On bandwidth selection in partial linear regression models under dependence
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.
Volume (Year): 57 (2002)
Issue (Month): 4 (May)
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References listed on IDEAS
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- Bradley, Richard C., 1981. "Central limit theorems under weak dependence," Journal of Multivariate Analysis, Elsevier, vol. 11(1), pages 1-16, March.
- Oliver Linton, 1993.
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Cowles Foundation Discussion Papers
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