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 highdimensional 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.
Item Type:  MPRA Paper 

Original Title:  Machine Learning Econometrics: Bayesian algorithms and methods 
Language:  English 
Keywords:  MCMC; approximate inference; scalability; parallel computation 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C49  Other C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology ; Computer Programs > C88  Other Computer Software 
Item ID:  100165 
Depositing User:  Dimitris Korobilis 
Date Deposited:  06 May 2020 14:14 
Last Modified:  06 May 2020 14:14 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/100165 