This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we specify a new dynamic variable/model selection strategy for TVP dynamic regression models in the presence of a large...

This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible...

We apply Bayesian methods to study a common VAR-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy which involves...

We examine the pricing of initial public offering (IPO) and seasoned equity offering (SEO) firms using a stochastic frontier methodology. The stochastic frontier framework models the difference between the maximum possible value of the firm and its actual market capitalization at the time of the...

Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible parametric model that accommodates virtually any of these specifications - and does so in a simple way that allows for straightforward Bayesian...

This handbook is aimed at researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest...