Correcting for Cross-Sectional and Time-Series Dependence in Accounting Research

• 成果类型: Article
• 署名作者: Gow, Ian D.; Ormazabal, Gaizka; Taylor, Daniel J.
• 署名单位: Northwestern University; Stanford University
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/accr.2010.85.2.483
• 发表日期: 2010
• 页码: 483-512
• 关键词: research-and-development; institutional investors; asymmetric timeliness; corporate governance; private information; VALUE RELEVANCE; stock returns; cost; earnings; QUALITY

摘要

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 in which 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 standard errors 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 implied 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.