Dynamic Panel Models in Politics
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.
Catatan sumber
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- 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 10.2307/2082979
- 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 10.2307/2297968
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