Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914213722587136 |
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| author | Yang, Yucheng Wang, Chiyuan Schaab, Andreas Moll, Benjamin |
| author_facet | Yang, Yucheng Wang, Chiyuan Schaab, Andreas Moll, Benjamin |
| contents | We present a new approach to formulating and solving heterogeneous agent models with aggregate risk. We replace the cross-sectional distribution with low-dimensional prices as state variables and let agents learn equilibrium price dynamics directly from simulated paths. To do so, we introduce a structural reinforcement learning (SRL) method which treats prices via simulation while exploiting agents' structural knowledge of their own individual dynamics. Our SRL method yields a general and highly efficient global solution method for heterogeneous agent models that sidesteps the Master equation and handles problems traditional methods struggle with, in particular nontrivial market-clearing conditions. We illustrate the approach in the Krusell-Smith model, the Huggett model with aggregate shocks, and a HANK model with a forward-looking Phillips curve, all of which we solve globally within minutes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18892 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics Yang, Yucheng Wang, Chiyuan Schaab, Andreas Moll, Benjamin Theoretical Economics Artificial Intelligence Machine Learning We present a new approach to formulating and solving heterogeneous agent models with aggregate risk. We replace the cross-sectional distribution with low-dimensional prices as state variables and let agents learn equilibrium price dynamics directly from simulated paths. To do so, we introduce a structural reinforcement learning (SRL) method which treats prices via simulation while exploiting agents' structural knowledge of their own individual dynamics. Our SRL method yields a general and highly efficient global solution method for heterogeneous agent models that sidesteps the Master equation and handles problems traditional methods struggle with, in particular nontrivial market-clearing conditions. We illustrate the approach in the Krusell-Smith model, the Huggett model with aggregate shocks, and a HANK model with a forward-looking Phillips curve, all of which we solve globally within minutes. |
| title | Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics |
| topic | Theoretical Economics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.18892 |