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    经济经纬 2016 Issue (3) :144-149
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    省级地方政府性债务风险测度、分解与归因——基于2014年省级地方债审计的因子-聚类分析
    赵剑锋
    广东金融学院 会计系,广东 广州510521
    The Risk Measure, Decomposition and Attribution of Local Government Debts at Province Level——Based on Factor-Cluster Analysis on 2014 Debts Audit
    ZHAO Jian-feng
    Department of Accounting, Guangdong University of Finance, Guangzhou 510521, China
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摘要 笔者基于2014年省级地方债审计数据,采用因子——聚类分析方法,构建了地方债综合风险测度体系。研究发现,省级地方债风险最重要来源为增量风险,经济转型期和风险因子交叉影响更易导致地方债综合风险失控。因此,抑制地方债非正常增长、发挥地方债对战略新兴产业等经济转型的杠杆效应,完善地方债风险的短期措施和长期制度建设的综合解决方案。
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赵剑锋
关键词政府性债务   风险测度   因子分析   聚类分析     
Abstract: Based on firsthand 2014 national audit data of 30 local governments debt announcements at province-level in China, the study constructs measure index system of debt risk of local government from the perspective of incremental debt risk, stock debt risk, financial support risk and short-term liquidity risk using factor analysis method. And attribute to risk factors ranking of local government debts, the principal risks decomposed and analyzed the risk origin attributions. Then there comes the results that the most important risk source is the incremental debt risk, economic transition and cross-over factors are likely to leading local government debt risks out of control. Therefore, the government should regulate the abnormal speeding of debts, exert the leverage effect on economic transformation from investing strategic emerging industries, and improve the complete packaging solutions of short-term measures and long-term institutional construction of local debt risks control.
KeywordsLocal Government Debts at Province Level   Risk Measure   Factor Analysis   Cluster Analysis     
收稿日期 2014-06-15; 接受日期 2016-05-16;
基金资助:广东省哲学社会科学规划项目(GD14CYJ08)
作者简介: 赵剑锋(1978-),男,河北邢台人,博士,讲师,主要从事公司金融、城镇化投融资研究。
引用本文:   
赵剑锋.省级地方政府性债务风险测度、分解与归因——基于2014年省级地方债审计的因子-聚类分析[J].  经济经纬, 2016,3: 144-149
ZHAO Jian-feng.The Risk Measure, Decomposition and Attribution of Local Government Debts at Province Level——Based on Factor-Cluster Analysis on 2014 Debts Audit[J]  Economic Survey, 2016,V33(3): 144-149
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