Machine Learning Econometrics: Bayesian algorithms and methods

Dimitris Korobilis and Davide Pettenuzzo ()

No 130, Working Papers from Brandeis University, Department of Economics and International Business School

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 (search for similar items in EconPapers)
JEL-codes: C11 C15 C49 C88 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2020-04
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP130.pdf (application/pdf)

Related works:
Working Paper: Machine Learning Econometrics: Bayesian algorithms and methods (2020)
Working Paper: Machine Learning Econometrics: Bayesian algorithms and methods (2020)
Working Paper: Machine Learning Econometrics: Bayesian algorithms and methods (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:brd:wpaper:130

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