Premium
A Dependence Metric for Possibly Nonlinear Processes
Author(s) -
Granger C. W.,
Maasoumi E.,
Racine J.
Publication year - 2004
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.1467-9892.2004.01866.x
Subject(s) - mathematics , nonlinear system , series (stratigraphy) , chaotic , measure (data warehouse) , nonparametric statistics , metric (unit) , entropy (arrow of time) , permutation (music) , kernel (algebra) , econometrics , discrete mathematics , computer science , data mining , paleontology , operations management , physics , quantum mechanics , artificial intelligence , acoustics , economics , biology
. 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.