基于宏观经济指数波动相关矩阵分析的EU-15国家集群结构
Cluster structure of EU-15 countries derived from the correlation matrix analysis of macroeconomic index fluctuations
摘要：
国家之间的统计距离，计算各种移动平均时间窗口，映射到超度量次优势空间，在经典的最小生成树方法。移动平均最小长度路径（MAMLP）算法允许相对于系统的质心的波动与质心本身的移动解耦。一个由因子图给出的哈密顿表示被使用，并起到成本函数的作用。本分析涉及11个宏观经济指标，即国内生产总值（x1）、最终消费支出（x2）、资本形成总额（x3）、净出口（x4）、消费物价指数（y1）、中央银行利率（y2）、劳动力（z1）、失业（z2）、国内生产总值/工作小时（z3）、人均国内生产总值（w1）和基尼系数（w2）。目标国家组由15个欧盟国家组成，数据取自1995年至2004年。通过两种不同的方法（二分因子图分析法和相关矩阵特征系统分析法），发现宏观经济指标波动的强相关国家可以划分为稳定的集群。
英文摘要：
The statistical distances between countries, calculated for various moving average time windows, are mapped into the ultrametric subdominant space as in classical Minimal Spanning Tree methods. The Moving Average Minimal Length Path (MAMLP) algorithm allows a decoupling of fluctuations with respect to the mass center of the system from the movement of the mass center itself. A Hamiltonian representation given by a factor graph is used and plays the role of cost function. The present analysis pertains to 11 macroeconomic (ME) indicators, namely the GDP (x1), Final Consumption Expenditure (x2), Gross Capital Formation (x3), Net Exports (x4), Consumer Price Index (y1), Rates of Interest of the Central Banks (y2), Labour Force (z1), Unemployment (z2), GDP/hour worked (z3), GDP/capita (w1) and Gini coefficient (w2). The target group of countries is composed of 15 EU countries, data taken between 1995 and 2004. By two different methods (the Bipartite Factor Graph Analysis and the Correlation Matrix Eigensystem Analysis) it is found that the strongly correlated countries with respect to the macroeconomic indicators fluctuations can be partitioned into stable clusters.
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