• Graduate Programs
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
    • Applied Public Policy Evaluation
    • Deep Learning
    • Development Economics
    • Economics of Blockchain and Digital Currencies
    • Economics of Climate Change
    • The Economics of Crime
    • Foundations of Machine Learning with Applications in Python
    • From Preference to Choice: The Economic Theory of Decision-Making
    • Inequalities in Health and Healthcare
    • Marketing Research with Purpose
    • Markets with Frictions
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
    • Tuition Fees and Payment
  • Alumni
Home | Events Archive | Approximately Parallel Trends: Event Study Inference under Trend Violations
Seminar

Approximately Parallel Trends: Event Study Inference under Trend Violations


  • Location
    Erasmus University Rotterdam, Campus Woudestein, Langeveld 3.02
    Rotterdam
  • Date and time

    June 01, 2026
    11:30 - 12:30

Abstract

Examining pre-trend violations is a common approach to validating the parallel trends assumption necessary for difference-in-difference designs. When the data reject parallel trends, however, applied researchers commonly continue to interpret treatment effect estimates, provided the observed pre-trend violations are deemed sufficiently small. To rationalize this behavior, we recast difference-in-differences designs as relying solely on an approximate version of parallel trends, which allows parallel trends to fail in some realizations of the data. Our reformulation delivers new inference procedures that account for uncertainty about possible deviations from parallel trends.