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Abstract

Predictive regressions are linear specifications linking a noisy variable such as stock returns to past values of a very persistent regressor with the aim of assessing the presence of predictability. Key complications that arise are the potential presence of endogeneity and the poor adequacy of asymptotic approximations. In this article, we develop tests for uncovering the presence of predictability in such models when the strength or direction of predictability may alternate across different economically meaningful episodes. An empirical application reconsiders the dividend yield-based return predictability and documents a strong predictability that is countercyclical, occurring solely during bad economic times. This article has online supplementary materials.

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File URL: http://hdl.handle.net/10.1080/07350015.2011.652053
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Article provided by American Statistical Association in its journal Journal of Business & Economic Statistics.

Volume (Year): 30 (2012)
Issue (Month): 2 (April)
Pages: 229-241

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Handle: RePEc:taf:jnlbes:v:30:y:2012:i:2:p:229-241

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Find related papers by JEL classification:

• C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
• C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models

References

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Cited by:

1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.

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