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Financial frictions and the wealth distribution

Author

Listed:
  • Jesús Fernández-Villaverde

    (University of Pennsylvania, NBER and CEPR)

  • Samuel Hurtado

    (Banco de España)

  • Galo Nuño

    (Banco de España)

Abstract

We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an endogenous regime-switching process for output, the risk-free rate, excess returns, debt, and leverage. The regime-switching generates i) multimodal distributions of the variables above; ii) time-varying levels of volatility and skewness for the same variables; and iii) supercycles of borrowing and deleveraging. All of these are important properties of the data. In comparison, the representative household version of the model cannot generate any of these features. Methodologically, we discuss how nonlinear DSGE models with heterogeneous agents can be efficiently computed using machine learning and how they can be estimated with a likelihood function, using inference with diffusions.

Suggested Citation

  • Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020. "Financial frictions and the wealth distribution," Working Papers 2013, Banco de España.
  • Handle: RePEc:bde:wpaper:2013
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    Cited by:

    1. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation: With an Application to Option Pricing," Papers 2102.09209, arXiv.org.
    2. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2021. "Multimodality In Macrofinancial Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 861-886, May.
    3. Lepetyuk, Vadym & Maliar, Lilia & Maliar, Serguei, 2019. "When the U.S. catches a cold, Canada sneezes: a lower-bound tale told by deep learning," CEPR Discussion Papers 14025, C.E.P.R. Discussion Papers.
    4. Papp, Tamás K. & Reiter, Michael, 2020. "Estimating linearized heterogeneous agent models using panel data," Journal of Economic Dynamics and Control, Elsevier, vol. 115(C).
    5. Peri, Alessandro, 2020. "A hardware approach to value function iteration," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    6. Auclert, Adrien & Bardoczy, Bence & Rognlie, Matthew & Straub, Ludwig, 2019. "Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models," CEPR Discussion Papers 13890, C.E.P.R. Discussion Papers.
    7. Jesus Fernandez-Villaverde & Pablo Guerron-Quintana, 2020. "Uncertainty Shocks and Business Cycle Research," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 37, pages 118-166, August.
    8. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. Lepetyuk, Vadym & Maliar, Lilia & Maliar, Serguei, 2020. "When the U.S. catches a cold, Canada sneezes: A lower-bound tale told by deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    10. Michael Reiter, 2019. "Solving Heterogeneous Agent Models with Non-convex Optimization Problems: Linearization and Beyond %," 2019 Meeting Papers 1048, Society for Economic Dynamics.
    11. Duarte, Victor & Duarte, Diogo & Fonseca, Julia & Montecinos, Alexis, 2020. "Benchmarking machine-learning software and hardware for quantitative economics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).

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    More about this item

    Keywords

    heterogeneous agents; wealth distribution; financial frictions; continuoustime; machine learning; neural networks; structural estimation; likelihood function;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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