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

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 …

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 address the curse of dimensionality in dynamic covariance estimation by modeling the
underlying co-volatility dynamics of a time series vector through latent time-varying …

We develop a Bayesian vector autoregressive (VAR) model with multivariate stochastic
volatility that is capable of handling vast dimensional information sets. Three features are …

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

In this paper we investigate dynamics of inflation and short-run inflation expectations. We
estimate a global vector autoregressive (GVAR) model using Bayesian techniques. We then …

High dimensional vector autoregressive (VAR) models require a large number of
parameters to be estimated and may suffer of inferential problems. We propose a new …

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 …

Large Bayesian vector autoregressions with various forms of stochastic volatility have
become increasingly popular in empirical macroeconomics. One main difficulty for …