Journal of Probability and Statistics
Volume 2009 (2009), Article ID 487194, 37 pages
Research Article

Model and Variable Selection Procedures for Semiparametric Time Series Regression

Department of Management Science, Faculty of Engineering, Tokyo University of Science, Kudankita 1-14-6, Chiyoda, Tokyo 102-0073, Japan

Received 13 March 2009; Accepted 26 June 2009

Academic Editor: Junbin Gao

Copyright © 2009 Risa Kato and Takayuki Shiohama. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.