Year: 2025
Type: preprint
Abstract: Distinguishing long-memory behaviour from nonstationarity is challenging, as both produce slowly decaying sample autocovariances. Existing stationarity tests either fail to account for long-memory processes or exhibit poor empirical size, particularly near the boundary between stationarity and nonstationarity. We propose a new, parameter-free testing procedure based on the evaluation of periodograms across multiple epochs. The limiting distributions derived here are obtained under stationarity and nonstationarity assumptions and analytically tractable, expressed as finite sums of weighted independent $χ^2$ random variables. Simulation studies indicate that the proposed method performs favorably compared to existing approaches.
Source: arXiv (Cornell University)
Authors: Haye, Mohamedou Ould, Philippe, Anne
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7102407077
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