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Change point detection in heteroscedastic time series

Tomasz Górecki, Lajos Horvath and Piotr Kokoszka

Econometrics and Statistics, 2018, vol. 7, issue C, 63-88

Abstract: Many time series exhibit changes both in level and in variability. Generally, it is more important to detect a change in the level, and changing or smoothly evolving variability can confound existing tests. A framework for testing for shifts in the level of a series which accommodates the possibility of changing variability is developed. The resulting tests are robust both to heteroskedasticity and serial dependence. They rely on a new functional central limit theorem for dependent random variables whose variance can change or trend in a substantial way. This new result is of independent interest as it can be applied in many inferential contexts applicable to time series. Its application to change point tests relies on a new approach which utilizes Karhunen–Loéve expansions of the limit Gaussian processes. After presenting the theory in the most commonly encountered setting of the detection of a change point in the mean, it is shown how it can be extended to linear and nonlinear regression. Finite sample performance is examined by means of a simulation study and an application to yields on US treasury bonds.

Keywords: Change point; Functional central limit theorem; Heteroskedastic time series; Karhunen–Loéve expansion (search for similar items in EconPapers)
Date: 2018
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