全球智库信息集成服务系统(G2TT): Should We Trust Clustered Standard Errors? A Comparison with Randomization-Based Methods

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w25926
来源ID	Working Paper 25926
	Should We Trust Clustered Standard Errors? A Comparison with Randomization-Based Methods
	Lourenço S. Paz; James E. West
发表日期	2019-06-10
出版年	2019
语种	英语
摘要	We compare the precision of critical values obtained under conventional sampling-based methods with those obtained using sample order statics computed through draws from a randomized counterfactual based on the null hypothesis. When based on a small number of draws (200), critical values in the extreme left and right tail (0.005 and 0.995) contain a small bias toward failing to reject the null hypothesis which quickly dissipates with additional draws. The precision of randomization-based critical values compares favorably with conventional sampling-based critical values when the number of draws is approximately 7 times the sample size for a basic OLS model using homoskedastic data, but considerably less in models based on clustered standard errors, or the classic Differences-in-Differences. Randomization-based methods dramatically outperform conventional methods for treatment effects in Differences-in-Differences specifications with unbalanced panels and a small number of treated groups.
主题	Econometrics ; Estimation Methods
URL	https://www.nber.org/papers/w25926
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/583600
推荐引用方式 GB/T 7714	Lourenço S. Paz,James E. West. Should We Trust Clustered Standard Errors? A Comparison with Randomization-Based Methods. 2019.

条目包含的文件
文件名称/大小	资源类型	版本类型	开放类型	使用许可
w25926.pdf（1044KB）	智库出版物		限制开放	CC BY-NC-SA	浏览