摘要翻译：
在计量经济学中，所谓的有序选择模型是流行的，当兴趣在于估计具有内在有序性的类别结果变量的特定值的概率时，以协变量为条件。本文提出了一种新的基于随机森林算法的机器学习估计器。所提出的有序森林估计器提供了一种灵活的条件选择概率估计方法，可以自然地处理数据中的非线性，同时显式地考虑了有序信息。除了一般的机器学习估计器之外，它还能估计边际效应以及对边际效应进行推断，从而提供与基于有序logit或probit模型的经典计量经济学估计器相同的输出。大量的仿真研究检验了有序森林的有限样本性质，并揭示了其良好的预测性能，特别是在预测因子之间的多重共线性和非线性泛函形式的情况下。实证应用进一步说明了边际效应及其标准差的估计，并证明了与参数基准模型相比，灵活估计的优势。该有序森林的软件实现是以高斯以及在CRAN上可用的R-package orf中提供的。
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英文标题：
《Random Forest Estimation of the Ordered Choice Model》
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作者：
Michael Lechner and Gabriel Okasa
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最新提交年份：
2020
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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：
  In econometrics so-called ordered choice models are popular when interest is in the estimation of the probabilities of particular values of categorical outcome variables with an inherent ordering, conditional on covariates. In this paper we develop a new machine learning estimator based on the random forest algorithm for such models without imposing any distributional assumptions. The proposed Ordered Forest estimator provides a flexible estimation method of the conditional choice probabilities that can naturally deal with nonlinearities in the data, 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 thereof and thus providing the same output as classical econometric estimators based on ordered logit or probit models. An extensive simulation study examines the finite sample properties of the Ordered Forest and reveals its good predictive performance, particularly in settings with multicollinearity among the predictors and nonlinear functional forms. An empirical application further illustrates the estimation of the marginal effects and their standard errors and demonstrates the advantages of the flexible estimation compared to a parametric benchmark model. A software implementation of the Ordered Forest is provided in GAUSS as well as in the R-package orf available on CRAN.
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PDF链接：
https://arxiv.org/pdf/1907.02436