Sammenlign metoder

	Pesaran-Timmermann-testen for retningsbestemt forudsigelsesnøjagtighed	Wald-Wolfowitz Runs Test	Tegn-testen
Fagområde	Økonometri	Statistik	Statistik
Familie	Hypothesis test	Hypothesis test	Hypothesis test
Oprindelsesår	1992	1940	1946
Ophavsperson	M. Hashem Pesaran & Allan Timmermann	Abraham Wald & Jacob Wolfowitz	W. J. Dixon & A. M. Mood
Type	Nonparametric one-sided test	Nonparametric randomness test	Nonparametric median test
Oprindelig kilde	Pesaran, M. H., & Timmermann, A. (1992). A simple nonparametric test of predictive performance. Journal of Business & Economic Statistics, 10(4), 461–465.	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.
Aliasser	PT Test, Directional Accuracy Test, Nonparametric Predictive Performance Test, Pesaran-Timmermann Yön Testi	Wald-Wolfowitz test, runs test for randomness, Runs Testi (Wald-Wolfowitz)	İşaret Testi (Sign Test), one-sample sign test, paired sign test
Relaterede	3	5	4
Resumé	Introduced by Pesaran and Timmermann (1992), the PT test is a nonparametric procedure that evaluates whether a forecasting model correctly predicts the direction (sign) of a target variable more often than would be expected by chance. It is widely used in financial econometrics and macroeconomic forecasting to assess the practical utility of a model beyond simple error metrics, particularly when the economic cost of getting the direction wrong is high.	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.