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 …

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

This paper discusses how the forecast accuracy of a Bayesian vector autoregression (BVAR) is affected by introducing …, estimated shrinkage, and no nonlinearity. Then I entertain alternative specifications of the zero lower bound: replace the … interest rate expectations to deal with the nonlinearity in the policy rate. Since the policy rate will remain low for some …

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 … evaluated in a small simulation study and found to perform competitively in applications to real world data. -- Bayesian model …

methodology of constructing Dynamic Stochastic General Equilibrium (DSGE) consistent prior distributions for Bayesian Vector … Chernozhukov and Hong (2003) and Theodoridis (2011) to derive the quasi Bayesian posterior distribution of the DSGE parameter …

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 …

parametric discrete time series models estimated with Bayesian methods. The subset of variables may vary across forecast horizons …