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Estimating error correlation in nonparametric regression

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  • Altman, Naomi Simone

Abstract

Estimates of error correlations in kernel nonparametric regression are obtained using the method of moments. A high order asymptotic expansion of the estimators shows that they are consistent and asymptotically normal at parametric rates, and provides heuristics for the choice of bandwidth and kernel.

Suggested Citation

  • Altman, Naomi Simone, 1993. "Estimating error correlation in nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 18(3), pages 213-218, October.
    • Handle: RePEc:eee:stapro:v:18:y:1993:i:3:p:213-218
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    Cited by:

    1. Ferreira, Eva & Núñez-Antón, Vicente & Rodríguez-Póo, Juan, 1997. "Kernel regression estimates of growth curves using nonstationary correlated errors," Statistics & Probability Letters, Elsevier, vol. 34(4), pages 413-423, June.
    2. Bastian Schäfer, 2021. "Bandwidth selection for the Local Polynomial Double Conditional Smoothing under Spatial ARMA Errors," Working Papers CIE 146, Paderborn University, CIE Center for International Economics.
    3. Dalla, Violetta & Giraitis, Liudas & Robinson, Peter M., 2020. "Asymptotic theory for time series with changing mean and variance," Journal of Econometrics, Elsevier, vol. 219(2), pages 281-313.

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