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Autocopulas: investigating the interdependence structure of stationary time series. (English) Zbl 1241.62126
Summary: We present a novel approach to the description of the lagged interdependence structure of stationary time series. The idea is to extend the use of copulas to the lagged (one-dimensional) series, to an analogy of the autocorrelation function. The use of such autocopulas can reveal the specifics of the lagged interdependence in a much finer way. However, the lagged interdependence is resulted from the dynamics, governing the series, therefore the known and popular copula models have little to do with that type of interdependence. True though, it seems rather cumbersome to calculate the exact form of the autocopula even for the simplest nonlinear time series models, so we confine ourselves here to an empirical and simulation based approach. The advantage of using autocopulas lays in the fact that they represent nonlinear dependencies as well, and make it possible, e.g., to study the interdependence of high (or low) values of the series separately. The presented methods are capable to check whether autocopulas of an observed process can be distinguished significantly from the autocopulas a of given time series model. The proposed approach is based on the Kendall transform which reduces the multivariate problem to one dimension. After illustrating the use of our approach in detecting conditional heteroscedasticity in the AR-ARCH vs. the AR case, we apply the proposed methods to investigate the lagged interdependence of river flow time series with particular focus on model choice based on the synchronized appearance of high values.

MSC:
 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) 62H05 Characterization and structure theory for multivariate probability distributions; copulas 62G10 Nonparametric hypothesis testing 65C60 Computational problems in statistics (MSC2010) 62P12 Applications of statistics to environmental and related topics
QRM
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