• Graduate Program
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • Summer School
      • Deep Learning
      • Economics of Blockchain and Digital Currencies
      • Foundations of Machine Learning with Applications in Python
      • Marketing Research with Purpose
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Tinbergen Institute Summer School Program
    • Annual Tinbergen Institute Conference archive
  • News
  • Summer School
    • Deep Learning
    • Economics of Blockchain and Digital Currencies
    • Foundations of Machine Learning with Applications in Python
    • Marketing Research with Purpose
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
  • Alumni

Hommes, C. and Poledna, S. (2026). Forecasting economic crises: The Great Recession, the sovereign debt crisis, and COVID-19 in the euro area Economic Modelling, 158.


  • Journal
    Economic Modelling

This study investigates the potential of agent-based modelling to forecast economic crises, addressing the failure of standard macroeconomic models to predict the 2008 financial crisis and capture crisis dynamics. While dynamic stochastic general equilibrium models have incorporated financial frictions, solving them typically requires linearisation around steady states, which suppresses the non-linear feedback loops through which crises emerge. Agent-based models avoid this limitation by numerically simulating heterogeneous agents, preserving non-linear dynamics without approximation. We develop such an agent-based model for the euro area and show that out-of-sample forecasts outperform benchmarks. We further demonstrate that the model can forecast economic crises without exogenous shocks and accurately reproduce crisis dynamics. The model endogenously predicts the onset of the Great Recession, explains the persistence of the sovereign debt crisis, and reproduces the sharp contraction and swift recovery of the COVID-19 recession. The findings suggest that preserving non-linear feedback loops is essential for crisis prediction.