This paper investigates the use of different priors to improve the inflation forecasting performance of BVAR models with Litterman’s prior. A Quasi-Bayesian method, with several different priors, is applied to a VAR model of the Iranian economy from 1981:Q2 to 2007:Q1. A novel feature with this paper is the use of g-prior in the BVAR models to alleviate poor estimation of drift parameters of Traditional BVAR models. Some results are as follows: (1) our results show that in the Quasi-Bayesian framework, BVAR models with Normal-Wishart prior provides the most accurate forecasts of Iranian inflation; (2) The results also show that generally in the parsimonious models, the BVAR with g-prior performs better than BVAR with Litterman’s prior.
