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A Dependence Metric for Possibly Nonlinear Processes


E. Maasoumi, Department of Economics, Southern Methodist University, Dallas, TX 75275-0496, USA. Tel.: (214) 768-4298; E-mail:


Abstract.  A transformed metric entropy measure of dependence is studied which satisfies many desirable properties, including being a proper measure of distance. It is capable of good performance in identifying dependence even in possibly nonlinear time series, and is applicable for both continuous and discrete variables. A nonparametric kernel density implementation is considered here for many stylized models including linear and nonlinear MA, AR, GARCH, integrated series and chaotic dynamics. A related permutation test of independence is proposed and compared with several alternatives.