Researchers have found that machine learning methods are typically better at prediction than econometric models when the choice environment is stable. We study hospital demand models, and evaluate the relative performance of machine learning algorithms when the choice environment changes substantially due to natural disasters that closed previously available hospitals. While machine learning algorithms outperform traditional econometric models in prediction, the gain they provide shrinks when patients’ choice sets are more profoundly affected. We show that traditional econometric methods provide important additional information when there are major changes in the choice environment.

Researchers have found that machine learning methods are typically better at prediction than econometric models when the choice environment is stable. 我们研究医院需求模型，并在选择环境因自然灾害而导致先前可用医院关闭而发生重大变化时评估机器学习算法的相对性能。虽然机器学习算法在预测方面优于传统的计量经济学模型，但当患者的选择集受到更​​深刻的影响时，它们提供的收益就会缩小。We show that traditional econometric methods provide important additional information when there are major changes in the choice environment.