Сравнение методов

	Набор доверительных моделей (Model Confidence Set, MCS)	Тест Джакомини-Уайта на условную предсказательную способность
Область	Эконометрика	Эконометрика
Семейство	Hypothesis test	Hypothesis test
Год появления	2011	2006
Автор метода	Hansen, Lunde & Nason	Raffaella Giacomini & Halbert White
Тип	Sequential hypothesis testing procedure for model comparison	Non-nested forecast comparison test
Основополагающий источник	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.
Другие названия	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
Связанные	3	3
Сводка	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.