Сравнение на методи

	Набор от доверие на модели (MCS)	Тест на Диболд-Мариано за равна прогнозна точност
Област	Иконометрия	Иконометрия
Семейство	Hypothesis test	Hypothesis test
Година на възникване	2011	1995
Създател	Hansen, Lunde & Nason	Francis Diebold & Roberto Mariano
Тип	Sequential hypothesis testing procedure for model comparison	Non-parametric forecast comparison test
Основополагащ източник	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.
Други названия	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
Свързани	3	3
Резюме	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.