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.
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Volume (Year): 57 (2002)
Issue (Month): 4 (May)
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- Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
- Bradley, Richard C., 1981. "Central limit theorems under weak dependence," Journal of Multivariate Analysis, Elsevier, vol. 11(1), pages 1-16, March.
- Linton, Oliver, 1995.
"Second Order Approximation in the Partially Linear Regression Model,"
Econometric Society, vol. 63(5), pages 1079-1112, September.
- Oliver Linton, 1993. "Second Order Approximation in the Partially Linear Regression Model," Cowles Foundation Discussion Papers 1065, Cowles Foundation for Research in Economics, Yale University.
- 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. Full references (including those not matched with items on IDEAS)
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