Rutger Lit, Ph.D.

Rutger Lit holds a Bachelor’s degree and Master’s degree in Econometrics and Operations Research from the Vrije Universiteit Amsterdam (VU). In April 2013, he started working as a PhD candidate at the Econometrics department at the VU. On February 9, 2016, he successfully defended his dissertation titled Time-varying parameter models for discrete valued time series. Currently, he is a research fellow at the VU and Netherlands Institute for the Study of Crime and Law Enforcement. Rutger published in top statistical journals like the Journal of the American Statistical Association and Journal of Econometrics.

In 2019, he founded Time Series Lab, a family of software programs designed to model and forecast time series.

Currently, he is working as Lead Data Scientist at ACMetric.

Skills & Experience

Fundamentals

• Probability & Statistics
  Advanced
• Linear Algebra
  Advanced
• Calculus
  Intermediate - Advanced

Data Science & Modelling

• Time Series Analysis:
  State space models and the Kalman filter
  Expert
• Time Series Analysis:
  SARIMAX · Exponential smoothing · Holt-Winters · Dynamic conditional score models · GARCH models · Stochastic volatility · Dynamic factor models · Particle filters
  Advanced
• Regression:
  OLS · WLS · GLS · Logistic · Lasso · Ridge · Polynomial · Cubic Spline
  Advanced
• Various topics:
  Principal component analysis · Clustering · Importance sampling · Model averaging · Monte Carlo simulation · Panel models · Copulas · Maximum likelihood · Stochastic gradient descent · XGBoost · Random forest
  Advanced

Programming Languages

• Python:
  Including Pandas · Numpy · Scipy · Scikit-learn · Matplotlib
  Advanced
• OxMetrics
  Advanced
• Matlab
  Intermediate
• HTML · CSS · PHP
  Intermediate
• SQL
  Intermediate
• R
  Basic
• C
  Basic

Data Visualization

• GUI design (tkinter)
  Advanced
• Matplotlib
  Intermediate - Advanced
• Excel
  Intermediate - Advanced

Additional Skills

• Media Mix Modelling
  Advanced
• Git
  Intermediate - Advanced
• Uplift Modelling
  Advanced
• Causal inference
  Intermediate - Advanced
• A/B Testing
  Intermediate - Advanced
• Google Cloud Platform
  Intermediate
• Latex
  Advanced
• Markdown
  Basic

• Estimation of final standings in football competitions with premature ending: the case of COVID-19 [link] with Gorgi, P.  and Koopman, S.J. AStA - Advances in Statistical Analysis (2020)
• The analysis and forecasting of tennis matches using a high-dimensional dynamic model [link] with Gorgi, P.  and Koopman, S.J. Journal of the Royal Statistical Society, Series A (2019)
• Long Term Forecasting of El Niño Events via Dynamic Factor Simulations [link] with Li, M., Koopman, S.J., Lit, R. and Desislava Petrova Journal of Econometrics (2020)
• Forecasting football match results in national league competitions using score-driven time series models [link] with Koopman, S.J. International Journal of Forecasting (2019)
• Dynamic Discrete Copula Models for High Frequency Stock Price Changes [link] with Koopman, S.J. , Lucas, A. and Opschoor, A.  Journal of Applied Econometrics (2018)
• Modified Efficient Importance Sampling for partially non-Gaussian State Space Models [link] with Koopman, S.J.  and T.M. Nguyen Statistica Neerlandica (2019)
• Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model [link] with Koopman, S.J.  and Lucas, A.  Journal of the American Statistical Association (2017), 112, 1490-1503
• Model-Based Business Cycle and Financial Cycle Decomposition for Europe and the United States [link] with Koopman, S.J.  and Lucas, A.  Systemic Risk Tomography: Signals, Measurements and Transmission Channels, ISTE-Elsevier (2016)
• A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League [link], with Koopman, S.J.  Journal of the Royal Statistical Society, Series A (2015), 178(1), 167-186