• Graduate program
    • Why Tinbergen Institute?
    • Research Master
    • Admissions
    • Course Registration
    • Facilities
    • PhD Vacancies
    • Selected PhD Placements
    • Research Master Business Data Science
  • Research
  • Browse our Courses
  • Summer School
  • Events
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Gender in Society
      • Machine Learning for Business
      • Marketing Research with Purpose
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • 16th Tinbergen Institute Annual Conference
    • Annual Tinbergen Institute Conference
  • News
  • Alumni
Home | Events Archive | Stationary or non-stationary? An investigation on the initial conditions for panel maximum likelihood estimation.
Research Master Pre-Defense

Stationary or non-stationary? An investigation on the initial conditions for panel maximum likelihood estimation.


  • Speaker(s)
    Sander Tromp , Sander Tromp
  • Location
    Tinbergen
    Amsterdam
  • Date and time

    July 07, 2025
    10:00 - 11:30

This thesis investigates some theoretical properties of the First-Difference Maximum Likelihood (FDML) estimator. The properties are derived by casting the estimator into the alternative Transformed Maximum Likelihood (TML) framework. Subsequently, the impact is investigated when one deviates from the covariance-stationary initial condition. Under this deviation, the expected score of the autoregressive parameter of interest will be biased. The analytical form of the bias is derived for both the time-series homoskedastic and heteroskedastic cases. Moreover, one can derive the limiting distribution of the estimator. The results imply that a deviation of the covariance-stationary initial condition impacts the possibility of inference on the estimated parameters. The analytical results are verified using a Monte Carlo study.