The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

Modeling and predicting extreme movements in GDP is notoriously difficult, and the
selection of appropriate covariates and/or possible forms of nonlinearities are key in …

We propose a mixed‑frequency regression prediction approach that models a time‑varying
trend, stochastic volatility and fat tails in the variable of interest. The coefficients of high …

Macroeconomists using large datasets often face the choice of working with either a large
vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate …

We propose a fast and flexible method to scale multivariate return volatility predictions up to
high-dimensions using a dynamic risk factor model. Our approach increases parsimony via …

We propose a novel variational Bayes approach to estimate high-dimensional Vector
Autoregressive (VAR) models with hierarchical shrinkage priors. Our approach does not rely …

This chapter describes Bayesian methods for macroeconomic forecasting using VARs. It
covers various priors which have been proposed which achieve the shrinkage and …

Uno degli aspetti piu discussi negli ultimi anni dalla ricerca in statistica bayesianae la stima
delle distribuzioni a posteriori nel contesto dell'analisi dei big data. I problemi principali che i …

Vector autoregressive (VAR) models have been widely used for modeling the temporal
dependence of multivariate time series. Globallocal priors are widely used to induce …