Das, SonaliGupta, RKabundi, A2010-10-072010-10-072010Das, 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 1-150277-6693http://onlinelibrary.wiley.com/doi/10.1002/for.1182/abstracthttp://hdl.handle.net/10204/4452This 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 providedThis 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.enBayesian modelsForecast accuracySpatial modelsNon-spatial modelsHouse pricesHouse price inflationMacro-economicDynamic factor modelArticleDas, S., Gupta, R., & Kabundi, A. (2010). Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models. http://hdl.handle.net/10204/4452Das, Sonali, R Gupta, and A Kabundi "Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models." (2010) http://hdl.handle.net/10204/4452Das S, Gupta R, Kabundi A. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models. 2010; http://hdl.handle.net/10204/4452.TY - Article AU - Das, Sonali AU - Gupta, R AU - Kabundi, A AB - 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. DA - 2010 DB - ResearchSpace DP - CSIR KW - Bayesian models KW - Forecast accuracy KW - Spatial models KW - Non-spatial models KW - House prices KW - House price inflation KW - Macro-economic KW - Dynamic factor model LK - https://researchspace.csir.co.za PY - 2010 SM - 0277-6693 T1 - Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models TI - Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models UR - http://hdl.handle.net/10204/4452 ER -