Philipp Adämmer is a researcher with a focus on financial econometrics and machine learning, text mining and big data. His teaching activities aim at providing students from different faculties of social sciences and humanities with a deeper understanding and competence in data science.
Email: philipp.adaemmer (@) uni-greifswald.de.
Phone: +49 3834 420 5506
University of Greifswald
Felix-Hausdorff-Straße 18
17489 Greifswald
Short CV:
Philipp Adämmer received his doctorate in Economics at the University of Münster in 2016. Between 2016 and 2022, Philipp Adämmer worked as a Postdoc at the Department of Mathematics and Statistics at the Helmut Schmidt University Hamburg. From 2020 to 2021, he was a substitute professor at the Chair of Econometrics and Statistics at the Technical University Dortmund.
Selected Publications and Working Papers:
- Adämmer, P., Prüser, J. and Schüssler, R. (2024): Forecasting Macroeconomic Tail Risk in Real Time: Do Textual Data Add Value?
- Adämmer, P., Lehmann, S. and Schüssler, R. (2023): Local Predictability in High Dimensions.
- Adämmer, P. and Schüssler,R. (2023):Economic Time Series Predictions and the Illusion of Support Recovery.
- Adämmer, P. and Schüssler, R. (2020):Forecasting the Equity Premium: Mind the News! Review of Finance.
- Adämmer, P. (2019): lpirfs: An R Package to Estimate Impulse Response Functions by Local Projections.
- Dybowski, T.P. and Adämmer, P. (2018):The Economic Effects of U.S. Presidential Tax Communication: Evidence from a Correlated Topic Model. European Journal of Political Economy.
Research grants:
- German Research Foundation (DFG): Development and Application of Super Learning Algorithms for Predicting Economic and Financial Time Series.
Software:
- lpirfs: An R package to estimate impulse response functions by local projections [CRAN] [GitHub].
- R package of Local Predictability in High Dimensions [CRAN] [GitHub].
Teaching experience:
- Data Science and Machine Learning using R
- Machine Learning for Economic Data
- Statistics for Economists
- Statistics for Political Scientists
- Econometrics
- Applied Econometrics
- Quantitative Methods
- Quantitative Risk Management