Hypothesis Testing in Predictive Regressions

49 Pages Posted: 3 Nov 2008

See all articles by Yakov Amihud

Yakov Amihud

New York University - Stern School of Business

Clifford M. Hurvich

Stern School of Business, New York University; New York University (NYU) - Department of Information, Operations, and Management Sciences

Yi Wang

New York University (NYU) - Department of Information, Operations, and Management Sciences

Date Written: November 2004

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 regression 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.

Suggested Citation

Amihud, Yakov and Hurvich, Clifford M. and Wang, Yi, Hypothesis Testing in Predictive Regressions (November 2004). NYU Working Paper No. FIN-04-030. Available at SSRN: https://ssrn.com/abstract=1294483

Yakov Amihud (Contact Author)

New York University - Stern School of Business ( email )

44 West 4th Street
Suite 9-190
New York, NY 10012-1126
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212-998-0720 (Phone)
212-995-4233 (Fax)

Clifford M. Hurvich

Stern School of Business, New York University ( email )

44 West 4th Street
New York, NY 10012-1126
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

Yi Wang

New York University (NYU) - Department of Information, Operations, and Management Sciences ( email )

44 West Fourth Street
New York, NY 10012
United States

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