Random Forest estimation of the ordered choice model - Michael Lechner; Gabriel Okasa (Empirical Economics, 2024)
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 nonlinearities and high correlation among covariates. 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.
Keywords: C14; C25; C40; 330; Ordered choice models; Random Forests; Probabilities; Marginal effects; Machine learning