Ryoya Nakano, Takahiro Hoshino
arXiv 1 Oct 2026 · Econometrics
arXiv:2610.01464 · PDF · Extracted main text
In difference-in-differences (DiD), researchers may use pre-treatment trends to select a control group for which the parallel-trends assumption appears plausible, with the aim of estimating the average treatment effect on the treated (ATT). Our earlier paper,Nakano and Hoshino (2016), and the present paper jointly provide the first selective-inference approach to the ATT that explicitly accounts for this control selection. We generalize our exact Gaussian procedure with known covariance to allow the covariance matrix to be estimated from the same individual-level data used for control selection and DiD estimation. We use this estimate to compute the variance, conditioning direction, residual, and truncation set. With fixed numbers of regions and periods, we establish uniform conditional coverage for selection events with probabilities bounded away from zero, and marginal coverage of the selected target without that restriction. We allow unequal regional sample sizes, heterogeneous covariances, ties in population fit, and regional sample shares that converge to zero. We establish asymptotic equivalence between the plug-in and known-covariance interval endpoints and derive rates for interval length. For staggered adoption, the control pools may differ across cohorts and periods, controls may be not yet treated, observations may be reused, and treatment effects may be heterogeneous. We also construct inference conditional on unions of selection paths that leave the reported parameter unchanged, together with simultaneous confidence bands for finitely many event-time effects. Under parallel trends and the other identifying conditions, the coverage results apply to the ATT. We give sufficient sampling conditions for individual panels and independent repeated cross-sections.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ryoya Nakano, Takahiro Hoshino (2026) Exact inference after data-driven control-unit selection in difference-in-differences self | 1.000 | 5 | 4 | 100% |
| 2 | Isaiah Andrews, Toru Kitagawa, Adam McCloskey (2024) Inference on winners | 0.644 | 2 | 2 | 100% |
| 3 | Brantly Callaway, Pedro H. C. Sant'Anna (2021) Difference-in-differences with multiple time periods | 0.644 | 2 | 2 | 100% |
| 4 | Ashesh Rambachan, Jonathan Roth (2023) A more credible approach to parallel trends | 0.644 | 2 | 2 | 100% |
| 5 | Liyang Sun, Sarah Abraham (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.644 | 2 | 2 | 100% |
| 6 | Timothy G. Conley, Christopher R. Taber (2011) difference in differences | 0.405 | 1 | 1 | 100% |
| 7 | Stephen G. Donald, Kevin Lang (2007) Inference with difference-in-differences and other panel data | 0.405 | 1 | 1 | 100% |
| 8 | Bruno Ferman, Cristine Pinto (2019) Inference in differences-in-differences with few treated groups and heteroskedasticity | 0.405 | 1 | 1 | 100% |
| 9 | Joshua R. Loftus, Jonathan E. Taylor (2015) Selective inference in regression models with groups of variables | 0.405 | 1 | 1 | 100% |
| 10 | Jelena Markovic, Lucy Xia, Jonathan Taylor (2018) Unifying approach to selective inference with applications to cross-validation | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 23 scored citations.