On bandwidth selection in partial linear regression models under dependence

Germán Aneiros-Pérez

Statistics & Probability Letters, 2002, vol. 57, issue 4, 393-401

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.

Keywords: Partial; linear; models; Kernel; smoothing; Bandwidth; selection; Mixing (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (1)

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