经济政策不确定性视角下的房价波动：以长江经济带为例

doi:10.16382/j.cnki.1000-5579.2021.06.016

摘要/Abstract

摘要：

近年来，为了应对房价过快上涨，我国政府频繁出台各类宏观经济政策对房地产市场进行调控，由此带来的经济政策不确定性不断冲击着我国房地产市场。因此，明确我国经济政策不确定性对房价的影响，具有较大的理论和现实意义。基于此，利用2006年1月至2021年3月长江经济带及其三大城市群25个城市的数据，对我国经济政策不确定性与房价的互动关系进行实证分析，其结论是：面板协整检验表明，经济政策不确定性与新房价格和二手房价格均存在长期协整关系；面板格兰杰因果检验显示，较之二手房价格，经济政策不确定性与新房价格的因果关系更为显著；尽管面板向量自回归模型显示经济政策不确定性对房价波动没有直接的显著影响，但条件收敛结果表明其对新房和二手房价格的收敛性均有显著的促进作用。因此，政府应重视经济政策变动对房地产市场的影响，在合理预期内“因地制宜，因城施策”，针对新房和二手房市场制定差别化的调控政策；同时，可利用经济政策不确定性的溢出效应对区域房价进行系统性调控，从而实现房地产市场的区域协同发展。

关键词: 经济政策不确定性, 房价波动, 长江经济带, 长三角城市群, 长江中游城市群, 成渝城市群

Abstract:

In recent years, in order to dampen the excessive rise of housing prices in China, governments have frequently implemented a series of macroeconomic policies to adjust the housing market. To some extent, house prices in urban China have been shocked by economic policy uncertainty （EPU）. By using the data of 25 big cities in Yangtze River Economic Belt and three urban agglomerations from January 2006 to March 2021, we find that there is a long-term integration between EPU and housing prices through panel integration test. Panel Granger causality test shows that, compared with second-hand housing price, EPU has more significant causality impact on new housing price. PVAR model indicates EPU has no direct significant effect on housing prices, however, the conditional β convergence analysis shows EPU has significantly accelerated the convergence of new housing price and second-hand housing price. Therefore, government should pay attention to the different effects of EPU on the new-built and second-hand housing markets, and implement city-specific policies based on reasonable expectations. Meanwhile, governments can systematically adjust the housing market by taking advantage of the spillover effect of economic policies to achieve the synergetic development of regional housing market.

Key words: economic policy uncertainty, fluctuation of housing prices, Yangtze River Economic Belt, urban agglomeration in the Yangtze River Delta, urban agglomeration in the middle reaches of Yangtze River, Chengdu-Chongqing urban agglomeration

张方. 经济政策不确定性视角下的房价波动：以长江经济带为例[J]. 华东师范大学学报(哲学社会科学版), 2021, 53(6): 165-176.

Fang ZHANG. The Effect of Economic Policy Uncertainty on the Fluctuation of House Prices：Evidence from Cities of Yangtze River Economic Belt[J]. Journal of East China Normal University (Philosophy and Social Sciences), 2021, 53(6): 165-176.

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参考文献 41

1	陈丰龙、王美昌、徐康宁, 2018, 《中国区域经济协调发展的演变特征: 空间收敛的视角》, 《财贸经济》第7期.
2	陈晓玲、张毅, 2017, 《金融发展、产业升级与经济增长的动态关系研究——基于省际数据的面板VAR分析》, 《财贸研究》第10期.
3	胡国庆, 2017, 《汇率预期对房价波动的影响——基于经济政策不确定性的研究》, 《价格月刊》第11期.
4	刘金全、毕振豫, 2018, 《不确定性会影响货币政策对房价的调控效应吗?——基于LT-TVP-VAR模型的实证检验》, 《财经论丛》第10期.
5	谭政勋、王聪, 2015, 《房价波动、货币政策立场识别及其反应研究》, 《经济研究》第10期.
6	王允、杜萌, 2015, 《汇率波动、国际收支账户传导与经济增长——基于面板VAR的实证分析》, 《管理现代化》第4期.
7	吴国维、章颖、朱萌、沈祥成、罗治情, 2018, 《金融发展与新型城镇化互动效应研究——基于2004—2015年276个地级市数据的异质性面板Granger因果检验》, 《生态经济》第8期.
8	游士兵、蔡远飞, 2017, 《人口老龄化对经济增长影响的动态分析——基于面板VAR模型的实证分析》, 《经济与管理》第1期.
9	张浩、李仲飞、邓柏峻, 2015, 《政策不确定、宏观冲击与房价波动——基于LSTVAR模型的实证分析》, 《金融研究》第10期.
10	Antonakakis, N. and Floros, C. , 2016, “Dynamic Interdependencies among the Housing Market, Stock Market, Policy Uncertainty and the Macroeconomy in the United Kingdom”, International Review of Financial Analysis, Vol. 44.
11	Baker, S. R. , Bloom, N. and Davis, S. J. , 2016, “Measuring Economic Policy Uncertainty”, Quarterly Journal of Economics, Vol. 131, No. 4.
12	Barro, R. J. , 1991, “Economic Growth in a Cross Section of Countries”, The Quarterly Journal of Economies, Vol. 106, No. 2.
13	Barro, R. J. and Sala-i-Martin, X. , 1992, “Convergence”, Journal of Political Economy, Vol. 100, No. 2.
14	Berkovec, J. A, 1989, “General Equilibrium Model of Housing Consumption and Investment”, Journal of Real Estate Finance and Economics, Vol. 2.
15	Bloom, N. , 2009, “The Impact of Uncertainty Shocks”, Econometrica, Vol. 77, No. 3.
16	Canova, F. and Ciccarelli, M. , 2013, “Panel Vector Autoregressive Models: A Survey”, Working Paper.
17	Chow, S. , Cunado, J. , Gupta, R. and Wong, W. K. , 2018, “Causal Relationships between Economic Policy Uncertainty and Housing Market Returns in China and India: Evidence from Linear and Nonlinear Panel and Time Series Models”, Studies in Nonlinear Dynamics & Econometrics, Vol. 22, No. 2.
18	Christidou, M. and Fountas, S. , 2018, “Uncertainty in the Housing Market: Evidence from US States”, Studies in Nonlinear Dynamics & Econometrics, Vol. 22, No. 2.
19	Christou, C. , Gupta, R. and Hassapis, C. , 2017, “Does Economic Policy Uncertainty Forecast Real Housing Returns in a Panel of OECD Countries? A Bayesian Approach”, The Quarterly Review of Economics and Finance, Vol. 65.
20	Dumitrescu, E. I. and Hurlin, C. , 2012, “Testing for Granger Non-Causality in Heterogeneous Panels”, Economic Modelling, Vol. 29, No. 4.
21	El-Montasser, G. , Ajmi, A. N. , Chang, T. , Simo-Kengne, B. D. , André, C. and Gupta, R. , 2016, “Cross-Country Evidence on the Causal Relationship between Policy Uncertainty and Housing Prices”, Journal of Housing Research, Vol. 25, No. 2.
22	Engle, R. F. and Granger, C. W. J. , 1987, “Cointegration and Error Correction Representation, Estimation, and Testing”, Econometrica, Vol. 55, No. 2.
23	Giavazzi, F. and McMahon, M. , 2012, “Policy Uncertainty and Household Savings”, The Review of Economics and Statistics, Vol. 94, No. 2.
24	Gilchrist, S. , Sim, J. and Zakrajsek, E. , 2011, “Uncertainty, Financial Frictions and Investment Dynamics”. NBER Working Paper, No. 14 863.
25	Glaeser, E. L. and Gyourko, J. , 2007, “Arbitrage in Housing Markets”, National Bureau of Economic Research Working Paper, No. 13 704.
26	Jeon, J. H. , 2018, “The Impact of Asian Economic Policy Uncertainty: Evidence from Korean Housing Market”, Journal of Asian Finance, Economics and Business, Vol. 5, No. 2.
27	Kao, C. , 1999, “Spurious Regression and Residual-Based Tests for Cointegration in Panel Data”, Journal of Econometrics, Vol. 90.
28	Huang, W. L. , Lin, W. Y. and Ning, S. L. , 2020, “The Effect of Economic Policy Uncertainty on China’s Housing Market”, North American Journal of Economics and Finance, Vol. 54.
29	Huang, Y. and Luk, P. , 2020, “Measuring Economic Policy Uncertainty in China”, China Economic Review, Vol. 59.
30	International Monetary Fund, 2012 World Economic Outlook: Coping with High Debt and Sluggish Growth, Washington, DC: IMF Press.
31	International Monetary Fund, 2013 World Economic Outlook: Hopes, Realities, Risks, Washington, DC: IMF Press.
32	Pastor, L. and Veronesi, P. , 2012, “Uncertainty about Government Policy and Stock Prices”, Journal of Finance, Vol. 64, No. 4.
33	Pastor, L. and Veronesi, P. , 2013, “Political Uncertainty and Risk Premia”, Journal of Financial Economics, Vol. 110, No. 3.
34	Pedroni, P. , 1999, “Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors”, Oxford Bulletin of Economics and Statistics, Vol. 61.
35	Pedroni, P. , 2004, “Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis”, Econometric Theory, Vol. 20.
36	Ramey, V. and Shapiro, M. , 1998, “Costly Capital Reallocation and the Effects of Government Spending”, Carnegie-Rochester Conference Series on Public Policy, Vol. 48.
37	Rosen, H. S. , Rosen, K. T. and Holtz-Eakin, D. , 1983, “Housing Tenure, Uncertainty, and Taxation”, National Bureau of Economic Research Working Paper, No. 1 168.
38	Sinai, T. and Souleles, N. S. , 2005, “Owner-Occupied Housing as a Hedge against Rent Risk”, Quarterly Journal of Economics, Vol. 120, No. 2.
39	Su, D. , Li, X. , Lobonţ, O. R. and Zhao, Y. , 2016, “Economic Policy Uncertainty and Housing Returns in Germany: Evidence from a Bootstrap Rolling Window”, Journal of Economics and Business, Vol. 34.
40	Westerlund, J. , 2005, “New Simple Tests for Panel Cointegration”, Econometric Reviews, Vol. 24.
41	Zhang, C. and Zhang, F. , 2019, “Effects of Housing Wealth on Subjective Well-Being in Urban China”, Journal of Housing and the Built Environment, Vol. 34.

国家级城市群	国务院批复时间	印发时间	涵盖城市^④
长江中游城市群	2015-03-26	2015-04-13	南昌、九江、武汉、宜昌、襄阳、长沙、岳阳、常德
成渝城市群	2016-04-12	2016-04-27	重庆、成都、泸州、南充、贵阳^、遵义^
长三角城市群	2016-05-22	2016-06-1	上海、南京、无锡、扬州、杭州、宁波、金华、合肥、安庆、徐州^、温州^

城市		HPI		HPI2
城市		ADF test	PP test	ADF test	PP test
长三角城市群	上海	?4.085^***	?3.990^***	?5.168^***	?5.007^***
	南京	?4.463^***	?4.453^***	?4.503^***	?4.504^***
	无锡	?5.311^***	?5.308^***	?6.573^***	?6.410^***
	扬州	?6.378^***	?6.509^***	?5.953^***	?5.959^***
	杭州	?5.499^***	?5.424^***	?6.071^***	?6.027^***
	宁波	?5.498^***	?5.357^***	?5.706^***	?5.655^***
	金华	?7.938^***	?8.056^***	?7.150^***	?6.871^***
	合肥	?4.042^***	?4.303^***	?4.676^***	?4.748^***
	安庆	?7.132^***	?7.228^***	?6.142^***	?6.172^***
	徐州	?6.827^***	?6.856^***	?8.004^***	?8.159^***
	温州	?7.907^***	?8.145^***	?8.304^***	?8.414^***
长江中游城市群	武汉	?4.650^***	?4.543^***	?5.841^***	?5.795^***
	襄阳	?13.622^***	?13.807^***	?7.282^***	?7.327^***
	宜昌	?8.498^***	?8.753^***	?7.473^***	?7.478^***
	长沙	?6.933^***	?6.853^***	?6.925^***	?6.916^***
	岳阳	?6.439^***	?6.453^***	?7.147^***	?7.018^***
	常德	?8.055^***	?8.190^***	?7.633^***	?7.769^***
	南昌	?6.687^***	?6.727^***	?6.256^***	?6.341^***
	九江	?8.082^***	?8.224^***	?6.589^***	?6.636^***
成渝城市群	成都	?6.620^***	?6.617^***	?7.463^***	?7.670^***
	重庆	?6.138^***	?6.149^***	?6.871^***	?6.879^***
	泸州	?7.413^***	?7.485^***	?6.995^***	?7.187^***
	南充	?6.607^***	?6.595^***	?6.439^***	?6.420^***
	贵阳	?6.324^***	?6.357^***	?8.576^***	?8.649^***
	遵义	?8.918^***	?9.156^***	?7.950^***	?7.974^***

Test statistics	HPI				HPI2
Test statistics	长江经济带	长三角城市群	长江中游城市群	成渝城市群	长江经济带	长三角城市群	长江中游城市群	成渝城市群
LLC test	?47.626^***	?30.191^***	?29.389^***	?22.444^***	?45.433^***	?27.626^***	?28.522^***	?22.505^***
IPS test	?49.264^***	?33.269^***	?27.741^***	?23.480^***	?48.709^***	?32.064^***	?27.409^***	?24.363^***

HPI	EPU		长江经济带	长三角城市群	长江中游城市群	成渝城市群
	Kao test	Modified Dickey-Fuller	?40.109^***	?26.042^***	?23.581^***	?19.812^***
		Dickey-Fuller	?35.360^***	?23.657^***	?20.185^***	?16.651^***
		Augmented Dickey-Fuller	?23.052^***	?14.891^***	?13.659^***	?10.991^***
		Unadjusted Modified Dickey-Fuller	?3.0e+02^***	?2.0e+02^***	?1.7e+02^***	?1.4e+02^***
		Unadjusted Dickey-Fuller	?73.863^***	?50.523^***	?41.053^***	?33.678^***
	Pedroni test	Modified Phillips-Perron	?118.543^***	?81.977^***	?64.514^***	?52.410^***
		Phillips-Perron	?81.185^***	?56.362^***	?48.587^***	?37.555^***
		Augmented Dickey-Fuller	?80.283^***	?55.436^***	?44.820^***	?37.063^***
	Westerlund test	Variance Ratio	?8.899^***	?5.894^***	?5.051^***	?4.352^***
HPI2	EPU		长江经济带	长三角城市群	长江中游城市群	成渝城市群
	Kao test	Modified Dickey-Fuller	?70.404^***	?66.708^***	?28.315^***	?24.682^***
		Dickey-Fuller	?46.308^***	?36.199^***	?21.971^***	?19.039^***
		Augmented Dickey-Fuller	?29.796^***	?23.722^***	?13.799^***	?11.869^***
		Unadjusted Modified Dickey-Fuller	?2.9e+02^***	?1.9e+02^***	?1.6e+02^***	?1.4e+02^***
		Unadjusted Dickey-Fuller	?70.309^***	?46.130^***	?40.051^***	?34.545^***
	Pedroni test	Modified Phillips-Perron	?116.792^***	?76.360^***	?64.329^***	?59.156^***
		Phillips-Perron	?79.998^***	?52.754^***	?46.662^***	?39.868^***
		Augmented Dickey-Fuller	?78.011^***	?50.880^***	?44.690^***	?38.744^***
	Westerlund test	Variance Ratio	?8.808^***	?5.845^***	?4.968^***	?4.329^***

	EPU lags		长江经济带	长三角城市群	长江中游城市群	成渝城市群
HPI	1	Z-bar	1.154	1.132	0.816	?0.118
	1	Z-bar tilde	1.093	1.083	0.777	?0.135
	2	Z-bar	2.949^**	1.056	1.190	3.217^**
	2	Z-bar tilde	2.826^**	0.995	1.131	3.117^**
	3	Z-bar	10.474^***	5.306^***	7.845^***	5.137^***
	3	Z-bar tilde	10.133^***	5.122^***	7.603^***	4.970^***
	4	Z-bar	14.763^***	9.303^***	10.437^***	5.487^***
	4	Z-bar tilde	14.251^***	8.977^***	10.087^***	5.287^***
HPI2	1	Z-bar	1.877^*	?0.769	4.727^***	?0.585
	1	Z-bar tilde	1.802^*	?0.780	4.612^***	?0.593
	2	Z-bar	1.771^*	?1.400	5.066^***	?0.339
	2	Z-bar tilde	1.675^*	?1.405	4.920^***	?0.359
	3	Z-bar	8.406^***	6.544^***	6.573^***	0.708
	3	Z-bar tilde	8.119^***	6.329^***	6.364^***	0.655
	4	Z-bar	11.456^***	10.733^***	6.834^***	0.960
	4	Z-bar tilde	11.040^***	10.366^***	6.588^***	0.892