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On bandwidth selection in partial linear regression models under dependence

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  • Aneiros-Pérez, Germán

Abstract

We obtain the expression of an asymptotically optimal bandwidth for a semiparametric least-squares estimator of [beta] in the model y=xT[beta]+m(t)+[var epsilon], where x is random, t is fixed, m is unknown and [var epsilon] is strong mixing. The selection method is based on second-order approximations for the variance and bias. Asymptotic normality is also established.

Suggested Citation

  • Aneiros-Pérez, Germán, 2002. "On bandwidth selection in partial linear regression models under dependence," Statistics & Probability Letters, Elsevier, vol. 57(4), pages 393-401, May.
    • Handle: RePEc:eee:stapro:v:57:y:2002:i:4:p:393-401
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    1. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    2. Bradley, Richard C., 1981. "Central limit theorems under weak dependence," Journal of Multivariate Analysis, Elsevier, vol. 11(1), pages 1-16, March.
    3. Linton, Oliver, 1995. "Second Order Approximation in the Partially Linear Regression Model," Econometrica, Econometric Society, vol. 63(5), pages 1079-1112, September.
    4. Roussas, George G. & Tran, Lanh T. & Ioannides, D. A., 1992. "Fixed design regression for time series: Asymptotic normality," Journal of Multivariate Analysis, Elsevier, vol. 40(2), pages 262-291, February.
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    Cited by:

    1. Germán Aneiros-Pérez, 2004. "Plug-in bandwidth choice for estimation of nonparametric part in partial linear regression models with strong mixing errors," Statistical Papers, Springer, vol. 45(2), pages 191-210, April.

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