Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļa autoregresijas (Panel AR) modelis× | Arellano-Bond GMM novērtētājs× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1980s-2000s | 1991 |
| Autors≠ | Hsiao, C.; Arellano, M. | Manuel Arellano and Stephen Bond |
| Tips≠ | Autoregressive time-series model for panel data | GMM estimator for dynamic panel data |
| Pirmavots≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | 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 | panel autoregressive model, PAR model, AR model for panel data, panel AR(p) | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Panel AR model extends the classical univariate autoregressive model to panel data, capturing how each unit's own past values predict its current value while controlling for unobserved individual heterogeneity through fixed or random effects. It is foundational for modelling dynamic persistence in micro or macro panel datasets. | 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. |
| ScholarGateDatu kopa ↗ |
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