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Individual rationality, model-consistent expectations and learning

  • Liam Graham

To isolate the impact of the assumption of model-consistent expectations, this paper proposes a baseline case in which households are individually rational, have full information and learn using forecast rules specified as in the minimum state variable representation of the economy. Applying this to the benchmark stochastic growth model shows that the economy with learning converges quickly to an equi-librium very similar to that with model-consistent expectations. In other words, if households are individually rational, the assumption that they can also form model-consistent expectations does not seem a strong one. The mechanism by which learning affects the model is considered in detail and the implications of relaxing the assumptions of the baseline case are explored.

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Paper provided by Centre for Dynamic Macroeconomic Analysis in its series CDMA Working Paper Series with number 201112.

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Date of creation: 20 Aug 2011
Date of revision:
Handle: RePEc:san:cdmawp:1112
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  1. Stefano Eusepi & Bruce Preston, 2011. "Expectations, Learning, and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 101(6), pages 2844-72, October.
  2. Milani, Fabio, 2010. "Expectation Shocks and Learning as Drivers of the Business Cycle," CEPR Discussion Papers 7743, C.E.P.R. Discussion Papers.
  3. Christian Hellwig, . "Monetary Business Cycle Models: Imperfect Information (Review Article, March 2006)," UCLA Economics Online Papers 377, UCLA Department of Economics.
  4. Evans, George W & Ramey, Garey, 2001. "Adaptive Expectations, Underparameterization and the Lucas Critique," University of California at San Diego, Economics Working Paper Series qt41f2h196, Department of Economics, UC San Diego.
  5. George W. Evans & Seppo Honkapohja & Kaushik Mitra, 2007. "Anticipated Fiscal Policy and Adaptive Learning," University of Oregon Economics Department Working Papers 2007-5, University of Oregon Economics Department, revised 13 Dec 2008.
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  8. William Branch & George W. Evans & Bruce McGough, 2012. "Finite Horizon Learning," CDMA Working Paper Series 201204, Centre for Dynamic Macroeconomic Analysis.
  9. Ma, Xingliang & Shi, Guanming, 2011. "A Dynamic Adoption Model with Bayesian Learning: Application to the U.S. Soybean Market," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 104577, Agricultural and Applied Economics Association.
  10. Chryssi Giannitsarou & Eva Carceles-Poveda, 2004. "Adaptive Learning in Practice," Computing in Economics and Finance 2004 271, Society for Computational Economics.
  11. Martin Ellison & Joseph Pearlman, 2010. "Saddlepath Learning," Economics Series Working Papers 505, University of Oxford, Department of Economics.
  12. James B. Bullard & Kaushik Mitra, 2002. "Learning about monetary policy rules," Working Papers 2000-001, Federal Reserve Bank of St. Louis.
  13. Campbell, John, 1994. "Inspecting the Mechanism: An Analytical Approach to the Stochastic Growth Model," Scholarly Articles 3196342, Harvard University Department of Economics.
  14. Ferrero, Giuseppe, 2007. "Monetary policy, learning and the speed of convergence," Journal of Economic Dynamics and Control, Elsevier, vol. 31(9), pages 3006-3041, September.
  15. Adam, Klaus & Marcet, Albert, 2011. "Internal rationality, imperfect market knowledge and asset prices," Journal of Economic Theory, Elsevier, vol. 146(3), pages 1224-1252, May.
  16. Tai-Yu Ma, 2011. "A cross entropy multiagent learning algorithm for solving vehicle routing problems with time windows," Post-Print halshs-00592118, HAL.
  17. Herbert Dawid, 2005. "Long horizon versus short horizon planning in dynamic optimization problems with incomplete information," Economic Theory, Springer, vol. 25(3), pages 575-597, 04.
  18. Liam Graham, 2011. "Learning, information and heterogeneity," CDMA Working Paper Series 201113, Centre for Dynamic Macroeconomic Analysis.
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