Hierarchical shrinkage priors for dynamic regressions with many predictors

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Prior Selection for Vector Autoregressions (2015, unknown): “…growing literature on forecasting using factors extracted from large information sets (Forni et al., 2000; Stock & Watson, 2002b), large Bayesian VARs (Bańbura et al., 2010; Koop, 2013), and empirical Bayes regressions with large sets of predictors (Knox, Stock, & Watson, 2000; Korobilis, 2013).”
Sparse Bayesian time-varying covariance estimation in many dimensions (2016, methodology): “In particular, it is straightforward to combine it with mean models such as (sparse) vector autoregressions (e.g., Bańbura et al. 2010; Kastner and Huber 2017), dynamic regressions (e.g., Korobilis 2013), or time-varying parameter models (e.g., Koop and Korobilis 2013; Huber et al. 2017).”
Complete subset regressions (2013, methodology): “…to the individual predictors in a datadependent adaptive manner, the Elastic Net (Zou and Hastie, 2005; Zou and Zhang, 2009) which introduces extra parameters to control the penalty for inclusion of additional variables, and Bayesian methods such as adaptive Monte Carlo (Lamnisos et al., 2012).”