Volatility has been one of the most active and successful areas of research in time series
econometrics and economic forecasting in recent decades. This chapter provides a selective …

The literature on multivariate stochastic volatility (MSV) models has developed significantly
over the last few years. This paper reviews the substantial literature on specification …

This chapter provides an overview of and user's guide to dynamic factor models (DFMs),
their estimation, and their uses in empirical macroeconomics. It also surveys recent …

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

While there have been few theoretical contributions on the Markov Chain Monte Carlo
(MCMC) methods in the past decade, current understanding and application of MCMC to the …

For a VAR with drifting coefficients and stochastic volatilities, we present posterior densities
for several objects that are pertinent for designing and evaluating monetary policy. These …

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

We focus on sparse modelling of high-dimensional covariance matrices using Bayesian
latent factor models. We propose a multiplicative gamma process shrinkage prior on the …

High dimensionality comparable to sample size is common in many statistical problems. We
examine covariance matrix estimation in the asymptotic framework that the dimensionality p …