Linganisha mbinu

	Mtihani wa Pesaran-Timmermann wa Usahihi wa Utabiri wa Mwelekeo	Jaribio la Diebold-Mariano la Usahihi Sawa wa Utabiri	Jaribio la Migongano la Wald-Wolfowitz
Nyanja	Ekonometriki	Ekonometriki	Takwimu
Familia	Hypothesis test	Hypothesis test	Hypothesis test
Mwaka wa asili	1992	1995	1940
Mwanzilishi	M. Hashem Pesaran & Allan Timmermann	Francis Diebold & Roberto Mariano	Abraham Wald & Jacob Wolfowitz
Aina	Nonparametric one-sided test	Non-parametric forecast comparison test	Nonparametric randomness test
Chanzo asilia	Pesaran, M. H., & Timmermann, A. (1992). A simple nonparametric test of predictive performance. Journal of Business & Economic Statistics, 10(4), 461–465.	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.
Majina mbadala	PT Test, Directional Accuracy Test, Nonparametric Predictive Performance Test, Pesaran-Timmermann Yön Testi	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)
Zinazohusiana	3	3	5
Muhtasari	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 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.