Multiple-Predictor Regressions: Hypothesis Testing

Posted: 3 Jan 2009

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: January 2009

Abstract

We propose a new hypothesis-testing method for multipredictor regressions in small samples, where the dependent variable is regressed on lagged variables that are autoregressive. The new test is based on the augmented regression method (Amihud and Hurvich, ), which produces reduced-bias coefficients and is easy to implement. The method's usefulness is demonstrated by simulations and by testing a model where stock returns are predicted by two variables, income-to-consumption and dividend yield.

Keywords: C32, G12

Suggested Citation

Amihud, Yakov and Hurvich, Clifford M. and Wang, Yi, Multiple-Predictor Regressions: Hypothesis Testing (January 2009). The Review of Financial Studies, Vol. 22, Issue 1, pp. 413-434, 2009. Available at SSRN: https://ssrn.com/abstract=1320562 or http://dx.doi.org/hhn056

Yakov Amihud (Contact Author)

New York University - Stern School of Business ( email )

44 West 4th Street
Suite 9-190
New York, NY 10012-1126
United States
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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