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

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  • Bingduo Yang
  • Wei Long
  • Liang Peng
  • Zongwu Cai

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.

Suggested Citation

  • Bingduo Yang & Wei Long & Liang Peng & Zongwu Cai, 2020. "Testing the Predictability of U.S. Housing Price Index Returns Based on an IVX-AR Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1598-1619, December.
    • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:532:p:1598-1619
    DOI: 10.1080/01621459.2019.1686392
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    Cited by:

    1. Yang, Bingduo & Long, Wei & Yang, Zihui, 2022. "Testing predictability of stock returns under possible bubbles," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 246-260.
    2. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    3. Christis Katsouris, 2023. "Limit Theory under Network Dependence and Nonstationarity," Papers 2308.01418, arXiv.org, revised Aug 2023.
    4. Shuping Shi & Peter C.B. Phillips, 2023. "Diagnosing housing fever with an econometric thermometer," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 159-186, February.
    5. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.
    6. Liu, Yanbo & Phillips, Peter C.B., 2023. "Robust inference with stochastic local unit root regressors in predictive regressions," Journal of Econometrics, Elsevier, vol. 235(2), pages 563-591.
    7. Shuping Shi & Peter C. B. Phillips, 2022. "Econometric Analysis of Asset Price Bubbles," Cowles Foundation Discussion Papers 2331, Cowles Foundation for Research in Economics, Yale University.
    8. Li, Zhenxiong & Yao, Xingzhi & Izzeldin, Marwan, 2023. "On the right jump tail inferred from the VIX market," International Review of Financial Analysis, Elsevier, vol. 86(C).
    9. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    10. Yang Bai, 2022. "150 Years of Return Predictability Around the World: A Holistic View," Papers 2209.00121, arXiv.org.
    11. Hurn, Stan & Shi, Shuping & Wang, Ben, 2022. "Housing networks and driving forces," Journal of Banking & Finance, Elsevier, vol. 134(C).

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