论文标题
离散选择模型的估计：机器学习方法
Estimation of Discrete Choice Models: A Machine Learning Approach
论文作者
论文摘要
在本文中，我们提出了一种新的估算方法，即当可用单个级别数据时，对离散选择需求模型进行了估算。该方法采用两步过程。步骤1将选择概率视为观察到的个体水平特征的功能。步骤2使用特定关注点和力矩限制的估计选择概率估算模型的结构参数。从本质上讲，该方法使用非参数近似（其次是矩估计）。因此名称---名称。我们使用仿真将名称的性能与标准方法进行比较。我们发现我们的方法提高了精度和收敛时间。我们通过提供所提出的估计量的大样本特性来补充分析。
In this paper we propose a new method of estimation for discrete choice demand models when individual level data are available. The method employs a two-step procedure. Step 1 predicts the choice probabilities as functions of the observed individual level characteristics. Step 2 estimates the structural parameters of the model using the estimated choice probabilities at a particular point of interest and the moment restrictions. In essence, the method uses nonparametric approximation (followed by) moment estimation. Hence the name---NAME. We use simulations to compare the performance of NAME with the standard methodology. We find that our method improves precision as well as convergence time. We supplement the analysis by providing the large sample properties of the proposed estimator.
