Real-time forecasting of the Australian macroeconomy using flexible Bayesian VARs

Bo Zhang and Bao H. Nguyen

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

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.

Keywords: Australia; real-time forecast; Non-Gaussian; Stochastic Volatility (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 C55 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2020-10
New Economics Papers: this item is included in nep-ets, nep-for, nep-mac and nep-ore
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2020-91

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