Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| GMM ar mainīgiem parametriem laika gaitā× | GMM atšķirību metode (Arellano–Bonda novērtētājs)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2000s–2010s | 1991 |
| Autors≠ | Extends Arellano & Bond (1991) difference GMM; TVP panel extensions developed in the 2000s–2010s literature | Manuel Arellano and Stephen Bond |
| Tips≠ | Dynamic panel estimator with time-varying parameters | GMM panel estimator |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | TVP-DGMM, time-varying GMM, TVP difference GMM, dynamic panel TVP estimator | Arellano-Bond estimator, AB-GMM, first-difference GMM, difference GMM estimator |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | Time-varying parameter difference GMM combines the Arellano-Bond first-difference GMM estimator for dynamic panels with a state-space or local-smoothing framework that allows regression coefficients to drift over time. It handles endogeneity and lagged dependent variables while relaxing the assumption that structural relationships remain constant across all periods. | 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. |
| ScholarGateDatu kopa ↗ |
|
|