Variable selection in partially time-varying coefficient models
| dc.contributor.author | Li, Degui | en |
| dc.contributor.author | Chen, Jia | en |
| dc.contributor.author | Lin, Zhengyan | en |
| dc.contributor.school | School of Economics | en |
| dc.date.issued | 2009 | en |
| dc.description | © 2009 Taylor & Francis | en |
| dc.description.abstract | A partially time-varying coefficient model is introduced to characterise the nonlinearity and trending phenomenon. To enhance predictability and to select significant variables in the parametric component of the model, the penalised least squares method with the help of the profile local linear technique is developed in this article. The convergence rate and the oracle property of the resulting estimator are established under mild conditions. A remarkable achievement of our results is that it does not require undersmoothing of the nonparametric component. Meanwhile, some extensions of the proposed model and method are also discussed. Furthermore, some numerical examples are provided to show that our theory and method work well in practice. | en |
| dc.description.statementofresponsibility | Degui Li, Jia Chen and Zhengyan Lin | en |
| dc.identifier.citation | Journal of Nonparametric Statistics, 2009; 21(5):553-566 | en |
| dc.identifier.doi | 10.1080/10485250902912694 | en |
| dc.identifier.issn | 1048-5252 | en |
| dc.identifier.uri | http://hdl.handle.net/2440/56443 | |
| dc.language.iso | en | en |
| dc.publisher | Gordon Breach Sci Publ | en |
| dc.subject | oracle property; penalised least squares; profile local linear technique; semiparametric regression; time-varying coefficient model | en |
| dc.title | Variable selection in partially time-varying coefficient models | en |
| dc.type | Journal article | en |