Dimitris Korobilis,Kenichi Shimizu
Bayesian Approaches to Shrinkage and Sparse Estimation
Bayesian Approaches to Shrinkage and Sparse Estimation
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Bayesian Approaches to Shrinkage and Sparse Estimation provides an introduction to Bayesian model determination, surveying modern shrinkage and variable selection algorithms and methodologies. It discusses exact and approximate inference, and how priors developed for simple regression can be extended to various econometric models. A MATLAB package and technical manual are available for replication.
Format: Paperback / softback
Length: 136 pages
Publication date: 29 June 2022
Publisher: now publishers Inc
Bayesian Approaches to Shrinkage and Sparse Estimation is a comprehensive guide that delves into the realm of Bayesian model determination, exploring modern shrinkage and variable selection algorithms and methodologies. This approach is a natural probabilistic framework for quantifying uncertainty and learning about model parameters, particularly valuable in inference for modern models characterized by high dimensions and increased complexity.
The authors begin by introducing various classes of priors that lead to shrinkage/sparse estimators of comparable value to popular penalized likelihood estimators (e.g., ridge, LASSO). They examine various methods of exact and approximate inference, discussing their strengths and limitations. Furthermore, they explore how priors developed for simple regression settings can be extended to various classes of interesting econometric models.
To demonstrate the practical applications of Bayesian shrinkage and variable selection strategies, the book includes case studies covering a range of popular econometric contexts. These case studies include vector autoregressive models, factor models, time-varying parameter regressions, confounder selection in treatment effects models, and quantile regression models. A MATLAB package and an accompanying technical manual are provided to allow readers to replicate many of the algorithms described in this review.
By employing Bayesian Approaches to Shrinkage and Sparse Estimation, researchers and practitioners can gain a deeper understanding of the underlying principles of Bayesian inference and apply them to address complex econometric problems with greater efficiency and accuracy.
Weight: 202g
Dimension: 234 x 156 (mm)
ISBN-13: 9781638280347
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