WP-277: Fiona Burlig, Louis Preonas, and Matt Woerman, "Panel Data and Experimental Design" (Revised October 2017) | Full Paper
How should researchers design experiments with panel data? We derive analytical expressions for the variance of panel estimators under non-i.i.d. error structures, which inform power calculations in panel data settings. Using Monte Carlo simulation, data from a randomized experiment in China, and high-frequency U.S. electricity consumption data, we demonstrate that traditional methods produce experiments that are incorrectly powered with proper inference. Failing to account for serial correlation yields overpowered experiments in short panels and underpowered experiments in long panels. Our theoretical results enable us to achieve correctly powered experiments in both simulated and real data.