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Finite-Sample Size Control Of Ivx-Based Tests In Predictive Regressions

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  • Hosseinkouchack, Mehdi
  • Demetrescu, Matei

Abstract

In predictive regressions with variables of unknown persistence, the use of extended IV (IVX) instruments leads to asymptotically valid inference. Under highly persistent regressors, the standard normal or chi-squared limiting distributions for the usual t and Wald statistics may, however, differ markedly from the actual finite-sample distributions which exhibit in particular noncentrality. Convergence to the limiting distributions is shown to occur at a rate depending on the choice of the IVX tuning parameters and can be very slow in practice. A characterization of the leading higher-order terms of the t statistic is provided for the simple regression case, which motivates finite-sample corrections. Monte Carlo simulations confirm the usefulness of the proposed methods.

Suggested Citation

  • Hosseinkouchack, Mehdi & Demetrescu, Matei, 2021. "Finite-Sample Size Control Of Ivx-Based Tests In Predictive Regressions," Econometric Theory, Cambridge University Press, vol. 37(4), pages 769-793, August.
    • Handle: RePEc:cup:etheor:v:37:y:2021:i:4:p:769-793_5
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    Cited by:

    1. Christis Katsouris, 2023. "Unified Inference for Dynamic Quantile Predictive Regression," Papers 2309.14160, arXiv.org, revised Nov 2023.
    2. Yijie Fei & Yiu Lim Lui & Jun Yu, 2024. "Testing Predictability in the Presence of Persistent Errors," Working Papers 202401, University of Macau, Faculty of Business Administration.
    3. Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    4. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Extensions to IVX methods of inference for return predictability," Journal of Econometrics, Elsevier, vol. 237(2).
    5. Zhishui Hu & Ioannis Kasparis & Qiying Wang, 2020. "Locally trimmed least squares: conventional inference in possibly nonstationary models," Papers 2006.12595, arXiv.org.
    6. Cai, Zongwu & Chen, Haiqiang & Liao, Xiaosai, 2023. "A new robust inference for predictive quantile regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 227-250.

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