방법 비교

	차분 추정량	이중차분법 (Diff-in-Diff)	패널 데이터 고정 효과 모형
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	2010	1994	2014
창시자	Jeffrey Wooldridge (treatment)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)	Hsiao (textbook treatment); within transformation of panel data
유형	Panel data estimator	Causal inference / panel regression	Panel data regression
원전	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0-262-23258-8	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
별칭	FD Estimator, First-Difference Panel Estimator, First-Difference OLS, Birinci Fark Tahmincisi	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
관련	2	5	5
요약	The First-Difference (FD) estimator is a panel data method that eliminates unobserved, time-invariant individual heterogeneity by subtracting each unit's observation in period t-1 from its observation in period t. By operating on changes rather than levels, FD removes any fixed individual effect that would otherwise confound causal inference. It is widely used in labor economics, program evaluation, and applied microeconomics whenever researchers suspect persistent unobserved differences across individuals, firms, or countries.	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.	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).