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Wasserstein Distance Bounds on the Normal Approximation of Empirical Autocovariances and Cross-covariances Under Non-stationarity and Stationarity by Anastasiou, Andreas; Kley, Tobias is a scholarly article available to read on EtoBox.

What is Wasserstein Distance Bounds on the Normal Approximation of Empirical Autocovariances and Cross-covariances Under Non-stationarity and Stationarity about?

The autocovariance and cross-covariance functions naturally appear in many time series procedures (e.g., autoregression or prediction). Under assumptions, empirical versions of the autocovariance and cross-covariance are asymptotically normal with covariance structure depending on the second and fourth order spectra. Under non-restrictive assumptions, we derive a bound for the Wasserstein distance of the finite sample distribution of the estimator of the autocovariance and cross-covariance to the Gaussian limit. An error of approximation to the second-order moments of the estimator and an $m$-dependent approximation are the key ingredients in order to obtain the bound. As a worked example, we discuss how to compute the bound for causal autoregressive processes of order 1 with different distributions for the innovations. To assess our result, we compare our bound to Wasserstein distances obtained via simulation.

Author

Anastasiou, Andreas; Kley, Tobias

Published

2023

Language

EN