Introduces stacked DDD that uses appended four-cell stacks and saturated fixed effects to identify a cell-size-weighted average of stack-level conditional average treatment effects under staggered adoption.
arXiv preprint arXiv:2307.02735 , year=
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A staggered difference-in-differences estimator that identifies treatment effects by extrapolating a pre-treatment polynomial gap, instead of requiring a flat gap.
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Stacked Triple Differences
Introduces stacked DDD that uses appended four-cell stacks and saturated fixed effects to identify a cell-size-weighted average of stack-level conditional average treatment effects under staggered adoption.
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Beyond Parallel Trends in Staggered Difference-in-Differences: Identification under Higher-Order Parallelism
A staggered difference-in-differences estimator that identifies treatment effects by extrapolating a pre-treatment polynomial gap, instead of requiring a flat gap.