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Modelling Social Learning in an Agent-Based New Keynesian Macroeconomic Model

  • Isabelle SALLE (GREThA, CNRS, UMR 5113)
  • Martin ZUMPE (GREThA, CNRS, UMR 5113)
  • Murat YILDIZOGLU (GREThA, CNRS, UMR 5113)
  • Marc-Alexandre SENEGAS (GREThA, CNRS, UMR 5113)

We propose an agent-based macroeconomic model (ABM) inspired by the New Keynesian general equilibrium model (NKM, Woodford 2003). We analyse the aggregate economic dynamics resulting from social learning of agents (households and firms). Households’ labour supply and consumption demand, as well as firms\' labour demand and wage offers evolve through imitation and random experimenting by the agents. We study, in this setting, the aggregate properties of the economy and the ability of those learning agents to coordinate on the intra-temporal equilibrium of the original model. Our approach is quite different from the existing learning literature in the NKM (à la Evans & Honkapohja, that mainly focuses on learning for testing local stability of equilibria), since learning is directly embedded in the behaviour of the individual agents. This original approach opens new perspectives about the NKM, and allows us to ask new questions about the coordination problems that can result from social learning. First, our computational analysis (Monte Carlo simulations) shows that social learning does not allow the agents to correctly learn about the interdependence between markets, because of the emergence of coordination problems that result in insufficient labour supply and depressive dynamics. Second, we shed light on the general properties of social learning that are behind these results in a general (dis)equilibrium setting, and prove that their neutralisation, at least on the one side of the markets, can significantly improve the performance of the economy. Our results point to the importance of carefully modelling learning mechanisms within macroeconomic ABMs.

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Paper provided by Groupe de Recherche en Economie Théorique et Appliquée in its series Cahiers du GREThA with number 2012-20.

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Date of creation: 2012
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Handle: RePEc:grt:wpegrt:2012-20
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  1. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880 Elsevier.
  2. George W. Evans & Seppo Honkapohja, 2008. "Learning and Macroeconomics," University of Oregon Economics Department Working Papers 2008-3, University of Oregon Economics Department.
  3. George W. Evans & Seppo Honkapohja & Kaushik Mitra, 2007. "Anticipated Fiscal Policy and Adaptive Learning," CDMA Working Paper Series 200717, Centre for Dynamic Macroeconomic Analysis.
  4. Jasmina Arifovic & James Bullard & Olena Kostyshyna, 2013. "Social Learning and Monetary Policy Rules," Economic Journal, Royal Economic Society, vol. 123(567), pages 38-76, 03.
  5. Branch, William & Evans, George W & McGough, Bruce, 2012. "Finite Horizon Learning," SIRE Discussion Papers 2012-16, Scottish Institute for Research in Economics (SIRE).
  6. Bruce Preston, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
  7. Vallée, Thomas & YIldIzoglu, Murat, 2009. "Convergence in the finite Cournot oligopoly with social and individual learning," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 670-690, November.
  8. Arifovic, Jasmina, 2000. "Evolutionary Algorithms In Macroeconomic Models," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 373-414, September.
  9. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
  10. Tesfatsion, Leigh, 2006. "Agent-Based Computational Modeling And Macroeconomics," Staff General Research Papers 12402, Iowa State University, Department of Economics.
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