Publication: DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks
DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks
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Han, J., Yang, Y., & E, W. (2025). DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks (No. 25–06; Swiss Finance Institute Research Paper). https://doi.org/10.2139/ssrn.3990409
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We propose an efficient, reliable, and interpretable global solution method, the Deep learning-based algorithm for Heterogeneous Agent Models (DeepHAM), for solving high dimensional heterogeneous agent models with aggregate shocks. The state distribution is approximately represented by a set of optimal generalized moments. Deep neural networks are used to approximate the value and policy functions, and the objective is optimized over directly simulated paths. In addition to being an accurate global solver, this method has three additi
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Han, J., Yang, Y., & E, W. (2025). DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks (No. 25–06; Swiss Finance Institute Research Paper). https://doi.org/10.2139/ssrn.3990409
