Comparer des méthodes

	Modèle ARIMA Robuste	Régression quantile
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1986–1993	1978
Auteur d'origine	Tsay (1986); Chen & Liu (1993)	Koenker & Bassett
Type	Robust time series model	Conditional quantile regression
Source fondatrice	Tsay, R. S. (1986). Time series model specification in the presence of outliers. Journal of the American Statistical Association, 81(393), 132–141.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	robust ARIMA, outlier-resistant ARIMA, robust time series estimation, ARIMA with outlier detection	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	4	5
Résumé	Robust ARIMA extends the classical ARIMA framework to detect and correct the influence of outliers and structural breaks during estimation. By jointly identifying anomalous observations and re-estimating model parameters, it produces coefficient estimates and forecasts that are far less distorted by isolated shocks or data errors than standard ARIMA.	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.