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	Dynamic Panel Models in Politics	Panel Data Analysis
Field	Political Science	Econometrics
Family	Regression model	Regression model
Year of origin	1995	1966–1978
Originator	Nathaniel Beck & Jonathan Katz; Manuel Arellano & Stephen Bond	Balestra & Nerlove (1966); Mundlak (1978); Hausman (1978)
Type	Dynamic regression model for time-series cross-section data	Panel regression framework
Seminal source	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.	Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. ISBN: 978-3030539528
Aliases	Dynamic TSCS models, Lagged dependent variable panel models, Time-series cross-section dynamic models, Dynamic time-series cross-section analysis	longitudinal data analysis, pooled cross-sectional time-series analysis, panel regression, data panel analysis
Related	4	5
Summary	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.	Panel data analysis models data that track multiple units — countries, firms, individuals — over time, enabling researchers to control for unobserved unit-level heterogeneity that would otherwise bias cross-sectional or time-series estimates. The two core specifications are fixed effects and random effects, selected via the Hausman test.