Partial Identification and Estimation of Semiparametric Ordered Response Models with Interval Regressor Data*
研究了当解释变量为区间数据时，有序响应模型的部分识别问题，提出了广义修正最大得分集估计量，并通过蒙特卡洛模拟和美国工作满意度数据验证了方法。
Abstract In many micro‐data studies, the dependent variable often involves ordered categories and at least one regressor is measured by the interval rather than the precise value. This paper considers partial identification of such an ordered response model when point identification fails. We show the identified set of non‐intercept coefficients is the intersection of those for composite binary response models. We also propose a generalized modified maximum score set (GMMS) estimator. A practical implication of our finding is researchers can shrink the identified set and obtain more precise inference by designing as many as categories of response in a questionnaire during data collection. Another advantage is our theoretical finding can be used to infer the identified region in the multinomial choice model. A Monte Carlo study is conducted to illustrate the main finding in a finite sample. Finally, we apply GMMS estimator to a job satisfaction study using US data with the interval income.