Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs

08/28/2020
by   Florian Huber, et al.
27

This paper develops Bayesian econometric methods for posterior and predictive inference in a non-parametric mixed frequency VAR using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of the extreme observations produced by the pandemic due to their flexibility and ability to model outliers. In a nowcasting application involving four major countries in the European Union, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR. A detailed examination of the predictive densities in the first six months of 2020 shows where these improvements are achieved.

READ FULL TEXT
research
08/17/2021

Semi-parametric Bayesian Additive Regression Trees

We propose a new semi-parametric model based on Bayesian Additive Regres...
research
10/07/2021

Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model

We develop a Bayesian non-parametric quantile panel regression model. Wi...
research
11/13/2020

Estimating the Copula of a class of Time-Changed Brownian Motions: A non-parametric Approach

Within a high-frequency framework, we propose a non-parametric approach ...
research
04/14/2022

Hierarchical Embedded Bayesian Additive Regression Trees

We propose a simple yet powerful extension of Bayesian Additive Regressi...
research
10/04/2018

Accelerated Bayesian Additive Regression Trees

Although less widely known than random forests or boosted regression tre...
research
05/24/2018

Model-based inference of conditional extreme value distributions with hydrological applications

Multivariate extreme value models are used to estimate joint risk in a n...
research
11/15/2012

Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-d...

Please sign up or login with your details

Forgot password? Click here to reset