Sieve Extremum Estimates for Weakly Dependent Data

作者：Xiaohong Chen, Xiaotong T. Shen · 发表于：Econometrica · 年份：1998 · DOI：10.2307/2998559 · 被引用次数：285 · 研究领域：Statistical Methods and Inference

Many non/semi-parametric time series estimates may be regarded as different forms of sieve extremum estimates. For stationary β-mixing observations, we obtain convergence rates of sieve extremum estimates and root-n asymptotic normality of plug-in sieve extremum estimates of smooth functionals. As applications to time series models, we give convergence rates for nonparametric ARX(p,q) regression via neural networks, splines, and wavelets; root-n asymptotic normality for partial linear additive AR(p) models, and monotone transformation AR(1) models.