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| Modelis ar strukturālām pārtraukumiem dinamiskajos paneļu datos× | Arellano-Bond GMM novērtētājs× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991–1998 | 1991 |
| Autors≠ | Bai & Perron (break detection); Arellano & Bond (dynamic panel GMM) | Manuel Arellano and Stephen Bond |
| Tips≠ | Dynamic panel model with regime change | GMM estimator for dynamic panel data |
| Pirmavots≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. 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 | dynamic panel with breaks, panel dynamic model structural change, DPDSB, panel dynamic structural break estimator | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | The structural break dynamic panel data model extends the standard dynamic panel framework by allowing regression coefficients or the autoregressive parameter to shift at one or more unknown break dates. It combines GMM-based dynamic panel estimation with formal structural change tests, enabling researchers to study how economic relationships evolve across distinct regimes while controlling for unobserved individual heterogeneity and endogeneity of the lagged dependent variable. | The Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous. |
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