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TESTING SERIAL INDEPENDENCE USING THE SAMPLE DISTRIBUTION FUNCTION
Author(s) -
Delgado Miguel A.
Publication year - 1996
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.1467-9892.1996.tb00276.x
Subject(s) - mathematics , resampling , null distribution , nonparametric statistics , test statistic , statistics , monte carlo method , independence (probability theory) , statistic , series (stratigraphy) , null hypothesis , sample size determination , sample (material) , statistical hypothesis testing , econometrics , chemistry , chromatography , paleontology , biology
. This paper presents and discusses a nonparametric test for detecting serial dependence. We consider a Cramèer‐von Mises statistic based on the difference between the joint sample distribution and the product of the marginals. Exact critical values can be approximated from the asymptotic null distribution, or by resampling, randomly permuting the original series. A Monte Carlo experiment illustrates the test performance with small sample sizes. The paper also includes an application, testing the random walk hypothesis of exchange rate returns for several currencies.