The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …

Dynamic model averaging (DMA) has become a widely used estimation technique in
macroeconomic applications. Since its introduction in econom (etr) ics by Gary Koop and …

We model permanent and transitory changes of the predictive density of US GDP growth. A
substantial increase in downside risk to US economic growth emerges over the last 30 …

We develop multivariate time‐series models using Bayesian additive regression trees that
posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged …

A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector
autoregressions (BVARs) has recently been proposed. We question whether three such …

We propose smooth monotone concave probabilistic regression trees for the estimation of
efficiency and productivity. In particular we modify these techniques to allow for the use of …

We run a 'horse race'among popular forecasting methods, including machine learning (ML)
and deep learning (DL) methods, that are employed to forecast US GDP growth. Given the …

Vector autoregression (VAR) models are widely used for multivariate time series analysis in
macroeconomics, finance, and related fields. Bayesian methods are often employed to deal …

In this paper, we assess whether using non-linear dimension reduction techniques pays off
for forecasting inflation in real-time. Several recent methods from the machine learning …