EconStor >
Institute for Fiscal Studies (IFS), London >
cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS) >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/64773
  

Full metadata record

DC FieldValueLanguage
dc.contributor.authorVogt, Michaelen_US
dc.date.accessioned2012-09-12en_US
dc.date.accessioned2012-10-16T13:09:00Z-
dc.date.available2012-10-16T13:09:00Z-
dc.date.issued2012en_US
dc.identifier.pidoi:10.1920/wp.cem.2012.2212en_US
dc.identifier.urihttp://hdl.handle.net/10419/64773-
dc.description.abstractIn this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the time-varying regression function and provide asymptotic theory for our estimates. Moreover, we show that the main conditions of the theory are satis ed for a large class of nonlinear autoregressive processes with a time-varying regression function. Finally, we examine structured models where the regression function splits up into time-varying additive components. As will be seen, estimation in these models does not su er from the curse of dimensionality. We complement the technical analysis of the paper by an application to financial data.en_US
dc.language.isoengen_US
dc.publisherCentre for Microdata Methods and Practice Londonen_US
dc.relation.ispartofseriescemmap working paper CWP22/12en_US
dc.subject.ddc330en_US
dc.subject.keywordlocal stationarityen_US
dc.subject.keywordnonparametric regressionen_US
dc.subject.keywordsmooth backfittingen_US
dc.titleNonparametric regression for locally stationary time seriesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn725565934en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

Files in This Item:
File Description SizeFormat
725565934.pdf593.65 kBAdobe PDF
No. of Downloads: Counter Stats
Show simple item record
Download bibliographical data as: BibTeX

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.