摘要
通过有向无环图(DAG)技术和基于DAG的预测方差分解技术,本文较为系统地研究了社会融资规模、实际利率和货币供应量这三种政策工具对房地产市场调控的有效性问题。实证显示,中国住房价格的持续上涨主要源于近年来一直超预期上涨的惯性作用;社会融资规模和实际利率对房地产市场的调控效果比较明显,而货币供应量对房地产价格的影响则十分有限。本文认为,社会融资规模可以作为住房价格调控的一个政策变量,这为政府调控住房价格提供了一个更为有效的政策工具。
By a directed acyclic graph (DAG) technology and prediction variance decomposition method based on DAG, the paper explores the effectiveness of three policy tools on regulation of the real estate market, including the scale of social financing, real interest rates and the money supply. The results show that China' s real estate price continues to rise mainly due to its own rising inertia beyond anticipation in recent years. In terms of effects of policy regulation, social financing scale and real interest rate have more obvious effects on the real estate market regulation, and the impact of money supply on real estate price is very limited. This paper argues that the scale of social financing can be used as a policy variable for housing price regulation, which provides a more effective policy tool for government regulation of housing price.
出处
《统计研究》
CSSCI
北大核心
2014年第10期29-36,共8页
Statistical Research
基金
教育部重大课题攻关项目"利率市场化背景下的金融风险研究"(项目编号:13JZD006)的阶段性成果
关键词
社会融资规模
住房价格
有向无环图
Scale of Social Financing
Housing Price
Directed Acyclic Graph