Testing the Predictability of US Housing Price Index Returns Based on an IVX-AR Model

• 成果类型: Article
• 署名作者: Yang, Bingduo; Long, Wei; Peng, Liang; Cai, Zongwu
• 署名单位: Sun Yat Sen University; Tulane University; University System of Georgia; Georgia State University; University of Kansas
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1686392
• 发表日期: 2020
• 页码: 1598-1619
• 关键词: robust econometric inference; time-series; PREDICTIVE REGRESSIONS; EFFICIENT TESTS; unit-root; MARKET

摘要

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. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.