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| イベントスタディデザイン（因果イベントスタディ）× | シフトシェア操作変数（Bartik操作変数）× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2021 | 2020 |
| 提唱者≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Bartik (1991); identification framework by Goldsmith-Pinkham, Sorkin & Swift (2020) and Borusyak, Hull & Jaravel (2022) |
| 種類≠ | Dynamic causal panel regression | Instrumental-variable design |
| 原典≠ | Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199. DOI ↗ | Goldsmith-Pinkham, P., Sorkin, I. & Swift, H. (2020). Bartik Instruments: What, When, Why, and How. American Economic Review, 110(8), 2586–2624. DOI ↗ |
| 別名≠ | dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model | Bartik instrument, shift-share instrument, Shift-Share Araç Değişkeni (Bartik Instrument) |
| 関連 | 5 | 5 |
| 概要≠ | The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021). | The shift-share instrumental variable, widely known as the Bartik instrument, is a causal-inference strategy that builds an instrument by interacting national or sector-level shocks (the shifts) with local composition weights (the shares). Its modern identification framework was set out by Goldsmith-Pinkham, Sorkin and Swift (2020) and Borusyak, Hull and Jaravel (2022). |
| ScholarGateデータセット ↗ |
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