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Model Confidence Set (MCS)×Test Diebolda-Mariana na rovnakú prediktívnu presnosť×
OdborEkonometriaEkonometria
RodinaHypothesis testHypothesis test
Rok vzniku20111995
TvorcaHansen, Lunde & NasonFrancis Diebold & Roberto Mariano
TypSequential hypothesis testing procedure for model comparisonNon-parametric forecast comparison test
Pôvodný zdrojHansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497. DOI ↗Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263. DOI ↗
Ďalšie názvyMCS Procedure, Superior Set of Models, Model Selection Confidence Set, Model Güven KümesiDM Test, Test of Equal Forecast Accuracy, Diebold-Mariano Forecast Comparison Test, Tahmin Doğruluğu Eşitliği Testi
Príbuzné33
ZhrnutieThe 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.
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ScholarGatePorovnať metódy: Model Confidence Set · Diebold-Mariano Test. Získané 2026-06-19 z https://scholargate.app/sk/compare