Philipp Adämmer is a scientific researcher with focus on financial econometrics and mechine learning, text mining, and big data. His teaching activities aim to bring basic understanding and competency in data science to students from different faculties of the social sciences and humanities.
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