Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| GMM atšķirību metode (Arellano–Bonda novērtētājs)× | Sistēmas GMM panelim (Blundell-Bonda novērtētājs)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 | 1998 |
| Autors≠ | Manuel Arellano and Stephen Bond | Blundell & Bond (1998); Arellano & Bover (1995) |
| Tips≠ | GMM panel estimator | GMM estimator for dynamic panel data |
| Pirmavots≠ | 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 ↗ | Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. DOI ↗ |
| Citi nosaukumi | Arellano-Bond estimator, AB-GMM, first-difference GMM, difference GMM estimator | System GMM, Blundell-Bond estimator, SYS-GMM, two-step System GMM |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Difference GMM, introduced by Arellano and Bond (1991), estimates dynamic panel data models by first-differencing the equation to remove fixed effects, then using lagged levels of the endogenous variables as GMM instruments. It is the standard approach when a lagged dependent variable or other endogenous regressors are present in a panel with many units and few time periods. | Panel System GMM is a two-equation GMM estimator for dynamic panel data that stacks the differenced equation (using lagged levels as instruments) with the levels equation (using lagged differences as instruments). Developed by Blundell and Bond (1998) on the foundation of Arellano and Bover (1995), it is the preferred tool when the lagged dependent variable is highly persistent or individual effects are large. |
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