INFERENCE ON TWO-COMPONENT MIXTURES UNDER TAIL RESTRICTIONS

被引：5

作者：

Jochmans, Koen ^{[1
]}

Henry, Marc ^{[2
]}

Salanie, Bernard ^{[3
]}

机构：

[1] Sci Po, Paris, France

[2] Penn State Univ, University Pk, PA 16802 USA

[3] Columbia Univ, New York, NY 10027 USA

来源：

ECONOMETRIC THEORY | 2017年 / 33卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

LATENT-STRUCTURE MODELS; SEMIPARAMETRIC ESTIMATION; NONPARAMETRIC-ESTIMATION; FINITE MIXTURES; IDENTIFICATION; MISCLASSIFICATION; DISTRIBUTIONS;

D O I：

10.1017/S0266466616000098

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Many econometric models can be analyzed as finite mixtures. We focus on two-component mixtures, and we show that they are nonparametrically point identified by a combination of an exclusion restriction and tail restrictions. Our identification analysis suggests simple closed-form estimators of the component distributions and mixing proportions, as well as a specification test. We derive their asymptotic properties using results on tail empirical processes and we present a simulation study that documents their finite-sample performance.

引用

收藏

页码：610 / 635

页数：26

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