Roberto Casarin

TL;DR: In this article, a Bayesian graphical VAR (BGVAR) model is proposed to identify the causal structures of the structural VAR model, which is shown to be quite effective in dealing with model identification and selection in multivariate time series of moderate dimension.

TL;DR: In this paper, a Bayesian graphical VAR (BGVAR) model is proposed to identify the causal structures of the structural VAR model, where the contemporaneous and temporal causal structures are represented by two different graphs.

TL;DR: The authors proposed a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights and several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive density.

TL;DR: In this article, a Bayesian combination approach for multivariate predictive densities is proposed, which relies upon a distributional state space representation of the combination weights, with a particular focus on weight dynamics driven by the past performance of the predictive density and the use of learning mechanisms.

TL;DR: In this article, the authors compare three regularized particle filters in an online data processing context, considering a Bayesian paradigm and a univariate stochastic volatility model, and show that the regularized auxiliary particle filter (R-APF) outperforms the Regularized Sequential Importance Sampling (SIS), Regularized Sampling Importance Resampling(R-SIR), and Regularized SIS.