Deep Learning for Search and Matching Models

Jonathan Payne, Adam Rebei and Yucheng Yang
Additional contact information
Jonathan Payne: Princeton University
Adam Rebei: Stanford University
Yucheng Yang: University of Zurich; Swiss Finance Institute

No 25-05, Swiss Finance Institute Research Paper Series from 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.

Keywords: Search and Matching; Distribution Feedback; Two-sided Heterogeneity; Business Cycles; Sorting; Over-the-Counter Financial Markets; Deep learning (search for similar items in EconPapers)
Pages: 64 pages
Date: 2025-01
New Economics Papers: this item is included in nep-cmp and nep-dge
References: Add references at CitEc
Citations:

Downloads: (external link)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4768566 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2505

Access Statistics for this paper

More papers in Swiss Finance Institute Research Paper Series from Swiss Finance Institute Contact information at EDIRC.
Bibliographic data for series maintained by Ridima Mittal ().