Long memory and changepoint models : a spectral classification procedure

Norwood, Ben and Killick, Rebecca Claire (2018) Long memory and changepoint models : a spectral classification procedure. Statistics and Computing, 28 (2). pp. 291-302. ISSN 0960-3174

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Time series within fields such as finance and economics are often modelled using long memory processes. Alternative studies on the same data can suggest that series may actually contain a ‘changepoint’ (a point within the time series where the data generating process has changed). These models have been shown to have elements of similarity, such as within their spectrum. Without prior knowledge this leads to an ambiguity between these two models, meaning it is difficult to assess which model is most appropriate. We demonstrate that considering this problem in a time-varying environment using the time-varying spectrum removes this ambiguity. Using the wavelet spectrum, we then use a classification approach to determine the most appropriate model (long memory or changepoint). Simulation results are presented across a number of models followed by an application to stock cross-correlations and US inflation. The results indicate that the proposed classification outperforms an existing hypothesis testing approach on a number of models and performs comparatively across others.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9731-0
Uncontrolled Keywords:
?? classification long memory changepointwavelet spectrumnon-stationarity computational theory and mathematicstheoretical computer sciencestatistics and probabilitystatistics, probability and uncertainty ??
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Deposited On:
10 Feb 2017 13:54
Last Modified:
18 Apr 2024 00:28