This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the … particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors … examine their forecast performance using a US macroeconomic data set containing 168 variables. We ?nd that Bayesian VARs do …

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the … particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors … examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do …

field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly …

The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a … observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations …

variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable … problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the …

, static and dynamic prediction pools, as well as Bayesian and dynamic model averaging. Since real-time data involves one …

variables to include in the model in addition to the forecast variables. The key di erence from traditional Bayesian variable … problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the …

variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable … problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the …

The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a … observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations …