An Evaluation of Alternative BVAR Models for Forecasting Iranian Inflation

ارزيابي مدلهاي BVAR جايگزين در پيش بيني تورم ايران

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.