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Abstract

This paper evaluates the real-time forecast performance of alternative Bayesian Vector Autoregressive (VAR) models for the Australian macroeconomy. To this end, we construct an updated vintage database and estimate a set of model specifications with different covariance structures. The results suggest that a large VAR model
with 20 variables tends to outperform a small VAR model when forecasting GDP growth, CPI inflation and unemployment rate. We find consistent evidence that the models with more flexible error covariance structures forecast GDP growth and inflation better than the standard VAR, while the standard VAR does better than
its counterparts for unemployment rate. The results are robust under alternative priors and when the data includes the early stage of the COVID-19 crisis.

Item Type:	Report (Discussion Paper)
Authors/Creators:	Zhang, B and Nguyen, BH
Keywords:	Australia, real-time forecast, non-Gaussian, stochastic volatility
Publisher:	University of Tasmania
Copyright Information:	Copyright 2020 University of Tasmania
Additional Information:	Discussion Paper Series N 2020-12 JEL Classification Numbers: C11, C32, C53, C55
Item Statistics:	View statistics for this item

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