A refined asymptotic framework for dividend yield in predictive regressions

Kaihua Deng

Economics Letters, 2016, vol. 138, issue C, 60-63

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.

Keywords: Local-to-unity; Local-to-zero; Long-horizon R2; Signal-to-noise ratio (search for similar items in EconPapers)
JEL-codes: C22 C53 G12 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:138:y:2016:i:c:p:60-63

DOI: 10.1016/j.econlet.2015.11.022

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