Inference for the Fourth-Order Innovation Cumulant in Linear Time Series

Maria Fragkeskou and Efstathios Paparoditis∗

Journal of Time Series Analysis, 2016, vol. 37, issue 2, 240-266

Abstract: type="main" xml:id="jtsa12160-abs-0001"> The rescaled fourth-order cumulant of the unobserved innovations of linear time series is an important parameter in statistical inference. This article deals with the problem of estimating this parameter. An existing nonparametric estimator is first discussed, and its asymptotic properties are derived. It is shown how the autocorrelation structure of the underlying process affects the behaviour of the estimator. Based on our findings and on an important invariance property of the parameter of interest with respect to linear filtering, a pre-whitening-based nonparametric estimator of the same parameter is proposed. The estimator is obtained using the filtered time series only; that is, an inversion of the pre-whitening procedure is not required. The asymptotic properties of the new estimator are investigated, and its superiority is established for large classes of stochastic processes. It is shown that for the particular estimation problem considered, pre-whitening can reduce the variance and the bias of the estimator. The finite sample performance of both estimators is investigated by means of simulations. The new estimator allows for a simple modification of the multiplicative frequency domain bootstrap, which extends its considerable range of validity. Furthermore, the problem of testing hypotheses about the rescaled fourth-order cumulant of the unobserved innovations is also considered. In this context, a simple test for Gaussianity is proposed. Some real-life data applications are presented.

Date: 2016
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