We contrast machine learning (ML) and structural econometrics (SE), focusing on areas where ML can advance the goals of SE. Our views have been informed and inspired by the contributions to this special issue and by papers presented at the second conference on dynamic structural econometrics at the University of Copenhagen in 2018, ‘Methodology and Applications of Structural Dynamic Models and Machine Learning'. ML offers a promising class of techniques that can significantly extend the set of questions we can analyse in SE. The scope, relevance and impact of empirical work in SE can be improved by following the lead of ML in questioning and relaxing the assumption of unbounded rationality. For the foreseeable future, however, ML is unlikely to replace the essential role of human creativity and knowledge in model building and inference, particularly with respect to the key goal of SE, counterfactual prediction.

中文翻译：

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机器学习与结构计量经济学：对比与协同作用

我们对比机器学习（ML）和结构计量经济学（SE），重点是ML可以促进SE的目标的领域。对此问题的贡献以及2018年在哥本哈根大学举行的第二届动态结构计量经济学会议上发表的论文``结构动力学模型和机器学习的方法和应用''，使我们的观点得到启发和启发。ML提供了一类很有前途的技术，可以显着扩展我们可以在SE中分析的问题集。遵循ML的质疑和放宽对无限理性的假设，可以改善SE中经验工作的范围，相关性和影响。但是，在可预见的未来，机器学习不太可能取代人类创造力和知识在模型构建和推理中的重要作用，