房地产市场高风险价格膨胀识别及其预警机制研究——基于国际经验数据  

Study on the High Cost Booms Identification in Real Estate Market and Its Real Time Early Warning Mechanism——Based on International Experience Data

作　　者：扈文秀[1] 吴婷婷[1] 付强[1] HU Wen-xiu WU Ting-ting FU Qiang(School of Economics and Management, Xi＇ an University of Technology, Xi＇ an 710054, China)

机构地区：[1]西安理工大学经济与管理学院,陕西西安710054

出　　处：《预测》2017年第2期51-56,共6页Forecasting

基　　金：国家自然科学基金资助项目(71373204);陕西省教育厅哲学社会科学重点研究基地科学研究计划资助项目(13JZ036);西安市科技计划资助项目(SF1504(1))

摘　　要：为了有效控制房地产价格非理性上涨对宏观经济的影响,本文对房地产价格膨胀进行区分。根据27个国家,1990年至2014年的房地产价格数据,通过约束平滑B-样条模型(COBS)识别出每一国家的房地产价格的膨胀区间,根据不同膨胀区间后宏观经济的不同反应,区分出高风险资产价格膨胀与低风险资产价格膨胀。并根据确定出的高风险资产价格膨胀的预警指标,通过二元离散选择模型,构建针对房地产市场的高风险价格膨胀动态预警机制。最后,运用我国的房地产市场数据,对得出的预警模型进行样本内及样本外检测。In order to effectively control the effect of house price＇ s irrationally rising on macroeconomic, this paper dis- tinguishes the house price booms. According to 27 countries from 1990 to 2014 real estate price data, by constraining the smooth B-spline model（ COBS）, this paper identifies each country＇s real estate price inflation interval. Subsequently, it distinguishes the high cost booms and the low cost booms by the macroeconomic reactions after different inflation intervals. Then, using the determinants through non-parametric tests, it establishes the real time early warning mechanism of high cost booms in real estate market, based on discrete choice model. Finally, this paper uses China＇ s real estate data to make in-sample test and out-of-sample test of that real time early warning mechanism.

关 键 词：高风险资产价格膨胀 COBS模型 二元离散选择模型 预警机制 

分 类 号：F830.2[经济管理—金融学]