Comparer des méthodes

	Analyse de données de panel avec Fourier	Modèle à effets aléatoires sur données de panel
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2006 (Fourier framework); panel extensions 2010s	1966
Auteur d'origine	Becker, Enders, and Lee (Fourier unit root framework); extended to panel data by subsequent applied econometricians	Balestra & Nerlove
Type	Panel regression with Fourier terms	Panel data estimator
Source fondatrice	Becker, R., Enders, W., & Lee, J. (2006). A stationary test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.	Balestra, P., & Nerlove, M. (1966). Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34(3), 585–612.
Alias	Fourier panel regression, smooth structural break panel model, trigonometric panel data model, Fourier-flexible panel estimator	random effects estimator, RE model, GLS random effects, error components model
Apparentées	6	5
Résumé	Fourier panel data analysis embeds trigonometric sine and cosine terms into a standard panel regression to approximate smooth, gradual structural shifts in the data-generating process. Rather than assuming a sharp break at a known date, the Fourier approach lets the data reveal the timing and shape of any structural change through a flexible trigonometric approximation, while retaining the cross-sectional and time-series structure of panel data.	The panel random effects (RE) model treats individual-specific effects as random draws from a population distribution rather than fixed constants, enabling efficient estimation by generalised least squares and allowing inference about time-invariant regressors that are swept away in fixed effects estimation.