Dr Niko Hauzenberger
Senior Lecturer
Economics
Personal statement
Niko Hauzenberger is a Senior Lecturer in the Department of Economics.
His research focuses on developing novel econometric methods for the efficient use of big data in macroeconomics. Specifically, he combines modeling techniques from the machine learning and Bayesian learning literature with multivariate time series models that macroeconomists commonly work with (e.g., vector autoregressions).
Niko's work has been published in the Journal of Business & Economic Statistics, the Journal of Applied Econometrics, the International Journal of Forecasting, the Journal of International Money & Finance, and the Scandinavian Journal of Economics, among others.
Niko has 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). His typical scope of activities in this context is to tailor econometric methods to the needs of policy institutions and central banks.
For further information, please visit Niko's personal website: https://nhauzenb.github.io/.
Prize And Awards
- EFaB/ISBA Junior Paper Awards
- Recipient
- 2025
- Würdigungspreis - Award for Outstanding Academic Performance
- Recipient
- 2018
Publications
- Bayesian neural networks for macroeconomic analysis
- Hauzenberger Niko, Huber Florian, Klieber Karin, Marcellino Massimilano
- Journal of Econometrics Vol 249 (2025)
- https://doi.org/10.1016/j.jeconom.2024.105843
- Machine learning the macroeconomic effects of financial shocks
- Hauzenberger Niko, Huber Florian, Klieber Karin, Marcellino Massimiliano
- Economics Letters Vol 250 (2025)
- https://doi.org/10.1016/j.econlet.2025.112260
- Predictive density combination using Bayesian machine learning
- Chernis Tony, Hauzenberger Niko, Huber Florian, Koop Gary, Mitchell James
- International Economic Review (2025)
- https://doi.org/10.1111/iere.12759
- Gaussian process vector autoregressions and macroeconomic uncertainty
- Hauzenberger Niko, Huber Florian, Marcellino Massimiliano, Petz Nico
- Journal of Business and Economic Statistics Vol 43, pp. 27-43 (2025)
- https://doi.org/10.1080/07350015.2024.2322089
- Sparse time-varying parameter VECMs with an application to modeling electricity prices
- Hauzenberger Niko, Pfarrhofer Michael, Rossini Luca
- International Journal of Forecasting Vol 41, pp. 361-376 (2025)
- https://doi.org/10.1016/j.ijforecast.2024.09.001
- A discussion of 'Sparse Bayesian factor analysis when the number of factors is unknown' by Sylvia Fruhwirth-Schnatter, Darjus Hosszejni and Hedibert Freitas Lopes
- Hauzenberger Niko, Koop Gary
- Bayesian Analysis (2024)
- https://doi.org/10.1214/24-BA1423
Professional Activities
- Horizon Europe: Practical Experience from a Case Study LAMASUS
- Organiser
- 24/6/2025
- Applied Bayesian Econometrics Workshop
- Organiser
- 17/6/2025
- Tamás Krisztin
- Host
- 16/6/2025
- 15th RCEA Bayesian Econometrics Workshop
- Speaker
- 9/6/2025
- 15th RCEA Bayesian Econometrics Workshop
- Speaker
- 9/6/2025
- ESCoE Conference on Economic Measurement 2025
- Speaker
- 21/5/2025
Projects
- Transforming Forecasting Capacity in Government
- McIntyre, Stuart (Co-investigator) De Polis, Andrea (Research Co-investigator) Hauzenberger, Niko (Research Co-investigator) Koop, Gary (Research Co-investigator) Wu, Ping (Research Co-investigator)
- 01-Jan-2023 - 31-Jan-2028
- Non-parametric volatility modeling in macroeconomics and finance
- Hauzenberger, Niko (Principal Investigator)
- During recessionary episodes (such as the global financial crisis or the Covid-19 pandemic) many important macroeconomic and financial quantities experience substantial shifts in the volatility process, while during tranquil periods the shock volatilities remain relatively stable. This project sets forth a research agenda to develop and apply more flexible volatility models for macroeconomic and financial data. These methods will be also useful for practitioners working on emerging market economies. These countries often feature short time series which are subject to several structural breaks. The methods we propose in this project are exactly designed for such situations where time series exhibit substantial shifts in the variance. We will illustrate the models developed in this project through various now- and forecasting applications. Apart from focusing on predictions, our models can be used to produce timely indicators of macroeconomic stability. These indicators can be applied to emerging market economies and implemented such that policy institutions have alternative high frequency measures of the overall state of a given economy.
- 01-Jan-2022 - 31-Jan-2026
Contact
Dr
Niko
Hauzenberger
Senior Lecturer
Economics
Email: niko.hauzenberger@strath.ac.uk
Tel: Unlisted