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Long memory and changepoint models: a spectral classification procedure

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>03/2018
<mark>Journal</mark>Statistics and Computing
Issue number2
Number of pages12
Pages (from-to)291-302
Early online date13/02/17
<mark>Original language</mark>English


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.

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The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9731-0