题 目: Adaptive Nonparametric Regression with Conditional Heteroskedasticity

汇报人: 苏良军博士(lsu@gsm.pku.edu.cn)

拉斯维加斯9888

时 间: 2006年3月29日下午3:30-5:00

地 点: 拉斯维加斯9888楼201室

Abstract

We extend the nonparametric adaptive estimation of Linton and Xiao (2005) to

allow for conditional heteroskedasticity of unknown form. We demonstrate that

both the conditional mean and conditional variance functions in a nonparametric

regression model can be estimated adaptively based on the local profile

likelihood principle. We show that the proposed estimators are asymptotically

equivalent to the infeasible local likelihood estimators (e.g., Aerts and

Claeskens, 1997), which require knowledge of the error distribution. Simulation

evidence suggests that significant gains can be achieved in finite samples with

our approach in comparison with the conventional local polynomial estimators.

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