Detection of non-constant long memory parameter

TL;DR

This paper proposes methods to detect changes in the long memory parameter of time series, distinguishing between constant and varying persistence levels, including abrupt or gradual shifts.

Contribution

It introduces new statistical techniques for identifying nonconstant long memory parameters in time series data, addressing a gap in existing change detection methods.

Findings

Effective detection of changes in long memory parameters

Ability to distinguish between stationary and nonstationary series with varying persistence

Applicable to both abrupt and gradual changes in long memory

Abstract

This article deals with detection of nonconstant long memory parameter in time series. The null hypothesis presumes stationary or nonstationary time series with constant long memory parameter, typically an I(d) series with d>-.5. The alternative corresponds to an increase in persistence and includes in particular an abrupt or gradual change from I(d_1) to I(d_2).