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浙江大学学报（理学版）
浙江大学学报（理学版）  2020, Vol. 47 Issue (3): 380-390    DOI: 10.3785/j.issn.1008-9497.2020.03.016
旅游学     
基于CEEMD的重大事件对香港住宅价格影响的实证分析
张凌, 杨霖尊
浙江大学 建筑工程学院，浙江 杭州 310058
The impact of major events on Hong Kong housing prices: A CEEMD based empirical analysis
ZHANG Ling, YANG Linzun
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
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摘要： 应用互补集成经验模态分解（CEEMD）方法对香港1997―2018年的住宅价格月度数据进行了分解，将经过重构后的数据分成高频序列、低频序列与残差项。将BP多断点检测应用于低频序列，并结合样本时段内的重大事件进行实证分析。结果表明：1997年亚洲金融风暴对房价的影响大于2008年金融危机；外部经济体的救市政策间接地影响香港房价；在经济不景气的大环境下“孙九招”政策没有立即见效；资本投资者入境计划、住房供给调整与按揭贷款调整对房价的影响较为显著；税收调整对房价影响不显著、对交易量影响显著；SARS爆发使住宅价格下降约1%。
关键词： 住宅价格;  重大事件;  互补集成经验模态分解;  BP多断点    
Abstract: Monthly housing price data of Hong Kong from 1997 to 2018 are decomposed by the complementary ensemble empirical mode decomposition (CEEMD). Then the reconstructed data are divided into high frequency series, low frequency series and residuals. Bai-Perron multi-breakpoint detection is applied to the low frequency series, and the impact of the major events in the sample period is analyzed. The results show that the impact of the 1997 Asian financial crisis on housing prices is greater than the 2008 financial crisis; the rescue policies of external economies indirectly affected the housing prices in Hong Kong; the Capital Investment Entrant Scheme, housing supply adjustment and mortgage adjustment have a significant impact on house prices; tax adjustments have no significant influence on house prices, but affect transaction volume obviously; SARS outbreak causes the housing prices to fall by around 1%.
Key words: major events    BP multi-breakpoint    complementary ensemble empirical mode decomposition (CEEMD)    Housing prices
收稿日期: 2019-01-17 出版日期: 2020-06-25
CLC:  F290  
作者简介: 张凌（1972—），ORCID:http://orcid. org/0000-0002-3864-2602，女，博士，副教授，主要从事房地产经济与管理研究,E-mail: zlcivil@163. com.
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引用本文:

张凌, 杨霖尊. 基于CEEMD的重大事件对香港住宅价格影响的实证分析[J]. 浙江大学学报（理学版）, 2020, 47(3): 380-390.

ZHANG Ling, YANG Linzun. The impact of major events on Hong Kong housing prices: A CEEMD based empirical analysis. Journal of Zhejiang University (Science Edition), 2020, 47(3): 380-390.

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https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2020.03.016    或    https://www.zjujournals.com/sci/CN/Y2020/V47/I3/380

1 LAI L W C, CHAU K W, HO D C W, et al. Impact of political incidents, financial crises, and severe acute respiratory syndrome on Hong Kong property buyers[J]. Environment and Planning B （Planning and Design）, 2006, 33(3): 413-433. DOI:10.1068/b31125a
2 BUCCHIANERI G W. Has SARS infected the housing market? Evidence from Hong Kong[J]. Journal of Urban Economics, 2008, 63(1): 74-95. DOI:10.1016/j.jue.2006.12.007
3 GUPTA R, JURGILAS M, KABUNDI A. The effect of monetary policy on real house price growth in South Africa: A factor-augmented vector autoregression (FAVAR) approach[J]. Economic Modelling, 2010, 27(1): 315-323.DOI:10.1016/j.econmod.2009.09.011
4 YEH J R, SHIEH J S, HUANG N E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2): 135-156.DOI:10.1142/S1793536910000422
5 阮连法,包洪洁,温海珍.重大事件对城市住宅价格的影响——来自杭州市的证据[J].中国土地科学,2012,26(12):41-47.DOI:10.3969/j.issn.1001-8158.2012.12.007 RUAN L F, BAO H J.WEN H Z, Impacts of social events on urban housing prices: Evidence from Hangzhou city[J]. China Land Sciences, 2012,26(12):41-47. DOI:10.3969/j.issn.1001-8158.2012.12.007
6 李仲飞,肖仁华,杨利军.基于集合经验模态分解技术的中国房地产周期识别研究[J].经济评论,2014(4):108-121. DOI:10.19361/j.er.2014.04.009 LI Z F, XIAO R H, YANG L J. Identifying real estate cycle in China: A study based on EEMD method[J]. Economic Review, 2014(4):108-121.DOI:10.1007/s00484-016-1277-x
7 PLAKANDARAS V, GUPTA R, GOGAS P, et al. Forecasting the US real house price index[J]. Economic Modelling, 2015, 45: 259-267.DOI: 10.1016/j.econmod.2014.10.050
8 李媛. 基于经验模态分解方法的房地产价格重大影响因素及短期预测分析[D].大连：东北财经大学,2017. LI Y. A Study on Influence Factors and Short Forecast of Real Estate Price Based on EMD[D]. Dalian: Dongbei University of Finance and Economics, 2017.
9 TANG L, DAI W, YU L, et al. A novel CEEMD-based EELM ensemble learning paradigm for crude oil price forecasting[J]. International Journal of Information Technology & Decision Making, 2015, 14(1): 141-169. DOI:10.1142/s0219622015400015
10 CHEN R Y, PAN B. Chinese stock index futures price fluctuation analysis and prediction based on complementary ensemble empirical mode decomposition[J]. Mathematical Problems in Engineering, 2016:1-13. DOI:10.1155/2016/3791504
11 JOTHIMANI D, YADAV S S. Stock trading decisions using ensemble-based forecasting models: A study of the Indian stock market[J]. Journal of Banking and Financial Technology, 2019, 3(2):113-129. DOI:10.1007/s42786-019-00009-7
12 WEN F, YANG X, GONG X, et al. Multi-scale volatility feature analysis and prediction of gold price[J]. International Journal of Information Technology & Decision Making, 2017, 16(1): 205-223. DOI:10.1142/S0219622016500504
13 HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings A, 1998, 454(1971):903-995.DOI:10.1098/rspa.1998.0193
14 WU Z, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41. DOI:10.1142/S1793536909000047
15 ZHANG X, YU L, WANG S, et al. Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method[J]. Energy Economics, 2009, 31(5): 768-778.DOI:10.1016/j.eneco.2009.04.003
16 BAI J, PERRON P. Computation and analysis of multiple structural change models[J]. Journal of Applied Econometrics, 2003, 18(1): 1-22.DOI:10.2307/30035185
17 ZEILEIS A, LEISCH F, HORNIK K, et al. Strucchange: An R package for testing for structural change in linear regression models[J]. Journal of Statistical Software, 2002, 7(2): 1-38. DOI:info:DOI/10.18637/jss.v007.i02
18 陈李蔼伦. 香港经济近况和展望（2009—2010年）[C]//刘树成,李扬,陈佳贵,等. 2010年中国经济形势分析与预测. 北京：社会科学文献出版社, 2009：329-346. CHEN L A H. Recent situation and forecast of Hong Kong economy[C]// LIU S C, LI Y, CHEN J G, et al. Economy of China Analysis and Forecast. Beijing: Social Sciences Academic Press, 2009: 329-346.
19 徐颖. 深港住房价格波动相关性研究[D].昆明：云南财经大学,2017. XU Y. The Correlation of Housing Price Fluctuation in Shenzhen and Hong Kong[D]. Kunming: Yunnan University of Finance and Economics, 2017.
20 张红,李洋,王悦.中国内地与香港房地产市场有效性及互动关系研究[J].经济问题探索, 2012(9):15-21.DOI:10.3969/j.issn.1006-2912.2012.09.003 ZHANG H, LI Y,WANG Y. A Study on the effectiveness and interaction of real estate market in mainland China and Hong Kong[J]. Inquiry into Economic Issues, 2012(9):15-21.DOI:10.3969/j.issn.1006-2912.2012.09.003
21 CHO D, RHEE C. Effects of quantitative easing on Asia: Capital flows and financial markets[J]. The Singapore Economic Review, 2014, 59(3): 1450018. DOI:10.1142/S0217590814500180
22 YU K H, HUI E C M. Housing market dynamics under a pegged exchange rate:A study of Hong Kong[J]. International Journal of Strategic Property Management, 2018, 22(2): 93-109.DOI:10.3846/ijspm.2018.441
23 韩昱. 香港特区政府治理房地产泡沫经验及其启示[J]. 价格理论与实践, 2017(9):160-163.DOI:10.19851/j.cnki.cnu-1010/f.2017.09.040 HAN Y. Research on long-term mechanism of real estate regulation in Hong Kong[J]. Price: Theory & Practice, 2017(9): 160-163. DOI:10.19851/j.cnki.cnu-1010/f.2017.09.040
24 薛凤旋. 香港发展报告（2012）[M]. 北京：社会科学文献出版社，2012. XUE F X. Annual Report on Development of Hong Kong (2012)[M]. Beijing: Social Sciences Academic Press, 2012.
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