Thursday 17 June 2021, 5.00pm – 6.30pm * Via  Zoom , Gary Koop, University of Strathclyde

* Please note change of time as Seminar Speaker is Zooming in from Scotland

Meeting ID : 853 8136 5283           Password : 488318 

Co-authors:

• Todd E. Clark (Federal Reserve Bank of Cleveland)
• Florian Huber (University of Salzburg)
• Massimiliano Marcellino (Bocconi University, IGIER and CEPR)
• Michael Pfarrhofer (University of Salzburg)

Abstract

We develop novel multivariate time series models using Bayesian additive regression trees which posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a novel non-parametric specification. We develop scalable Markov Chain Monte Carlo estimation algorithms for each specification. We evaluate the real-time density and tail forecasting performance of the various models for a set of US macroeconomic and financial indicators. Our results suggest that using non-parametric models generally leads to improved forecast accuracy. Especially when interest centers on the tails of the posterior predictive, exible models improve upon standard VAR models with SV. Another key finding is that if we allow for non-linearities in the conditional mean, allowing for heteroscedasticity becomes less important.

For further information contact:

Ye Lu | ph: 9351 4429 |email: ye.lu1@sydney.edu.au

Alastair Fraser | ph: 9351 5689 |email: alastair.fraser@sydney.edu.au

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