FORECASTING REAL US HOUSE PRICE: PRINCIPAL COMPONENTS VERSUS BAYESIAN REGRESSIONS

Rangan Gupta () and Alain Kabundi ()

No 200907, Working Papers from University of Pretoria, Department of Economics

Abstract: This 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.

Keywords: Bayesian Regressions; Principal Components; Large-Cross Sections (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ure
Date: 2009-02

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Persistent link: http://EconPapers.repec.org/RePEc:pre:wpaper:200907

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