Compare methods

	Dynamic Panel Models in Politics	System GMM
Field	Political Science	Econometrics
Family	Regression model	Regression model
Year of origin	1995	1998
Originator	Nathaniel Beck & Jonathan Katz; Manuel Arellano & Stephen Bond	Arellano & Bover (1995); Blundell & Bond (1998)
Type	Dynamic regression model for time-series cross-section data	Dynamic panel data estimator
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.	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.
Aliases	Dynamic TSCS models, Lagged dependent variable panel models, Time-series cross-section dynamic models, Dynamic time-series cross-section analysis	Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond)
Related	4	4
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.	System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small.