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 …

We develop Bayesian techniques for estimation and model comparison in a novel Generalised Stochastic Unit Root (GSTUR …

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

A general Bayesian Markov Chain Monte Carlo methodology is utilized for conducting an analysis of the intensity process …

-time capacity to forecast GDP and consumption. A Bayesian error correction approach augmented with the consumer sentiment index and …

Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have … paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds …

-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 …