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Segmenting mean-nonstationary time series via trending regressions

Alexander Aue, Lajos Horvath and Marie Hušková

Journal of Econometrics, 2012, vol. 168, issue 2, 367-381

Abstract: In this paper, we provide a segmentation procedure for mean-nonstationary time series. The segmentation is obtained by casting the problem into the framework of detecting structural breaks in trending regression models in which the regressors are generated by suitably smooth functions. As test statistics we propose to use the maximally selected likelihood ratio statistics and a related statistics based on partial sums of weighted residuals. The main theoretical contribution of the paper establishes the extreme value distribution of these statistics and their consistency. To circumvent the slow convergence to the extreme value limit, we propose to employ a version of the circular bootstrap. This procedure is completely data-driven and does not require knowledge of the time series structure. In an empirical part, we show in a simulation study and applications to air carrier traffic and S&P500 data that the finite sample performance is very satisfactory.

Keywords: Change-point analysis; Circular bootstrap; Extreme value asymptotics; Gaussian processes; Gumbel distribution; Linear models; Polynomial regression; Resampling; Trending regression (search for similar items in EconPapers)
JEL-codes: C01 C22 C60 (search for similar items in EconPapers)
Date: 2012
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