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| Progettazione a Piegatura di Regressione (RKD)× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|
| Campo≠ | Inferenza causale | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2015 | 2019 |
| Ideatore≠ | Card, Lee, Pei & Weber | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Quasi-experimental design (slope-based RDD) | Linear regression |
| Fonte seminale≠ | Card, D., Lee, D. S., Pei, Z. & Weber, A. (2015). Inference on Causal Effects in a Generalized Regression Kink Design. Econometrica, 83(6), 2453-2483. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias | RKD, regression kink design, kink regression discontinuity, Regresyon Kırılma Tasarımı (RKD — Regression Kink Design) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | The Regression Kink Design is a quasi-experimental method that estimates a causal effect when a policy rule creates a change in slope (a kink) — rather than a jump — at a known threshold of a running variable. It was formalised as a generalized design by Card, Lee, Pei and Weber (2015) and is the slope-based counterpart of the regression discontinuity design. | 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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