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Semiparametric estimation of regression functions in autoregressive models

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  • Yu, Zhuoxi
  • Wang, Dehui
  • Shi, Ningzhong

Abstract

This paper proposes a semiparametric method for an autoregressive model by combining a parametric regression estimator with a nonparametric adjustment. The regression has a parametric framework. After the parameter is estimated through a general parametric method, the obtained regression function is adjusted by a nonparametric factor, and the nonparametric factor is obtained through a natural consideration of the local L2-fitting criterion. Some asymptotic and simulation results for the semiparametric method are discussed.

Suggested Citation

  • Yu, Zhuoxi & Wang, Dehui & Shi, Ningzhong, 2009. "Semiparametric estimation of regression functions in autoregressive models," Statistics & Probability Letters, Elsevier, vol. 79(2), pages 165-172, January.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:2:p:165-172
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    References listed on IDEAS

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    1. Fabienne Comte, 2004. "Kernel deconvolution of stochastic volatility models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(4), pages 563-582, July.
    2. Hardle, W. & Tsybakov, A., 1997. "Local polynomial estimators of the volatility function in nonparametric autoregression," Journal of Econometrics, Elsevier, vol. 81(1), pages 223-242, November.
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    Cited by:

    1. Yang, Hu & Wu, Xingcui, 2011. "Semiparametric EGARCH model with the case study of China stock market," Economic Modelling, Elsevier, vol. 28(3), pages 761-766.

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