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 …

State space models have gained tremendous popularity in recent years in as disparate
fields as engineering, economics, genetics and ecology. After a detailed introduction to …

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 …

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …

This paper revisits the topic of forecasting aggregate stock market volatility using financial
and macroeconomic predictors in a comprehensive Bayesian model averaging framework …

The R package spikeSlabGAM implements Bayesian variable selection, model choice, and
regularized estimation in (geo-) additive mixed models for Gaussian, binomial, and Poisson …

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 briefly review the main developments of Bayesian dynamic models. The emphasis is on
marketing applications. Typical examples in this area are discussed. The concepts of …

The Gaussian assumption generally employed in many state‐space models is usually not
satisfied for real time series. Thus, in this work, a broad family of non‐Gaussian models is …