论文标题
确定错误分类的二进制内源性回归器的效果
Identifying the effect of a mis-classified, binary, endogenous regressor
论文作者
论文摘要
本文研究了当可用的离散值仪器变量时，研究了错误分类，二元，内源性回归器的效果。我们首先证明该模型的唯一现有点标识结果是不正确的。我们继续在仪器的平均独立性假设和测量误差下得出尖锐的识别设置。由此产生的界限是新颖且内容丰富的，但未能指出识别感兴趣的效果。这促使我们考虑替代性和稍强的假设：我们表明，添加第二和第三刻独立性假设足以识别模型。
This paper studies identification of the effect of a mis-classified, binary, endogenous regressor when a discrete-valued instrumental variable is available. We begin by showing that the only existing point identification result for this model is incorrect. We go on to derive the sharp identified set under mean independence assumptions for the instrument and measurement error. The resulting bounds are novel and informative, but fail to point identify the effect of interest. This motivates us to consider alternative and slightly stronger assumptions: we show that adding second and third moment independence assumptions suffices to identify the model.
