We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and...

We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...

In this paper, I study the drop of real GDP volatility which has been observed in the United States during the postwar period. This paper thoroughly estimates how much sectoral shifts contributed to this phenomenon called the Great Moderation. In a short section, Stock and Watson (2003) find...

In this paper we investigate whether the dynamic properties of the U.S. business cycle have changed in the last fifty years. For this purpose we develop a flexible business cycle indicator that is constructed from a moderate set of macroeconomic time series. The coincident economic indicator is...

This paper studies the joint dynamics of U.S. inflation and a term structure of average inflation predictions taken from the Survey of Professional Forecasters (SPF). We estimate these joint dynamics by combining an unobserved components (UC) model of inflation and a sticky‐information...

In the aftermath of the 2008 Global Financial Crisis (GFC), scholars and policymakers turned their attention to the role of uncertainty in amplifying the effects of economic or financial shocks on economic activity. A growing literature has focused on addressing this question. Most works find...

Using monthly data for Belgium, France, Germany, Italy and Spain for the period 2002-2019, we build a Hierarchical Euro Area Dynamic Nelson-Siegel model that allows for time varying exposures of national factors on the common components, and for stochastic volatility both at the regional and...

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...