This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using …

approach using a Bayesian MS-VAR which is net of these arbitrary components. This method allows for the consistent … method on artificial as well a real data and conduct an empirical backtest, in which generated scenarios are compared to the … actual development during the financial crisis. The method is challenged with the DSGE model and conditional forecasting. …

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using …

. The model is estimated with sequential Monte Carlo methods that include a particle learning filter and a Rao …

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 …

used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian … using the Bayesian or frequentist approach. We show that mitigating model uncertainty by combining forecasts leads to … substantial gains in forecasting performance, especially when applying Bayesian model averaging. …

for richer forecasting specifications, the paper shows, using Bayesian model averaging techniques (BMA), that the …

-time forecasts from a richly-specified DSGE model to those from the Survey of Professional Forecasters, Bayesian VARs and VARs using …

In this paper we review the methodology of forecasting with log-linearised DSGE models using Bayesian methods. We focus … nonstructural benchmarks, such as Bayesian vector autoregressions (BVARs). Overall, the empirical evidence indicates that the NAWM … multivariate point and density forecasts using the log determinant and the log predictive score, respectively, suggest that a large …