Sammenlign metoder

	Giacomini-White-test af betinget prædiktiv evne	Model Confidence Set (MCS)
Fagområde	Økonometri	Økonometri
Familie	Hypothesis test	Hypothesis test
Oprindelsesår	2006	2011
Ophavsperson	Raffaella Giacomini & Halbert White	Hansen, Lunde & Nason
Type	Non-nested forecast comparison test	Sequential hypothesis testing procedure for model comparison
Oprindelig kilde	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.
Aliasser	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
Relaterede	3	3
Resumé	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.