Neue Website:
an der Hochschule Merseburg, E-Mail: malte.jahn (at) hs-merseburg.de.
Publikationen (Fachzeitschriften):
- Jahn, M. (2025): Discrete-Valued Time Series and Recurrent Neural Network Response Functions. In: Steland, A., Rafajłowicz, E., Parolya, N. (eds) Stochastic Models, Statistics and Their Applications. SMSA 2024. Springer Proceedings in Mathematics & Statistics, vol 499. Springer, Cham.
- Jahn, M.; Weiß, C.H. (2025): Modeling multivariate ordinal time series. Journal of Applied Statistics, 1-28.
- Jahn, M. (2024): A flexible likelihood-based neural network extension of the classic spatio-temporal model. Spatial Statistics 59, 100801.
- Jahn, M. (2024): Artificial neural networks and time series of counts: A class of nonlinear INGARCH models. Studies in Nonlinear Dynamics & Econometrics 28(5), 761-765.
- Jahn, M.; Weiß, C.H. (2023): Nonlinear GARCH-type models for ordinal time series. Stochastic Environmental Research and Risk Assessment 38, 637-649.
- Jahn, M.; Weiß, C.H.; Kim, H-Y. (2023): Approximately linear INGARCH models for spatio-temporal counts. Journal of the Royal Statistical Society Series C: Applied Statistics 72(2), 476-497.
- Weiß, C.H.; Jahn, M. (2022): Soft-clipping INGARCH models for time series of bounded counts. Statistical Modelling 24(4), 295-319.
- Jahn, M. (2020): Artificial neural network regression models in a panel setting: Predicting economic growth, Economic Modelling 91, 148-154.
- Jahn, M.; Flegg, A.; Tohmo, T. (2020): Testing and implementing a new approach to estimating interregional output multipliers using input–output data for South Korean regions, Spatial Economic Analysis 15(2), 165-185.
- Boll, C.; Jahn, M.; Lagemann, A. (2017): The gender lifetime earnings gap—exploring gendered pay from the life course perspective, Journal of Income Distribution 26(1), 1-53.
- Jahn, M. (2017): Extending the FLQ formula: a location quotient-based interregional input–output framework, Regional Studies 51, 1518-1529.
- Jahn, M. (2015): Economics of extreme weather events: Terminology and regional impact models, Weather and Climate Extremes, Volume 10, Part B, 29-39.
Lehrveranstaltungen:
- HT 25: Mathematik für WiWi (Vorlesung + Übung)
- WT 25: Quantitative Methoden II (Übung)
- FT 24: Quantitative Methoden III (Vorlesung)
- WT 24: Quantitative Methoden II (Vorlesung)
- WT 23: Quantitative Methoden II (Übung)
- HT 22: Statistical Computing (Vorlesung + Übung)
- FT 22: Quantitative Methoden III (Übung)
- HT 21: Stochastik I für WI (Vorlesung)
- FT 21: Stochastik II für WI (Vorlesung), Zeitreihenanalyse (Übung)
- WT 21: Quantitative Methoden II (Übung)
- HT 20: Quantitative Methoden I (Übung)
Letzte Änderung: 9. April 2026