dc.contributor.author |
Das, Sonali
|
|
dc.contributor.author |
Gupta, R
|
|
dc.contributor.author |
Kabundi, A
|
|
dc.date.accessioned |
2009-12-04T07:10:41Z |
|
dc.date.available |
2009-12-04T07:10:41Z |
|
dc.date.issued |
2009 |
|
dc.identifier.citation |
Das, S, Gupta, R and Kabundi, A. 2009. Could we have predicted the recent downturn in the South African housing market?. Journal of housing economics, Vol. 18(4), pp 325–335 |
en |
dc.identifier.issn |
1051-1377 |
|
dc.identifier.uri |
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WJR-4W6Y81X-2&_user=958262&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000049363&_version=1&_urlVersion=0&_userid=958262&md5=8a3e2ba8b97d10e1620478d6790d7e54
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/3803
|
|
dc.description |
Copyright: 2009 Elsevier. This is the pre print version of the work. It is posted here by permission of Elsevier for your personal use. Not for redistribution. The definitive version is published in the Journal of housing economics, Vol. 18(2009), pp 325-335 |
en |
dc.description.abstract |
This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01–2000:04, the large-scale BVARs, estimated under alternative hyperparameter values specifying the priors, are used to forecast real house price growth rates over a 24-quarter out-of-sample horizon of 2001:01–2006:04. The forecast performance of the large-scale BVARs are then compared with classical and Bayesian versions of univariate and multivariate Vector Autoregressive (VAR) models, merely comprising of the real growth rates of the large-, medium- and small-middle-segment houses, and a large-scale Dynamic Factor Model (DFM), which comprises of the same 268 variables included in the large-scale BVARs. Based on the one- to four-quarters-ahead Root Mean Square Errors (RMSEs) over the out-of-sample horizon, we find the large-scale BVARs to not only outperform all the other alternative models, but to also predict the recent downturn in the real house price growth rates for the three categories of the middle-segmenthousing over the period of 2003:01–2008:02. |
en |
dc.language.iso |
en |
en |
dc.publisher |
Elsevier |
en |
dc.subject |
Dynamic factor model |
en |
dc.subject |
Forecast accuracy |
en |
dc.subject |
Housing market |
en |
dc.subject |
Housing economics |
en |
dc.subject |
Bayesian vector autoregressive |
en |
dc.subject |
BVAR |
en |
dc.title |
Could we have predicted the recent downturn in the South African housing market? |
en |
dc.type |
Article |
en |
dc.identifier.apacitation |
Das, S., Gupta, R., & Kabundi, A. (2009). Could we have predicted the recent downturn in the South African housing market?. http://hdl.handle.net/10204/3803 |
en_ZA |
dc.identifier.chicagocitation |
Das, Sonali, R Gupta, and A Kabundi "Could we have predicted the recent downturn in the South African housing market?." (2009) http://hdl.handle.net/10204/3803 |
en_ZA |
dc.identifier.vancouvercitation |
Das S, Gupta R, Kabundi A. Could we have predicted the recent downturn in the South African housing market?. 2009; http://hdl.handle.net/10204/3803. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Das, Sonali
AU - Gupta, R
AU - Kabundi, A
AB - This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01–2000:04, the large-scale BVARs, estimated under alternative hyperparameter values specifying the priors, are used to forecast real house price growth rates over a 24-quarter out-of-sample horizon of 2001:01–2006:04. The forecast performance of the large-scale BVARs are then compared with classical and Bayesian versions of univariate and multivariate Vector Autoregressive (VAR) models, merely comprising of the real growth rates of the large-, medium- and small-middle-segment houses, and a large-scale Dynamic Factor Model (DFM), which comprises of the same 268 variables included in the large-scale BVARs. Based on the one- to four-quarters-ahead Root Mean Square Errors (RMSEs) over the out-of-sample horizon, we find the large-scale BVARs to not only outperform all the other alternative models, but to also predict the recent downturn in the real house price growth rates for the three categories of the middle-segmenthousing over the period of 2003:01–2008:02.
DA - 2009
DB - ResearchSpace
DP - CSIR
KW - Dynamic factor model
KW - Forecast accuracy
KW - Housing market
KW - Housing economics
KW - Bayesian vector autoregressive
KW - BVAR
LK - https://researchspace.csir.co.za
PY - 2009
SM - 1051-1377
T1 - Could we have predicted the recent downturn in the South African housing market?
TI - Could we have predicted the recent downturn in the South African housing market?
UR - http://hdl.handle.net/10204/3803
ER -
|
en_ZA |