Kabundi, Alain2009-03-032009-03-032009-02Gupta, R & Kabundi, A 2009, 'Forecasting real US house price: principal components versus Bayesian regressions', University of Pretoria, Department of Economics, Working paper series, no. 2009-07. [http://web.up.ac.za/default.asp?ipkCategoryID=736&sub=1&parentid=677&subid=729&ipklookid=3]http://hdl.handle.net/2263/9106This paper analyzes the ability of principal component regressions and Bayesian regression methods under Gaussian and double-exponential prior in forecasting the real house price of the United States (US), based on a monthly dataset of 112 macroeconomic variables. Using an in-sample period of 1992:01 to 2000:12, Bayesian regressions are used to forecast real US house prices at the twelve-months-ahead forecast horizon over the out-of-sample period of 2001:01 to 2004:10. In terms of the Mean Square Forecast Errors (MSFEs), our results indicate that a principal component regression with only one factor is best-suited for forecasting the real US house price. Amongst the Bayesian models, the regression based on the double exponential prior outperforms the model with Gaussian assumptions.enUniversity of Pretoria, Department of EconomicsBayesian regressionsPrincipal componentsLarge-cross sectionsHousing -- Prices -- United States -- ForecastingWorking Paper