Institut für Ökonometrie und Wirtschaftsstatistik
Am Stadtgraben 9
48143 Münster

Raum 308
Tel.: +49 (0)251-83 22914
Fax: +49 (0)251-83 22012
manuel.stapper@wiwi.uni-muenster.de

Sprechstunde:
Mittwoch, 13-14 Uhr (nach Absprache)
 

• Über

  Ausbildung

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  11/2017 -  aktuell           Doktorand, Institut für Ökonometrie und Wirtschaftsstatistik, WWU Münster
  10/2015 - 10/2017        M.Sc. Statistik, TU Dortmund
  10/2011 - 06/2015        B.Sc. Statistik, TU Dortmund

  Berufserfahrung

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  11/2017 -  aktuell           Wissenschaftlicher Mitarbeiter, Institut für Ökonometrie und Wirtschaftsstatistik,
                                               WWU Münster
  05/2016 - 09/2017        Wissenschaftfliche Hilfskraft, Lehrstuhl für Statistik in den Biowissenschaften, TU Dortmund

  Forschungsschwerpunkte

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  • Count Data Models
  • Software Package Development
  • Robust Statistics
  • Disease Spread
  • Long Memory Count Data 

• Veranstaltungen

  • Macroeconometrics (Master, WiSe23/24)
  • Bayesian Econometrics and MCMC (Master, SoSe23)
  • Advanced Statistics (Bachelor, WiSe22/23)
  • Advanced Time Series Analysis (Master, SoSe22)
  • Econometrics (Bachelor, WiSe21/22)
  • Advanced Time Series Analysis (Master, SoSe21)
  • Advanced Statistics (Bachelor, WiSe20/21)
  • Empirische Wirtschaftsforschung (Bachelor, SoSe20)
  • Advanced Time Series Analysis (Master, WiSe19/20)
  • Introduction to R (Master, SoSe19)
  • Econometrics PhD (PhD level, WiSe18/19)
  • Ökonometrie II (Bachelor, SoSe18)
  • Ökonometrie I (Bachelor, WiSe17/18)

   

• Publikationen

  Aufsätze

  • Segnon, Stapper (2019), "Long Memory Conditional Heteroscedasticity in Count Data", CQE Working Papers, 82/2019 Article
  • Stapper (2021), "Count Data Time Series Modelling in Julia - The CountTimeSeries.jl Package and Applications", Entropy 23(6) Article

  Software

  • CountTimeSeries.jl - Julia Package for Count Time Series
  • RandomVariables.jl - Julia Package for Random Variables, Transformations and Probabilities
  • RobustMFit.jl - Methods to fit distribution robustly

  Konferenzbeiträge

  • 12/2018 - „Long Memory Conditional Heteroscedasticity in Count Data”, CFE/CMStatistics, Pisa
  • 03/2019 - „The INFIGARCH Model and its Application in Trading Activity”, SMSA, Dresden
  • 03/2019 - „Long Memory Conditional Heteroscedasticity in Count Data”, DAGStat, München
  • 12/2019 - „Sources of Global Trading Activity”, CFE/CMStatistics, London
  • 08/2022 - „Accounting for Asymmetry in M-Estimation”, COMPSTAT, Bologna
  • 09/2022 - „Accounting for Asymmetry in M-Estimation”, Statistische Woche, Münster
  • 09/2022 - „CountTimeSeries.jl - A Julia Package for Integer-Valued Time Series”, Statistische Woche, Münster
  • 12/2022 - „RandomVariables.jl ‑ A Julia Package for Random Variables and Probabilities”, CFE/CMStatistics, London
  • 08/2023 - „Commuting and the Spread of Infectious Diseases - Influenza in Germany”, ASA Joint Statistical Meeting, Toronto
  • 08/2023 - „Commuting and the Spread of Infectious Diseases - Influenza in Germany”, COMPSTAT, London

• Abschlussarbeiten

  Betreute Abschlussarbeiten

  • The Effect of Wind Turbines on House Prices in Germany – Evidence from a Machine Learning based Estimation Approach (Master)
  • Bayesian Latent Cluster Detection in the International Arms Trade Network (Master)
  • Carbon Price Acceptance: An Empirical Application of Machine Learning Methods for Estimating Heterogeneous Treatment Effects (Master)
  • Prediction of disruption ticket volumes based on a time series analysis using the eTTs reporting system of Deutsche Telekom AG as an example (Bachelor)
  • Non-Parametric Machine Learning Regression under Misspecification (Bachlor)
  • Robust Fitting of INGARCH Processes - A Generalized Method of Moments Approach (Bachelor)
  • INARMA Models - Parameter Estimation by Indirect Inference (Bachelor)