摘要翻译：
动态离散选择模型通常对状态向量进行离散化和维数限制，以实现有效的推理。在不完全竞争动态模型中引入高维状态空间，提出了一种新的集辨识结构参数的两阶段估计器。在第一阶段，利用机器学习工具估计状态变量的运动规律和均衡策略函数。在第二阶段，我把第一阶段的估计插入一个矩不等式，求解结构参数。矩函数表示为两个分量之和，其中第一个分量表示平衡假设，第二个分量是一个偏差校正项，使之和对第一级偏差不敏感（即正交）。所提出的估计量一致收敛于根-n率，我用它来构造置信区域。本文的结果可用于将高维状态空间纳入经典的动态离散选择模型，例如Rust(1987)，Bajari等人所考虑的模型。（2007年）和斯科特（2013年）。
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英文标题：
《Machine Learning for Dynamic Discrete Choice》
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作者：
Vira Semenova
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Dynamic discrete choice models often discretize the state vector and restrict its dimension in order to achieve valid inference. I propose a novel two-stage estimator for the set-identified structural parameter that incorporates a high-dimensional state space into the dynamic model of imperfect competition. In the first stage, I estimate the state variable's law of motion and the equilibrium policy function using machine learning tools. In the second stage, I plug the first-stage estimates into a moment inequality and solve for the structural parameter. The moment function is presented as the sum of two components, where the first one expresses the equilibrium assumption and the second one is a bias correction term that makes the sum insensitive (i.e., orthogonal) to first-stage bias. The proposed estimator uniformly converges at the root-N rate and I use it to construct confidence regions. The results developed here can be used to incorporate high-dimensional state space into classic dynamic discrete choice models, for example, those considered in Rust (1987), Bajari et al. (2007), and Scott (2013).
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PDF链接：
https://arxiv.org/pdf/1808.02569