Dr. Andreas Masuhr

Institute of Econometrics and Economic Statistics
Am Stadtgraben 9
48143 Münster

Room 307
Phone: +49 (0)251-83 22974
Zoom: wwu.zoom.us/my/andreas.masuhr
andreas.masuhr@wiwi.uni-muenster.de

Consultation hours:
by appointment

  • About

    Education

    10/2015 - 07/2019      PhD Student, Institute of Econometrics and Economic Statistics,
                                               WWU Münster
    10/2013 - 09/2015      M.Sc. Economics, WWU Münster
    10/2010 - 09/2013      B.Sc. Volkswirtschaftslehre, WWU Münster

    Job Experience

    since 10/2015               Research Assistant, Institute of Econometrics and Economic Statistics,
                                               WWU Münster
    08/2015 - 10/2015      Internship WGZ Bank, Credit Risk Methods
    04/2015 - 09/2015      Student Assistant, Chair of Empirical Economics

    Research Focuses


    • Volatility Transmission and Spillovers
    • Nonparametric Copulas
    • Reinforcement Learning
  • Lectures

    • Statistik 2 (Bachelor, Summer 2020)
    • Introduction to R (Master, Summer 2020)
    • Introduction to R (Master, Winter 19/20)
    • Machine Learning: Dynamic Optimization and Reinforcement Learning (Winter 19/20)
    • Econometric Poliy Evaluation (Winter 19/20)
    • Introduction to R (Master, Summer 2019)
    • Introduction to R (Master, Winter 2018/2019)
    • Bayesian Econometrics (PhD level, Summer 2018)
    • Econometrics PhD (PhD level, Winter 2016/2017)
    • Empirical Economics (Bachelor, Summer 2016)
    • Advanced Statistics (Bachelor, Winter 2015/2016)
    • Empirical Economics (Bachelor, Summer 2015)
  • Publications

    • Masuhr (2018), "Bayesian Estimation of Generalized Partition of Unity Copulas ", CQE Working Papers, 73/2018, .pdf download
    • Masuhr (2017), " Volatility Transmission in Overlapping Trading Zones ", CQE Working Papers, 67/2017, .pdf download
  • Theses Supervision

    If you're interested in one of the theses below, please let know!

    • Masters: Optimal use of a Battery in a Solar Power Plant - a Reinforcement Learning Approach