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Estimating Nonlinear Heterogeneous Agents Models with Neural Networks

Author

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

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.

Suggested Citation

  • Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2022. "Estimating Nonlinear Heterogeneous Agents Models with Neural Networks," CEPR Discussion Papers 17391, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17391
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    1. Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," NBER Working Papers 33117, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    Neural networks;

    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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