Rutger Lit
Rutger Lit is a Lead Data Scientist at Amsterdam Data Collective (ADC), where he works on pricing, experimentation, and panel data modeling in complex, real-world systems. His work focuses on applying statistical models in Python at scale, with particular attention to high-dimensional data, temporal dependence, and model specification.
Session
Two-way fixed effects (TWFE) models are a workhorse for analyzing large-scale panel data in applications such as pricing and demand modeling. In practice, running these models at scale in Python comes with challenges: high-dimensional fixed effects, clustering, memory constraints, and specification choices that can lead to different conclusions.
In this talk, we show how to use pyfixest to estimate TWFE models efficiently in Python, and what actually matters when moving from textbook examples to production settings. Using real-world airline pricing applications as a case study, we illustrate how to handle large numbers of fixed effects without exploding memory, how to specify clustered standard errors under temporal dependence, and how to avoid identification issues when fixed effects absorb key variation.
Attendees will leave with practical patterns for running fixed effects models in Python, along with a clear understanding of common failure modes and how to avoid them.