Panel data and experimental design

• 成果类型: Article
• 署名作者: Burlig, Fiona; Preonas, Louis; Woerman, Matt
• 署名单位: University of Chicago; National Bureau of Economic Research; University System of Maryland; University of Maryland College Park; University of Massachusetts System; University of Massachusetts Amherst
• 刊物名称: JOURNAL OF DEVELOPMENT ECONOMICS
• ISSN/ISSBN: 0304-3878
• DOI: 10.1016/j.jdeveco.2020.102458
• 发表日期: 2020
• 关键词: POWER; Experimental design; panel data; sample size

摘要

How should researchers design panel data experiments? We analytically derive the variance of panel estimators, informing power calculations in panel data settings. We generalize Frison and Pocock (1992) to fully arbitrary error structures, thereby extending McKenzie (2012) to allow for non-constant serial correlation. Using Monte Carlo simulations and real-world panel data, we demonstrate that failing to account for arbitrary serial correlation ex ante yields experiments that are incorrectly powered under proper inference. By contrast, our serialcorrelation-robust power calculations achieve correctly powered experiments in both simulated and real data. We discuss the implications of these results, and introduce a new software package to facilitate proper power calculations in practice.