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Modeling the Evolution of Expectations and Uncertainty in General Equilibrium

  • Francesco Bianchi
  • Leonardo Melosi

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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Paper provided by Duke University, Department of Economics in its series Working Papers with number 13-14.

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Length: 53
Date of creation: 2013
Date of revision:
Handle: RePEc:duk:dukeec:13-14
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Web page: http://econ.duke.edu/

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  1. Francesco Bianchi, 2010. "Regime Switches, Agents' Beliefs, and Post-World War II U.S. Macroeconomic Dynamics," Working Papers 10-39, Duke University, Department of Economics.
  2. 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..
  3. Troy Davig & Eric M. Leeper, 2006. "Generalizing the Taylor Principle," Caepr Working Papers 2006-001, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  4. 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.
  5. Kristoffer Nimark, 2007. "Dynamic Pricing and Imperfect Common Knowledge," RBA Research Discussion Papers rdp2007-12, Reserve Bank of Australia.
  6. 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.
  7. Christian Matthes & Argia M. Sbordone & Timothy Cogley, 2011. "Optimal Disinflation Under Learning," 2011 Meeting Papers 74, Society for Economic Dynamics.
  8. David Andolfatto & Paul Gomme, 1997. "Monetary Policy Regimes and Beliefs," Cahiers de recherche CREFE / CREFE Working Papers 48, CREFE, Université du Québec à Montréal, revised Apr 2001.
  9. Farmer, Roger E.A. & Waggoner, Daniel F. & Zha, Tao, 2009. "Understanding Markov-switching rational expectations models," Journal of Economic Theory, Elsevier, vol. 144(5), pages 1849-1867, September.
  10. 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.
  11. Taeyoung Doh & Troy Davig, 2009. "Monetary Policy Regime Shifts and Inflation Persistence," 2009 Meeting Papers 182, Society for Economic Dynamics.
  12. 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).
  13. Leonardo Melosi, 2014. "Estimating Models with Dispersed Information," American Economic Journal: Macroeconomics, American Economic Association, vol. 6(1), pages 1-31, January.
  14. Leonardo Melosi, 2012. "Signaling effects of monetary policy," Working Paper Series WP-2012-05, Federal Reserve Bank of Chicago.
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