TESTING SERIAL INDEPENDENCE USING THE SAMPLE DISTRIBUTION FUNCTION

Miguel A. Delgado

91 citations0/0 files storedJournal of Time Series Analysis

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Financial Risk and Volatility Modeling Monetary Policy and Economic Impact Complex Systems and Time Series Analysis

Abstract

Abstract. 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.

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1996A test for independence based on the correlation dimension 1991Consistent Nonparametric Entropy-Based Testing

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2008A non-parametric independence test using permutation entropy 2001A Nonparametric Test of Serial Independence for Time Series and Residuals 1998Testing for Pairwise Serial Independence Via the Empirical Distribution Function 2006Testing the martingale difference hypothesis using integrated regression functions 2009Tests of serial independence for continuous multivariate time series based on a Möbius decomposition of the independence empirical copula process 2011Generalized runs tests for the IID hypothesis

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