Bayesian Approaches to Shrinkage and Sparse Estimation

Dimitris Korobilis and Kenichi Shimizu

MPRA Paper from University Library of Munich, Germany

Abstract: In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where high-dimensional and nonlinear/noparametric inference is the norm in several fields of applied econometric work. The purpose of this paper is to introduce the reader to the realm of Bayesian model determination, by surveying modern shrinkage and variable selection algorithms and methodologies. Bayesian inference is a natural probabilistic framework for quantifying uncertainty and learning about model parameters, and this feature is particularly important for inference in modern models of high dimensions and increased complexity. We begin with a linear regression setting in order to introduce various classes of priors that lead to shrinkage/sparse estimators of comparable value to popular penalized likelihood estimators (e.g. ridge, lasso). We explore various methods of exact and approximate inference, and discuss their pros and cons. Finally, we explore how priors developed for the simple regression setting can be extended in a straightforward way to various classes of interesting econometric models. In particular, the following case-studies are considered, that demonstrate application of Bayesian shrinkage and variable selection strategies to popular econometric contexts: i) vector autoregressive models; ii) factor models; iii) time-varying parameter regressions; iv) confounder selection in treatment effects models; and v) quantile regression models. A MATLAB package and an accompanying technical manual allow the reader to replicate many of the algorithms described in this review.

Keywords: Bayesian inference; sparsity; shrinkage; hierarchical priors; computation (search for similar items in EconPapers)
JEL-codes: C11 C12 C13 C15 C20 C30 C45 C46 C51 C52 C53 C55 C58 C61 C63 C88 (search for similar items in EconPapers)
Date: 2021-12-03
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Bayesian Approaches to Shrinkage and Sparse Estimation (2022)
Working Paper: Bayesian Approaches to Shrinkage and Sparse Estimation (2022)
Working Paper: Bayesian Approaches to Shrinkage and Sparse Estimation (2021)
Working Paper: Bayesian Approaches to Shrinkage and Sparse Estimation (2021)
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