Functional-coefficient regression models for nonlinear time series

Zongwu Cai (caiz@ku.edu), Jianqing Fan and Qiwei Yao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: α\-mixing; asymptotic normality; bootstrap; forecasting; goodness\-of\-fit test; local linear regression; Nonlinear time series; varying\-coefficient models. (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2000-09
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Citations: View citations in EconPapers (236)

Published in Journal of the American Statistical Association, September, 2000, 95(451), pp. 941-956. ISSN: 0162-1459

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