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Modeling the Evolution of Expectations and Uncertainty in General Equilibrium

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  • 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 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.

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Bibliographic Info

Paper provided by Society for Economic Dynamics in its series 2013 Meeting Papers with number 67.

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Date of creation: 2013
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Handle: RePEc:red:sed013:67

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References

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  1. Frank Schorfheide, 2003. "Learning and monetary policy shifts," Working Paper 2003-23, Federal Reserve Bank of Atlanta.
  2. Kristoffer Nimark, 2007. "Dynamic Pricing and Imperfect Common Knowledge," RBA Research Discussion Papers rdp2007-12, Reserve Bank of Australia.
  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, 2005. "Generalizing the Taylor principle," Research Working Paper RWP 05-13, Federal Reserve Bank of Kansas City.
  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, 2013. "Signaling Effects of Monetary Policy," PIER Working Paper Archive 13-029, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  7. Francesco Bianchi & Leonardo Melosi, 2012. "Constrained Discretion and Central Bank Transparency," PIER Working Paper Archive 13-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  8. Christopher A. Sims & Tao Zha, 2005. "Were There Regime Switches in U.S. Monetary Policy?," Working Papers 92, Princeton University, Department of Economics, Center for Economic Policy Studies..
  9. Leonardo Melosi, 2014. "Estimating Models with Dispersed Information," American Economic Journal: Macroeconomics, American Economic Association, vol. 6(1), pages 1-31, January.
  10. Zheng Liu & Daniel F. Waggoner & Tao Zha, 2010. "Sources of Macroeconomic Fluctuations: A Regime-switching DSGE Approach," Emory Economics 1002, Department of Economics, Emory University (Atlanta).
  11. Roger E.A. Farmer & Tao Zha & Daniel F. Waggoner, 2009. "Understanding Markov-Switching Rational Expectations Models," NBER Working Papers 14710, National Bureau of Economic Research, Inc.
  12. Troy Davig & Taeyoung Doh, 2008. "Monetary policy regime shifts and inflation persistence," Research Working Paper RWP 08-16, Federal Reserve Bank of Kansas City.
  13. Christian Matthes & Argia M. Sbordone & Timothy Cogley, 2011. "Optimal Disinflation Under Learning," 2011 Meeting Papers 74, Society for Economic Dynamics.
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Cited by:
  1. Francesco Bianchi & Leonardo Melosi, 2013. "Dormant Shocks and Fiscal Virtue," Working Papers 13-12, Duke University, Department of Economics.

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