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Estimating Nonlinear Heterogeneous Agent Models with Neural Networks

Author

Listed:
  • Kase, Hanno

    (European Central Bank)

  • Melosi, Leonardo

    (University of Warwick, FRB Chicago, DNB, & CEPR)

  • Rottner, Matthias

    (Deutsche Bundesbank)

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, 2024. "Estimating Nonlinear Heterogeneous Agent Models with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1499, University of Warwick, Department of Economics.
    • Handle: RePEc:wrk:warwec:1499
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    More about this item

    Keywords

    Neural networks ; likelihood ; global solution ; heterogeneous agents ; nonlinearity ; aggregate uncertainty ; HANK ; zero lower bound. JEL Codes: C11 ; C45 ; D31 ; E32 ; E52.;
    All these keywords.

    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

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