Tests for the error distribution in nonparametric possibly heteroscedastic regression models

被引：15

作者：

Huskova, Marie ^{[1
,2
]}

Meintanis, Simos G. ^{[3
]}

机构：

[1] Charles Univ Prague, Dept Stat, Prague 18675 8, Czech Republic

[2] UTIA Czech Acad Sci, Prague 18600, Czech Republic

[3] Univ Athens, Dept Econ, Athens 10559, Greece

来源：

TEST | 2010年 / 19卷 / 01期

关键词：

Empirical characteristic function; Kernel regression estimator; Goodness-of-fit; Parametric bootstrap; GOODNESS-OF-FIT; LIMIT-THEOREMS; SCALE;

D O I：

10.1007/s11749-008-0135-5

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：

Consistent procedures are constructed for testing the goodness-of-fit of the error distribution in nonparametric regression models. The test starts with a kernel-type regression fit and proceeds with the construction of a test statistic in the form of an L (2) distance between a parametric and a nonparametric estimates of the residual characteristic function. The asymptotic null distribution and the behavior of the test statistic under alternatives are investigated. A simulation study compares bootstrap versions of the proposed test to corresponding procedures utilizing the empirical distribution function.

引用

页码：92 / 112

页数：21

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