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An Improved Local-linear Estimator For Nonparametric Regression With Autoregressive Errors

Author

Listed:
  • Ke Yang

    (University of Hartford)

Abstract

In this paper we propose a modification of the local linear smoother to account for the autocorrelated errors in a nonparametric regression model with random-design. The proposed estimator has a closed-form expression and is simple to calculate. The asymptotic bias and variance of the proposed estimator are studied for AR(1) case. Compared to the standard local linear smoother, the proposed estimator retains the same design-adaptive bias but has a smaller asymptotic variance. Therefore the proposed method improves the estimation efficiency in kernel regression

Suggested Citation

  • Ke Yang, 2013. "An Improved Local-linear Estimator For Nonparametric Regression With Autoregressive Errors," Economics Bulletin, AccessEcon, vol. 33(1), pages 19-27.
    • Handle: RePEc:ebl:ecbull:eb-12-00517
    as

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    References listed on IDEAS

    as
    1. Naisyin Wang, 2003. "Marginal nonparametric kernel regression accounting for within-subject correlation," Biometrika, Biometrika Trust, vol. 90(1), pages 43-52, March.
    2. Kani Chen & Zhezhen Jin, 2005. "Local polynomial regression analysis of clustered data," Biometrika, Biometrika Trust, vol. 92(1), pages 59-74, March.
    3. Martins-Filho, Carlos & Yao, Feng, 2009. "Nonparametric regression estimation with general parametric error covariance," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 309-333, March.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Nonparametric method; Kernel regression; Local linear regression; autoregressive; Variance reduction;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C0 - Mathematical and Quantitative Methods - - General

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