Model order selection for cascade autoregressive (CAR) models
| dc.contributor.author | Köhler, Steffen | |
| dc.date.accessioned | 2021-01-29T13:26:48Z | |
| dc.date.available | 2021-01-29T13:26:48Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | In recent years, Cascade Autoregression (CAR) models enjoy increasing popularity in applied econometrics. This is due to the fact that they are able to approximate both short- and long-memory processes and are easy to implement. However, their model order, namely the timing of the steps, relies on ad-hoc decisions rather than being data-driven. In this paper, techniques for model order selection of CAR models in finite samples are presented. The approaches are evaluated in an extensive simulation study, as well as in an empirical application. The results suggest that model order selection may provide gains in both in- and out-of-sample performance. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/40014 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-21897 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | Discussion Paper / SFB823;3/2021 | |
| dc.subject | cascade models | en |
| dc.subject | cross-validation | en |
| dc.subject | forecasting | en |
| dc.subject | time-series modeling | en |
| dc.subject | long-memory | en |
| dc.subject | model selection | en |
| dc.subject | HAR | en |
| dc.subject.ddc | 310 | |
| dc.subject.ddc | 330 | |
| dc.subject.ddc | 620 | |
| dc.title | Model order selection for cascade autoregressive (CAR) models | en |
| dc.type | Text | de |
| dc.type.publicationtype | workingPaper | de |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | false | de |