An Evaluation of Alternative BVAR Models for Forecasting Iranian Inflation

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.