DSpace
 

Researchspace >
General science, engineering & technology >
General science, engineering & technology >
General science, engineering & technology >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/4452

Title: Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models
Authors: Das, S
Gupta, R
Kabundi, A
Keywords: Bayesian models
Forecast accuracy
Spatial models
Non-spatial models
House prices
House price inflation
Macro-economic
Dynamic factor model
Issue Date: 2010
Publisher: Wiley-Blackwell
Citation: Das, S, Gupta, R and Kabundi, A. 2010. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models. Journal of Forecasting, Vol (2010), pp 1-15
Series/Report no.: Pre Print Version
Abstract: This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, the authors forecast house price inflation for five metropolitan areas of South Africa using principal components obtained from 282 quarterly macroeconomic time series in the period 1980:1 to 2006:4. The results, based on the root mean square errors of one to four quarters ahead out-of-sample forecasts over the period 2001:1 to 2006:4 indicate that, in the majority of the cases, the Dynamic Factor Model statistically outperforms the vector autoregressive models, using both the classical and the Bayesian treatments. The authors also consider spatial and non-spatial specifications. Their results indicate that macroeconomic fundamentals in forecasting house price inflation are important.
Description: This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in Journal of Forecasting, copyright Wiley-Blackwell after peer review. To access the final edited and published work see the link provided
URI: http://onlinelibrary.wiley.com/doi/10.1002/for.1182/abstract
http://hdl.handle.net/10204/4452
ISSN: 0277-6693
Appears in Collections:Logistics and quantitative methods
Advanced mathematical modelling and simulation
General science, engineering & technology

Files in This Item:

File Description SizeFormat
Das_2010.pdf183.15 kBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback