Estimating Nonlinear Heterogeneous Agent Models with Neural Networks

Hanno Kase, Leonardo Melosi and Matthias Rottner
Additional contact information
Hanno Kase: European Central Bank
Matthias Rottner: Deutsche Bundesbank

The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics

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. JEL Codes: C11; C45; D31; E32; E52. (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-big, nep-cmp and nep-dge
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Citations: View citations in EconPapers (1)

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https://warwick.ac.uk/fac/soc/economics/research/w ... rp_1499_-_melosi.pdf

Related works:
Working Paper: Estimating Nonlinear Heterogeneous Agents Models with Neural Networks (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:warwec:1499

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