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Estimating nonlinear heterogeneous agent models with neural networks
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Kase, Hanno, Melosi, Leonardo and Rottner, Matthias (2024) Estimating nonlinear heterogeneous agent models with neural networks. Working Paper. Coventry, UK: University of Warwick. Department of Economics. Warwick economics research papers series (WERPS) (1499). (Unpublished)
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Official URL: https://warwick.ac.uk/fac/soc/economics/research/w...
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.
Item Type: | Working or Discussion Paper (Working Paper) | ||||||
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Subjects: | H Social Sciences > HB Economic Theory H Social Sciences > HG Finance |
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Divisions: | Faculty of Social Sciences > Economics | ||||||
Library of Congress Subject Headings (LCSH): | Finance -- Mathematical models, Economics, Mathematical, Macroeconomics, Equilibrium (Economics), Finance -- Mathematical models -- Data processing | ||||||
Series Name: | Warwick economics research papers series (WERPS) | ||||||
Publisher: | University of Warwick. Department of Economics | ||||||
Place of Publication: | Coventry, UK | ||||||
ISSN: | 2059-4283 | ||||||
Official Date: | May 2024 | ||||||
Dates: |
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Number: | 1499 | ||||||
Institution: | University of Warwick | ||||||
Status: | Not Peer Reviewed | ||||||
Publication Status: | Unpublished | ||||||
Access rights to Published version: | Free Access (unspecified licence, 'bronze OA') | ||||||
RIOXX Funder/Project Grant: |
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Open Access Version: |
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