有序选择模型的随机森林估计
Random Forest Estimation of the Ordered Choice Model
摘要：
在本文中，我们开发了一个新的机器学习估计的有序选择模型的基础上随机森林。建议有序森林灵活地估计的条件选择概率，同时考虑到明确的排序信息。除了常见的机器学习估计器外，它还可以估计边际效应并进行推理，从而提供与经典计量经济学估计器相同的输出。一个广泛的模拟研究揭示了良好的预测性能，特别是在设置与非线性和近多重共线性。一个实证应用对比了边际效应及其标准误的估计与有序logit模型。有序森林的软件实现分别在CRAN和PyPI上可用的orf包中以R和Python提供。
英文摘要：
In this paper we develop a new machine learning estimator for ordered choice models based on the random forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with non-linearities and near-multicollinearity. An empirical application contrasts the estimation of marginal effects and their standard errors with an ordered logit model. A software implementation of the Ordered Forest is provided both in R and Python in the package orf available on CRAN and PyPI, respectively.
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