Machine Learning Econometrics: Bayesian algorithms and methods
Computation
2020-04-27 v1 Econometrics
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.
Cite
@article{arxiv.2004.11486,
title = {Machine Learning Econometrics: Bayesian algorithms and methods},
author = {Dimitris Korobilis and Davide Pettenuzzo},
journal= {arXiv preprint arXiv:2004.11486},
year = {2020}
}