Research

Working Paper

Sudden stop: supply and demand shocks in the German natural gas market, (with Jochen Güntner and Maik H. Wolters, conditionally accepted at Journal of Applied Econometrics)

We propose a structural vector-autoregressive model for the German natural gas market to investigate the impact of the 2022 Russian supply stop on the German economy. We combine conventional and narrative sign restrictions to leverage information about supply cuts for identification and find that gas supply and demand shocks have large and persistent price effects, while output effects are rather moderate. The 2022 natural gas price spike was driven by adverse flow supply shocks and positive storage demand shocks, as Germany filled its inventories before the winter. Counterfactual simulations of an embargo on natural gas imports from Russia indicate similar positive price and negative output effects compared to what we observe in the data.

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.