The goal of this paper is to contrast and survey the major advances in two of the most
commonly used high-dimensional techniques, namely, the Lasso and horseshoe …

Time-varying parameter (TVP) models have the potential to be over-parameterized,
particularly when the number of variables in the model is large. Global-local priors are …

Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the
effect of explanatory variables on the outcome variable. However, in particular when the …

This article develops a Bayesian partitioning prior model from spanning trees of a graph, by
first assigning priors on spanning trees, and then the number and the positions of removed …

A comprehensive methodology for inference in vector autoregressions (VARs) using sign
and other structural restrictions is developed. The reduced-form VAR disturbances are …

We develop a fully Bayesian framework for function-on-scalars regression with many
predictors. The functional data response is modeled nonparametrically using unknown basis …

State–space models are commonly used in the engineering, economic, and statistical
literature. They are flexible and encompass many well-known statistical models, including …

We address the problem of dynamic variable selection in time series regression with
unknown residual variances, where the set of active predictors is allowed to evolve over …

This article addresses the issue of inference in time‐varying parameter regression models in
the presence of many predictors and develops a novel dynamic variable selection strategy …

We develop Bayesian neural networks (BNNs) that permit to model generic nonlinearities
and time variation for (possibly large sets of) macroeconomic and financial variables. From a …