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Unified Tests for a Dynamic Predictive Regression

Author

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  • Bingduo Yang

    (School of Finance, Jiangxi University of Finance and Economics, Nanchang, China)

  • Xiaohui Liu

    (School of Finance, Jiangxi University of Finance and Economics, Nanchang, China)

  • Liang Peng

    (Department of Risk Management and Insurance, Georgia State University)

  • Zongwu Cai

    (Department of Economics, University of Kansas)

Abstract

Testing for predictability of asset returns has been a long history in economics and finance. Recently, based on a simple predictive regression, Kostakis, Magdalinos and Stamatogiannis (2015, Review of Financial Studies) derived a Wald type test based on the context of the extended instrumental variable (IVX) methodology for testing predictability of stock returns and Demetrescu (2014) showed that the local power of the standard IVX-based test could be improved in some cases when a lagged predicted variable is added to the predictive regression on purpose, which poses a general important question on whether a lagged predicted variable should be included in the model or not. This paper proposes novel robust procedures for testing both the existence of a lagged predicted variable and the predictability of asset returns in a predictive regression regardless of regressors being stationary or nearly integrated or unit root and the AR model for regressors with or without intercept. A simulation study confirms the good finite sample performance of the proposed tests before applying the proposed tests to some real datasets in finance to illustrate their practical usefulness.

Suggested Citation

  • Bingduo Yang & Xiaohui Liu & Liang Peng & Zongwu Cai, 2018. "Unified Tests for a Dynamic Predictive Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201808, University of Kansas, Department of Economics, revised Sep 2018.
    • Handle: RePEc:kan:wpaper:201808
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    References listed on IDEAS

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    1. Cai, Zongwu & Wang, Yunfei, 2014. "Testing predictive regression models with nonstationary regressors," Journal of Econometrics, Elsevier, vol. 178(P1), pages 4-14.
    2. Amihud, Yakov & Hurvich, Clifford M., 2004. "Predictive Regressions: A Reduced-Bias Estimation Method," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 813-841, December.
    3. Jing, Bing-Yi & Yuan, Junqing & Zhou, Wang, 2009. "Jackknife Empirical Likelihood," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1224-1232.
    4. Magdalinos, Tassos & Phillips, Peter C.B., 2009. "Limit Theory For Cointegrated Systems With Moderately Integrated And Moderately Explosive Regressors," Econometric Theory, Cambridge University Press, vol. 25(2), pages 482-526, April.
    5. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    6. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    7. Willa W. Chen & Rohit S. Deo & Yanping Yi, 2013. "Uniform Inference in Predictive Regression Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 525-533, October.
    8. Michael Jansson & Marcelo J. Moreira, 2006. "Optimal Inference in Regression Models with Nearly Integrated Regressors," Econometrica, Econometric Society, vol. 74(3), pages 681-714, May.
    9. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    10. Phillips, Peter C.B. & Magdalinos, Tassos, 2007. "Limit theory for moderate deviations from a unit root," Journal of Econometrics, Elsevier, vol. 136(1), pages 115-130, January.
    11. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    12. Chenxue Li & Deyuan Li & Liang Peng, 2017. "Uniform Test for Predictive Regression With AR Errors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 29-39, January.
    13. Choi, Yongok & Jacewitz, Stefan & Park, Joon Y., 2016. "A reexamination of stock return predictability," Journal of Econometrics, Elsevier, vol. 192(1), pages 168-189.
    14. Cavanagh, Christopher L. & Elliott, Graham & Stock, James H., 1995. "Inference in Models with Nearly Integrated Regressors," Econometric Theory, Cambridge University Press, vol. 11(5), pages 1131-1147, October.
    15. Demetrescu, Matei, 2014. "Enhancing the local power of IVX-based tests in predictive regressions," Economics Letters, Elsevier, vol. 124(2), pages 269-273.
    16. Phillips, Peter C.B. & Lee, Ji Hyung, 2013. "Predictive regression under various degrees of persistence and robust long-horizon regression," Journal of Econometrics, Elsevier, vol. 177(2), pages 250-264.
    17. Peter C. B. Phillips, 2015. "Pitfalls and Possibilities in Predictive Regression," Cowles Foundation Discussion Papers 2003, Cowles Foundation for Research in Economics, Yale University.
    18. John Y. Campbell, 2007. "Estimating the Equity Premium," NBER Working Papers 13423, National Bureau of Economic Research, Inc.
    19. Breitung, Jörg & Demetrescu, Matei, 2015. "Instrumental variable and variable addition based inference in predictive regressions," Journal of Econometrics, Elsevier, vol. 187(1), pages 358-375.
    20. Liu, Jun M. & Chen, Rong & Yao, Qiwei, 2010. "Nonparametric transfer function models," LSE Research Online Documents on Economics 28868, London School of Economics and Political Science, LSE Library.
    21. Liu, Jun M. & Chen, Rong & Yao, Qiwei, 2010. "Nonparametric transfer function models," Journal of Econometrics, Elsevier, vol. 157(1), pages 151-164, July.
    22. Xiao Z. & Linton O.B. & Carroll R.J. & Mammen E., 2003. "More Efficient Local Polynomial Estimation in Nonparametric Regression With Autocorrelated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 980-992, January.
    23. Chen, Willa W. & Deo, Rohit S., 2009. "Bias Reduction And Likelihood-Based Almost Exactly Sized Hypothesis Testing In Predictive Regressions Using The Restricted Likelihood," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1143-1179, October.
    24. Alexandros Kostakis & Tassos Magdalinos & Michalis P. Stamatogiannis, 2015. "Robust Econometric Inference for Stock Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 28(5), pages 1506-1553.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Autoregressive Errors; Empirical Likelihood; Predictive Regression; Weighted Score.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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