In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the...

We show that under standard assumptions the elements of the impact matrix of the structural vector autoregression (SVAR) are always at least set identified and bounded by the standard deviations of the corresponding reduced form errors. This result facilitates valid Bayesian inference without...