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Estimating Nonlinear Heterogeneous Agents Models with Neural Networks

Author

Listed:
  • Kase, Hanno
  • Melosi, Leonardo
  • Rottner, Matthias

Abstract

We leverage recent advances in machine learning to develop an integrated framework that globally solves and estimates models featuring agent heterogeneity and nonlinear constraints, while accounting for aggregate risk. The framework enables the solution and estimation of a frontier quantitative Heterogeneous Agent New Keynesian (HANK) model in its nonlinear form, while allowing for the estimation of parameters governing the steady state. We apply it to U.S. data to estimate a HANK model with a zero lower bound (ZLB) constraint. In the estimated model, the interaction between nonlinearities, such as the ZLB, and wealth heterogeneity emerges as a quantitatively important driver of aggregate volatility.

Suggested Citation

  • Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2022. "Estimating Nonlinear Heterogeneous Agents Models with Neural Networks," CEPR Discussion Papers 17391, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:17391
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    1. is not listed on IDEAS
    2. repec:ecb:ecbdps:202527 is not listed on IDEAS
    3. Douglas Araujo & Rafael Schmidt & Olivier Sirello & Bruno Tissot & Ricardo Villarreal, 2025. "Governance and implementation of artificial intelligence in central banks," IFC Reports 18, Bank for International Settlements.
    4. Sofia Velasco, 2025. "Let the Tree Decide: FABART A Non-Parametric Factor Model," Papers 2506.11551, arXiv.org.
    5. Sarah Bell & Matthieu Chavaz & Boris Hofmann & Daniel Rees & Matthias Rottner, 2026. "Evolving approaches to monetary policy communication in the face of uncertainty: fan charts, scenarios and guidance," BIS Quarterly Review, Bank for International Settlements, March.
    6. Fernández-Villaverde, Jesús & Nuño, Galo & Perla, Jesse, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," CEPR Discussion Papers 19636, Centre for Economic Policy Research.
    7. Darougheh, Saman & Faccini, Renato & Melosi, Leonardo & Villa, Alessandro, 2024. "On-the-Job Search and Inflation under the Microscope," CEPR Discussion Papers 19795, Centre for Economic Policy Research.
    8. Hubrich, Kirstin & Schüler, Yves & Waggoner, Daniel, 2026. "Financial shocks and leverage of financial institutions: When do they matter?," Journal of Monetary Economics, Elsevier, vol. 158(C).
    9. Yang, Yucheng & Wang, Chiyuan & Schaab, Andreas & Moll, Benjamin, 2025. "Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics," CEPR Discussion Papers 20980, Centre for Economic Policy Research.
    10. Hanno Kase & Leonardo Melosi & Sebastian Rast & Matthias Rottner, 2026. "The perils of narrowing fiscal spaces," BIS Working Papers 1328, Bank for International Settlements.
    11. Yves Achdou & Johannes Brumm & Lukas Frank, 2026. "Mastering Stochastic OLG Models in Continuous Time," Papers 2608.11134, arXiv.org.

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    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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