Hypothesis Testing in Predictive Regressions

Yakov Amihud, Clifford Hurvich and Yi Wang
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Clifford Hurvich: New York University
Yi Wang: New York University

Finance from University Library of Munich, Germany

Abstract: We propose a new hypothesis testing method for multi-predictor regressions with finite samples, where the dependent variable is regressed on lagged variables that are autoregressive. It is based on the augmented regressiom method (ARM; Amihud and Hurvich (2004)), which produces reduced-bias coefficients and is easy to implement. The method's usefulness is demonstrated by simulations and by an empirical example, where stock returns are predicted by dividend yield and by bond yield spread. For single-predictor regressions, we show that the ARM outperforms bootstrapping and that the ARM performs better than Lewellen's (2003) method in many situations.

Keywords: Augmented Regression Method (ARM); Bootstrapping; Hypothesis Testing (search for similar items in EconPapers)
JEL-codes: G (search for similar items in EconPapers)
Pages: 47 pages
Date: 2004-12-15
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
Note: Type of Document - pdf; pages: 47
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpfi:0412022

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