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

Citation

Hou, C and Nguyen, B and Zhang, B, Real-time forecasting of the Australian macroeconomy using flexible Bayesian VARs, Journal of Forecasting pp. 1-34. ISSN 0277-6693 (2022) [Refereed Article]

Copyright Statement

Copyright 2022 John Wiley & Sons Ltd.

DOI: doi:10.1002/for.2913

Abstract

This paper evaluates the real-time forecast performance of alternative Bayesian autoregressive (AR) and vector autoregressive (VAR) models for the Australian macroeconomy. To this end, we construct an updated vintage database and compare the predictive ability of a wide set of specifications that takes into account almost all possible combinations of nonstandard errors existing in the current literature. In general, we find that the models with flexible covariance structures can improve the forecast accuracy as compared with the standard variant. For forecasting GDP, both point and density forecasts consistently suggest small VARs tend to outperform their counterparts while AR models often predict inflation better. With the unemployment rate, large VAR models provide superior forecasts to the alternatives at almost all forecast horizons. The forecasting performance of these models slightly changes when we consider the first, second, and latest-available vintage as actual values, highlighting the importance of using real-time data vintages in forecasting.

Item Details

Item Type:Refereed Article
Keywords:Australia, non-Gaussian, real-time forecast, stochastic volatility
Research Division:Economics
Research Group:Applied economics
Research Field:Macroeconomics (incl. monetary and fiscal theory)
Objective Division:Economic Framework
Objective Group:Macroeconomics
Objective Field:Economic growth
UTAS Author:Nguyen, B (Dr Bao Nguyen)
ID Code:153898
Year Published:2022
Web of Science® Times Cited:11
Deposited By:Economics
Deposited On:2022-10-14
Last Modified:2023-01-10
Downloads:0

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