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 International Economic Review, the Journal of Econometrics, the Journal of Business & Economic Statistics, the Journal of Applied Econometrics, and the International Journal of Forecasting, 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/.