Research

Working Paper

Real-time Forecasting using mixed-frequency VARs with time-varying parameters, (with Markus Heinrich, revisions requested at Journal of Forecasting) 

This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying parameter VAR with stochastic volatility (MF-TVP-SV-VAR).  Overall, the MF-TVP-SV-VAR delivers accurate now- and forecasts and, on average, outperforms its competitors. We assess the models' accuracy relative to expert forecasts and show that the MF-TVP-SV-VAR delivers better inflation nowcasts in this regard. Using an optimal prediction pool, we moreover demonstrate that the MF-TVP-SV-VAR has gained importance since the Great Recession.

A latent weekly GDP indicator for Germany, (with Sercan Eraslan)

This paper introduces a weekly GDP indicator to track real economic activity in Germany in real-time. We use a mixed-frequency dynamic factor model with quarterly, monthly, and weekly indicators and obtain the weekly GDP indicator as the weighted common component of the mixed-frequency dataset. Our indicator is able to approximate latent week-on-week growth of German GDP. In addition, it enables computing a weekly GDP series in levels, which is also of great interest for central bankers, policy makers, and practitioners interested in analysing the current state of the economy in a timely manner. Finally, we demonstrate the benefits of our indicator for high-frequency tracking of the German economy using a recursive nowcasting exercise.