Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models
离散选择模型异质参数估计中的深度学习AI 解读 ↓
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离散选择模型异质参数估计中的深度学习
- 研究问题
- 在离散选择模型中利用深度学习估计异质参数时，正则化与重复样本分割如何影响有限样本估计和推断表现？
- 方法与思路
- 作者沿用结合经济模型结构与深度学习的估计方法，通过蒙特卡洛实验比较常规稳健标准误、影响函数及稳定化方案。
- 材料陈述的贡献
- 摘要称正则化虽能稳定估计，却可能因额外偏差导致无效推断；重复样本分割在其模拟中稳定估计且未增加偏差。
- 阅读关注点
- 应核对离散选择结构、异质参数与目标平均量的定义，以及影响函数、正则化强度和重复分割的具体实现方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；结论来自所述模拟，摘要未说明其能否推广至其他模型与样本。
原文摘要
This paper studies the finite sample performance of the flexible estimation approach of Farrell, Liang, and Misra (2021a), who propose to use deep learning for the estimation of heterogeneous parameters in economic models, in the context of discrete choice models. The approach combines the structure imposed by economic models with the flexibility of deep learning, which assures the interpretebility of results on the one hand, and allows estimating flexible functional forms of observed heterogeneity on the other hand. For inference after the estimation with deep learning, Farrell et al. (2021a) derive an influence function that can be applied to many quantities of interest. We conduct a series of Monte Carlo experiments that investigate the impact of regularization on the proposed estimation and inference procedure in the context of discrete choice models. The results show that the deep learning approach generally leads to precise estimates of the true average parameters and that regular robust standard errors lead to invalid inference results, showing the need for the influence function approach for inference. Without regularization, the influence function approach can lead to substantial bias and large estimated standard errors caused by extreme outliers. Regularization reduces this property and stabilizes the estimation procedure, but at the expense of inducing an additional bias. The bias in combination with decreasing variance associated with increasing regularization leads to the construction of invalid inferential statements in our experiments. Repeated sample splitting, unlike regularization, stabilizes the estimation approach without introducing an additional bias, thereby allowing for the construction of valid inferential statements.