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| 정책 평가 패널 사건 연구× | 인과 추론을 위한 도구 변수(IV) 방법× | |
|---|---|---|
| 분야≠ | 인과추론 | 보건경제학 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 2021 | 1990s (modern applications) |
| 창시자≠ | Callaway & Sant'Anna (2021); Borusyak, Jaravel & Spiess (2024); Sun & Abraham (2021) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 유형≠ | Causal inference / quasi-experimental panel design | Method |
| 원전≠ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| 별칭 | panel event study, event-study DiD, staggered event study, difference-in-differences event study | IV, two-stage least squares, TSLS, causal estimation |
| 관련≠ | 6 | 3 |
| 요약≠ | A panel event study is a quasi-experimental design that traces how an outcome evolves in periods before and after a policy event, using unit and time fixed effects to identify the causal effect. Widely used in economics and policy research, it tests for anticipation effects, verifies parallel pre-trends, and estimates dynamic treatment effects across post-treatment horizons — making it the standard toolkit for rigorous policy evaluation with observational panel data. | Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes. |
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