Misclassification of the dependent variable in a discrete-response setting
Misclassification of the dependent variable in a discrete-response setting
复制标题
DOI：
10.1016/s0304-4076(98)00015-3
复制
发表时间：
1998-12-01
影响因子：
6.3
通讯作者：
Scott-Morton, FM
中科院分区：
文献类型：
--
作者：
Hausman, JA;Abrevaya, J;Scott-Morton, FM
当使用传统估计技术（例如概率或合法性）时，离散响应模型中因变量的错误分类会导致系数估计不一致。提出了一种修正错误分类的改进的最大似然估计器。半参数方法将 Han (1987) (Journal of Econometrics 35, 303-316) 的最大等级相关估计与等渗回归相结合，允许比最大似然方法更常见的错误分类形式。参数和半参数估计技术应用于具有两个常用数据集的工作变动模型，即当前人口调查（CPS）和收入动态面板研究（PSID）。 (C) 1998 Elsevier Science S.A. 保留所有权利。
Misclassification of dependent variables in a discrete-response model causes inconsistent coefficient estimates when traditional estimation techniques (e.g., probit or legit) are used. A modified maximum likelihood estimator that corrects for misclassification is proposed. A semiparametric approach, which combines the maximum rank correlation estimator of Han (1987) (Journal of Econometrics 35, 303-316) with isotonic regression, allows for more general forms of misclassification than the maximum likelihood approach. The parametric and semiparametric estimation techniques are applied to a model of job change with two commonly used data sets, the Current Population Survey (CPS) and the Panel Study of Income Dynamics (PSID). (C) 1998 Elsevier Science S.A. All rights reserved.