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A refined asymptotic framework for dividend yield in predictive regressions

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  • Deng, Kaihua

Abstract

I model predictability by dividend yield using a local-to-zero signal-to-noise ratio refinement. Under the local-to-unity assumption, I study the limiting behavior of the R2statistic and the slope estimate as functions of forecast horizon and sample size. The new asymptotic framework provides a theoretical explanation for many previous simulation-based results in the finance literature.

Suggested Citation

  • Deng, Kaihua, 2016. "A refined asymptotic framework for dividend yield in predictive regressions," Economics Letters, Elsevier, vol. 138(C), pages 60-63.
    • Handle: RePEc:eee:ecolet:v:138:y:2016:i:c:p:60-63
    DOI: 10.1016/j.econlet.2015.11.022
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    References listed on IDEAS

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    More about this item

    Keywords

    Local-to-unity; Local-to-zero; Long-horizon R2; Signal-to-noise ratio;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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