Comparer des méthodes

	Ensemble de Confiance des Modèles (ECM)	Test de Giacomini-White de la capacité prédictive conditionnelle	Régression par étapes
Domaine	Économétrie	Économétrie	Statistique
Famille	Hypothesis test	Hypothesis test	Regression model
Année d'origine	2011	2006	1960
Auteur d'origine	Hansen, Lunde & Nason	Raffaella Giacomini & Halbert White	M. A. Efroymson
Type	Sequential hypothesis testing procedure for model comparison	Non-nested forecast comparison test	Automated variable selection
Source fondatrice	Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497.	Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.	Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley.
Alias	MCS Procedure, Superior Set of Models, Model Selection Confidence Set, Model Güven Kümesi	GW Test, Conditional Predictive Ability Test, Giacomini-White CPA Test, Koşullu Tahmin Yeteneği Testi	stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection
Apparentées	3	3	5
Résumé	The Model Confidence Set (MCS) is a sequential hypothesis-testing procedure introduced by Hansen, Lunde, and Nason (2011) that identifies the smallest collection of forecasting or predictive models statistically indistinguishable from the best-performing model at a given confidence level. Instead of selecting a single winner, MCS returns a set of superior models, making it especially valuable in econometric forecast comparisons where the true best model is unknown.	The Giacomini-White (GW) test, introduced by Raffaella Giacomini and Halbert White in 2006, evaluates whether two competing forecasting methods have equal conditional predictive ability given information available at the time of forecast. Unlike unconditional tests such as the Diebold-Mariano test, it asks whether one method systematically outperforms the other in specific economic or market conditions, making it especially useful for practitioners who need state-dependent forecast comparisons.	Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library.