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 …

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 propose a novel class of dynamic shrinkage processes for Bayesian time series and
regression analysis. Building on a global–local framework of prior construction, in which …

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

Vector autoregressive (VAR) models assume linearity between the endogenous variables
and their lags. This assumption might be overly restrictive and could have a deleterious …

The paper develops a global vector auto‐regressive model with time varying parameters
and stochastic volatility to analyse whether international spillovers of US monetary policy …

Time-varying parameter VARs have become the workhorse models in empirical
macroeconomics. These models are usually equipped with tightly parametrized prior …

In this paper, we compare the transmission of a conventional monetary policy shock with that
of an unexpected decrease in the term spread, which mirrors quantitative easing. Employing …

This paper presents new empirical evidence concerning the time-varying responses of
China's macroeconomy to US economic uncertainty shocks through a novel TVP-VAR …

We introduce a loss discounting framework for model and forecast combination, which
generalises and combines Bayesian model synthesis and generalized Bayes …