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On the plausibility of adaptive learning in macroeconomics: A puzzling conflict in the choice of the representative algorithm

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  • Michele Berardi
  • Jaqueson K. Galimberti

Abstract

The literature on bounded rationality and learning in macroeconomics has often used recursive algorithms such as least squares and stochastic gradient to depict the evolution of agents' beliefs over time. In this work, we try to assess the plausibility of such practice from an empirical perspective, by comparing forecasts obtained from these algorithms with survey data. In particular, we show that the relative performance of the two algorithms in terms of forecast errors depends on the variable being forecasted, and we argue that rational agents would therefore use different algorithms when forecasting different variables. By using survey data, then, we show that agents instead always behave as least squares learners, irrespective of the variable being forecasted. We thus conclude that such findings point to a puzzling conflict between rational and actual behaviour when it comes to expectations formation.

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File URL: http://www.socialsciences.manchester.ac.uk/medialibrary/cgbcr/discussionpapers/dpcgbcr177.pdf
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Bibliographic Info

Paper provided by Economics, The Univeristy of Manchester in its series Centre for Growth and Business Cycle Research Discussion Paper Series with number 177.

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Length: 29 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:man:cgbcrp:177

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References

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  1. Kevin X.D. Huang & Zheng Liu & Tao Zha, 2008. "Learning, adaptive expectations, and technology shocks," Working Paper Series 2008-18, Federal Reserve Bank of San Francisco.
  2. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
  3. George W. Evans & Seppo Honkapohja & Noah Williams, 2005. "Generalized Stochastic Gradient Learning," CESifo Working Paper Series 1576, CESifo Group Munich.
  4. James Bullard & Stefano Eusepi, 2003. "Did the Great Inflation occur despite policymaker commitment to a Taylor rule?," Working Paper 2003-20, Federal Reserve Bank of Atlanta.
  5. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, 05.
  6. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
  7. Evans, George, 1985. "Expectational Stability and the Multiple Equilibria Problem in Linear Rational Expectations Models," The Quarterly Journal of Economics, MIT Press, vol. 100(4), pages 1217-33, November.
  8. Martin Ellison & Joseph Pearlman, 2010. "Saddlepath Learning," Economics Series Working Papers 505, University of Oxford, Department of Economics.
  9. Stefano Eusepi & Bruce Preston, 2008. "Expectations, Learning and Business Cycle Fluctuations," NBER Working Papers 14181, National Bureau of Economic Research, Inc.
  10. Barucci, Emilio & Landi, Leonardo, 1997. "Least mean squares learning in self-referential linear stochastic models," Economics Letters, Elsevier, vol. 57(3), pages 313-317, December.
  11. James H. Stock & Mark W. Watson, 1994. "Evidence on Structural Instability in Macroeconomic Time Series Relations," NBER Technical Working Papers 0164, National Bureau of Economic Research, Inc.
  12. Weber, Anke, 2007. "Heterogeneous expectations, learning and European inflation dynamics," Discussion Paper Series 1: Economic Studies 2007,16, Deutsche Bundesbank, Research Centre.
  13. Bennett T. McCallum, 2006. "E-Stability vis-a-vis Determinacy Results for a Broad Class of Linear Rational Expectations Models," NBER Working Papers 12441, National Bureau of Economic Research, Inc.
  14. Orphanides, Athanasios & Williams, John C, 2005. "The Decline of Activist Stabilization Policy: Natural Rate Misperceptions, Learning and Expectations," CEPR Discussion Papers 4865, C.E.P.R. Discussion Papers.
  15. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
  16. Heinemann, Maik, 2000. "Convergence Of Adaptive Learning And Expectational Stability: The Case Of Multiple Rational-Expectations Equilibria," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 263-288, September.
  17. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
  18. Fabio Milani, 2005. "Learning, Monetary Policy Rules, and Macroeconomic Stability," Macroeconomics 0508019, EconWPA.
  19. Michele Berardi & Jaqueson K. Galimberti, 2012. "A note on exact correspondences between adaptive learning algorithms and the Kalman filter," Centre for Growth and Business Cycle Research Discussion Paper Series 170, Economics, The Univeristy of Manchester.
  20. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  21. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
  22. James B. Bullard & Stefano Eusepi, 2009. "When does determinacy imply expectational stability?," Working Papers 2008-007, Federal Reserve Bank of St. Louis.
  23. Fabio Milani, 2005. "Expectations, Learning and Macroeconomic Persistence," Working Papers 050608, University of California-Irvine, Department of Economics.
  24. Margaret Bray, 2010. "Learning, Estimation, and the Stability of Rational Expectations," Levine's Working Paper Archive 205, David K. Levine.
  25. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
  26. Evans, G.W. & Honkapohja, S., 1998. "Stochastic Gradient Learning in the Cobweb Model," University of Helsinki, Department of Economics 438, Department of Economics.
  27. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
  28. Evans, George W & Honkapohja, Seppo, 1998. "Economic Dynamics with Learning: New Stability Results," Review of Economic Studies, Wiley Blackwell, vol. 65(1), pages 23-44, January.
  29. Michele Berardi & Jaqueson K. Galimberti, 2012. "On the initialization of adaptive learning algorithms: A review of methods and a new smoothing-based routine," Centre for Growth and Business Cycle Research Discussion Paper Series 175, Economics, The Univeristy of Manchester.
  30. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
  31. Stark, Tom & Croushore, Dean, 2002. "Reply to the comments on 'Forecasting with a real-time data set for macroeconomists'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 563-567, December.
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Cited by:
  1. Agnieszka Markiewicz & Andreas Pick, 2013. "Adaptive Learning and Survey Data," CDMA Working Paper Series 201305, Centre for Dynamic Macroeconomic Analysis.

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