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New Approaches to Macroeconomic Modeling

Author

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  • Aoki,Masanao
Abstract
This book contributes substantively to state-of-the-art macroeconomic modeling by providing a method for modeling large collections of heterogeneous agents subject to non-pairwise externality called field effects, i.e. feedback of aggregate effects on individual agents or agents using state-dependent strategies. Adopting a level of microeconomic description which keeps track of compositions of fractions of agents by 'types' or 'strategies', time evolution of the microeconomic states is described by (backward) Chapman-Kolmogorov equations. Macroeconomic dynamics naturally arise by expansion of the solution in some power series of the number of participants. Specification of the microeconomic transition rates thus leads to macroeconomic dynamic models. This approach provides a consistent way for dealing with multiple equilibria of macroeconomic dynamics by ergodic decomposition and associated calculations of mean first passage times, and stationary probabilities of equilibria further provide useful information on macroeconomic behavior.

Suggested Citation

  • Aoki,Masanao, 1998. "New Approaches to Macroeconomic Modeling," Cambridge Books, Cambridge University Press, number 9780521637695, September.
    • Handle: RePEc:cup:cbooks:9780521637695
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    Cited by:

    1. Gallegati, Mauro & Kirman, Alan, 2019. "20 years of WEHIA: A journey in search of a safer road," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 5-14.
    2. Imre Kondor & István Csabai & Gábor Papp & Enys Mones & Gábor Czimbalmos & Máté Sándor, 2014. "Strong random correlations in networks of heterogeneous agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 203-232, October.
    3. Catalano, Michele & Di Guilmi, Corrado, 2019. "Uncertainty, rationality and complexity in a multi-sectoral dynamic model: The dynamic stochastic generalized aggregation approach," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 117-144.
    4. Chiarella, Carl & Di Guilmi, Corrado, 2011. "The financial instability hypothesis: A stochastic microfoundation framework," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1151-1171, August.
    5. Charles D. Brummitt & Kenan Huremović & Paolo Pin & Matthew H. Bonds & Fernando Vega-Redondo, 2017. "Contagious disruptions and complexity traps in economic development," Nature Human Behaviour, Nature, vol. 1(9), pages 665-672, September.
    6. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    7. Aoki, Masanao & Yoshikawa, Hiroshi, 2006. "Uncertainty, policy ineffectiveness, and long stagnation of the macroeconomy," Japan and the World Economy, Elsevier, vol. 18(3), pages 261-272, August.
    8. Andrea Caravaggio & Lorenzo Cerboni Baiardi & Mauro Sodini, 2021. "A note on symmetry breaking in a non linear marketing model," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 507-531, December.
    9. Alfarano, Simone & Milakovic, Mishael, 2009. "Network structure and N-dependence in agent-based herding models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(1), pages 78-92, January.
    10. Albrecht Irle & Jonas Kauschke & Thomas Lux & Mishael Milaković, 2011. "Switching Rates And The Asymptotic Behavior Of Herding Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 359-376.
    11. Alfarano Simone & Milakovic Mishael, 2012. "Identification of Interaction Effects in Survey Expectations: A Cautionary Note," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-23, October.
    12. N. Ding & N. Xi & Y. Wang, 2003. "Effects of saving and spending patterns on holding time distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 36(1), pages 149-153, November.
    13. Josip Stepanic, 2004. "Social Equivalent of Free Energy," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 2(1), pages 53-60.
    14. Power, Gabriel J. & Turvey, Calum G., 2008. "On Term Structure Models of Commodity Futures Prices and the Kaldor-Working Hypothesis," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37608, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    15. Alan D. Zimm, 2005. "Derivation of a Logistic Equation for Organizations, and its Expansion into a Competitive Organizations Simulation," Computational and Mathematical Organization Theory, Springer, vol. 11(1), pages 37-57, May.
    16. Shu-Heng Chen & Sai-Ping Li, 2011. "Econophysics: Bridges over a Turbulent Current," Papers 1107.5373, arXiv.org.
    17. Bury, Thomas, 2013. "Market structure explained by pairwise interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1375-1385.
    18. Thomas Bury, 2012. "Statistical pairwise interaction model of stock market," Papers 1206.4420, arXiv.org, revised Jan 2014.
    19. LI, XI HAO & Gallegati, Mauro, 2015. "Stock-Flow Dynamic Projection," MPRA Paper 62047, University Library of Munich, Germany.
    20. M. Gallegati & A. Palestrini & D. Gatti & E. Scalas, 2006. "Aggregation of Heterogeneous Interacting Agents: The Variant Representative Agent Framework," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(1), pages 5-19, May.

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