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Complexity and Empirical Economics

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  • Steven N. Durlauf

Abstract

This article explores the state of interplay between recent efforts to introduce complex systems methods into economics and the understanding of empirical phenomena. The empirical side of economic complexity may be divided into three general branches: historical studies, the identification of power and scaling laws, and analyses of social interactions. I argue that, while providing useful 'stylised facts', none of these empirical approaches has produced compelling evidence that economic contexts exhibit the substantive microstructure or properties of complex systems. This failure reflects inadequate attention to identification problems. Identification analysis should therefore be at the centre of future work on the empirics of complexity. Copyright 2005 Royal Economic Society.

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-0297.2005.01003.x
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Bibliographic Info

Article provided by Royal Economic Society in its journal The Economic Journal.

Volume (Year): 115 (2005)
Issue (Month): 504 (06)
Pages: F225-F243

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Handle: RePEc:ecj:econjl:v:115:y:2005:i:504:p:f225-f243

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References

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Citations

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Cited by:
  1. Katarzyna Ostasiewicz & Michal H. Tyc & Piotr Goliczewski & Piotr Magnuszewski & Andrzej Radosz & Jan Sendzimir, 2006. "Integrating economic and psychological insights in binary choice models with social interactions," Papers physics/0609170, arXiv.org.
  2. Antonelli Cristiano & Patrucco Pierpaolo & Rossi Federica, 2008. "The economics of knowledge interaction and the changing role of universities," Dipartimento di Economia "S. Cognetti de Martiis" LEI & BRICK - Laboratorio di economia dell'innovazione "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio Carlo 200802, University of Turin.
  3. Thomas Lux, 2008. "Stochastic Behavioral Asset Pricing Models and the Stylized Facts," Working Papers wp08-03, Warwick Business School, Financial Econometrics Research Centre.
  4. Robert Marks, 2007. "Validating Simulation Models: A General Framework and Four Applied Examples," Computational Economics, Society for Computational Economics, vol. 30(3), pages 265-290, October.
  5. Antonelli Cristiano & Scellato Giuseppe, 2007. "Complexity and innovation: social interactions and firm level total factor productivity," Dipartimento di Economia "S. Cognetti de Martiis" LEI & BRICK - Laboratorio di economia dell'innovazione "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio Carlo 200709, University of Turin.
  6. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, 05.
  7. Antonelli Cristiano & Ferraris Gianluigi, 2009. "Innovation as an emerging system property : an agent based model," Dipartimento di Economia "S. Cognetti de Martiis" LEI & BRICK - Laboratorio di economia dell'innovazione "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio Carlo 200911, University of Turin.
  8. Patrucco Pierpaolo, 2008. "Complexity and organizational change in the coordination of technological knowledge: evidence from the automobile cluster in Turin," Dipartimento di Economia "S. Cognetti de Martiis" LEI & BRICK - Laboratorio di economia dell'innovazione "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio Carlo 200808, University of Turin.
  9. Kieran Donaghy, 2011. "Models of travel demand with endogenous preference change and heterogeneous agents," Journal of Geographical Systems, Springer, vol. 13(1), pages 17-30, March.

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