This paper examines frequentist risks of Bayesian estimates of VAR regression coefficient and error covariance matrices under competing loss functions, under a variety of non-informative priors, and in the normal and Student-t models. Simulation results show that for the regression coefficient matrix an asymmetric LINEX estimator does better overall than the posterior mean. For the error covari...

The main purpose of  this paper is to make the connexion between stochastic analysis, Bayesian Statistics, and time series analysis for policy analysis. This approach solves problem of mathematical modelling - presence uncertainties in models parameters -  that reduces the  forecasting   effectiveness. By using multiple It\^o  integral, mult...

The purpose of this study is to test the Brenner hypothesis using a Bayesian vector autoregressive approach, to compute impulse response function and to conclude if evidence of the hypothesis exist in Canadian data.

This paper analyzes whether a wealth of information contained in 126 monthly series used by large-scale Bayesian Vector Autoregressive (LBVAR) models, as well as Factor Augmented Vector Autoregressive (FAVAR) models, either Bayesian or classical, can prove to be more useful in forecasting real house price growth rate of the nine census divisions of the US, compared to the small-scale VAR models...