Assistant Professor, Faculty of Economics, Urmia University
Abstract
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
Heydari,H . (2012). An Evaluation of Alternative BVAR Models for Forecasting Iranian Inflation. Iranian Journal of Economic Research, 17(50), 65-81.
MLA
Heydari,H . "An Evaluation of Alternative BVAR Models for Forecasting Iranian Inflation", Iranian Journal of Economic Research, 17, 50, 2012, 65-81.
HARVARD
Heydari H. (2012). 'An Evaluation of Alternative BVAR Models for Forecasting Iranian Inflation', Iranian Journal of Economic Research, 17(50), pp. 65-81.
CHICAGO
H Heydari, "An Evaluation of Alternative BVAR Models for Forecasting Iranian Inflation," Iranian Journal of Economic Research, 17 50 (2012): 65-81,
VANCOUVER
Heydari H. An Evaluation of Alternative BVAR Models for Forecasting Iranian Inflation. Iranian Journal of Economic Research. 2012;17(50):65-81 (In Persian).
