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	Model Confidence Set (MCS)	Test di Diebold-Mariano per l'Accuratezza Predittiva Equivalente	Test di Giacomini-White sulla Capacità Predittiva Condizionale
Campo	Econometria	Econometria	Econometria
Famiglia	Hypothesis test	Hypothesis test	Hypothesis test
Anno di origine	2011	1995	2006
Ideatore	Hansen, Lunde & Nason	Francis Diebold & Roberto Mariano	Raffaella Giacomini & Halbert White
Tipo	Sequential hypothesis testing procedure for model comparison	Non-parametric forecast comparison test	Non-nested forecast comparison test
Fonte seminale	Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497.	Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.	Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.
Alias	MCS Procedure, Superior Set of Models, Model Selection Confidence Set, Model Güven Kümesi	DM Test, Test of Equal Forecast Accuracy, Diebold-Mariano Forecast Comparison Test, Tahmin Doğruluğu Eşitliği Testi	GW Test, Conditional Predictive Ability Test, Giacomini-White CPA Test, Koşullu Tahmin Yeteneği Testi
Correlati	3	3	3
Sintesi	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 Diebold-Mariano (DM) test, introduced by Diebold and Mariano in 1995, is a widely used non-parametric procedure for formally comparing the predictive accuracy of two competing forecasting models. It evaluates whether the difference in forecast errors between two models is statistically significant, without requiring nested models or specific distributional assumptions about the forecasts, making it broadly applicable across economics, finance, and time-series analysis.	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.