Salīdzināt metodes

	Robustais Arellano-Bonda GMM novērtētājs	Dinamiskais paneļa datu modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1991	1988–1991
Autors	Arellano & Bond (1991); robust inference extensions by Windmeijer (2005)	Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)
Tips	Dynamic panel GMM estimator with robust inference	Dynamic regression / GMM estimation
Pirmavots	Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297.	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	Robust Difference GMM, AB-GMM with robust standard errors, Robust first-difference GMM, Arellano-Bond robust estimator	dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model
Saistītās	6	5
Kopsavilkums	The Robust Arellano-Bond GMM estimator applies the Arellano-Bond first-difference GMM approach to dynamic panel data while computing heteroscedasticity- and autocorrelation-consistent (robust) standard errors. This combination handles the Nickell bias from lagged dependent variables and simultaneously yields reliable inference when error variances differ across units or periods.	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.