Porovnat metody

	Giacomini-Whiteův test podmíněné prediktivní schopnosti	Model Confidence Set (MCS)
Obor	Ekonometrie	Ekonometrie
Rodina	Hypothesis test	Hypothesis test
Rok vzniku	2006	2011
Tvůrce	Raffaella Giacomini & Halbert White	Hansen, Lunde & Nason
Typ	Non-nested forecast comparison test	Sequential hypothesis testing procedure for model comparison
Původní zdroj	Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.	Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497.
Další názvy	GW Test, Conditional Predictive Ability Test, Giacomini-White CPA Test, Koşullu Tahmin Yeteneği Testi	MCS Procedure, Superior Set of Models, Model Selection Confidence Set, Model Güven Kümesi
Příbuzné	3	3
Shrnutí	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.	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.