Deep Learning for Search and Matching Models

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.