Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfumo wa Data wa Paneli Wenye Kigezo Teule× | Kiendesha cha GMM cha Tofauti (Kiendesha cha Arellano-Bond)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1988–1991 | 1991 |
| Mwanzilishi≠ | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) | Manuel Arellano and Stephen Bond |
| Aina≠ | Dynamic regression / GMM estimation | GMM panel estimator |
| Chanzo asilia | 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 ↗ | 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 ↗ |
| Majina mbadala | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model | Arellano-Bond estimator, AB-GMM, first-difference GMM, difference GMM estimator |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy. | 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. |
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