FORECASTING WITH GENERALIZED BAYESIAN VECTOR AUTOREGRESSIONS

The effects of using different distributions to parameterize the prior beliefs in a Bayesian analysis of vector autoregressions are studied. The well-known Minnesota prior of Litterman as well as four less restrictive distributions are considered. Two of these prior distributions are new to vector autoregressive models. When the forecasting performance of the different parameterizations of the prior beliefs are compared it is found that the prior distributions that allow for dependencies between the equations of the VAR give rise to better forecasts.