Quantile regression for dynamic partially linear varying coefficient time series models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 49-66 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 141 |
Publication status | Published - 4 Jul 2015 |
Externally published | Yes |
Link(s)
Abstract
In this article, we consider quantile regression method for partially linear varying coefficient models for semiparametric time series modeling. We propose estimation methods based on general series estimation. We establish convergence rates of the estimator and the root-n asymptotic normality of the finite-dimensional parameter in the linear part. We further propose penalization-based method for automatically specifying the linear part of the model as well as performing variable selection, and show the model selection consistency of this approach. We illustrate the performance of estimates using a simulation study.
Research Area(s)
- Autoregressive models, Model structure recovery, SCAD penalty, Schwarz information criterion (SIC), Splines
Citation Format(s)
Quantile regression for dynamic partially linear varying coefficient time series models. / Lian, Heng.
In: Journal of Multivariate Analysis, Vol. 141, 04.07.2015, p. 49-66.
In: Journal of Multivariate Analysis, Vol. 141, 04.07.2015, p. 49-66.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review