Functional-coefficient regression models for nonlinear time series

Cai, Zongwu, Fan, Jianqing and Yao, Qiwei ORCID: 0000-0003-2065-8486 (2000) Functional-coefficient regression models for nonlinear time series. Journal of the American Statistical Association, 95 (451). pp. 941-956. ISSN 0162-1459

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Abstract

The local linear regression technique is applied to estimation of functional-coefficient regression models for time series data. The models include threshold autoregressive models and functional-coefficient autoregressive models as special cases but with the added advantages such as depicting finer structure of the underlying dynamics and better postsample forecasting performance. Also proposed are a new bootstrap test for the goodness of fit of models and a bandwidth selector based on newly defined cross-validatory estimation for the expected forecasting errors. The proposed methodology is data-analytic and of sufficient flexibility to analyze complex and multivariate nonlinear structures without suffering from the “curse of dimensionality.” The asymptotic properties of the proposed estimators are investigated under the α-mixing condition. Both simulated and real data examples are used for illustration.

Item Type:	Article
Official URL:	http://www.tandfonline.com/doi/abs/10.1080/0162145...
Additional Information:	© 2000 American Statistical Association
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	02 Jul 2008 10:08
Last Modified:	20 Oct 2021 00:41
Projects:	DMS-9803200, L16358, 96/MMI09785
Funders:	National Science Foundation, National Science Administration 96-1-0015, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council/Engineering, Physical Sciences Research Council
URI:	http://eprints.lse.ac.uk/id/eprint/6314

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