Quantifying Long-Range Dependence in Object-Valued Time Series

2026-09-21

AI Summary

本文针对对象值时间序列中缺乏线性结构的问题，提出了一种基于距离的内在框架来定义和估计长记忆，并通过多种方法进行估计。

Abstract

Object-valued time series, including distributions, covariance matrices, networks, and compositions, lack the linear structure required by conventional autocovariance-based definitions of long memory. We develop an intrinsic framework for defining and estimating long memory in metric spaces of negative type. An isometric embedding yields a centered Hilbert-valued process, and the trace of its lag-covariance operator provides a signed, additive measure of temporal dependence. Crucially, this trace equals the difference between the marginal mean pairwise distance and expected lagged distance, so the framework and its estimators use only distances between the original objects. We define the memory parameter through the nonsummable decay of this trace and show that it coincides with the usual parameter in Hilbert-valued settings. Estimation uses Bartlett aggregates of distance-based lag measures. Estimating the common marginal distance from the same dependent sample induces a common-centering bias in these aggregates. We derive its finite-sample form and propose iterated block-difference corrections, log-ratio and multi-bandwidth log-slope estimators, and a localized self-consistency refinement. We establish consistency, document substantial bias reduction in simulations, and find long-memory evidence in foreign-exchange return distributions and U.S. electricity-generation compositions.

Problem

• long-range dependence
• object-valued time series
• metric spaces

Innovation

• long-range dependence
• metric spaces of negative type
• isometric embedding
• lag-covariance operator
• Bartlett aggregates