A distribution free test for changes in the trend function of locally stationary processes
| dc.contributor.author | Heinrichs, Florian | |
| dc.contributor.author | Dette, Holger | |
| dc.date.accessioned | 2020-05-27T11:25:57Z | |
| dc.date.available | 2020-05-27T11:25:57Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | In the common time series model Xi,n = μ(i/n)+"i,n with non-stationary errors we consider the problem of detecting a significant deviation of the mean function g(μ) from a benchmark g(μ) (such as the initial value μ(0) or the average trend R 1 0 μ(t)dt). The problem is motivated by a more realistic modelling of change point analysis, where one is interested in identifying relevant deviations in a smoothly varying sequence of means (μ(i/n))i=1,...,n and cannot assume that the sequence is piecewise constant. A test for this type of hypotheses is developed using an appropriate estimator for the integrated squared deviation of the mean function and the threshold. By a new concept of self-normalization adapted to non-stationary processes an asymptotically pivotal test for the hypothesis of a relevant deviation is constructed. The results are illustrated by means of a simulation study and a data example. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/39154 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-21072 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | Discussion Paper / SFB823;15/2020 | en |
| dc.subject | change point analysis | en |
| dc.subject | nonparametric regression | en |
| dc.subject | nonparametric regression | en |
| dc.subject.ddc | 310 | |
| dc.subject.ddc | 330 | |
| dc.subject.ddc | 620 | |
| dc.title | A distribution free test for changes in the trend function of locally stationary processes | en |
| dc.type | Text | de |
| dc.type.publicationtype | workingPaper | de |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | false | de |
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