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Institutional Affiliation: University of Maryland
Information about this author at RePEc
NBER Working Papers and Publications
|September 2019||Panel Data and Experimental Design|
with , : w26250
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 “serial-correlation-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.
Published: Fiona Burlig & Louis Preonas & Matt Woerman, 2020. "Panel data and experimental design," Journal of Development Economics, .