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

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References

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  1. Troy Davig & Eric M. Leeper, 2007. "Generalizing the Taylor Principle," American Economic Review, American Economic Association, vol. 97(3), pages 607-635, June.
  2. 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.
  3. Nimark, Kristoffer, 2008. "Dynamic pricing and imperfect common knowledge," Journal of Monetary Economics, Elsevier, vol. 55(2), pages 365-382, March.
  4. Leonardo Melosi, 2014. "Estimating Models with Dispersed Information," American Economic Journal: Macroeconomics, American Economic Association, vol. 6(1), pages 1-31, January.
  5. Francesco Bianchi, 2012. "Regime Switches, Agents’ Beliefs, and Post-World War II U.S. Macroeconomic Dynamics," Working Papers 12-04, Duke University, Department of Economics.
  6. Leonardo Melosi, 2012. "Signaling effects of monetary policy," Working Paper Series WP-2012-05, Federal Reserve Bank of Chicago.
  7. Francesco Bianchi & Leonardo Melosi, 2012. "Constrained Discretion and Central Bank Transparency," PIER Working Paper Archive 13-041, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  8. Timothy Cogley & Christian Matthes & Argia M. Sbordone, 2011. "Optimal disinflation under learning," Staff Reports 524, Federal Reserve Bank of New York.
  9. Christopher A. Sims & Tao Zha, 2004. "Were there regime switches in U.S. monetary policy?," Working Paper 2004-14, Federal Reserve Bank of Atlanta.
  10. Frank Schorfheide, 2003. "Learning and monetary policy shifts," Working Paper 2003-23, Federal Reserve Bank of Atlanta.
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Cited by:
  1. Francesco Bianchi & Leonardo Melosi, 2013. "Dormant Shocks and Fiscal Virtue," NBER Chapters, in: NBER Macroeconomics Annual 2013, Volume 28, pages 1-46 National Bureau of Economic Research, Inc.

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