|
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/10419/64711
|
| | |
Full metadata record
| DC Field | | Value | | Language |
| dc.contributor.author | | Li, Degui | | en_US |
| dc.contributor.author | | Linton, Oliver | | en_US |
| dc.contributor.author | | Lu, Zudi | | en_US |
| dc.date.accessioned | | 2012-09-24 | | en_US |
| dc.date.accessioned | | 2012-10-16T13:09:21Z | | - |
| dc.date.available | | 2012-10-16T13:09:21Z | | - |
| dc.date.issued | | 2012 | | en_US |
| dc.identifier.pi | | doi:10.1920/wp.cem.2012.2812 | | en_US |
| dc.identifier.uri | | http://hdl.handle.net/10419/64711 | | - |
| dc.description.abstract | | We consider approximating a multivariate regression function by an affine combination of one-dimensional conditional component regression functions. The weight parameters involved in the approximation are estimated by least squares on the first-stage nonparametric kernel estimates. We establish asymptotic normality for the estimated weights and the regression function in two cases: the number of the covariates is finite, and the number of the covariates is diverging. As the observations are assumed to be stationary and near epoch dependent, the approach in this paper is applicable to estimation and forecasting issues in time series analysis. Furthermore, the methods and results are augmented by a simulation study and illustrated by application in the analysis of the Australian annual mean temperature anomaly series. We also apply our methods to high frequency volatility forecasting, where we obtain superior results to parametric methods. | | en_US |
| dc.language.iso | | eng | | en_US |
| dc.publisher | | Centre for Microdata Methods and Practice London | | en_US |
| dc.relation.ispartofseries | | cemmap working paper CWP28/12 | | en_US |
| dc.subject.jel | | C14 | | en_US |
| dc.subject.jel | | C22 | | en_US |
| dc.subject.ddc | | 330 | | en_US |
| dc.subject.keyword | | asymptotic normality | | en_US |
| dc.subject.keyword | | model averaging | | en_US |
| dc.subject.keyword | | Nadaraya-Watson kernel estimation | | en_US |
| dc.subject.keyword | | near epoch dependence | | en_US |
| dc.subject.keyword | | semiparametric method | | en_US |
| dc.title | | A flexible semiparametric model for time series | | en_US |
| dc.type | | Working Paper | | en_US |
| dc.identifier.ppn | | 726303968 | | en_US |
| dc.rights | | http://www.econstor.eu/dspace/Nutzungsbedingungen | | en_US |
| Appears in Collections: | | cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)
|
Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.
|