Innovations in Bayesian Econometrics: Theory, Techniques, and Economic Analysis
A special issue of Econometrics (ISSN 2225-1146).
Deadline for manuscript submissions: 31 May 2025 | Viewed by 1144
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, ongoing innovations in Bayesian econometric theories and estimation techniques have demonstrated the substantial advantages of Bayesian econometrics over other methods. In this context, this Special Issue seeks to present a carefully curated selection of research articles that exemplify these advancements in a timely manner.
Both theoretical papers and applied works addressing important economic and financial issues are welcome. Areas of Bayesian analysis may include (but are not limited to) the following:
- Cluster analysis;
- Density forecasting;
- Distribution theory;
- Factor models;
- Global–local shrinkage;
- Graphical models;
- Machine learning;
- Mixed-frequency methods;
- Mixture models;
- Model averaging and selection;
- Multivariate analysis;
- Network models;
- Non- and semiparametric inferences;
- Optimization;
- Quantile regression;
- Spatial models;
- State-space models;
- Tensor analysis;
- Uncertainty quantification;
- Variable selection.
I look forward to receiving your original contributions.
Dr. Deborah Gefang
Guest Editor
Manuscript Submission Information
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Keywords
- non- and semiparametric inferences
- mixture models
- vector autoregressions
- quantile regression
- mixed-frequency methods
- state-space models
- forecasting
- parameter shrinkage and variable selection
- uncertainty
- machine learning
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