Decoupling Shrinkage and Selection for the Bayesian Quantile Regression
This incremental work improves interpretability for policymakers by identifying variables driving downside economic risk, using a domain-specific approach.
The paper tackles the problem of variable selection in Bayesian Quantile Regression by decoupling shrinkage and sparsity, showing in simulations that their method reduces bias regardless of data sparsity and retains prediction accuracy in a high-dimensional growth-at-risk application.
This paper extends the idea of decoupling shrinkage and sparsity for continuous priors to Bayesian Quantile Regression (BQR). The procedure follows two steps: In the first step, we shrink the quantile regression posterior through state of the art continuous priors and in the second step, we sparsify the posterior through an efficient variant of the adaptive lasso, the signal adaptive variable selection (SAVS) algorithm. We propose a new variant of the SAVS which automates the choice of penalisation through quantile specific loss-functions that are valid in high dimensions. We show in large scale simulations that our selection procedure decreases bias irrespective of the true underlying degree of sparsity in the data, compared to the un-sparsified regression posterior. We apply our two-step approach to a high dimensional growth-at-risk (GaR) exercise. The prediction accuracy of the un-sparsified posterior is retained while yielding interpretable quantile specific variable selection results. Our procedure can be used to communicate to policymakers which variables drive downside risk to the macro economy.