Dimitris Korobilis, Davide Pettenuzzo
arXiv 23 Apr 2020 · Statistics — Computation · 3 citations (OpenAlex)
arXiv:2004.11486 · PDF · DOI · OpenAlex · Extracted main text
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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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian Approaches to Shrinkage and Sparse Estimation large A guide for applied econometricians large | 0.405 | 1 | 1 |