Projects per year
Personal profile
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/.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Collaborations and top research areas from the last five years
Projects
- 1 Active
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Transforming Forecasting Capacity in Government
McIntyre, S. (Co-investigator), De Polis, A. (Research Co-investigator), Hauzenberger, N. (Research Co-investigator), Koop, G. (Research Co-investigator) & Wu, P. (Research Co-investigator)
ESRC (Economic and Social Research Council)
1/04/23 → 31/03/28
Project: Research - Internally Allocated
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Machine learning the macroeconomic effects of financial shocks
Hauzenberger, N., Huber, F., Klieber, K. & Marcellino, M., Apr 2025, In: Economics Letters. 250, 112260.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Predictive density combination using Bayesian machine learning
Chernis, T., Hauzenberger, N., Huber, F., Koop, G. & Mitchell, J., 27 Feb 2025, (E-pub ahead of print) In: International Economic Review. 29 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile4 Downloads (Pure)
Prizes
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Würdigungspreis - Award for Outstanding Academic Performance
Hauzenberger, N. (Recipient), 2018
Prize: Prize (including medals and awards)
Activities
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Bayesian Analysis Webinar (ISBA)
Hauzenberger, N. (Participant)
13 Feb 2025Activity: Participating in or organising an event types › Participation in workshop, seminar, course
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Journal of Business and Economic Statistics (Journal)
Hauzenberger, N. (Peer reviewer)
2025 → …Activity: Publication peer-review and editorial work types › Journal peer review