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| Dynamic Panel Models in Politics× | Model dynamických panelových dat× | |
|---|---|---|
| Obor≠ | Political Science | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1995 | 1988–1991 |
| Tvůrce≠ | Nathaniel Beck & Jonathan Katz; Manuel Arellano & Stephen Bond | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Typ≠ | Dynamic regression model for time-series cross-section data | Dynamic regression / GMM estimation |
| Původní zdroj≠ | Beck, N., & Katz, J. N. (1995). What to Do (and Not to Do) with Time-Series Cross-Section Data. American Political Science Review, 89(3), 634–647. 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 ↗ |
| Další názvy | Dynamic TSCS models, Lagged dependent variable panel models, Time-series cross-section dynamic models, Dynamic time-series cross-section analysis | dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model |
| Příbuzné≠ | 4 | 5 |
| Shrnutí≠ | Dynamic panel models for political science analyze time-series cross-section (TSCS) data — repeated observations on countries, dyads, states, or other units over many years — where the outcome today depends on its own past. By including a lagged dependent variable alongside unit fixed effects, these models capture persistence and inertia common in comparative politics and international relations, but doing so introduces the Nickell bias. Estimators such as Arellano-Bond and system GMM, and design choices such as Beck-Katz panel-corrected standard errors, were developed to recover credible dynamic estimates from such data. | 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. |
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