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June 3, 2008Review of Financial Studies11,411 citationsOpen Access

Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches

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MPMitchell A. Petersen

Key Points

  • To examine and compare the performance of standard error estimation techniques in financial panel datasets characterized by cross-sectional and time-series correlation.
  • Evaluated standard error estimation techniques commonly used across corporate finance and asset pricing literatures, including ordinary least squares (OLS), Rogers clustered standard errors, and the Fama-MacBeth procedure.
  • Analyzed the theoretical and practical conditions under which standard error estimation approaches produce equivalent results versus when they diverge.
  • Identified that standard OLS standard errors are biased in the presence of firm-level and time-series residual correlation.
  • Clarified the distinct data structures under which Rogers clustered standard errors and Fama-MacBeth estimates yield consistent versus divergent standard error estimates.

Abstract

In both 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 and across time, and OLS standard errors can be biased. Historically, the two literatures have used different solutions to this problem. Corporate finance has relied on Rogers standard errors, while asset pricing has used the Fama-MacBeth procedure to estimate standard errors. This paper will examine the different methods used in the literature and explain 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 thus give researchers guidance for their use.

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Cite This Study

Mitchell A. Petersen (2008) studied this question.

synapsesocial.com/papers/69d72a92faf9bc6d3dbef2fahttps://doi.org/10.1093/rfs/hhn053
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