Munich Personal RePEc Archive

Machine Learning Econometrics: Bayesian algorithms and methods

Korobilis, Dimitris and Pettenuzzo, Davide (2020): Machine Learning Econometrics: Bayesian algorithms and methods.

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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.

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