RFS Advance Access published online on June 3, 2008
Review of Financial Studies, doi:10.1093/rfs/hhn053
Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches
Northwestern University
Address correspondence to Mitchell A. Petersen, Kellogg School of Management, Northwestern University, and NBER, 2001 Sheridan Road, Evanston, IL 60208; telephone: 847-467-1281; email: mpetersen{at}northwestern.edu.
JEL Classification: G12, G3, C01, C15
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Abstract |
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In corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms or across time, and OLS standard errors can be biased. Historically, researchers in the two literatures have used different solutions to this problem. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use.
I thank the Center for Financial Institutions and Markets at Northwestern University's Kellogg School for support. In writing this paper, I have benefited greatly from discussions with John Ammer, Robert Chirinko, Toby Daglish, Kent Daniel, Joey Engelberg, Gene Fama, Michael Faulkender, Wayne Ferson, Mariassunta Giannetti, John Graham, William Greene, Chris Hansen, Wei Jiang, Toby Moskowitz, Chris Polk, Joshua Rauh, Michael Roberts, Paola Sapienza, Georgios Skoulakis, Doug Staiger, Jeff Wooldridge, Annette Vissing-Jorgensen, the editor, and referees, as well as the comments of seminar participants at the American Finance Association Meetings, Arizona State University, Boston College, Case Western Reserve University, Cornell University, Duke University, Federal Reserve Bank of Chicago, Financial Management Association Meetings, Barclay's Global Investors, Goldman Sachs Asset Management, Harvard Business School, Hong Kong University of Science and Technology, National University of Singapore, Northwestern University, Singapore Management University, Rice University, Stanford University, Stockholm School of Economics, and the Universities of California at Berkeley, Chicago, Columbia, Florida, Iowa, Michigan, Pennsylvania (Wharton), Texas at Dallas, and Washington. The research assistance of Marie Grabinski, Nick Halpern, Casey Liang, Matt Withey, Sungjoon Park, Amit Patel, and Calvin Zhang is greatly appreciated.