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

Gneiting, T., Lerch, S. and Schulz, B. (2023). Probabilistic solar forecasting: benchmarks, post-processing and verification. Solar Energy, 252, 72-80. [doi.org/10.1016/j.solener.2022.12.054] Code on Github.

Sherratt K, Gruson H, Johnson H, [...], Bracher J, Funk S (2023). Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. eLife, accepted. Preprint available here: [https://doi.org/10.1101/2022.06.16.22276024]

Buse R, Schienle M, Urban J (2022). Assessing the Impact of Policy and Regulation Interventions in European Sovereign Credit Risk Networks: What worked best? Journal of International Economics (139) [https://doi.org/10.1016/j.jinteco.2022.103673]

Phipps, K., Lerch, S., Andersson, M., Mikut, R., Hagenmeyer, V. and Ludwig, N. (2022). Evaluating ensemble post-processing for wind power forecasts. Wind Energy, 25(8):1379-1405. [https://doi.org/10.1002/we.2736] Code on Github.

Bracher J, Wolffram D, Deuschel J, Görgen K, Ketterer J et al (2022). National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021. Communications Medicine 2 (136), [https://doi.org/10.1038/s43856-022-00191-8].

Cramer EY, Huang Y, Wang Y, [...], Bracher J, [...], Reich NG (2022). The United States COVID-19 Forecast Hub dataset. Scientific Data 9(1). [https://doi.org/10.1038/s41597-022-01517-w].

Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A et al (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences 119(5). [https://doi.org/10.1073/pnas.2113561119].

Ray EL, Brooks L, Bien J, [...], Bracher J, [...], Reich NG (2022). Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States. International Journal of Forecasting. [https://doi.org/10.1016/j.ijforecast.2022.06.005].

Lerch, S. and Polsterer, K.L. (2022). Convolutional autoencoders for spatially-informed ensemble post-processing. International Conference on Learning Representations (ICLR) 2022 - AI for Earth and Space Science Workshop [arxiv.org/abs/2204.05102] Code on Github.

Chapman, W.E., Delle Monache, L., Alessandrini, S., Subramanian, A., Ralph, F.M., Xie, S.-P., Lerch, S. and Hayatbini, N. (2022). Probabilistic predictions from deterministic atmospheric river forecasts with deep learning. Monthly Weather Review, 150(1): 215-234. [https://doi.org/10.1175/MWR-D-21-0106.1] Code on Github.

Gneiting T,  Wolffram D, Resin J, Kraus K, Bracher J et al (2022). Model Diagnostics and Forecast Evaluation for Quantiles. Annual Review of Statistics and Its Application (10), [https://doi.org/10.1146/annurev-statistics-032921-020240].

Schanzer, S, Koch, M, [...], Bracher J, Müller C (2022). Analysis of pesticide and persistent organic pollutant residues in German bats. Chemosphere (305): 135342. [https://doi.org/10.1016/j.chemosphere.2022.135342].

Silini, R., Lerch, S.,  Mastrantonas, N., Kantz, H., Barreiro, M., and Masoller, C. (2022). Improving the predictino of the Madden-julian Oscillation of the ECMWF model by post-processing. Earth System Dynamics, 13, 1157-1165 [doi.org/10.5194/esd-13-1157-2022].

Bosse NI, Abbott S, Bracher J, Hain H, Quilty BJ et al (2022). Comparing human and model-based forecasts of COVID-19 in Germany and Poland. PLOS Computational Biology 18(9). [https://doi.org/10.1371/journal.pcbi.1010405].

Reich NG, Lessler J, Funk S, [...], Schienle, M, [...], Bracher J, [...], Biggerstaff, M (2022). Collaborative hubs: making the most of predictive epidemic modeling. American Journal of Public Health 112(6). [https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2022.306831].

Bracher J, Wolffram D, Deuschel J, Görgen K, Ketterer J et al (2021). A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave. Nature Communications, 12, 5173. Preprint available here.

Bracher J, Ray EL, Gneiting T, Reich NG (2021). Evaluating epidemic forecasts in an interval format. PLOS Computational Biology 18(10). [https://doi.org/10.1371/journal.pcbi.1008618].

Schulz, B. and Lerch, S. (2021). Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison. Monthly Weather Review, accepted for publication. [https://doi.org/10.1175/MWR-D-21-0150.1] Code on Github.

Bracher J and Littek JM (2021). An empirical assessment of influenza intensity thresholds obtained from the moving epidemic and WHO methods. Preprint available here.

Christian Conrad and Melanie Schienle (2020) Testing for an Omitted Long-Term Component in Multiplicative GARCH Models, Journal of Business & Economic Statistics, Vol.38, No.2, 229-242; [doi.org/10.1080/07350015.2018.1482759 ]

Carsten Bormann and Melanie Schienle (2020) Detecting structural differences in tail dependence of financial time series, Journal of Business & Economic Statistics, Vol.38, No.2, 380-392; [doi:10.1080/07350015.2018.1506343] 

Shi Chen, Wolfgang K Härdle and Brenda L Cabrera (2019) Regularization Approach for Network Modeling of German Power Derivative Market, Energy Economics, in press [doi:10.1016/j.eneco.2019.06.021]

Liang, C.;Schienle, M. (2018+) Determination of Vector Error Correction Models in High Dimensions (with Chong Liang), Journal of Econometrics, 2018+, in press [doi:10.1016/j.jeconom.2018.09.018], [working paper] , [online appendix]

Buse, R.; Schienle, M. (2019) Measuring Connectedness of Euro Area Sovereign Risk, International Journal of Forecasting,  Vol.35 , No.1, 25-44; [doi:10.1016/j.ijforecast.2018.07.010], [working paper]

Nazemi, A.; Heidenreich, K.; Fabozzi, F. J. (2018). Improving Corporate Bond Recovery Rate Prediction Using Multi-Factor Support Vector Regressions, European Journal of Operational Research, forthcoming. [https://www.sciencedirect.com/science/article/pii/S0377221718304247]

Nazemi, A., Fabozzi, Frank, J. (2018)  Macroeconomic Variable Selection for Creditor Recovery Rates, Journal of Banking and Finance, Vol. 89, pp. 14-25. [https://www.sciencedirect.com/science/article/pii/S037842661830013X]

Nazemi, A.; Fatemipour, F.; Heidenreich, K.; Fabozzi, F. J. (2017). Fuzzy decision fusion approach for loss-given-default modeling. European Journal of Operational Research, forthcoming [http://www.sciencedirect.com/science/article/pii/S0377221717303417 ]

Betz, F., Hautsch, N., Peltonen, T., Schienle, M. (2016) Systemic Risk Spillovers in the European Banking and Sovereign Network, Journal of Financial Stability, Vol.25 , 206–224 [doi:10.1016/j.jfs.2015.10.006] , [working paper version], - [covered in the ECB Financial Stability Review [May 2013] (pages 71-73 (box 6))  and [Nov.2013] (page 74 ,chart 3.15)]

Bormann, C., Schaumburg, J., Schienle, M. (2016) Beyond dimension two: A test for higher-order tail risk (with Carsten Bormann and Julia Schaumburg), Journal of Financial Econometrics, Vol. 14, No 3,  552-580 [doi: 10.1093/jjfinec/nbv022], [working paper version]

Mammen, E., Rothe, C., Schienle, M. (2016) Semiparametric Estimation with Generated Covariates, Econometric Theory, Vol. 32, No.5, 1140-1177 [doi:10.1017/S0266466615000134] , [working paper version]

Hautsch, N., Schaumburg, J., Schienle, M. (2015)  Financial Network Systemic Risk Contributions, Review of Finance, Vol. 19, No 2, 685-738, [doi:10.1093/rof/rfu010], [working paper version]

Malec, P., Schienle, M. (2014) Nonparametric Kernel Density Estimation Near the Boundary, Computational Statistics and Data Analysis,  Vol. 72, 57-72 [doi:10.1016/j.csda.2013.10.023], [working paper version]

Hautsch, N., Schaumburg, J.,  Schienle, M. (2014) Forecasting systemic impact in financial networks, International Journal of Forcasting, Vol. 30, Issue 3, 781–794 [doi:10.1016/j.ijforecast.2013.09.004], [working paper version]

Hautsch, N., Malec, P.,  Schienle, M. (2013) Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes, Journal of Financial Econometrics, Vol. 12 No.1, 89-121 [doi: 10.1093/jjfinec/nbt002] , [working paper version], [webappendix]

Mammen, E., Rothe, C., Schienle, M. (2012) Nonparametric Regression with Nonparametrically Generated Regressors, the Annals of Statistics, Vol. 40, No. 2, 1132-1170 [doi:10.1214/12-AOS995] or via arXiv [pdf

Mammen, E., Park, B. U., Schienle, M. (2014) Additive Models: Extensions and Related Models, in Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics (editors Racine, Ullah), Oxford University Press  [working paper version]

Mammen, E., Rothe, C., Schienle, M. (2013) Generated Regressors in Nonparametric Estimation: A Short Review,  in Recent Developments in Modeling and Applications in Statistics (editors Oliveira, da GraccaTemido, Henriques and Vichi), Springer 2013 [working paper version]

Grith, M. , Härdle, W.K., Schienle, M. (2012)Nonparametric Estimation of Risk-Neutral Densities, in Handbook of Computational Finance, Springer 2012 [working paper version]