Cookies?
Library Header Image
LSE Research Online LSE Library Services

Functional-coefficient regression models for nonlinear time series

Cai, Zongwu and 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

[img]
Preview
PDF
Download (465kB) | Preview

Identification Number: 10.1080/01621459.2000.10474284

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
Subjects: H Social Sciences > HA Statistics
Sets: Collections > Economists Online
Departments > Statistics
Date Deposited: 02 Jul 2008 10:08
Last Modified: 25 Jun 2014 09:24
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

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics