Testing the Predictability of U.S. Housing Price Index Returns Based on an IVX-AR Model

Bingduo Yang, Wei Long, Liang Peng and Zongwu Cai

Journal of the American Statistical Association, 2020, vol. 115, issue 532, 1598-1619

Abstract: We use ten common macroeconomic variables to test for the predictability of the quarterly growth rate of house price index (HPI) in the United States during 1975:Q1–2018:Q2. We extend the instrumental variable based Wald statistic (IVX-KMS) proposed by Kostakis, Magdalinos, and Stamatogiannis to a new instrumental variable based Wald statistic (IVX-AR) which accounts for serial correlation and heteroscedasticity in the error terms of the linear predictive regression model. Simulation results show that the proposed IVX-AR exhibits excellent size control regardless of the degree of serial correlation in the error terms and the persistency in the predictive variables, while IVX-KMS displays severe size distortions. The empirical results indicate that the percentage of residential fixed investment in GDP is fairly a robust predictor of the growth rate of HPI. However, other macroeconomic variables’ strong predictive ability detected by IVX-KMS is likely to be driven by the highly correlated error terms in the predictive regressions and thus becomes insignificant when the proposed IVX-AR method is implemented. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Date: 2020
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DOI: 10.1080/01621459.2019.1686392

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