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Teaching

Main Courses

Time Series Analysis and Forecasting

Course Coordinator

Academic years: 2023-2024, 2024-2025, and 2025-2026

Minor Econometrics and Mathematical Economics and Premaster Econometrics

University of Amsterdam

This third-year BSc econometrics course covers univariate and multivariate time series methods for economic and financial applications. Topics include ARMA and ARIMA models and their theoretical properties, unit root testing, GARCH models, dynamic regressions, VAR models, cointegration, error correction, and state space methods. The course also introduces forecast evaluation, density forecasts, model confidence sets, and practical implementation in Python or R.

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Time Series Analysis

Teacher

Academic years: 2021-2022, 2022-2023, 2023-2024, 2024-2025, and 2025-2026

BSc Econometrics and Data Science, BSc Actuarial Science, BSc Business Analytics, and BSc Mathematics

University of Amsterdam

This course covers the same core material as Time Series Analysis and Forecasting, with emphasis on the theory and application of univariate and multivariate time series methods. 

Probability Theory and Statistics 3

Teacher

Academic years: 2021-2022, 2022-2023, 2023-2024, 2024-2025, and 2025-2026

BSc Econometrics and Data Science, BSc Actuarial Science, BSc Business Analytics, Minor Actuarial Science, Premaster Econometrics or Premaster Actuarial Science and Mathematical Finance.

University of Amsterdam

This course covers the mathematical foundations of statistical inference. Topics include convergence concepts, limiting distributions, asymptotic normality, method of moments, maximum likelihood, sufficiency, completeness, optimal estimation, confidence intervals, and hypothesis testing. Particular emphasis is placed on the theoretical properties of estimators and tests, including the Cramér-Rao lower bound, Neyman-Pearson lemma, uniformly most powerful tests, and generalized likelihood ratio tests.

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Other Courses

Academic years: 2016-2017 to 2020-2021

  • Mathematics M1. University Foundation Programme, Cambridge Education Group.

  • Mathematics M2. University Foundation Programme, Cambridge Education Group.

  • Programming and Numerical Analysis. BSc Econometrics and Operations Research, University of Amsterdam.

  • Probability Theory and Statistics 2. BSc Econometrics and Operations Research, University of Amsterdam.

  • Probability Theory and Statistics 3. BSc Econometrics and Operations Research, University of Amsterdam.

  • Econometrics 1. BSc Econometrics and Operations Research, University of Amsterdam.

  • Econometrics 2. BSc Econometrics and Operations Research, University of Amsterdam.

  • Time Series Analysis. BSc Econometrics and Operations Research, University of Amsterdam.

  • Advanced Econometrics 1. Research Master in Econometrics, Tinbergen Institute.​​

Thesis Supervision

2025-2026

  • How does individual loss aversion shape optimal life-cycle investment strategies in defined contribution pension schemes? Wietze Nicolaï (MSc).

  • Green Premiums and Collateral Risk: Evidence from the 2024 Dutch Mortgage Reform. Tim Bruinsma (MSc).

  • A Measure of Robustness of Score-Driven Models to Contaminated Observations. Bram Jakob Abbring (BSc).

  • Markov Regime Confidence-Based Weighting for Robust Score-Driven Volatility Filtering. Apoorva Rai (BSc).

  • Robust Score-Driven Time Series Models. Sebastian Marcus Jonathan Walonker (BSc).

  • Adaptive Convex-Censored Score Filter: Responsiveness and Robustness in GAS Models. Diego Zuccarino (BSc).

 

2024-2025

  • Robust Quasi Score-Driven Models with an Adaptive Loss Function. Mathijs Dijkstra (BSc).

  • Density Forecasting for Censored Air Quality Data: GAS Models for PM2.5 Modeling. Sean Park (BSc).

  • Robust Density Forecasting in GAS Models: Model Averaging, Likelihood Censoring, and Score Scaling. Nathan Lee (BSc).

 

2023-2024

  • Robustifying Score-Driven Updates by the PseudoSpherical Scoring Rule. Louis Gehringer (BSc).

  • ​​Expected Kullback Leibler Reductions by Accelerating Score Driven Update. Sebastiaan Veltman (BSc).

  • Accelerated Generalized Autoregressive Score-Based Weights in Dynamic Mixture Models. Hette Wempe (BSc).

 

2021-2022

  • Does the volatility of Bitcoins popularity affect the volatility of its returns? Olle Colenbrander (BSc).

  • A novel trimming approach to creating robust dynamic mixture models. Jesse Geerts (BSc).

  • Minimized tail risk portfolios with the use of copulas. Jelle van Paridon (BSc).

  • A comparison of dynamic and static time series model averaging. Zeb Albers (BSc).​

 

2020-2021

  • ​​Constructing self-learning densities: a score-driven approach. Mitzi van Eijk (BSc).

  • A Parameter-Driven Framework for Time-Varying Mixed Logit Models for Panel Data. Ruben Reijerse (BSc).

  • Comparison of Dynamic Model Averaging Methods. Vera Zentveld (BSc).

 

2019-2020

  • Dynamic Model Averaging for Density Forecasts. Denis Dvinskikh (BSc).

  • Dynamic Model Averaging on Observation Driven Models. Radu Alexandru Dunca (BSc).

  • Self-Learning Time Series Models. Max Roos (BSc).

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