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 flexibility 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.
Highlights
Mixed frequency vector autoregressions (MF-VARs) have enjoyed great popularity in recent years as a tool for producing timely high frequency nowcasts of low frequency variables
MF-VARs have been a standard tool for producing timely, high frequency nowcasts of low frequency variables for several years
Conventional linear MF-VARs nowcast poorly during the pandemic due to their inability to effectively deal with the extreme observations that have occurred
Summary
Mixed frequency vector autoregressions (MF-VARs) have enjoyed great popularity in recent years as a tool for producing timely high frequency nowcasts of low frequency variables. The fact that statistical agencies release data such as GDP with a delay, whereas appropriately chosen monthly variables are released with less of a delay further enhances the benefits of the MF-VAR. The pandemic lockdown of 2020 has further increased the need for timely, high frequency nowcasts of economic activity. The increasing availability of a variety of high frequency (i.e., monthly, weekly or daily) and quickly released data (i.e., some variables are released almost instantaneously) presents rich opportunities for the mixed frequency modeler. The pandemic poses challenges to the conventional, linear, MF-VAR. Linear time series econometric methods seek to find average patterns in past data. If current data is very different, using such patterns and linearly extrapolating them may be highly questionable
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
