{"title":"Simple Formulas for Standard Errors that Cluster by Both Firm and Time","authors":"S. B. Thompson","doi":"10.2139/ssrn.914002","DOIUrl":null,"url":null,"abstract":"When estimating finance panel regressions, it is common practice to adjust standard errors for correlation either across firms or across time. These procedures are valid only if the residuals are correlated either across time or across firms, but not across both. This paper shows that it is very easy to calculate standard errors that are robust to simultaneous correlation along two dimensions, such as firms and time. The covariance estimator is equal to the estimator that clusters by firm, plus the estimator that clusters by time, minus the usual heteroskedasticity-robust ordinary least squares (OLS) covariance matrix. Any statistical package with a clustering command can be used to easily calculate these standard errors.","PeriodicalId":437258,"journal":{"name":"Corporate Finance: Capital Structure & Payout Policies","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1420","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Corporate Finance: Capital Structure & Payout Policies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.914002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1420
Abstract
When estimating finance panel regressions, it is common practice to adjust standard errors for correlation either across firms or across time. These procedures are valid only if the residuals are correlated either across time or across firms, but not across both. This paper shows that it is very easy to calculate standard errors that are robust to simultaneous correlation along two dimensions, such as firms and time. The covariance estimator is equal to the estimator that clusters by firm, plus the estimator that clusters by time, minus the usual heteroskedasticity-robust ordinary least squares (OLS) covariance matrix. Any statistical package with a clustering command can be used to easily calculate these standard errors.