论文标题
在动态离散选择模型中连续永久性未观察的异质性
Continuous permanent unobserved heterogeneity in dynamic discrete choice models
论文作者
论文摘要
在动态离散选择（DDC）分析中，通常使用混合模型来控制未观察到的异质性。但是，一致的估计通常需要对未观察到的异质性的支持以及难以验证的高级注射率条件的限制。本文为具有多元连续永久性未观察的异质性的广泛的DDC模型的点鉴定提供了原始条件。结果适用于有限的和无限的摩托车DDC模型，不需要完全支持假设，也不需要长的面板，并且对未观察到的异质性的分布没有任何参数限制。此外，我提出了一个具有计算上有吸引力的seminonparametric估计器，可以使用熟悉的参数方法实现。
In dynamic discrete choice (DDC) analysis, it is common to use mixture models to control for unobserved heterogeneity. However, consistent estimation typically requires both restrictions on the support of unobserved heterogeneity and a high-level injectivity condition that is difficult to verify. This paper provides primitive conditions for point identification of a broad class of DDC models with multivariate continuous permanent unobserved heterogeneity. The results apply to both finite- and infinite-horizon DDC models, do not require a full support assumption, nor a long panel, and place no parametric restriction on the distribution of unobserved heterogeneity. In addition, I propose a seminonparametric estimator that is computationally attractive and can be implemented using familiar parametric methods.
