방법 비교

	회귀 불연속성 설계 (RDD)	이중차분법 (Diff-in-Diff)	최소제곱법(OLS) 회귀	패널 데이터 고정 효과 모형
분야	계량경제학	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model	Regression model
기원 연도	2008	1994	2019	2014
창시자	Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
유형	Quasi-experimental causal design	Causal inference / panel regression	Linear regression	Panel data regression
원전	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
별칭	RDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD)	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
관련	5	5	5	5
요약	Regression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the design developed for applied practice by Imbens and Lemieux (2008) and by Lee and Lemieux (2010).	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).