Nonparametric Analysis of Random Utility Models: Computational Tools for Statistical Testing
针对Kitamura和Stoye提出的随机效用模型非参数检验计算困难的问题，开发了列生成方法，大幅提升计算效率，使该检验更适用于实证研究。
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 paper, we develop a column generation approach to operationalize the test. These novel computational tools generate considerable computational gains in practice, which substantially increases the empirical usefulness of Kitamura and Stoye's statistical test.