TY - JOUR

T1 - Long memory and changepoint models

T2 - a spectral classification procedure

AU - Norwood, Ben

AU - Killick, Rebecca Claire

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9731-0

PY - 2018/3/31

Y1 - 2018/3/31

N2 - 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.

AB - 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.

KW - Classification

KW - Long memory

KW - Changepoint

KW - Wavelet spectrum

KW - Non-stationarity

U2 - 10.1007/s11222-017-9731-0

DO - 10.1007/s11222-017-9731-0

M3 - Journal article

VL - 28

SP - 291

EP - 302

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 2

ER -