# Column Generation Algorithms for Nonparametric Analysis of Random Utility Models

## TL;DR

This paper introduces more efficient column generation algorithms to perform nonparametric tests of Random Utility Models, enabling analysis of larger datasets by overcoming computational challenges.

## Contribution

The paper develops and implements novel column generation algorithms that significantly improve the computational efficiency of nonparametric tests for Random Utility Models.

## Key findings

- Algorithms enable analysis of larger datasets.
- Improved computational efficiency over previous methods.
- Facilitates practical application of nonparametric RUM testing.

## Abstract

Kitamura and Stoye (2014) develop a nonparametric test for linear inequality constraints, when these are are represented as vertices of a polyhedron instead of its faces. They implement this test for an application to nonparametric tests of Random Utility Models. As they note in their paper, testing such models is computationally challenging. In this paper, we develop and implement more efficient algorithms, based on column generation, to carry out the test. These improved algorithms allow us to tackle larger datasets.