Studies in Nonlinear Dynamics & Econometrics
Ed. by Mizrach, Bruce
IMPACT FACTOR 2017: 0.855
CiteScore 2017: 0.76
SCImago Journal Rank (SJR) 2017: 0.668
Source Normalized Impact per Paper (SNIP) 2017: 0.894
Mathematical Citation Quotient (MCQ) 2017: 0.02
The validity of any test for nonlinearity based on resampling techniques depends heavily on the consistency of the generated resampled data to the null hypothesis of linear stochastic process. The surrogate data generating algorithms AAFT, IAAFT and STAP, as well as a residual-based bootstrap algorithm, all used for the randomization or bootstrap test for nonlinearity, are reviewed and their performance is compared using different nonlinear statistics for the test. The simulations on linear and nonlinear stochastic systems, as well as chaotic systems, reveals a variation in the test outcome with the algorithm and statistic. Overall, the bootstrap algorithm led to smallest test power whereas the STAP algorithm gave consistently good results in terms of size and power of the test. The performance of the nonlinearity test with the resampling techniques is evaluated on volume and return time series of international stock exchange indices.
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