2020-2024       BSc, Msc Thesis Supervisor (BSc, MSc Econometrics & Data Science)

Students supervised: C. Bosch, D. Pei, J. Liem, M. Pruppers, R. Heijligers, S. Jingkaojai, I. Warmerdam, J. Kroezen, K. Mehagnoul, M. de Bast, M. Paijens, S. Wester, B. Kamphorst, I. Danser, B. Schippers, E. de Jong, P. Fijnvandraat, J. van den Bosch, O. Werkman, R. de Graaf, C. Csaky, M. Grigoriou, R. Teeuwissen, S. Snel, S. Wilhelm, T. Goslin.

Topics include: Inflation rate forecasting using ML, Global VAR analysis, epidemiological modelling of COVID-19 cases, GARCH models, ML for speech-based classfication of Alzheimer's disease, Bootstrap for integer-valued AR models, predicting bankruptcy using CNNs, gene selection for cancer classification

    Second reader for: L. Mahieu, F. Wieland, D. Giuliano Talledo Torres, A. Mughal, J. Qu, A. van Heyningen, M. van der Meulen,  E. Hashicho, D. Suurland,
    G. Smeets, J. ter Borch,  S. van Put, A. el Bakali, D. Popescu, R. Neelis, S. Ka Ping Lee, S. Kaur Sidhu, R. Torenvlied, M. Hekkema, M.Huiskes
    and V. Krivickaite.

2020-2024       Advanced Econometrics, Teaching Assistant, Vrije Universiteit Amsterdam (Msc Econometrics & Data Science)

Course description: This course covers both theoretical and practical aspects of complex dynamic econometric models that are used in the industry, by central banks, governments, think tanks, and other research institutes.  The students are introduced to stochastic theory that allows them to fully understand the dynamic properties of complex models featuring nonlinearities, time-varying parameters and latent variables. The students will also be introduced to advanced estimation theory that allows them to bring state-of-the-art models to data and conduct inference on parameters under very general conditions.
Important concepts include invertibility, stationarity, ergodicity, bounded moments, measurability, consistency and asymptotic normality of extremum, M and Z estimators. We also cover advanced topics in nonlinear model selection and specification, estimation and inference under incorrect specification, metric selection, structural models and causality.

    Responsibilities: Creating and grading the group assignments and offering guidance during weekly Python and R computer labs.
Given the general theory of the course, these practical assignments cover a wide range of advanced methods in applications in economics, finance, business,
and data science. From robust GARCH models with leverage to model financial data, to Smooth Transition Autoregressive (STAR) models with applications
to the FRED-MD datastet, to evaluating structural effects of prices on sales in a marketing context. 

2020-2024       Business Statistics, Teaching Assistant, Vrije Universiteit Amsterdam (BSc International Business Administration)

                        Course description: This course introduces business students to the fundamental concepts in statistics, that helps future graduates make sense of empirical evidence and provide informed, data-driven decision making. This course is a vital component in the methodological toolkit of students of the Business Adminstration program, and introduces them to programming in R and working with R Markdown and R notebooks.
Topics include: probability theory, continuous and discrete distributions, central limit theorem, one/two sample testing, ANOVA, linear regression, non-parametric tests. 

    Responsibilities: 

• Tutor: teaching 4 tutorial groups weekly, providing a recap of the material and facilitating interactive discussion on solutions to exercises.

• Development: Renew exercises and assist with implementation of automatic generation of exam questions in R Exams to provide students with unlimited practice material.