I'm a PhD researcher in Economics and Econometrics at Aarhus University, with research interests at the intersection of macroeconometrics, high- and multi-dimensional statistics, and monetary economics.

My work focuses on extracting economically meaningful signals from large and complex datasets, with particular attention to factor models, mixed-frequency data, real-time macroeconomic forecasting, and latent macroeconomic trends.

I'm especially interested in methodological tools that can improve our understanding of inflation, economic activity, and monetary policy in real time.

• 📊 High-dimensional macroeconometrics
• 🧩 Matrix and vector factor models
• ⏱️ Mixed-frequency modelling & ragged-edge data
• 🔮 Macroeconomic nowcasting and forecasting
• 📈 Inflation dynamics and latent trends
• 🏦 Monetary policy & central banking
• 🎯 Bayesian and state-space methods
• 🌍 Real-time macroeconomic data

I am working on methods that exploit the matrix structure of large macroeconomic datasets rather than vectorising the available information.

The project studies high-dimensional, mixed-frequency and ragged-edge environments, with applications to real-time GDP nowcasting in the euro area.

Matrix factor models · Mixed frequency · Nowcasting · Real-time data · Euro area

Another strand of my research studies the joint evolution of latent equilibrium concepts such as:

• trend inflation — π*
• natural rate of unemployment — u*
• potential output growth — g*
• natural real interest rate — r*

with applications to the Euro Area, United States, United Kingdom and Japan.

State-space models · Bayesian estimation · Inflation · r-star · Monetary policy

	Tools
Programming
Scientific writing
Development
Version control

More research code and replication packages coming soon.

My long-term goal is to contribute to research at the intersection of macroeconometrics and central banking, developing empirical tools that help understand the economy when information is incomplete, noisy, and arriving in real time.