Prediction in locally stationary time series
| dc.contributor.author | Dette, Holger | |
| dc.contributor.author | Wu, Weichi | |
| dc.date.accessioned | 2020-01-17T15:20:09Z | |
| dc.date.available | 2020-01-17T15:20:09Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in non-stationary time series. In contrast to the currently available methods for this problem the predictor developed here does not rely on fitting an autoregressive model and does not require a vanishing trend. The finite sample properties of the new methodology are illustrated by means of a simulation study and a data example. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/38530 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-20449 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | Discussion Paper / SFB823;1/2020 | en |
| dc.subject | locally stationary time series | en |
| dc.subject | high dimensional auto-covariance | en |
| dc.subject | matrices | en |
| dc.subject | prediction | en |
| dc.subject | local linear regression | en |
| dc.subject.ddc | 310 | |
| dc.subject.ddc | 330 | |
| dc.subject.ddc | 620 | |
| dc.title | Prediction in locally stationary time series | en |
| dc.type | Text | de |
| dc.type.publicationtype | workingPaper | de |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | false | de |