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Functional-coefficient regression models for nonlinear time series

Cai, Zongwu, Fan, Jianqing and Yao, Qiwei (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
Library of Congress subject classification: H Social Sciences > HA Statistics
Sets: Collections > Economists Online
Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
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
Projects: DMS-9803200, L16358, 96/MMI09785
Date Deposited: 02 Jul 2008 10:08
URL: http://eprints.lse.ac.uk/6314/

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