Salīdzināt metodes

	Pesaran-Timmermann testa virziena prognozēšanas precizitātei	Dībolda-Mariāno tests par prognožu precizitātes līdzvērtību	Zīmju tests
Nozare	Ekonometrija	Ekonometrija	Statistika
Saime	Hypothesis test	Hypothesis test	Hypothesis test
Izcelsmes gads	1992	1995	1946
Autors	M. Hashem Pesaran & Allan Timmermann	Francis Diebold & Roberto Mariano	W. J. Dixon & A. M. Mood
Tips	Nonparametric one-sided test	Non-parametric forecast comparison test	Nonparametric median test
Pirmavots	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.	Dixon, W. J. & Mood, A. M. (1946). The statistical sign test. Journal of the American Statistical Association, 41(236), 557–566.
Citi nosaukumi	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	İşaret Testi (Sign Test), one-sample sign test, paired sign test
Saistītās	3	3	4
Kopsavilkums	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 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.