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| Regression Discontinuity Design (RDD)× | Differens-i-differens (DiD)× | Vanligaste minsta kvadratmetoden (OLS) Regression× | |
|---|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 2008 | 1994 | 2019 |
| Upphovsperson≠ | Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Quasi-experimental causal design | Causal inference / panel regression | Linear regression |
| Ursprungskälla≠ | Imbens, 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: 978-0691120355 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | 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 |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | 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). |
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