Estimating the innovation distribution in nonparametric autoregression

被引：11

作者：

Mueller, Ursula U. ^{[2
]}

Schick, Anton ^{[1
]}

Wefelmeyer, Wolfgang ^{[3
]}

机构：

[1] SUNY Binghamton, Dept Math Sci, Binghamton, NY 13902 USA

[2] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA

[3] Univ Cologne, Math Inst, D-50931 Cologne, Germany

来源：

PROBABILITY THEORY AND RELATED FIELDS | 2009年 / 144卷 / 1-2期

关键词：

Residual-based empirical distribution function; Local linear smoother; Bahadur representation; EMPIRICAL DISTRIBUTION FUNCTION; WEAK-CONVERGENCE; ASYMPTOTIC-BEHAVIOR; ERROR DISTRIBUTION; SQUARED RESIDUALS; GARCH MODELS; REGRESSION; ERGODICITY; ARCH; PARAMETERS;

D O I：

10.1007/s00440-008-0141-2

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We prove a Bahadur representation for a residual-based estimator of the innovation distribution function in a nonparametric autoregressive model. The residuals are based on a local linear smoother for the autoregression function. Our result implies a functional central limit theorem for the residual-based estimator.

引用

页码：53 / 77

页数：25

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