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.

More on this topic

BFI Working Paper·Apr 15, 2025

Can Pollution Markets Work in Developing Countries? Experimental Evidence from India

Michael Greenstone, Rohini Pande, Nicholas Ryan, and Anant Sudarshan
Topics: Energy & Environment
BFI Working Paper·Mar 10, 2025

The Value of Clean Water: Experimental Evidence from Rural India

Fiona Burlig, Amir Jina, and Anant Sudarshan
Topics: Development Economics, Energy & Environment
BFI Working Paper·Jan 28, 2025

Drive Down the Cost: Learning by Doing and Government Policies in the Global EV Battery Industry

Panle Jia Barwick, Hyuk-soo Kwon, Shanjun Li Nahim, and Bin Zahur
Topics: Energy & Environment, Technology & Innovation