• Graduate program
    • Why Tinbergen Institute?
    • Program Structure
    • Courses
    • Course Registration
    • Facilities
    • Admissions
    • Recent PhD Placements
  • Research
  • News
  • Events
    • Summer School
      • Behavioral Macro and Complexity
      • Econometrics and Data Science Methods for Business and Economics and Finance
      • Experimenting with Communication – A Hands-on Summer School
      • Inequalities in Health and Healthcare
      • Introduction in Genome-Wide Data Analysis
      • Research on Productivity, Trade, and Growth
      • Summer School Business Data Science Program
    • Events Calendar
    • Tinbergen Institute Lectures
    • Annual Tinbergen Institute Conference
    • Events Archive
  • Summer School
  • Alumni
  • Times
Home | Events Archive | Binarization for Panel Models with Fixed Effects

Binarization for Panel Models with Fixed Effects

  • Series
    Seminars Econometric Institute
  • Speaker(s)
    Chris Muris (Bristol University, United Kingdom)
  • Field
  • Location
    Erasmus University Rotterdam, Polak Building Room 1-10
  • Date and time

    April 25, 2019
    14:00 - 15:00


Abstract In nonlinear panel models with fixed effects and fixed-T, the incidental parameter problem poses identification difficulties for structural parameters and partial effects. Existing solutions are model-specific, likelihood-based, impose time homogeneity, or restrict the distribution of unobserved heterogeneity. We provide new identification results for the structural function and for partial effects in a large class of Fixed Effects Linear Transformation (FELT) models with unknown, time-varying, weakly monotone transformation functions. Our results accommodate continuous and discrete outcomes and covariates, require only two time periods, and impose no parametric distributional assumptions. First, we provide a systematic solution to the incidental parameter problem in FELT. Second, we identify the distribution of counterfactual outcomes and a menu of time-varying partial effects without any assumptions on the distribution of unobserved heterogeneity. Third, we obtain new results for nonlinear difference-in-differences that accommodate both discrete and censored outcomes, and for FELT with random coefficients. Finally, we propose rank- and likelihood-based estimators that achieve \sqrt{n} rate of convergence.