Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/46472
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Type: Journal article
Title: Semiparametric non-linear time series model selection
Author: Gao, J.
Tong, H.
Citation: Journal of the Royal Statistical Society Series B-Statistical Methodology, 2004; 66(2):321-336
Publisher: Blackwell Publ Ltd
Issue Date: 2004
ISSN: 1369-7412
0035-9246
Statement of
Responsibility: 
Jiti Gao and Howell Tong
Abstract: Semiparametric time series regression is often used without checking its suitability, resulting in an unnecessarily complicated model. In practice, one may encounter computational difficulties caused by the curse of dimensionality.The paper suggests that to provide more precise predictions we need to choose the most significant regressors for both the parametric and the nonparametric time series components.We develop a novel cross-validation-based model selection procedure for the simultaneous choice of both the parametric and the nonparametric time series components, and we establish some asymptotic properties of the model selection procedure proposed. In addition, we demonstrate how to implement it by using both simulated and real examples. Our empirical studies show that the procedure works well.
Description: © 2004 Royal Statistical Society
RMID: 0020081884
DOI: 10.1111/j.1369-7412.2004.05303.x
Published version: http://www3.interscience.wiley.com/journal/118808460/abstract
Appears in Collections:Economics publications

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