Maximum entropy model for business cycle synchronization

The global economy is a complex dynamical system, whose cyclical fluctuations can mainly be characterized by simultaneous recessions or expansions of major economies. Thus, the researches on the synchronization phenomenon are key to understanding and controlling the dynamics of the global economy. Based on a pairwise maximum entropy model, we analyze the business cycle synchronization of the G7 economic system. We obtain a pairwise-interaction network, which exhibits certain clustering structure and accounts for 45% of the entire structure of the interactions within the G7 system. We also find that the pairwise interactions become increasingly inadequate in capturing the synchronization as the size of economic system grows. Thus, higher-order interactions must be taken into account when investigating behaviors of large economic systems.

[1]  R. Muneepeerakul,et al.  Inferring plant ecosystem organization from species occurrences. , 2010, Journal of theoretical biology.

[2]  Ifije E. Ohiorhenuan,et al.  Maximum-entropy network analysis reveals a role for tumor necrosis factor in peripheral nerve development and function , 2009, Proceedings of the National Academy of Sciences.

[3]  Christian Beck,et al.  Superstatistical distributions from a maximum entropy principle. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Jurgen Kurths,et al.  Synchronization in complex networks , 2008, 0805.2976.

[5]  Jayanth R Banavar,et al.  Inferring species interactions in tropical forests , 2009, Proceedings of the National Academy of Sciences.

[6]  A. Maritan,et al.  Applications of the principle of maximum entropy: from physics to ecology , 2010, Journal of physics. Condensed matter : an Institute of Physics journal.

[7]  Miroslav Dudík,et al.  Performance Guarantees for Regularized Maximum Entropy Density Estimation , 2004, COLT.

[8]  Michael J. Berry,et al.  Weak pairwise correlations imply strongly correlated network states in a neural population , 2005, Nature.

[9]  D. Osborn,et al.  Business Cycles for G7 and European Countries , 1997 .

[10]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[11]  A. Monfort,et al.  Is Economic Activity in the G7 Synchronized? Common Shocks Versus Spillover Effects , 2003 .

[12]  Jean Imbs.  Trade, Finance, Specialization and Synchronization , 2003 .

[13]  Patrick J. Kehoe,et al.  International Real Business Cycles , 1992, Journal of Political Economy.

[14]  A. Maritan,et al.  Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns , 2006, Proceedings of the National Academy of Sciences.

[15]  R. Segev,et al.  Sparse low-order interaction network underlies a highly correlated and learnable neural population code , 2011, Proceedings of the National Academy of Sciences.

[16]  Jonathon Shlens,et al.  The Structure of Multi-Neuron Firing Patterns in Primate Retina , 2006, The Journal of Neuroscience.

[17]  Hermann Haken,et al.  Information and Self-Organization: A Macroscopic Approach to Complex Systems , 2010 .

[18]  Dirk Helbing,et al.  Globally networked risks and how to respond , 2013, Nature.

[19]  R. Baierlein.  Probability Theory: The Logic of Science , 2004 .

[20]  E. Prasad,et al.  Global Business Cycles: Convergence or Decoupling? , 2008, SSRN Electronic Journal.

[21]  P. Bodman,et al.  Are business cycles independent in the G7? , 2005 .

[22]  Peter E. Latham,et al.  Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't , 2008, PLoS Comput. Biol..

[23]  A. Rose,et al.  The Endogeneity of the Optimum Currency Area Criteria , 1996 .

[24]  W. Bialek,et al.  Maximum entropy models for antibody diversity , 2009, Proceedings of the National Academy of Sciences.

[25]  A. Maritan,et al.  Maximum entropy approach for deducing amino Acid interactions in proteins. , 2008, Physical review letters.

[26]  Eric Garnier,et al.  From Plant Traits to Plant Communities: A Statistical Mechanistic Approach to Biodiversity , 2006, Science.

[27]  Amos Maritan,et al.  The maximum relative entropy principle , 2007 .

[28]  Michael J. Berry,et al.  Network information and connected correlations. , 2003, Physical review letters.