विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| सिस्टम जीएमएम (अरेलानो-बोवर / ब्लंडेल-बॉन्ड)× | साधारण न्यूनतम वर्ग (OLS) समाश्रयण× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1998 | 2019 |
| प्रवर्तक≠ | Arellano & Bover (1995); Blundell & Bond (1998) | Wooldridge (textbook treatment); classical least squares |
| प्रकार≠ | Dynamic panel data estimator | Linear regression |
| मौलिक स्रोत≠ | Arellano, M. & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277-297. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| उपनाम | Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| संबंधित≠ | 4 | 5 |
| सारांश≠ | System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small. | 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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