期刊名称：International Business & Economics Research Journal
印刷版ISSN：1535-0754
电子版ISSN：2157-9393
出版年度：2010
卷号：9
期号：7
语种：English
出版社：The Clute Institute for Academic Research
摘要：This paper analyzes the ability of principal component regressions and Bayesian regression methods under Gaussian and double-exponential prior in forecasting the real house prices of the United States (U.S.), 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 U.S. house prices at the twelve-months-ahead forecast horizon over the out-of-sample period of 2001:01 to 2004:10. I n 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 U.S. house prices. Among the Bayesian models, the regression based on the double exponential prior outperforms the model with Gaussian assumptions.
