Machine Learning Econometrics: Bayesian Algorithms and Methods

Abstract

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.

Keywords: MCMC, Approximate Inference, Scalability, Parallel Computation

JEL Classification: C11, C15, C49, C88