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Regresní diskontinuitní design (RDD)×Rozdíl v rozdílech (Diff-in-Diff)×Regrese metodou ordinárních nejmenších čtverců (OLS)×
OborEkonometrieEkonometrieEkonometrie
RodinaRegression modelRegression modelRegression model
Rok vzniku200819942019
TvůrceImbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & TitiunikCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)Wooldridge (textbook treatment); classical least squares
TypQuasi-experimental causal designCausal inference / panel regressionLinear regression
Původní zdrojImbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Další názvyRDD, 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
Příbuzné555
ShrnutíRegression 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).
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ScholarGatePorovnat metody: Regression Discontinuity Design · Difference-in-Differences · OLS Regression. Získáno 2026-06-17 z https://scholargate.app/cs/compare