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NONPARAMETRIC TESTS FOR SERIAL DEPENDENCE
Author(s) -
Chan Ngai Hang,
Tran Lanh Tat
Publication year - 1992
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1992.tb00092.x
Subject(s) - nonparametric statistics , mathematics , statistic , autocorrelation , test statistic , series (stratigraphy) , statistics , bilinear interpolation , independence (probability theory) , histogram , statistical hypothesis testing , null hypothesis , econometrics , artificial intelligence , computer science , paleontology , image (mathematics) , biology
. A nonparametric test statistic based on the distance between the joint and marginal densities is developed to test for the serial dependence for a given sequence of time series data. The key idea lies in observing that, under the null hypothesis of independence, the joint density of the observations is equal to the product of their individual marginals. Histograms are used in constructing such a statistic which is nonparametric and consistent. It possesses high power in capturing subtle or diffuse dependence structure. A bilinear time series model is used to illustrate its performance with the classical correlation approach.