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| 강건 계량 변수 추정× | 이중차분법 (Diff-in-Diff)× | |
|---|---|---|
| 분야≠ | 인과추론 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1949–2019 | 1994 |
| 창시자≠ | Anderson & Rubin (1949); Stock, Wright & Yogo (2002); Andrews, Stock & Sun (2019) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| 유형≠ | Causal inference / robust estimation | Causal inference / panel regression |
| 원전≠ | Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518-529. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 별칭≠ | Robust IV, Weak-instrument-robust IV, Robust 2SLS, Weak-instrument-robust inference | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| 관련≠ | 4 | 5 |
| 요약≠ | Robust Instrumental Variables estimation extends standard IV and two-stage least squares (2SLS) by guarding against weak-instrument bias and non-standard inference. Methods such as the Anderson-Rubin test, Limited Information Maximum Likelihood (LIML), and the Conditional Likelihood Ratio test provide valid confidence sets and hypothesis tests even when instruments are weak or only partially identified, making IV inference reliable in settings where standard 2SLS breaks down. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
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