# Random Forest Estimation of the Ordered Choice Model

## TL;DR

This paper introduces a new random forest-based estimator for ordered choice models that accurately estimates conditional probabilities, marginal effects, and allows for inference, outperforming traditional models especially with non-linearities.

## Contribution

The paper develops the Ordered Forest, a flexible machine learning estimator for ordered choice models that incorporates ordering information and provides inference capabilities.

## Key findings

- Good predictive performance in simulations with non-linearities
- Effective estimation of marginal effects and standard errors
- Comparable or superior to traditional ordered logit models

## Abstract

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.