Working papers


This paper introduces a novel "theory coherent'' shrinkage prior for time varying parameters VARs. The proposed prior can be used to sharpen inference about the time varying parameters by leveraging on prior information from an underlying economic theory about the macroeconomic variables in the model. The paper reveals that exploiting prior information from conventional economic theory to form a prior for the time varying parameters significantly improves inference precision and forecast accuracy over the standard TVP-VAR. More specifically, using the classical 3-equation New Keynesian block to form a prior for the TVP-VAR substantially enhances forecast accuracy of output growth and  of the inflation rate in a standard model of monetary policy. Additionally, prior information from economic theory can be used to address the inferential challenges faced by the standard TVP-VAR during the zero lower bound period. 


In this paper 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 factors. We find that the conditional distribution of the changes in the unemployment rate displays time-varying volatility and skewness, with peaks coinciding with the Global Financial Crisis and the COVID-19 pandemic. We take advantage of the multivariate nature of our parametric model to measure stagflation risk defined as the possible joint event of large increases in the unemployment rate and large annual rates of inflation. We find an increasing risk of stagflation for the euro area in 2022 while in the United States stagflation risk increased earlier in 2021 and started decreasing more recently. Notwithstanding the significantly high levels of inflation, stagflation risks have been contained by the resilient performance of the labour market in both areas. The degree of labour at risk is therefore important for the assessment of the inflation-unemployment trade-off. 


Monitoring downside risk and upside risk to the key macroeconomic indicators is critical for effective policymaking aimed at maintaining economic stability. In this paper I propose a parametric framework for modelling and forecasting macroeconomic risk based on stochastic volatility models with Skew-Normal and Skew-t shocks featuring time varying skewness. Exploiting a mixture stochastic representation of the Skew-Normal and Skew-t  random variables,  in the paper I develop efficient posterior simulation samplers for Bayesian estimation of both univariate and VAR models of this type. In an application, I use the models to predict downside risk to GDP growth and I show that these models represent a competitive alternative to semi-parametric approaches such as quantile regression. Finally, estimating a medium scale VAR on US data I show that time varying skewness is a relevant feature of macroeconomic and financial shocks. 

Policy notes