-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in …

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 …

present methods for Bayesian inference. An application involving US in?ation forecasting illustrates and compares the …

perception of a constrained multivariate normal distribution is crucial to our Gibbs sampler. Furthermore, the Markov property of … imputation, regardless of information loss or noises gain. We study a VAR model with varied frequency data in a Bayesian context …

generic (linear and nonlinear) VARs. The performance of the proposed variable selection method is assessed in a small Monte …This paper develops methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs … Carlo experiment, and in forecasting 4 macroeconomic series of the UK using time-varying parameters vector autoregressions …

development of adjacent age groups is assured allowing for consistent forecasts. We develop an appropriate Markov Chain Monte … Carlo algorithm to estimate the parameters and the latent variables in an efficient one-step procedure. Via the Bayesian …

development of adjacent age groups is assured allowing for consistent forecasts. We develop an appropriate Markov Chain Monte … Carlo algorithm to estimate the parameters and the latent variables in an efficient one-step procedure. Via the Bayesian …