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Social networks and macroeconomic stability

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  • Chen, Shu-Heng
  • Chang, Chia-Ling
  • Wen, Ming-Chang

Abstract

In this paper, the effect of the social network on macroeconomic stability is examined using an agent-based, network-based DSGE (dynamic stochastic general equilibrium) model. While the authors' primitive (first-stage) examination has the network generation mechanism as its main focus, their more in-depth second-stage analysis is based on a few main characteristics of network topologies, such as the degree, clustering coefficient, length, and centrality. Based on their econometric analysis of the simulation results, the authors find that the betweenness centrality contributes to the GDP instability and average path length contributes to the inflation instability. These results are robust under two augmentations, one taking into account non-linearity and one taking into account the shape of the degree distribution as an additional characteristic. Through these augmentations, the authors find that the effect of network topologies on economic stability can be more intriguing than their baseline model may suggest: in addition to the existence of non-linear or combined effects of network characteristics, the shape of the degree distribution is also found to be significant. --

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File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2014-16
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Bibliographic Info

Article provided by Kiel Institute for the World Economy in its journal Economics: The Open-Access, Open-Assessment E-Journal.

Volume (Year): 8 (2014)
Issue (Month): 16 ()
Pages: 1-40

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Handle: RePEc:zbw:ifweej:201416

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Keywords: New Keynesian DSGE models; Agent-based model; macroeconomic stability; social networks; Ising model; betweenness centrality; seemingly unrelated regression estimation;

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  1. Giovani Dosi & Giorgio Fagiolo & Andrea Roventini & Mauro Napoletano, 2012. "Income Distribution, Credit and Fiscal Policies in an Agent-Based Keynesian Model," INET Research Notes 11, Institute for New Economic Thinking (INET).
  2. Orphanides, Athanasios & Williams, John C., 2007. "Robust monetary policy with imperfect knowledge," Working Paper Series 0764, European Central Bank.
  3. K. Vela Velupillai, 2011. "DSGE And Beyond – Computable And Constructive Challenges," ASSRU Discussion Papers 1122, ASSRU - Algorithmic Social Science Research Unit.
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  6. Georg Zaklan & Frank Westerhoff & Dietrich Stauffer, 2008. "Analysing tax evasion dynamics via the Ising model," Papers 0801.2980, arXiv.org.
  7. Chang, Chia-ling & Chen, Shu-heng, 2011. "Interactions in DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics Discussion Papers 2011-25, Kiel Institute for the World Economy.
  8. Follmer, Hans, 1974. "Random economies with many interacting agents," Journal of Mathematical Economics, Elsevier, vol. 1(1), pages 51-62, March.
  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. Fabio Milani, 2009. "Adaptive Learning and Macroeconomic Inertia in the Euro Area," Journal of Common Market Studies, Wiley Blackwell, vol. 47, pages 579-599, 06.
  11. Joseph E. Stiglitz, 2011. "Rethinking Macroeconomics: What Failed, And How To Repair It," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 591-645, 08.
  12. Orlean, Andre, 1995. "Bayesian interactions and collective dynamics of opinion: Herd behavior and mimetic contagion," Journal of Economic Behavior & Organization, Elsevier, vol. 28(2), pages 257-274, October.
  13. Chen, Shu-Heng & Chang, Chia-Ling & Tseng, Yi-Heng, 2014. "Social networks, social interaction and macroeconomic dynamics: How much could Ernst Ising help DSGE?," Research in International Business and Finance, Elsevier, vol. 30(C), pages 312-335.
  14. Paul Grauwe, 2011. "Animal spirits and monetary policy," Economic Theory, Springer, vol. 47(2), pages 423-457, June.
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