离散选择模型的广义间接推理
Generalized Indirect Inference for Discrete Choice Models
摘要：
本文开发并实现了一种实用的基于模拟的方法来估计动态离散选择模型。该方法建立在间接推理的思想基础上，能够适应滞后因变量、序列相关误差、未观测变量和多种备选方案。在离散选择模型中实现间接推理的主要困难在于目标曲面是阶梯函数，使得基于梯度的最优化方法无用。为了克服这一障碍，本文介绍了如何平滑的目标表面。其核心思想是使用潜在效用的平滑函数作为辅助模型中的因变量。当平滑参数趋于零时，该函数提供潜在效用所隐含的离散选择，从而保证一致性。我们建立的平滑条件，使我们的估计享有相同的限制分布的间接推断估计，而在同一时间，确保平滑有利于收敛的梯度为基础的最优化方法。一组Monte Carlo实验表明，该方法是快速的，鲁棒性，几乎一样有效的最大似然辅助模型是足够丰富的。
英文摘要：
This paper develops and implements a practical simulation-based method for estimating dynamic discrete choice models. The method, which can accommodate lagged dependent variables, serially correlated errors, unobserved variables, and many alternatives, builds on the ideas of indirect inference. The main difficulty in implementing indirect inference in discrete choice models is that the objective surface is a step function, rendering gradient-based optimization methods useless. To overcome this obstacle, this paper shows how to smooth the objective surface. The key idea is to use a smoothed function of the latent utilities as the dependent variable in the auxiliary model. As the smoothing parameter goes to zero, this function delivers the discrete choice implied by the latent utilities, thereby guaranteeing consistency. We establish conditions on the smoothing such that our estimator enjoys the same limiting distribution as the indirect inference estimator, while at the same time ensuring that the smoothing facilitates the convergence of gradient-based optimization methods. A set of Monte Carlo experiments shows that the method is fast, robust, and nearly as efficient as maximum likelihood when the auxiliary model is sufficiently rich.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
