevier.
    10. Luca Gambetti & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2023. "Agreed and Disagreed Uncertainty," Working Paper series 23-01, Rimini Centre for Economic Analysis.
    11. Francesco Bianchi & Leonardo Melosi, 2014. "Dormant Shocks and Fiscal Virtue," NBER Macroeconomics Annual, University of Chicago Press, vol. 28(1), pages 1-46.
    12. 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.
    13. Andrew Foerster & Christian Matthes, 2022. "Learning About Regime Change," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1829-1859, November.
    14. Francesco Bianchi & Martin Lettau & Sydney C. Ludvigson, 2022. "Monetary Policy and Asset Valuation," Journal of Finance, American Finance Association, vol. 77(2), pages 967-1017, April.

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    More about this item

    Keywords

    Markov switching DSGE model; Bayesian econometrics; beliefs; uncertainty; Bayesian learning; rational expectations;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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