방법 비교

	Diebold-Mariano Test	발트-볼포위츠 연순위 검정	부호 검정 (Sign Test)
분야	계량경제학	통계학	통계학
계열	Hypothesis test	Hypothesis test	Hypothesis test
기원 연도	1995	1940	1946
창시자	Francis Diebold & Roberto Mariano	Abraham Wald & Jacob Wolfowitz	W. J. Dixon & A. M. Mood
유형	Non-parametric forecast comparison test	Nonparametric randomness test	Nonparametric median test
원전	Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.	Wald, A. & Wolfowitz, J. (1940). On a test whether two samples are from the same population. Annals of Mathematical Statistics, 11(2), 147–162.	Dixon, W. J. & Mood, A. M. (1946). The statistical sign test. Journal of the American Statistical Association, 41(236), 557–566.
별칭	DM Test, Test of Equal Forecast Accuracy, Diebold-Mariano Forecast Comparison Test, Tahmin Doğruluğu Eşitliği Testi	Wald-Wolfowitz test, runs test for randomness, Runs Testi (Wald-Wolfowitz)	İşaret Testi (Sign Test), one-sample sign test, paired sign test
관련	3	5	4
요약	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 Wald-Wolfowitz runs test is a nonparametric hypothesis test that determines whether a sequence of observations — coded as a series of binary symbols — follows a random pattern or contains systematic structure. Introduced by Abraham Wald and Jacob Wolfowitz in 1940, the test counts the number of uninterrupted runs of identical symbols and asks whether that count is consistent with random arrangement.	The sign test is the simplest nonparametric hypothesis test for deciding whether the median of paired differences — or of a single sample — differs significantly from a hypothesised value. Formalised by W. J. Dixon and A. M. Mood in 1946, it imposes virtually no distributional assumptions and can be applied to any data where individual differences can be classified as positive or negative.