BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//pydata-amsterdam2026//talk//FAAVMX
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-pydata-amsterdam2026-FAAVMX@pretalx.com
DTSTART;TZID=CET:20260910T142500
DTEND;TZID=CET:20260910T145500
DESCRIPTION:**Two-way fixed effects (TWFE) models** are a workhorse for ana
 lyzing large-scale panel data in applications such as pricing and demand m
 odeling. In practice\, running these models at scale in **Python** comes w
 ith challenges: **high-dimensional fixed effects**\, clustering\, memory c
 onstraints\, and specification choices that can lead to different conclusi
 ons.\n\nIn this talk\, we show how to use `pyfixest` to estimate **TWFE mo
 dels efficiently in Python**\, and what actually matters when moving from 
 **textbook examples to production settings**. Using real-world **airline p
 ricing applications** as a case study\, we illustrate how to handle large 
 numbers of fixed effects without exploding memory\, how to specify **clust
 ered standard errors under temporal dependence**\, and how to avoid **iden
 tification issues** when fixed effects absorb key variation.\n\nAttendees 
 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.
DTSTAMP:20260710T141121Z
LOCATION:Unconference
SUMMARY:Scaling Two-Way Fixed Effects Models in Python with pyfixest: Lesso
 ns from Airline Pricing - Rutger Lit
URL:https://pretalx.com/pydata-amsterdam2026/talk/FAAVMX/
END:VEVENT
END:VCALENDAR