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dc.contributor.author Gupta, R
dc.contributor.author Das, Sonali
dc.date.accessioned 2010-10-12T09:00:17Z
dc.date.available 2010-10-12T09:00:17Z
dc.date.issued 2010-10
dc.identifier.citation Gupta, R and Das, S. 2010. Predicting downturns in the US housing market: a Bayesian approach. Journal of Real Estate Finance and Economics, Vol. 41(3), pp 294-319 en
dc.identifier.issn 0895-5638
dc.identifier.uri http://www.springerlink.com/index/w752464096652861.pdf
dc.identifier.uri http://hdl.handle.net/10204/4481
dc.description Copyright: 2010 Springer. This is the post print version of the work. The definitive version is published in the Journal of Real Estate Finance and Economics, Vol. 41(3), pp 294-319 en
dc.description.abstract This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1–1994:Q4; and then forecasts one-to-four quarters-ahead real house price growth over the out-of- sample horizon of 1995:Q1–2006:Q4. The forecasts are evaluated by comparing them with those from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant of the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth over the recent period of 2007: Q1–2008:Q1. The results show that the BVARs, in whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in predicting the downturn in 18 of the 19 states en
dc.language.iso en en
dc.publisher Springer en
dc.relation.ispartofseries Journal Article en
dc.subject Bayesian vector autoregressive en
dc.subject BVAR en
dc.subject BVAR forecasts en
dc.subject Forecast accuracy en
dc.subject US housing market en
dc.title Predicting downturns in the US housing market: a Bayesian approach en
dc.type Article en


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