Rutger Lit, Ph.D.

Short Bio

Rutger Lit holds a PhD in Econometrics from the Vrije Universiteit Amsterdam (VU), where he defended his dissertation in 2016 titled "Time-varying parameter models for discrete-valued time series." He previously earned both a BSc and MSc in Econometrics and Operations Research from the same university. His academic work has been published in leading journals, including the Journal of the American Statistical Association and the Journal of Econometrics.

Rutger has held research positions at the Vrije Universiteit and the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR).

In 2021, he founded Time Series Lab, a suite of software tools for modeling, analyzing, and forecasting time series, used by researchers and practitioners alike.

Currently, Rutger is Senior Lead Data Scientist at ADC - Consulting, where he combines causal inference, time series modeling, and experimentation at scale. He also leads the Causal Inference Guild, supporting the adoption of rigorous causal methods across the organization.

Publications
  • 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