Comparer des méthodes

	Modèles à mémoire longue (ARFIMA, FIGARCH)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Finance	Économétrie
Famille	Regression model	Regression model
Année d'origine	1980	2019
Auteur d'origine	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)	Wooldridge (textbook treatment); classical least squares
Type	Fractionally integrated time series model	Linear regression
Source fondatrice	Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	ARFIMA, FIGARCH, fractionally integrated models, fractional integration	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	4	5
Résumé	Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).