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Modeling the Evolution of Expectations and Uncertainty in General Equilibrium

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  • Francesco Bianchi

    ()
    (Department of Economics, Duke University)

  • Leonardo Melosi

    ()
    (Federal Reserve Bank of Chicago)

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 .rst-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.

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Bibliographic Info

Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 13-030.

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Length: 52 pages
Date of creation: 01 Dec 2012
Date of revision:
Handle: RePEc:pen:papers:13-030

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Related research

Keywords: Markov switching DSGE model; Bayesian econometrics; beliefs; uncertainty; Bayesian learning; rational expectations;

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References

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  1. Christian Matthes & Argia M. Sbordone & Timothy Cogley, 2011. "Optimal Disinflation Under Learning," 2011 Meeting Papers 74, Society for Economic Dynamics.
  2. Christopher A. Sims & Tao Zha, 2004. "Were there regime switches in U.S. monetary policy?," Working Paper 2004-14, Federal Reserve Bank of Atlanta.
  3. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
  4. Troy Davig & Eric M. Leeper, 2007. "Generalizing the Taylor Principle," American Economic Review, American Economic Association, vol. 97(3), pages 607-635, June.
  5. Frank Schorfheide, 2005. "Learning and Monetary Policy Shifts," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 392-419, April.
  6. Taeyoung Doh & Troy Davig, 2009. "Monetary Policy Regime Shifts and Inflation Persistence," 2009 Meeting Papers 182, Society for Economic Dynamics.
  7. Kristoffer Nimark, 2007. "Dynamic Pricing and Imperfect Common Knowledge," RBA Research Discussion Papers rdp2007-12, Reserve Bank of Australia.
  8. Leonardo Melosi, 2013. "Signaling Effects of Monetary Policy," PIER Working Paper Archive 13-029, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  9. Francesco Bianchi, 2009. "Regime Switches, Agents’ Beliefs, and Post-World War II U.S. Macroeconomic Dynamics," 2009 Meeting Papers 198, Society for Economic Dynamics.
  10. Bianchi, Francesco & Melosi, Leonardo, 2014. "Constrained Discretion and Central Bank Transparency," CEPR Discussion Papers 9955, C.E.P.R. Discussion Papers.
  11. Leonardo Melosi, 2014. "Estimating Models with Dispersed Information," American Economic Journal: Macroeconomics, American Economic Association, vol. 6(1), pages 1-31, January.
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Cited by:
  1. Leonardo Melosi & Francesco Bianchi, 2012. "Dormant Shocks and Fiscal Virtue," 2012 Meeting Papers 44, Society for Economic Dynamics.

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