Salīdzināt metodes

	Model Confidence Set (MCS)	Giacomini-White kondicionālās prognozēšanas spējas tests
Nozare	Ekonometrija	Ekonometrija
Saime	Hypothesis test	Hypothesis test
Izcelsmes gads	2011	2006
Autors	Hansen, Lunde & Nason	Raffaella Giacomini & Halbert White
Tips	Sequential hypothesis testing procedure for model comparison	Non-nested forecast comparison test
Pirmavots	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.
Citi nosaukumi	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
Saistītās	3	3
Kopsavilkums	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.