This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal …-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered … – 2010 I exhaustively evaluate the forecasting properties of Bayesian shrinkage in regressions with many predictors. Results …

This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal …-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered … -- 2010 I exhaustively evaluate the forecasting properties of Bayesian shrinkage in regressions with many predictors. Results …

straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations …

This paper examines the properties of Bayes shrinkage estimators for dynamic regressions that are based on hierarchical … versions of the typical normal prior. Various popular penalized least squares estimators for shrinkage and selection in … for the period 1959–2010, I extensively evaluate the forecasting properties of Bayesian shrinkage in macroeconomic …

straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations …

There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large … develop priors for panel VARs (PVARs). The parametric and semi-parametric priors I suggest not only perform valuable shrinkage …

straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations …