• Graduate Programs
    • Facilities
    • Tinbergen Institute Research Master in Economics
      • Why Tinbergen Institute?
      • Research Master
      • Admissions
      • PhD Vacancies
      • Selected PhD Placements
    • Research Master Business Data Science
    • Education for external participants
    • Summer School
    • Tinbergen Institute Lectures
    • PhD Vacancies
  • Research
  • Browse our Courses
  • Events
    • 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
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
  • Alumni
    • PhD Theses
    • Master Theses
    • Selected PhD Placements
    • Key alumni publications
    • Alumni Community
Home | People | Lukas Hoesch
 placeholder

Lukas Hoesch

Candidate Fellow

University
Vrije Universiteit Amsterdam
Research field
Econometrics
Interests
Applied Econometrics, Econometric Methodology, Financial Econometrics, Macroeconometrics, Time Series Econometrics

Biography

I am an assistant professor at the Department of Econometrics and Data Science at Vrije Universiteit (VU) Amsterdam. My research interests lie in the fields of time series econometrics, forecasting and macroeconometrics. In particular, my current research focuses on hypothesis testing in the presence of instabilities, weak-identification-robust inference in non-gaussian models and methods for forecast evaluation.

List of publications

Hoesch, L., Lee, A. and Mesters, G. (2024). Locally robust inference for non-Gaussian SVAR models Quantitative Economics, 15(2):523--570.