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Prof. Dr. Jannis Kück

Professor für Volkswirtschaftslehre, insb. Data Science in Economics
Prof. Dr. Jannis Kück
Düsseldorf Institute for Competition Economics (DICE)
Heinrich-Heine-Universität Düsseldorf
Universitätsstr. 1
40225 Düsseldorf
Gebäude: 24.31
Etage/Raum: 01.07
+49 211 81-10238
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2022Forschungsaufenthalt an der Universität Freiburg, Schweiz (Lehrstuhl für Angewandte Ökonometrie), Prof. Martin Huber
2020Promotion in Statistik unter der Betreuung von Herr Prof. Dr. Martin Spindler
2019Forschungsaufenthalt an der University of California, Irvine (Department of Economics/Deep Data Lab), Prof. Matthew Harding
2016M.Sc. Wirtschaftsmathematik, Universität Hamburg
2014B.Sc. Wirtschaftsmathematik, Universität Hamburg
Seit 2023Professor für Volkswirtschaftslehre, insb. Data Science in Economics, Düsseldorf Institute for Competition Economics (DICE), Heinrich-Heine-Universität Düsseldorf
2021 - 2023Postdoc am Lehrstuhl für Statistik, Universität Hamburg
  • Bach, P., Klaassen, S., Kueck, J., & Spindler, M. (2025). Estimation and uniform inference in sparse high-dimensional additive models. Journal of Econometrics, 249(B), 105973.
  • Chernozhukov, V., Klaaßen, S., Kueck J., Spindler, M. (2022): Uniform Inference in High-Dimensional Gaussian Graphical Models. (Biometrika, available here)
  • Kueck, J., Luo, Y., Spindler, M., Wang, Z. (2022): Estimation and Inference of Treatment Effects with L2-Boosting in High-Dimensional Settings. (Journal of Econometrics, available here)
  • Felderer, B., Kueck, J., Spindler, M. (2022): Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-mode Online Panel (Social Science Computer Review, available here).
  • Klaaßen, S., Kueck, J., Spindler, M. (2021): Transformation Models in High Dimensions. (Journal of Business & Economic Statistics, available here)
  • Kueck, J. (2020): Advances in Machine Learning: Valid Inference about High-Dimensional Parameters. (available here) Dissertation, Staats-und Universitätsbibliothek Hamburg Carl von Ossietzky.
  • Bach, P.,  Klaaßen, S., Kueck, J., Spindler, M. (2020): Uniform Inference in High-Dimensional Additive Models. (R&R at Journal of Econometrics, available here)
  • Luo, Y., Spindler, M., Kueck, J. (2022): High-Dimensional L2-Boosting: Rate of Convergence. (R&R at Journal of Machine Learning Research, available here)
  • Huber, M., Kueck, J. (2022): Testing the Identification of Causal Effects in Observational Data. (available here)
  • Transformed Failure Time Models in High-Dimensions (with Oliver Schacht)
  • Adaptive Smoothing for Nonparametric Estimation (with Ye Luo and Martin Spindler)
  • Double Machine Learning for Partial Correlations and Partial Copulas (with Malte Kurz)
  • Hochdimensionale Statistik
  • Ökonometrie
  • Kausale Inferenz
  • Maschinelles Lernen
  • Deep Learning
  • Graphische Modelle