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| Metoda regresji z nieciągłością (RDD)× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|
| Dziedzina≠ | Wnioskowanie przyczynowe | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2008 | 2019 |
| Twórca≠ | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Quasi-experimental causal design | Linear regression |
| Źródło pierwotne≠ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy≠ | RDD, regression discontinuity design, sharp RDD, fuzzy RDD | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. | 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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