Panel data and experimental design

Fiona Burlig, Louis Preonas and Matt Woerman

Journal of Development Economics, 2020, vol. 144, issue C

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.

Keywords: Power; Experimental design; Panel data; Sample size (search for similar items in EconPapers)
JEL-codes: B4 C23 C9 O1 Q4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Related works:
Working Paper: Panel Data and Experimental Design (2019)
Working Paper: Panel Data and Experimental Design (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:deveco:v:144:y:2020:i:c:s030438782030033x

DOI: 10.1016/j.jdeveco.2020.102458

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