| 核心城市房地产市场的联动和溢出特征研究——基于中国“北上广深”样本的经验考察 |
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| 引用本文： | 王雪,韩永辉,赵贤,韦东明.核心城市房地产市场的联动和溢出特征研究——基于中国“北上广深”样本的经验考察[J].运筹与管理,2021,30(7):232-239. |
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| 作者姓名： | 王雪 韩永辉 赵贤 韦东明 |
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| 作者单位： | 1.西南财经大学 中国金融研究中心,四川 成都 610074; 2.广东外语外贸大学 广东国际战略研究院,广东 广州 510420; 3.山东省科学技术情报研究院,山东 济南 250101; 4.暨南大学 产业经济研究院,广东 广州 510632 |
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| 基金项目： | 国家自然科学基金资助项目(71603060,71873041,72073037);广东省自然科学基金项目(2021A1515011814);广东省软科学研究计划项目(2019A101002100) |
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| 摘 要： | 本文基于房价长期趋势和短期波动层面,基于HP滤波法分离北京、上海、广州和深圳四大一线城市的房价,采用有向无环图和信息溢出指数方法剖析了核心城市房价之间的同期联动效应和信息溢出效应,并结合滚动窗口估计法分析了信息溢出对外部信息和调控政策的反应程度。结果显示:一线城市房价之间有紧密的联系程度和较高的信息溢出规模。在同期联动效应上,深圳房价趋势的对外联动效应最明显,北京房价趋势在同期最易受其它城市影响;上海房价波动存在较强的对外联动效应,深圳房价波动受其它一线城市的波动冲击较迅速。在信息溢出效应上,深圳房价趋势有引领作用,对外溢出效应最强;上海房价波动处于引导地位,中长期对其它市场影响最大。核心城市房价趋势之间的溢出指数随着利好政策信息的出现而上升,随着限制政策等不利信息的出现而下降。核心城市房价波动之间的溢出效应对于外部信息反应更为灵敏,国家对房地产市场的调控政策增大核心城市房价波动的信息溢出规模。
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| 关 键 词： | 房地产市场 “北上广深” 有向无环图 溢出指数 |
| 收稿时间： | 2019-09-03 |
A Study of Connectedness and Spillovers in China’s Core Cities Real Estate Market: the Analysis of Pei.-Sha.-Gua.-She. Cities |
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| WANG Xue,HAN Yong-Hui,ZHAO Xian,WEI Dong-ming.A Study of Connectedness and Spillovers in China’s Core Cities Real Estate Market: the Analysis of Pei.-Sha.-Gua.-She. Cities[J].Operations Research and Management Science,2021,30(7):232-239. |
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| Authors: | WANG Xue HAN Yong-Hui ZHAO Xian WEI Dong-ming |
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| Affiliation: | 1. Institute of Chinese Financial Studies, Southwestern University of Finance and Economics, Chengdu 610074, China; 2. GIIS, Guangdong University of Foreign Studies, Guangzhou 510420, China; 3. Shandong Institute of Scientific and Technical Information, Jinan 250101, China; 4. Institute of Industrial Economics, Jinan University, Guangzhou 510632, China |
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| Abstract: | This article separates the housing prices in Beijing, Shanghai, Guangzhou and Shenzhen by using the HP filter method. Directed Acyclic Graph (DAG) and the Information Spillover Index methods are implemented innovatively to analyze the contemporaneous linkage effect and the information spillover effect among the housing prices and Rolling Window Estimate method to analyze the spillover to the external information and regulation policies. The results show that there are close linkage effect and relatively high market information spillover effect among the housing prices of the first-tier cities. In terms of the contemporaneous linkage effect, the price trend of Shen Zhen shows the most obvious external linkage effect, the price trend of Beijing is most easily affected by other cities over the same period; Shanghai’s price fluctuation has strong external linkage effect on other cities, while Shenzhen’s price fluctuation is influenced more quickly and clearly by the other cities. In terms of the information spillover effect, Shenzhen’s price trend plays a leading role with the strongest spillover effects; Shanghai’s price has the most influence on other markets in medium and long terms. Spillover index among the house prices trend rises with the favorable policies and information, and declines with the restriction policies and unfavorable information. Spillover effect of house price volatility is more sensitive to external information, and the restrictions on real estate market policy increases the information spillover scale among price fluctuations of core cities. |
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| Keywords: | housing market Bei -Shang -Guang -Shen cities directed acyclic graph spillover index |
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