Alternative bvar models for forecasting inflation

H. Heidari ()
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H. Heidari: Urmia University, Department of Economics, P.O.Box 165, Urmia, Iran

Acta Oeconomica, 2011, vol. 61, issue 1, 61-75

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 simulated data as well as to the Australian economy from 1978:Q2 to 2006:Q4. 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) In the Quasi-Bayesian framework, BVAR models with Normal-Wishart prior provide the most accurate forecasts of Australian inflation; (2) Generally in the parsimonious models, the BVAR with g-prior performs better than BVAR with Litterman’s prior; (3) In simulated data, the BVAR model with g-prior produces more accurate forecasts of driftless variable in the long-run horizons (first and second year forecast horizons).

Keywords: inflation forecasting; Bayesian VAR models; g-prior; Australia (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 E17 E31 (search for similar items in EconPapers)
Date: 2011
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