摘要
运用R型系统聚类法将我国划分为高、中、低房价地区,通过建立状态空间模型和运用卡尔曼滤波解法,对比分析了历年货币政策变化对区域房价的动态影响。实证结果表明:贷款规模对房价的影响力较大且区域差别显著,而实际利率对房价的影响力较小,也有一定的区域差异。针对我国房地产市场局部过热且高房价有向全国扩散的态势,应该根据货币政策工具对区域房价影响的特点,从以往的以价格手段调控为主,转变为以数量手段调控为主、价格手段为辅,才能使房地产市场调控取得预期效果。
By using R Hierarchical cluster to establish State-space Models combined with Kalman Filter solution, this article compared the dynamic influence of monetary policy changes on the high- price, middle-price as well as the low-price areas. The empirical test result shows that scales of loans have more impacts on the housing price, and the impact varies greatly according to different areas. However, the pricing tools such as interest adjustment have less influence, and the regional influence is invisible. Therefore; the macroeconomic control on housing market should make some adjustment according to the influence of monetary policies. It should shift from pricing tools to quantity tools so as to achieve the expected effect.
出处
《河北科技大学学报(社会科学版)》
2012年第2期1-7,共7页
Journal of Hebei University of Science and Technology:Social Sciences
基金
国家自然科学基金项目(71103121)
浙江省教育厅基金项目(Y201121210)
上海市哲学社会科学规划课题(2011EJL002)
上海市教委科研创新课题(12YS106)
关键词
货币政策
区域房价
状态空间模型
卡尔曼滤波
monetary policy
regional housing priee
state-space model
Kalman Filter