Abstract
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 by the COVID-19
pandemic of 2020. This is due to their exibility and ability to model outliers.
In an application involving four major euro area countries, we find substantial
improvements in nowcasting performance relative to a linear mixed frequency VAR.
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 by the COVID-19
pandemic of 2020. This is due to their exibility and ability to model outliers.
In an application involving four major euro area countries, we find substantial
improvements in nowcasting performance relative to a linear mixed frequency VAR.
| Original language | English |
|---|---|
| Number of pages | 39 |
| Journal | Journal of Econometrics |
| Publication status | Accepted/In press - 28 Nov 2020 |
Keywords
- regression tree models
- Bayesian
- macroeconomic forecasting
- vector autoregression
