Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution
利用工具变量识别和估计含误分类误差的非线性模型：一般解法AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究带离散解释变量误分类的潜在非线性模型，并利用工具变量实现识别与估计。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
利用工具变量识别和估计含误分类误差的非线性模型：一般解法
- 研究问题
- 当一般离散解释变量被误分类且误差可与解释变量相关时，如何利用工具变量识别并估计潜在的非线性模型？
- 方法与思路
- 作者用矩阵对角化求解识别方程，将潜在模型表示为观测分布的显式函数，并讨论单调性及如实报告概率条件。
- 材料陈述的贡献
- 摘要称，该方法给出非参数识别与直接估计路径，导出半参数估计量，并以模拟和经验应用展示有限样本表现。
- 阅读关注点
- 应核对工具变量条件、矩阵对角化的唯一性与标签区分，以及单调性和如实报告概率两类识别条件的准确范围。
- 阅读边界
- 仅据所给出版社摘要，无法确定完整识别条件或数值表现；收敛速度处原文缺失符号，不能据此补写为根号样本量一致。
原文摘要
This paper provides a general solution to the problem of identification and estimation of nonlinear models with misclassification error in a general discrete explanatory variable using instrumental variables. The misclassification error is allowed to be correlated with all the explanatory variables in the model. It is not enough to identify the model by simply generalizing the identification in the binary case with a claim that the number of restrictions is no less than that of the unknowns. Such a claim requires solving a complicated nonlinear system of equations. This paper introduces a matrix diagonalization technique which allows one to easily find the unique solution of the system. The solution shows that the latent model can be expressed as an explicit function of directly observed distribution functions. Therefore, the latent model is nonparametrically identifiable and directly estimable using instrumental variables. The results show that certain monotonicity restrictions on the latent model may lead to its identification with virtually no restrictions on the misclassification probabilities. An alternative identification condition suggests that the nonparametric identification may rely on the belief that people always have a higher probability of telling the truth than of misreporting. The nonparametric identification in this paper directly leads to a -consistent semiparametric estimator. The Monte Carlo simulation and empirical illustration show that the estimator performs well with a finite sample and real data.