Power calculations for regression-discontinuity designs

被引:42
|
作者
Cattaneo, Matias D. [1 ,2 ]
Titiunik, Rocio [3 ]
Vazquez-Bare, Gonzalo [4 ]
机构
[1] Univ Michigan, Econ, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Stat, Ann Arbor, MI 48109 USA
[3] Univ Michigan, Polit Sci, Ann Arbor, MI 48109 USA
[4] Univ Calif Santa Barbara, Econ, Santa Barbara, CA 93106 USA
来源
STATA JOURNAL | 2019年 / 19卷 / 01期
基金
美国国家科学基金会;
关键词
st0554; rdpow; rdsampsi; regression-discontinuity designs; power calculations; local polynomial methods; INFERENCE; RDROBUST;
D O I
10.1177/1536867X19830919
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
In this article, we introduce two commands, rdpow and rdsampsi, that conduct power calculations and survey sample selection when using local polynomial estimation and inference methods in regression-discontinuity designs. rdpow conducts power calculations using modern robust bias-corrected local polynomial inference procedures and allows for new hypothetical sample sizes and bandwidth selections, among other features. rdsampsi uses power calculations to compute the minimum sample size required to achieve a desired level of power, given estimated or user-supplied bandwidths, biases, and variances. Together, these commands are useful when devising new experiments or surveys in regression-discontinuity designs, which will later be analyzed using modern local polynomial techniques for estimation, inference, and falsification. Because our commands use the communitycontributed (and R) package rdrobust for the underlying bandwidths, biases, and variances estimation, all the options currently available in rdrobust can also be used for power calculations and sample-size selection, including preintervention covariate adjustment, clustered sampling, and many bandwidth selectors. Finally, we also provide companion R functions with the same syntax and capabilities.
引用
收藏
页码:210 / 245
页数:36
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