论文标题
机器学习计量经济学：贝叶斯算法和方法
Machine Learning Econometrics: Bayesian algorithms and methods
论文作者
论文摘要
随着全球产生的经济和其他数据的数量大大增加，对后代的计量经济学家将面临挑战，是针对具有大量信息集的经验模型来推断有效的算法。本章对计量经济学的贝叶斯推论的流行估计算法进行了综述，并调查了机器学习和计算科学中开发的替代算法，这些算法允许在高维环境中有效计算。重点是每种算法的可伸缩性和并行性，以及它们在经济学和金融经验的各种经验环境中采用的能力。
As the amount of economic and other data generated worldwide increases vastly, a challenge for future generations of econometricians will be to master efficient algorithms for inference in empirical models with large information sets. This Chapter provides a review of popular estimation algorithms for Bayesian inference in econometrics and surveys alternative algorithms developed in machine learning and computing science that allow for efficient computation in high-dimensional settings. The focus is on scalability and parallelizability of each algorithm, as well as their ability to be adopted in various empirical settings in economics and finance.
