Semiparametric non-linear time series model selection
AbstractSemiparametric time series regression is often used without checking its suitability, resulting in an unnecessarily complicated model. In practice, one may encounter computational difficulties caused by the curse of dimensionality. The paper suggests that to provide more precise predictions we need to choose the most significant regressors for both the parametric and the nonparametric time series components. We develop a novel cross-validation-based model selection procedure for the simultaneous choice of both the parametric and the nonparametric time series components, and we establish some asymptotic properties of the model selection procedure proposed. In addition, we demonstrate how to implement it by using both simulated and real examples. Our empirical studies show that the procedure works well. Copyright 2004 Royal Statistical Society.
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Bibliographic InfoArticle provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society Series B.
Volume (Year): 66 (2004)
Issue (Month): 2 ()
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- Patrick Saart & Jiti Gao, 2012. "Semiparametric Methods in Nonlinear Time Series Analysis: A Selective Review," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 21/12, Monash University, Department of Econometrics and Business Statistics.
- Jansen, Dennis W. & Li, Qi & Wang, Zijun & Yang, Jian, 2008. "Fiscal policy and asset markets: A semiparametric analysis," Journal of Econometrics, Elsevier, Elsevier, vol. 147(1), pages 141-150, November.
- Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, Princeton University Press, edition 1, volume 1, number 8355.
- Gao, Jiti & Tong, Howell, 2002. "Nonparametric and semiparametric regression model selection," MPRA Paper 11987, University Library of Munich, Germany, revised Feb 2004.
- Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
- Dong, Chaohua & Gao, Jiti & Tong, Howell, 2006. "Semiparametric penalty function method in partially linear model selection," MPRA Paper 11975, University Library of Munich, Germany, revised Aug 2006.
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