Main research areas

Statistical Methods

  • Probabilistic Forecasting
  • High-dimensional methods
  • Time series: nonstationarity, nonlinearity, high-dimensionality, and count data
  • Non- and semiparametric methods
  • Network dependencies, extreme dependencies, quantiles
  • Generated regressors

Applied Econometrics

  • Financial Systemic risk measurement
  • Interdependencies of risks
  • Large dimensional extreme risk
  • Methods for high-frequency finance
  • Evaluation of policy measures
  • Forecasting of Cryptocurrencies and house prices

Epidemiology (YIG-PP Johannes Bracher)

  • probabilistic forecasting of infectious diseases
  • Covid-19 forecast hubs: systematic comparison, evaluation, ensemble building
  • Nowcasting of hospitalisations for Covid-19

Weather Forecasting (YIG Sebastian Lerch)

  • Machine learning methods for probabilistic forecasting and uncertainty quantification
  • Forecast verification and extreme events
  • Probabilistic weather forecasting: ensemble post-processing, subseasonal forecasts and hybrid models


Collaborative nowcasting of COVID-19 hospitalization incidences in Germany
Wolffram, D.; Abbott, S.; an der Heiden, M.; Funk, S.; Günther, F.; Hailer, D.; Heyder, S.; Hotz, T.; Bracher, J. E.; Schienle, M.; u. a.
2023. PLOS Computational Biology, 19 (8), Art.-Nr.: e1011394. doi:10.1371/journal.pcbi.1011394
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
Ray, E. L.; Brooks, L. C.; Bien, J.; Biggerstaff, M.; Bosse, N. I.; Bracher, J.; Cramer, E. Y.; Funk, S.; Gerding, A.; Johansson, M. A.; u. a.
2023. International Journal of Forecasting, 39 (3), 1366–1383. doi:10.1016/j.ijforecast.2022.06.005
Statistical Postprocessing of Numerical Weather Prediction Forecasts using Machine Learning. Dissertation
Schulz, B.
2023, Mai 25. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000158905
Model Diagnostics and Forecast Evaluation for Quantiles
Gneiting, T.; Wolffram, D.; Resin, J.; Kraus, K.; Bracher, J.; Dimitriadis, T.; Hagenmeyer, V.; Jordan, A. I.; Lerch, S.; Schienle, M.; u. a.
2023. Annual Review of Statistics and Its Application, 10. doi:10.1146/annurev-statistics-032921-020240
The EUPPBench postprocessing benchmark dataset v1.0
Demaeyer, J.; Bhend, J.; Lerch, S.; Primo, C.; Van Schaeybroeck, B.; Atencia, A.; Ben Bouallègue, Z.; Chen, J.; Dabernig, M.; Evans, G.; u. a.
2023. Earth System Science Data, 15 (6), 2635–2653. doi:10.5194/essd-15-2635-2023
Scoring epidemiological forecasts on transformed scales
Bosse, N. I.; Abbott, S.; Cori, A.; van Leeuwen, E.; Bracher, J.; Funk, S.
2023. PLOS Computational Biology, 19 (8), Art.-Nr.: e1011393. doi:10.1371/journal.pcbi.1011393
Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
Sherratt, K.; Gruson, H.; Grah, R.; Johnson, H.; Niehus, R.; Prasse, B.; Sandmann, F.; Deuschel, J.; Wolffram, D.; Bracher, J.; u. a.
2023. eLife, 12, Art.-Nr.: e81916. doi:10.7554/eLife.81916
Learning to Forecast: The Probabilistic Time Series Forecasting Challenge
Bracher, J.; Koster, N.; Krüger, F.; Lerch, S.
2023. The American Statistician, 1–13. doi:10.1080/00031305.2023.2199800
Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts
Lakatos, M.; Lerch, S.; Hemri, S.; Baran, S.
2023. Quarterly Journal of the Royal Meteorological Society, 149 (752), 856–877. doi:10.1002/qj.4436
The United States COVID-19 Forecast Hub dataset
US COVID-19 Forecast Hub Consortium; Cramer, E. Y.; Huang, Y.; Wang, Y.; Ray, E. L.; Cornell, M.; Bracher, J.; Brennen, A.; Rivadeneira, A. J. C.; Wolffram, D.; u. a.
2022. Scientific Data, 9 (1), Art.-Nr.: 462. doi:10.1038/s41597-022-01517-w
Analysis of pesticide and persistent organic pollutant residues in German bats
Schanzer, S.; Koch, M.; Kiefer, A.; Jentke, T.; Veith, M.; Bracher, F.; Bracher, J.; Müller, C.
2022. Chemosphere, 305, Art.-Nr.: 135342. doi:10.1016/j.chemosphere.2022.135342
Collaborative Hubs: Making the Most of Predictive Epidemic Modeling
Reich, N. G.; Lessler, J.; Funk, S.; Viboud, C.; Vespignani, A.; Tibshirani, R. J.; Shea, K.; Schienle, M.; Runge, M. C.; Bracher, J.; u. a.
2022. American Journal of Public Health, 112 (6), 839–842. doi:10.2105/AJPH.2022.306831
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Cramer, E. Y.; Ray, E. L.; Lopez, V. K.; Bracher, J.; Brennen, A.; Castro Rivadeneira, A. J.; Gerding, A.; Gneiting, T.; House, K. H.; Huang, Y.; u. a.
2022. Proceedings of the National Academy of Sciences of the United States of America, 119 (15), e2113561119. doi:10.1073/pnas.2113561119
National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Schienle, M.; u. a.
2022. Communications Medicine, 2 (1), Art.-Nr.: 136. doi:10.1038/s43856-022-00191-8
Comparing human and model-based forecasts of COVID-19 in Germany and Poland
Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group; Bosse, N. I.; Abbott, S.; Bracher, J.; Hain, H.; Quilty, B. J.; Jit, M.; van Leeuwen, E.; Cori, A.; Funk, S.
2022. (J. M. McCaw, Hrsg.) PLOS Computational Biology, 18 (9), Art.Nr. e1010405. doi:10.1371/journal.pcbi.1010405
Large Spillover Networks of Nonstationary Systems
Chen, S.; Schienle, M.
2022. Journal of Business and Economic Statistics, 1–15. doi:10.1080/07350015.2022.2099870
Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
Silini, R.; Lerch, S.; Mastrantonas, N.; Kantz, H.; Barreiro, M.; Masoller, C.
2022. Earth System Dynamics, 13 (3), 1157–1165. doi:10.5194/esd-13-1157-2022
Evaluating ensemble post‐processing for wind power forecasts
Phipps, K.; Lerch, S.; Andersson, M.; Mikut, R.; Hagenmeyer, V.; Ludwig, N.
2022. Wind Energy, 25 (8), 1379–1405. doi:10.1002/we.2736
Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning
Chapman, W. E.; Delle Monache, L.; Alessandrini, S.; Subramanian, A. C.; Ralph, F. M.; Xie, S.-P.; Lerch, S.; Hayatbini, N.
2022. Monthly Weather Review, 150 (1), 215–234. doi:10.1175/MWR-D-21-0106.1
A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Schienle, M.; u. a.
2021. (List of Contributors by Team, Hrsg.) Nature communications, 12 (1), 5173. doi:10.1038/s41467-021-25207-0
Evaluating epidemic forecasts in an interval format
Bracher, J.; Ray, E. L.; Gneiting, T.; Reich, N. G.
2021. (V. E. Pitzer, Hrsg.) PLoS Computational Biology, 17 (2), Art.Nr. e1008618. doi:10.1371/JOURNAL.PCBI.1008618
Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world
Vannitsem, S.; Bremnes, J. B.; Demaeyer, J.; Evans, G. R.; Flowerdew, J.; Hemri, S.; Lerch, S.; Roberts, N.; Theis, S.; Atencia, A.; u. a.
2021. Bulletin of the American Meteorological Society, 102 (3), E681-E699. doi:10.1175/BAMS-D-19-0308.1
Machine learning for total cloud cover prediction
Baran, Á.; Lerch, S.; El Ayari, M.; Baran, S.
2021. Neural computing & applications, 33, 2605–2620. doi:10.1007/s00521-020-05139-4
Preface: Advances in post-processing and blending of deterministic and ensemble forecasts
Hemri, S.; Lerch, S.; Taillardat, M.; Vannitsem, S.; Wilks, D. S.
2020. Nonlinear processes in geophysics, 27 (4), 519–521. doi:10.5194/npg-27-519-2020
Predictive Inference Based on Markov Chain Monte Carlo Output
Krüger, F.; Lerch, S.; Thorarinsdottir, T.; Gneiting, T.
2020. International statistical review, 89 (2), 274–301. doi:10.1111/insr.12405
Simulation-based comparison of multivariate ensemble post-processing methods
Lerch, S.; Baran, S.; Möller, A.; Groß, J.; Schefzik, R.; Hemri, S.; Graeter, M.
2020. Nonlinear processes in geophysics, 27 (2), 349–371. doi:10.5194/npg-27-349-2020
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Lang, M. N.; Lerch, S.; Mayr, G. J.; Simon, T.; Stauffer, R.; Zeileis, A.
2020. Nonlinear processes in geophysics, 27 (1), 23–34. doi:10.5194/npg-27-23-2020
A retrospective assessment of different endodontic treatment protocols
Bartols, A.; Bormann, C.; Werner, L.; Schienle, M.; Walther, W.; Dörfer, C. E.
2020. PeerJ, 8, e8495. doi:10.7717/peerj.8495
Detecting Structural Differences in Tail Dependence of Financial Time Series
Bormann, C.; Schienle, M.
2020. Journal of business & economic statistics, 38 (2), 380–392. doi:10.1080/07350015.2018.1506343
Determination of vector error correction models in high dimensions
Liang, C.; Schienle, M.
2019. Journal of econometrics, 208 (2), 418–441. doi:10.1016/j.jeconom.2018.09.018
Evaluating Probabilistic Forecasts with
Jordan, A.; Krüger, F.; Lerch, S.
2019. Journal of statistical software, 90 (12), 1–37. doi:10.18637/jss.v090.i12
Testing for an Omitted Multiplicative Long-Term Component in GARCH Models
Conrad, C.; Schienle, M.
2019. Journal of business & economic statistics, 38 (2), 229–242. doi:10.1080/07350015.2018.1482759
Measuring connectedness of euro area sovereign risk
Buse, R.; Schienle, M.
2019. International journal of forecasting, 35 (1), 25–44. doi:10.1016/j.ijforecast.2018.07.010
Neural Networks for Postprocessing Ensemble Weather Forecasts
Rasp, S.; Lerch, S.
2018. Monthly weather review, 146 (11), 3885–3900. doi:10.1175/MWR-D-18-0187.1
High Dimensional Time Series — New Techniques and Applications. Dissertation
Liang, C.
2018. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000084976
Forecaster’s dilemma: Extreme events and forecast evaluation
Lerch, S.; Thorarinsdottir, T. L.; Ravazzolo, F.; Gneiting, T.
2017. Statistical science, 32 (1), 106–127. doi:10.1214/16-STS588
Similarity-based semilocal estimation of post-processing models
Lerch, S.; Baran, S.
2017. Journal of the Royal Statistical Society / C, 66 (1), 29–51. doi:10.1111/rssc.12153
Mixture EMOS model for calibrating ensemble forecasts of wind speed
Baran, S.; Lerch, S.
2016. Environmetrics, 27 (2), 116–130. doi:10.1002/env.2380
Systemic risk spillovers in the European banking and sovereign network
Betz, F.; Hautsch, N.; Peltonen, T. A.; Schienle, M.
2016. Journal of financial stability, 25, 206–224. doi:10.1016/j.jfs.2015.10.006
Semiparametric Estimation with Generated Covariates
Mammen, E.; Rothe, C.; Schienle, M.
2016. Econometric theory, 32 (5), 1140–1177. doi:10.1017/S0266466615000134
Beyond Dimension two: A Test for Higher-Order Tail Risk
Bormann, C.; Schaumburg, J.; Schienle, M.
2015. Journal of financial econometrics, 14 (3), 552–580. doi:10.1093/jjfinec/nbv022
Financial Network Systemic Risk Contributions
Hautsch, N.; Schaumburg, J.; Schienle, M.
2015. Review of finance, 19 (2), 685–738. doi:10.1093/rof/rfu010
Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes
Hautsch, N.; Malec, P.; Schienle, M.
2014. Journal of financial econometrics, 12 (1), 89–121. doi:10.1093/jjfinec/nbt002
Systemic Risk Spillovers in the European Banking and Sovereign Network
Betz, F.; Hautsch, N.; Peltonen, T.; Schienle, M.
2014. Univ.-Bibliothek Frankfurt am Main. doi:10.2139/ssrn.2504400
Additive Models: Extensions and Related Models
Mammen, E.; Park, B. U.; Schienle, M.
2014. The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics. Ed.: J.Racine, Oxford Univ. Press. doi:10.1093/oxfordhb/9780199857944.013.007
Forecasting systemic impact in financial networks
Hautsch, N.; Schaumburg, J.; Schienle, M.
2014. International Journal of Forecasting, 30 (3), 781–794. doi:10.1016/j.ijforecast.2013.09.004
Nonparametric Kernel Density Estimation Near the Boundary
Malec, P.; Schienle, M.
2014. Computational Statistics and Data Analysis, 72, 57–76. doi:10.1016/j.csda.2013.10.023
Nonparametric regression with nonparametrically generated covariates
Mammen, E.; Rothe, C.; Schienle, M.
2012. The annals of statistics, 40 (2), 1132–1170. doi:10.1214/12-AOS995