Correcting for Cross-Sectional and Time-Series Dependence in Accounting Research
Ian D. Gow
Harvard Business School
University of Navarra, IESE Business School
Daniel J. Taylor
University of Pennsylvania - The Wharton School
August 4, 2009
Accounting Review, Forthcoming
We review and evaluate the methods commonly used in the accounting literature to correct for cross-sectional and time-series dependence. While much of the accounting literature studies settings where variables are cross-sectionally and serially correlated, we find that the extant methods are not robust to both forms of dependence. Contrary to claims in the literature, we find that the Z2-statistic and Newey-West corrected Fama-MacBeth do not correct for both cross-sectional and time-series dependence. We show that extant methods produce misspecified test statistics in common accounting research settings, and that correcting for both forms of dependence substantially alters inferences reported in the literature. Specifically, several findings in the cost of equity capital literature, the cost of debt literature, and the conservatism literature appear not to be robust to the use of well-specified test statistics.
Number of Pages in PDF File: 49
Keywords: cross-sectional dependence, time-series dependence, autocorrelation, Fama-MacBeth, Newey-West, cluster-robust standard errors, cost of capital, credit ratings, conservatism
JEL Classification: C12, C15, C23, M41Accepted Paper Series
Date posted: July 31, 2008 ; Last revised: October 24, 2010
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