Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regresijas atšķirības dizains (RDD)× | Parastā mazāko kvadrātu (OLS) regresija× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2008 | 2019 |
| Autors≠ | Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik | Wooldridge (textbook treatment); classical least squares |
| Tips≠ | Quasi-experimental causal design | Linear regression |
| Pirmavots≠ | 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 |
| Citi nosaukumi | RDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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). | 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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