Teaching

Instructor

Introduction to R and the tidyverse (Workshop)

University of Nottingham, 3DI (Data-Driven Discovery Initiative) | Nottingham, UK | 2022

  • Conceptualisation of the workshop with Moritz Schwarz and Ebba Mark
  • Delivered three two-day workshops for 10-20 students each
  • Topics: Basic programming concepts, data cleaning, data wrangling, descriptive analysis, basic econometric modelling

Guest Lecture on Climate Econometrics

University of Oxford | Oxford, UK | 2021

  • Joint lecture with Ryan Rafaty for the MSc in Environmental Change and Management
  • Conceptualisation and slide preparation about econometric methods for environmental economics
  • Delivered part of the 90-minute lecture illustrating the usage of the difference-in-differences method to assess the effect of carbon prices on emissions

Teaching Assistance

Core Empirical Research Methods (Graduate)

University of Oxford | Oxford, UK | Trinity Term 2025

  • Responsibilities: Provided coding assistance during lectures, held office hours, advised students on their replication projects
  • Topics: Basic programming concepts, introduction to the tidyverse, linear regression, logistic regression, instrumental variables regression, local average treatment effects, regression discontinuity design, difference-in-differences, panel data basics, Monte Carlo simulations

Core Econometrics (Graduate)

University of Oxford | Oxford, UK | Hilary Terms 2022 - 2024

  • Responsibilities: Class teaching for 30-45 students in total (8 weeks), marking of problem sets
  • Topics:
    • Time series: ARMA models, VAR models, unit roots, cointegration, forecasting and structural breaks
    • Panel data: Three-way error components model, pooled OLS estimator, first differenced OLS estimator, fixed effects / within-groups estimator, random effects GLS estimator, Arellano-Bond type estimators
    • Limited dependent variables: Maximum likelihood estimation, Poisson regression, linear probability model, probit & logit model, random utility models, Heckman selection model

Econometrics (Graduate)

University of Oxford | Oxford, UK | Michaelmas and Hilary Terms 2019 - 2021

  • Responsibilities: Class teaching for 30-40 students in total (8 weeks), marking of problem sets
  • Topics:
    • OLS regression: Exact and asymptotic inference, omitted variables
    • IV regression: Estimation, identification, testing
    • GMM estimation: GMM asymptotics and hypothesis testing, identification, asymptotic efficiency, tests of overidentifying restrictions
    • ML estimation: Probit & Logit models, asymptotics and hypothesis testing, identification via Kullback-Leibler minimisation
    • Time series: ARMA models, VAR models, unit roots, cointegration, forecasting and structural breaks
    • Panel data: Three-way error components model, pooled OLS estimator, first differenced OLS estimator, fixed effects / within-groups estimator, random effects GLS estimator, Arellano-Bond type estimators, dynamic panel models, mean groups estimator, introduction to common factor models and spatial econometrics
    • Quasi-experimental methods: Difference-in-differences, propensity score matching, regression discontinuity design

Public Economics (Undergraduate)

University of Tübingen | Tübingen, DE | Fall semester 2016/17

  • Responsibilities: Class teaching for 10-20 students, focused on problem sets (10 weeks)
  • Topics:
    • Competition, equilibrium, efficiency
    • Market inefficiencies: public goods, externalities, incomplete competition, asymmetric information
    • Theory of taxation: principles of taxation, economic effects of taxation, taxing goods, international taxation, government debt and financing