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Semiparametric time series regression modeling with a diverging number of parameters

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  • Shengchao Zheng
  • Degao Li

Abstract

Variable selection and error structure determination of a partially linear model with time series errors are important issues. In this paper, we investigate the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation penalty for a partially linear model with a divergent number of covariates and finite order autoregressive time series errors. Both consistency and asymptotic normality of the proposed penalized estimators are derived. The oracle property of the resultant estimators is proved. Simulation studies are carried out to assess the finite†sample performance of the proposed procedure. A real data analysis is made to illustrate the usefulness of the proposed procedure as well.

Suggested Citation

  • Shengchao Zheng & Degao Li, 2018. "Semiparametric time series regression modeling with a diverging number of parameters," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(2), pages 90-108, May.
  • Handle: RePEc:bla:stanee:v:72:y:2018:i:2:p:90-108
    DOI: 10.1111/stan.12121
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