Comparer des méthodes

	ARFIMA : Modèle ARMA à intégration fractionnaire	Régression quantile
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1980	1978
Auteur d'origine	Granger & Joyeux (1980); Hosking (1981)	Koenker & Bassett
Type	Long-memory time series model	Conditional quantile 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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	5	5
Résumé	ARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.