Comparer des méthodes

	GMM de Fourier Arellano-Bond	Modèle dynamique de données de panel	Modèle à effets fixes pour données de panel
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	2010s	1988–1991	2014
Auteur d'origine	Extension of Arellano & Bond (1991) with Fourier flexible form augmentation	Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)	Hsiao (textbook treatment); within transformation of panel data
Type	Dynamic panel GMM estimator with smooth structural break accommodation	Dynamic regression / GMM estimation	Panel data regression
Source fondatrice	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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Alias	Fourier AB-GMM, Fourier first-differenced GMM, Fourier dynamic panel GMM, Fourier-extended Arellano-Bond estimator	dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Apparentées	2	5	5
Résumé	Fourier Arellano-Bond GMM is a dynamic panel estimator that augments the classic Arellano-Bond first-differenced GMM framework with Fourier trigonometric terms to capture smooth, gradual structural breaks in the time dimension. It handles endogeneity through lagged-level instruments while remaining robust to unknown nonlinear trends that standard difference GMM ignores.	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.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).