This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced...

We study the out-of-sample forecasting performance of 32 exchange rates vis-a-vis the New Taiwan Dollar (NTD) in a 32-variable vector autoregression (VAR) model. The Bayesian approach is applied to the large-scale VAR model (LBVAR), and its (timevarying) forecasting performance is compared to...

Recently, many countries were hit by a series of macroeconomic shocks, most notably as a consequence of the COVID-19 pandemic and Russia's invasion in Ukraine, raising inflation rates to multi-decade highs and suspending well-documented macroeconomic relationships. To capture these tail events,...

The Basel credit-to-GDP gap is the single most popular measure of excessive credit growth and the financial cycle in general. It is based, however, on a purely statistical understanding of excessiveness: Growth is excessive if the credit-to-GDP ratio (i.e. the ratio of credit to nominal GDP) is...

Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modeling flexibility, as...

Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this...

We nowcast and forecast Austrian economic activity, namely real gross domestic product (GDP), consumption and investment, which are available at a quarterly frequency. While nowcasting uses data up to (and including) the quarter to be predicted, forecasting uses only data up to the previous...

Dynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman...