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| 패널 데이터 플라시보 검정× | 패널 데이터 이중차분법 (패널 DiD / TWFE)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2004-2010 | 1985–2004 |
| 창시자≠ | Bertrand, Duflo & Mullainathan; Abadie, Diamond & Hainmueller | Ashenfelter & Card (1985); codified by Angrist & Pischke (2009); serial correlation critique by Bertrand, Duflo & Mullainathan (2004) |
| 유형≠ | Falsification / validation test | Causal inference / panel regression |
| 원전≠ | Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1), 249-275. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 별칭 | placebo regression test, falsification test, pseudo-treatment test, in-time placebo | Two-Way Fixed Effects DiD, TWFE, Panel DiD, Panel Diff-in-Diff |
| 관련 | 4 | 4 |
| 요약≠ | A panel data placebo test is a falsification procedure used to assess the credibility of causal estimates in quasi-experimental panel designs. By applying the same estimation strategy to a period, group, or outcome where no true effect should exist, researchers verify that the observed treatment effect is not merely an artifact of model specification, coincidental trends, or data patterns unrelated to the intervention. | Panel Data Difference-in-Differences extends the classic two-period DiD design to settings with multiple units observed across many time periods. By absorbing unit-level fixed effects and time fixed effects simultaneously, it isolates the causal effect of a treatment or policy change while controlling for both time-invariant unit heterogeneity and common time shocks affecting all units. |
| ScholarGate데이터셋 ↗ |
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