论文标题
离散选择模型中的较弱识别
Weak Identification in Discrete Choice Models
论文作者
论文摘要
我们研究了弱识别在离散选择模型中的影响，并为这些模型中识别强度的决定因素提供了见解。使用这些见解，我们提出了一种新型测试，该测试可以在常用的离散选择模型（例如概率，logit及其许多扩展）中始终检测到弱识别。此外，我们证明，当拒绝弱识别的零假设时，可以使用标准公式和临界值进行基于WALD的推断。一项蒙特卡洛研究将我们提出的测试方法与常用弱识别测试进行了比较。结果同时证明了我们方法的良好性能以及在离散选择模型上下文中使用线性模型的常规弱识别测试的根本失败。此外，我们将我们的方法与文献中通常在两个经验例子中使用的方法进行了比较：已婚妇女劳动力参与以及美国的粮食援助和民间冲突。
We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo study compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of using conventional weak identification tests for linear models in the discrete choice model context. Furthermore, we compare our approach against those commonly applied in the literature in two empirical examples: married women labor force participation, and US food aid and civil conflicts.
