Comparer des méthodes

	Modèle AR de Fourier	Modèle ARMA (Autoregressive Moving Average)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2012	1970
Auteur d'origine	Enders & Lee	George E. P. Box and Gwilym M. Jenkins
Type	Time series model with Fourier augmentation	Time series model
Source fondatrice	Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Alias	Fourier AR, trigonometric AR model, smooth transition AR with Fourier terms, FAR model	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)
Apparentées	6	5
Résumé	The Fourier AR model extends the standard autoregressive specification by adding trigonometric (sine and cosine) terms to the deterministic component. This allows the model to capture smooth, gradual shifts in the mean or trend of a time series without requiring the researcher to locate or count structural break points explicitly.	The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.