Salīdzināt metodes

	Dynamic Panel Models in Politics	Arellano-Bond GMM novērtētājs
Nozare	Political Science	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1995	1991
Autors	Nathaniel Beck & Jonathan Katz; Manuel Arellano & Stephen Bond	Manuel Arellano and Stephen Bond
Tips	Dynamic regression model for time-series cross-section data	GMM estimator for dynamic panel data
Pirmavots	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.
Citi nosaukumi	Dynamic TSCS models, Lagged dependent variable panel models, Time-series cross-section dynamic models, Dynamic time-series cross-section analysis	AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator
Saistītās	4	5
Kopsavilkums	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 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.