विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| रिग्रेशन डिसकंटीन्यूइटी डिज़ाइन (RDD)× | साधारण न्यूनतम वर्ग (OLS) समाश्रयण× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 2008 | 2019 |
| प्रवर्तक≠ | Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik | Wooldridge (textbook treatment); classical least squares |
| प्रकार≠ | Quasi-experimental causal design | Linear regression |
| मौलिक स्रोत≠ | 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 |
| उपनाम | RDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| संबंधित | 5 | 5 |
| सारांश≠ | 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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