Estimating nonlinear heterogeneous agent models with neural networks

Hanno Kase, Leonardo Melosi and Matthias Rottner

No 1241, BIS Working Papers from Bank for International Settlements

Abstract: We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate uncertainty. Using simulated data, we show that the proposed method accurately estimates the parameters of a nonlinear Heterogeneous Agent New Keynesian (HANK) model with a zero lower bound (ZLB) constraint. We further apply our method to estimate this HANK model using U.S. data. In the estimated model, the interaction between the ZLB constraint and idiosyncratic income risks emerges as a key source of aggregate output volatility.

Keywords: neural networks; likelihood; global solution; heterogeneous agents; nonlinearity; aggregate uncertainty; HANK; zero lower bound (search for similar items in EconPapers)
JEL-codes: C11 C45 D31 E32 E52 (search for similar items in EconPapers)
Date: 2025-01
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