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Deep Learning for Search and Matching Models

Author

Listed:
  • Jonathan Payne

    (Princeton University)

  • Adam Rebei

    (Stanford University)

  • Yucheng Yang

    (University of Zurich; Swiss Finance Institute)

Abstract

We develop a new method to globally solve and estimate search and matching models with aggregate shocks and heterogeneous agents. We characterize general equilibrium as a high-dimensional partial differential equation with the distribution as a state variable. We then use deep learning to solve the model and estimate economic parameters using the simulated method of moments. This allows us to study a wide class of search markets where the distribution affects agent decisions and compute variables (e.g. wages and prices) that were previously unattainable. In applications to labor search models, we show that distribution feedback plays an important role in amplification and that positive assortative matching weakens in prolonged expansions, disproportionately benefiting low-wage workers.

Suggested Citation

  • Jonathan Payne & Adam Rebei & Yucheng Yang, 2025. "Deep Learning for Search and Matching Models," Swiss Finance Institute Research Paper Series 25-05, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2505
    as

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