Robust Standard Errors in Small Samples: Some Practical Advice

• 成果类型: Article
• 署名作者: Imbens, Guido W.; Kolesar, Michal
• 署名单位: Stanford University; National Bureau of Economic Research; Princeton University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00552
• 发表日期: 2016-10
• 页码: 701-712
• 关键词: Bootstrap

摘要

We study the properties of heteroskedasticity-robust confidence intervals for regression parameters. We show that confidence intervals based on a degrees-of-freedom correction suggested by Bell and McCaffrey (2002) are a natural extension of a principled approach to the Behrens-Fisher problem. We suggest a further improvement for the case with clustering. We show that these standard errors can lead to substantial improvements in coverage rates even for samples with fifty or more clusters.We recommend that researchers routinely calculate the Bell-McCaffrey degrees-of-freedom adjustment to assess potential problems with conventional robust standard errors.