विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| Structural Break Difference GMM× | सिस्टम जीएमएम (अरेलानो-बोवर / ब्लंडेल-बॉन्ड)× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1991 / 1998 | 1998 |
| प्रवर्तक≠ | Arellano & Bond (Difference GMM); Bai & Perron (structural break testing) | Arellano & Bover (1995); Blundell & Bond (1998) |
| प्रकार≠ | Dynamic panel estimator with structural breaks | Dynamic panel data estimator |
| मौलिक स्रोत≠ | Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. DOI ↗ | 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 ↗ |
| उपनाम | Difference GMM with structural breaks, break-augmented Arellano-Bond GMM, dynamic panel GMM with regime shifts, structural change Difference GMM | Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond) |
| संबंधित≠ | 6 | 4 |
| सारांश≠ | Structural Break Difference GMM extends the Arellano-Bond first-difference GMM estimator to dynamic panel settings where the data-generating process shifts at one or more unknown breakpoints. By explicitly incorporating break indicators or allowing regime-specific parameters, the estimator avoids the biased coefficient and invalid moment conditions that arise when a structural change is ignored in a standard Difference GMM fit. | 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. |
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