An Efficient Fixed-Effects Estimator for Corporate Finance

73 Pages Posted: 22 May 2018 Last revised: 13 Apr 2020

See all articles by Daniela Osterrieder

Daniela Osterrieder

Rutgers Business School; CREATES

Darius Palia

Rutgers University, Newark, School of Business-Newark, Department of Finance & Economics; Columbia University - Law School

Ge Wu

University of Richmond

Date Written: April 10, 2020

Abstract

The use of panel data in corporate finance is ubiquitous to estimate the impact of managers' and/or shareholders’ choices on firm value. We evaluate the properties of four existing and widely used estimators (pooled OLS, random-effects, first-difference, and fixed-effects), and find them to be lacking consistency, efficiency, or both. Consequently, we introduce the new consistent efficient fixed-effects (EFE) estimator. When examining the relationship between CEO performance-pay sensitivity and firm value, we find the EFE estimator to be most appropriate. All estimators are presented as GMM estimators, allowing us to straightforwardly design and conduct tests for model misspecification, including endogeneity.

Suggested Citation

Osterrieder, Daniela and Palia, Darius and Wu, Ge, An Efficient Fixed-Effects Estimator for Corporate Finance (April 10, 2020). Available at SSRN: https://ssrn.com/abstract=3168947 or http://dx.doi.org/10.2139/ssrn.3168947

Daniela Osterrieder (Contact Author)

Rutgers Business School ( email )

Janice H. Levin Bldg., Room 121
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CREATES ( email )

Aarhus University
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DK-8210 Aarhus C
Denmark

Darius Palia

Rutgers University, Newark, School of Business-Newark, Department of Finance & Economics ( email )

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MEC 134
Newark, NJ 07102
United States
973-353-5981 (Phone)
973-353-1233 (Fax)

Columbia University - Law School ( email )

435 W 116th St.
New York, NY 10027
United States

Ge Wu

University of Richmond ( email )

102 UR Drive
Richmond, VA 23173
United States

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