Salīdzināt metodes

	Dinamiskais paneļdatu modelis	Dinamiskais paneļa datu modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1991–1998	1988–1991
Autors	Arellano & Bond (1991); Blundell & Bond (1998)	Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)
Tips	Dynamic panel regression	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. 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	dynamic panel model, lagged dependent variable panel model, Arellano-Bond type dynamic panel, GMM dynamic panel	dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model
Saistītās	5	5
Kopsavilkums	The dynamic panel data model extends standard panel regression by including one or more lagged values of the outcome variable as regressors. Because past outcomes directly predict current outcomes, the model captures persistence and adjustment dynamics — but it also introduces a correlation between the lagged dependent variable and the individual fixed effect, rendering OLS and standard fixed-effects estimators inconsistent. GMM-based approaches developed by Arellano-Bond and Blundell-Bond resolve this problem.	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.