Understanding Chinese provincial real estate investment: A Global VAR perspective

Chen, Y; He, M; Rudkin, S

Understanding Chinese provincial real estate investment: A Global VAR perspective

Chen, Y He, M Rudkin, S

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Publication Type:: Journal Article
Citation:: Economic Modelling, 2017, 67 pp. 248 - 260
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	Chen, Y	en_US
dc.contributor.author	He, M	en_US
dc.contributor.author	Rudkin, S https://orcid.org/0000-0001-8622-7318	en_US
dc.date.issued	2017-12-01	en_US
dc.identifier.citation	Economic Modelling, 2017, 67 pp. 248 - 260	en_US
dc.identifier.issn	0264-9993	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124291
dc.description.abstract	© 2016 Elsevier Ltd This article investigates the spatial interdependence within China's real estate industry, a sector assuming increasing importance in the national economy. The Global Vector Autoregressive (GVAR) model allows us to explicitly address the presence of spatial linkages, including spillover and backwash effects, without a stringent requirement on data. Applying the model to monthly Chinese provincial data for the first time we highlight clear advantages in forecasting and steady-state value prediction. We also demonstrate through the contemporaneous correlation coefficients a growing divide between the previously highly industrialized north and the rest of China. The insights provided by our empirical study have clear value to a wide range of audiences, including researchers, policy makers, and business investors.	en_US
dc.relation.ispartof	Economic Modelling	en_US
dc.relation.isbasedon	10.1016/j.econmod.2016.12.019	en_US
dc.subject.classification	Economics	en_US
dc.title	Understanding Chinese provincial real estate investment: A Global VAR perspective	en_US
dc.type	Journal Article
utslib.citation.volume	67	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	67	en_US

Abstract:

© 2016 Elsevier Ltd This article investigates the spatial interdependence within China's real estate industry, a sector assuming increasing importance in the national economy. The Global Vector Autoregressive (GVAR) model allows us to explicitly address the presence of spatial linkages, including spillover and backwash effects, without a stringent requirement on data. Applying the model to monthly Chinese provincial data for the first time we highlight clear advantages in forecasting and steady-state value prediction. We also demonstrate through the contemporaneous correlation coefficients a growing divide between the previously highly industrialized north and the rest of China. The insights provided by our empirical study have clear value to a wide range of audiences, including researchers, policy makers, and business investors.

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http://hdl.handle.net/10453/124291