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Erratum: New resampling method to assess the accuracy of the maximal Lyapunov exponent estimation (Physica D (2001) 155 (101-111) PII: S0167278901002676)
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tseriesEntropy: R package for Entropy based tests and analysis in time series
No full textR package for Entropy based tests and analysis in time serie
New resampling method to assess the accuracy of the maximal Lyapunov exponent estimation
No full textIn this paper, we propose a novel method to assess standard errors and confidence intervals for the maximal Lyapunov exponent estimated on time continuous chaotic systems. The method is based on resampling the original series by means of spline interpolation in the time domain. In such a way, new time series of increased size are obtained, and the sample distribution of the estimators is constructed. The method is explained and tested on the basis of computer simulations both for clean and noisy series. We give evidence that the distribution of the maximal Lyapunov exponent calculated by this method fairly agrees with the one obtained by true series with different initial conditions. An empirical criterion for the choice of the parameters of the resampling is also suggested. © 2001 Elsevier Science B.V
Entropy-Based Tests for Complex Dependence in Economic and Financial Time Series with the R Package tseriesEntropy
Full text linkTesting for complex serial dependence in economic and financial time series is a crucial task that bears many practical implications. However, the linear paradigm remains pervasive among practitioners as the autocorrelation function, because, despite its known shortcomings, it is still one of the most used tools in time series analysis. We propose a solution to the problem, by introducing the R package tseriesEntropy, dedicated to testing for serial/cross dependence and nonlinear serial dependence in time series, based on the entropy metric S-rho. The package implements tests for both continuous and categorical data. The nonparametric tests, based on S-rho, rely on minimal assumptions and have also been shown to be powerful for small sample sizes. The measure can be used as a nonlinear auto/cross-dependence function, both as an exploratory tool, or as a diagnostic measure, if computed on the residuals from a fitted model. Different null hypotheses of either independence or linear dependence can be tested by means of resampling methods, backed up by a sound theoretical background. We showcase our methods on a panel of commodity price time series. The results hint at the presence of a complex dependence in the conditional mean, together with conditional heteroskedasticity, and indicate the need for an appropriate nonlinear specification
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