Machine Learning Econometrics: Bayesian Algorithms and Methods

33 Pages Posted: 14 May 2020

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Davide Pettenuzzo

Brandeis University - International Business School

Date Written: April 19, 2020

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

Suggested Citation

Korobilis, Dimitris and Pettenuzzo, Davide, Machine Learning Econometrics: Bayesian Algorithms and Methods (April 19, 2020). Available at SSRN: https://ssrn.com/abstract=3580433

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

Davide Pettenuzzo

Brandeis University - International Business School ( email )

Mailstop 32
Waltham, MA 02454-9110
United States

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