Panel Data and Experimental Design

Fiona Burlig, Louis Preonas () and Matt Woerman

No 26250, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: B4 C23 C9 O1 Q4 (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (16)

Published as Fiona Burlig & Louis Preonas & Matt Woerman, 2020. "Panel data and experimental design," Journal of Development Economics, .

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Journal Article: Panel data and experimental design (2020)
Working Paper: Panel Data and Experimental Design (2017)
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