How should researchers design panel data experiments?
In "Panel Data and Experimental Design" (Fiona, 2020) the issue of up-front ex-ante consideration of realistic, non-constant serial correlations in the planning and design phase of panel data experiments is addressed. After noting the standard simplifying assumptions for variance structures that that are widely used in the simplest panel settings, for example pre-test - post-test two-group comparisons, the authors demonstrate how to relax the strict assumption of constant serial correlations and demonstrate their new, “serial-correlation-robust” power calculation approach, showing with simulations and an application to real data that it achieves correct power and thus provides sample size estimates that support valid inference for this important class of experiments.
In the authors own words,
Highlights:
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Existing power calculations (McKenzie, 2012) fail with non-constant serial correlation.
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We propose a new method for serial-correlation-robust power calculations.
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Our method achieves correct power with arbitrary correlation in simulated & real data.
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We introduce a new Stata package, pcpanel, which operationalizes this method.
See Fiona, Burlig, Preonas, and Woerman 2020, Panel Data and Experimental Design, J. Devel. Econ. Volume 144
The updated working paper is available here - a free download.
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