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Nonlinear and Complex Dynamics in Economics

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  • Barnett, William A.
  • Serletis, Apostolos
  • Serletis, Demitre

Abstract

This paper is an up-to-date survey of the state-of-the-art in dynamical systems theory relevant to high levels of dynamical complexity, characterizing chaos and near chaos, as commonly found in the physical sciences. The paper also surveys applications in economics and �finance. This survey does not include bifurcation analyses at lower levels of dynamical complexity, such as Hopf and transcritical bifurcations, which arise closer to the stable region of the parameter space. We discuss the geometric approach (based on the theory of differential/difference equations) to dynamical systems and make the basic notions of complexity, chaos, and other related concepts precise, having in mind their (actual or potential) applications to economically motivated questions. We also introduce specifi�c applications in microeconomics, macroeconomics, and �finance, and discuss the policy relevancy of chaos.

Suggested Citation

  • Barnett, William A. & Serletis, Apostolos & Serletis, Demitre, 2012. "Nonlinear and Complex Dynamics in Economics," MPRA Paper 41245, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:41245
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    Cited by:

    1. Barnett, William A. & Serletis, Apostolos & Serletis, Demitre, 2015. "Nonlinear And Complex Dynamics In Economics," Macroeconomic Dynamics, Cambridge University Press, vol. 19(08), pages 1749-1779, December.
    2. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 14645, University Library of Munich, Germany.
    3. Gomes, Orlando, 2013. "Information stickiness on general equilibrium and endogenous cycles," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-43.
    4. Orlando Gomes, 2007. "Routes to chaos in macroeconomic theory," Journal of Economic Studies, Emerald Group Publishing, vol. 33(6), pages 437-468, January.
    5. Vivaldo M. Mendes & Diana A. Mendes, 2007. "Controlling Endogenous Cycles in an OLG Economy by the OGY Method," Working Papers Series 1 ercwp0808, ISCTE-IUL, Business Research Unit (BRU-IUL).
    6. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    7. Orlando Gomes, 2006. "Endogenous Business Cycles in the Ramsey Growth Model," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 9(2), pages 13-36, November.
    8. Orlando Gomes, 2006. "Routes to chaos in macroeconomic theory," Journal of Economic Studies, Emerald Group Publishing, vol. 33(6), pages 437-468, November.
    9. William A. Barnett & Yijun He, 1999. "Center Manifold, Stability, and Bifurcations in Continuous Time Macroeconometric Systems," Macroeconomics 9901002, EconWPA.
    10. Libo Xu & Apostolos Serletis, "undated". "Communication Frictions, Sentiments, and Nonlinear Business Cycles," Working Papers 2016-35, Department of Economics, University of Calgary, revised 20 Jun 2016.
    11. Vivaldo M. Mendes & Diana A. Mendes, 2006. "Active Interest Rate Rules and the Role of Stabilization Policy R&D Tax Credits," Working Papers Series 1 ercwp0208, ISCTE-IUL, Business Research Unit (BRU-IUL).

    More about this item

    Keywords

    Complexity; chaos; endogenous business cycles;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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