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Inference of trends in time series

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We consider statistical inference of trends in mean non-stationary models. A test statistic is proposed for the existence of structural breaks in trends. On the basis of a strong invariance principle of stationary processes, we construct simultaneous confidence bands with asymptotically correct nominal coverage probabilities. The results are applied to global warming temperature data and Nile river flow data. Our confidence band of the trend of the global warming temperature series supports the claim that the trend is increasing over the last 150 years.

Keywords: Confidence bands; Global warming; Invariance principle; Non-linear time series; Non-parametric regression

Document Type: Research Article


Affiliations: University of Chicago, USA

Publication date: 2007-06-01

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