states and model variables, which is sparse and banded in many economic applications and allows for efficient sampling. The … existing literature on precision-based sampling is focused on complete-data applications, whereas the proposed samplers in this … easily integrated into Bayesian estimation procedures like the Gibbs sampler. By allowing for incomplete data sets, the …

Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions …

In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models …

have different dimensions; b) provides a suitable estimation procedure for matrix autoregression with lag structure; c …) facilitates the introduction of Bayesian estimators. We propose maximum likelihood and Bayesian estimation with Independent …

Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling …

Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of … Bayesian estimation approach called the density-tempered sequential Monte Carlo method. Our findings indicate that the …

vector can be recovered from the BVAR posterior estimates: a new 'quasi-Bayesian' DSGE estimation. An empirical application …