Premium
An evaluation of tests of distributional forecasts
Author(s) -
Noceti Pablo,
Smith Jeremy,
Hodges Stewart
Publication year - 2003
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.876
Subject(s) - kurtosis , statistic , statistics , econometrics , skewness , test statistic , mathematics , variance (accounting) , independence (probability theory) , statistical hypothesis testing , economics , accounting
One popular method for testing the validity of a model's forecasts is to use the probability integral transforms ( pits ) of the forecasts and to test for departures from the dual hypotheses of independence and uniformity, with departures from uniformity tested using the Kolmogorov–Smirnov (KS) statistic. This paper investigates the power of five statistics (including the KS statistic) to reject uniformity of the pits in the presence of misspecification in the mean, variance, skewness or kurtosis of the forecast errors. The KS statistic has the lowest power of the five statistics considered and is always dominated by the Anderson and Darling statistic. Copyright © 2003 John Wiley & Sons, Ltd.