Entropy-Based Tests for Complex Dependence in Economic and Financial Time Series with the R Package tseriesEntropy

Testing 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.

Automated summary

Testing for complex serial dependence in economic and financial time series is an important task. The R package tseriesEntropy provides a solution by introducing the entropy metric S-rho and implementing tests for both continuous and categorical data. The nonparametric tests, based on S-rho, have been shown to be powerful and require minimal assumptions. Our methods showcase the presence of complex dependence in commodity price time series, indicating the need for an appropriate nonlinear specification.