Nonparametric Analysis of Random Utility Models: Computational Tools for Statistical Testing

Kitamura and Stoye (2018) recently proposed a nonparametric statistical test for random utility models of consumer behavior. The test is formulated in terms of linear inequality constraints and a quadratic objective function. While the nonparametric test is conceptually appealing, its practical implementation is computationally challenging. In this note, we develop a column generation approach to operationalize the test. We show that these novel computational tools generate considerable computational gains in practice, which substantially increases the empirical usefulness of Kitamura and Stoye’s statistical test.