I am a Senior Lecturer in the Department of Economics at the University of Strathclyde and Director of the MSc programme in Economics, Data Science and AI.

My research focuses on developing novel econometric methods for the efficient use of big data in macroeconomics. In particular, I combine modeling techniques from the machine learning and Bayesian learning literature with multivariate time series models that macroeconomists commonly work with (e.g., vector autoregressions). My work has been published in the Journal of Econometrics, the Journal of Business & Economic Statistics, the International Economic Review, the Journal of Applied Econometrics, the International Journal of Forecasting, among others.

Selected (recently published) papers.

• Bayesian modeling of time-varying parameter vector autoregressions using regression trees, with F. Huber, G. Koop, and J. Mitchell. Annals of Applied Statistics, 2026.
• Bayesian neural networks for macroeconomic analysis, with F. Huber, K. Klieber, and M. Marcellino. Journal of Econometrics, 2025.
• Gaussian process VARs and macroeconomic uncertainty, with F. Huber, M. Marcellino, and N. Petz. Journal of Business & Economic Statistics, 2025.

Current work.

• Conditional projection methods for large-scale Bayesian VARs, with M. Pfarrhofer. Fast conditional forecasting and scenario analysis in high-dimensional VARs, applied to oil price scenarios after the 2026 closure of the Strait of Hormuz.
• A Bayesian Gaussian process dynamic factor model, with T. Chernis, H. Mumtaz, and M. Pfarrhofer. A factor model with a nonparametric observation equation, used to forecast US macro series and to extract drivers of global inflation.
• Direct Gaussian process predictive regressions with mixed frequency data, with M. Marcellino, M. Pfarrhofer, and A. Stelzer. Nonlinear nowcasting of GDP growth and inflation from high-dimensional mixed-frequency data.

I am principal investigator and first supervisor of two PhD projects. The project “Central Banks’ inflation gamble: Good luck vs. good models”, funded by a Research Excellence Award under the Strathclyde Research Studentship Scheme (with G. Koop and S. McIntyre; PhD student: Petra Gresó), uses machine learning methods to model and forecast inflation at a more granular level than usual, looking at inflation subcomponents rather than only the aggregate. The idea is to better understand what drove the recent inflation surge, and to contribute to the good luck versus good policy debate. The project “Addressing the economic data crisis: Bayesian statistical learning for real-time forecasting and decision support”, funded by a Student Excellence Award under the John Anderson Research Studentship Scheme (with C. Bueno Macedo Medeiros and G. Koop; PhD student: Thiago Da Cruz Alves Santos), is about real-time macroeconomic forecasting when official data arrive late, get revised, or are missing. We combine Bayesian machine learning with data at different frequencies and from less conventional sources, such as surveys and text, and also use the framework to measure tail risks to the economy.

I have actively participated in knowledge exchange, providing scientific consultancy services to the Joint Research Centre (JRC) Ispra of the European Commission, the Austrian Central Bank (OeNB), and the International Institute for Applied Systems Analysis (IIASA). My typical scope of activities in this context is to tailor econometric methods to the needs of policy institutions and central banks. I have also delivered a continuing professional development (CPD) course on modern macroeconometrics at the Banco de Portugal; the course materials are available here.

Background. Senior Lecturer, University of Strathclyde (since 2023). Postdoctoral researcher, University of Salzburg (2020–23), and postdoctoral fellow, WU Vienna (2022). PhD in Economics, WU Vienna (2020). Würdigungspreis of the Austrian Federal Ministry of Education, Science and Research (2018).