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Challenges in Learning from Panel Data
Fri, September 6, 12:00 to 1:30pm, Pennsylvania Convention Center (PCC), 110BSession Submission Type: Created Panel
Session Description
The panel offers innovative solutions to the pervasive issues of attrition, missing data, effect heterogeneity, and statistical power in panel data studies. Highlighting a blend of classical approaches and new methodological frameworks, the papers offer practical tools and theoretical guidance on common panel data challenges.
Sub Unit
Individual Presentations
The Attrition Permutation Test: ML for the Detection of Systematic Attrition - Sam Fuller, Harvard University; Jack Rametta, University of California, Davis
Difference-in-Differences Designs with Attrition Bias Correction - Sooahn Shin, Harvard University
History versus Unobserved Confounding in Causal Panel Analysis - Ye Wang, University of North Carolina, Chapel Hill; Yiqing Xu, Stanford University
How Much Should We Trust Modern Difference-in-Differences Estimates? - Amanda Weiss, Yale University
Recovering Lost Information: Imputing Data to Generate Synthetic Complete Dataset - Jeff Gill; Ali Amini, Center for Data Science - American University