论文标题
动态二进制选择面板数据模型的半参数估计
Semiparametric Estimation of Dynamic Binary Choice Panel Data Models
论文作者
论文摘要
我们提出了一种新方法，用于对具有固定效果和动力学（滞后因变量）的面板数据二进制选择模型的半参数分析。我们认为的模型具有与Honore和Kyriazidou（2000）相同的随机效用框架。我们证明，通过对确定性实用程序的额外依赖条件和对误差分布的尾巴限制的过程，该模型的（点）识别可以分为两个步骤进行，并且只需要与随时间的时间相匹配解释性变量的索引函数的值，就像每种解释性变量相对的相对。我们的识别方法激发了易于实现的两步最高分数（2SMS）过程 - 产生估计器，其收敛速率与Honore和Kyriazidou（2000）的方法相反，与模型维度无关。然后，我们得出2SMS程序的渐近特性，并提出基于自举的分布近似进行推理。蒙特卡洛证据表明，我们的程序在有限样品中进行了足够的性能。
We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model we consider has the same random utility framework as in Honore and Kyriazidou (2000). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions on the error distribution, the (point) identification of the model can proceed in two steps, and only requires matching the value of an index function of explanatory variables over time, as opposed to that of each explanatory variable. Our identification approach motivates an easily implementable, two-step maximum score (2SMS) procedure -- producing estimators whose rates of convergence, in contrast to Honore and Kyriazidou's (2000) methods, are independent of the model dimension. We then derive the asymptotic properties of the 2SMS procedure and propose bootstrap-based distributional approximations for inference. Monte Carlo evidence indicates that our procedure performs adequately in finite samples.
