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Functional-coefficient regression models for nonlinear time series

Author

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  • Cai, Zongwu
  • Fan, Jianqing
  • Yao, Qiwei

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.

Suggested Citation

  • Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:6314
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    File URL: http://eprints.lse.ac.uk/6314/
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    More about this item

    Keywords

    α-mixing; asymptotic normality; bootstrap; forecasting; goodness-of-fit test; local linear regression; Nonlinear time series; varying-coefficient models.; DMS-9803200; L16358; 96/MMI09785;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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