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A new test of asset return predictability with an unstable predictor

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  • Chang, Seong Yeon

Abstract

This study constructs predictive regressions in which the predictable variable exhibits a level shift at some unknown date. We establish novel procedures to test asset return predictability via empirical likelihood (EL) methods based on weighted score equations. Monte Carlo simulations confirm that the EL-based tests perform well in terms of size and power in finite samples.

Suggested Citation

  • Chang, Seong Yeon, 2020. "A new test of asset return predictability with an unstable predictor," Economics Letters, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:ecolet:v:196:y:2020:i:c:s0165176520303219
    DOI: 10.1016/j.econlet.2020.109529
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    References listed on IDEAS

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    1. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    2. Fukang Zhu & Zongwu Cai & Liang Peng, 2014. "Predictive regressions for macroeconomic data," Papers 1404.7642, arXiv.org.
    3. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    4. Lewellen, Jonathan, 2004. "Predicting returns with financial ratios," Journal of Financial Economics, Elsevier, vol. 74(2), pages 209-235, November.
    5. Chan, Ngai Hang & Li, Deyuan & Peng, Liang, 2012. "Toward A Unified Interval Estimation Of Autoregressions," Econometric Theory, Cambridge University Press, vol. 28(3), pages 705-717, June.
    6. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    7. David E. Rapach & Mark E. Wohar, 2006. "Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(2), pages 238-274.
    8. Alexandros Kostakis & Tassos Magdalinos & Michalis P. Stamatogiannis, 2015. "Robust Econometric Inference for Stock Return Predictability," Review of Financial Studies, Society for Financial Studies, vol. 28(5), pages 1506-1553.
    9. 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.
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    Cited by:

    1. Toloo, Mehdi & Tone, Kaoru & Izadikhah, Mohammad, 2023. "Selecting slacks-based data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1302-1318.

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    More about this item

    Keywords

    Autoregressive process; Empirical likelihood; Level shift; Local-to-unity; Weighted estimation;
    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
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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