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.
Real-time Forecasting using mixed-frequency VARs with time-varying parameters, Journal of Forecasting, forthcoming (with Markus Heinrich).
Sudden stop: supply and demand shocks in the German natural gas market, Journal of Applied Econometrics, 2024, 39(7), 1282-1300 (with Jochen Güntner and Maik H. Wolters), Replication Codes.
Time-Varying Dynamics of the German Business Cycle: A Comprehensive Investigation, Oxford Bulletin of Economics and Statistics, 2022, 84(1), 80-102.
Technological Growth and Hours in the Long Run: Theory and Evidence, Economica, 2021, 88(352), 1060-1053 (with Mewael Tesfasselassie and Maik H. Wolters).
Macroeconomic Uncertainty and Forecasting Macroeconomic Aggregates, Studies in Nonlinear Dynamics & Econometrics, 2021, 25(2).
Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle, International Journal of Forecasting, 2020, 36(3), 829-850 (with Kai Carstensen, Markus Heinrich and Maik H. Wolters).