We propose a Bayesian VAR model with stochastic volatility and time varying skewness to estimate the degree of labour at risk in the euro area and in the United States. We model the asymmetry of the shocks to changes in the unemployment rate as a function of real activity and financial risk...

This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a...

Purpose: The purpose of the paper was to estimate the interdependence between selected macroeconomic variables and non-performing loans in Ghana using a Bayesian Vector autoregressive approach. Design/methodology/approach: This paper used annual series from 2008-2017 which was interpolated into...

Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a high dimensional setting or when applied to large panel of time series, these models require a large number of parameters to be estimated and suffer of inferential problems.To...

We develop novel multivariate time series models using Bayesian additive regression trees that 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...

This paper proposes a Bayesian regression model with time-varying coefficients (TVC) that makes it possible to estimate jointly the degree of instability and the time-path of regression coefficients. Thanks to its computational tractability, the model proves suitable to perform the first (to our...

The number of short-time workers from January to April 2020 is used to now- and forecast quarterly GDP growth. We purge the monthly log level series from the systematic component to extract unexpected changes or shocks to log short-time workers. These monthly shocks are included in a univariate...

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...

The number of employees historically filed and registered from January to April 2020 for short-time compensation is used to obtain a nowcast for GDP growth in the first quarter and an outlook until the third quarter 2021. We purge the monthly log level series from the systematic component to...

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...