IDEAS home Printed from https://ideas.repec.org/p/ajk/ajkdps/311.html
   My bibliography  Save this paper

An Endogenous Gridpoint Method for Distributional Dynamics

Author

Listed:
  • Christian Bayer

    (University of Bonn)

  • Ralph Luetticke

    (Eberhard Karls University of Tübingen)

  • Maximilian Weiss

    (Eberhard Karls University of Tübingen)

  • Yannik Winkelmann

    (Eberhard Karls University of Tübingen)

Abstract

The "histogram method" (Young, 2010), while the standard approach for analyzing distributional dynamics in heterogeneous agent models, is linear in optimal policies. We introduce a novel method that captures nonlinearities of distributional dynamics. This method solves the distributional dynamics by interpolation instead of integration, which is made possible by making the grid endogenous. It retains the tractability and speed of the histogram method, while increasing numerical efficiency even in the steady state and producing significant economic differences in scenarios with aggregate risk. We document this by studying aggregate investment risk with a third-order solution using perturbation techniques.

Suggested Citation

  • Christian Bayer & Ralph Luetticke & Maximilian Weiss & Yannik Winkelmann, 2024. "An Endogenous Gridpoint Method for Distributional Dynamics," ECONtribute Discussion Papers Series 311, University of Bonn and University of Cologne, Germany.
    • Handle: RePEc:ajk:ajkdps:311
    as

    Download full text from publisher

    File URL: https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_311_2024.pdf
    File Function: First version, 2024
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Adrien Auclert & Bence Bardóczy & Matthew Rognlie & Ludwig Straub, 2021. "Using the Sequence‐Space Jacobian to Solve and Estimate Heterogeneous‐Agent Models," Econometrica, Econometric Society, vol. 89(5), pages 2375-2408, September.
    2. S. Rao Aiyagari, 1994. "Uninsured Idiosyncratic Risk and Aggregate Saving," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 109(3), pages 659-684.
    3. Reiter, Michael, 2009. "Solving heterogeneous-agent models by projection and perturbation," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 649-665, March.
    4. Christian Bayer & Benjamin Born & Ralph Luetticke, 2024. "Shocks, Frictions, and Inequality in US Business Cycles," American Economic Review, American Economic Association, vol. 114(5), pages 1211-1247, May.
    5. Carroll, Christopher D., 2006. "The method of endogenous gridpoints for solving dynamic stochastic optimization problems," Economics Letters, Elsevier, vol. 91(3), pages 312-320, June.
    6. Anmol Bhandari & Thomas Bourany & David Evans & Mikhail Golosov, 2023. "A Perturbational Approach for Approximating Heterogeneous Agent Models," NBER Working Papers 31744, National Bureau of Economic Research, Inc.
    7. Robert J. Barro, 2006. "Rare Disasters and Asset Markets in the Twentieth Century," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(3), pages 823-866.
    8. Den Haan, Wouter J. & Judd, Kenneth L. & Juillard, Michel, 2010. "Computational suite of models with heterogeneous agents: Incomplete markets and aggregate uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 34(1), pages 1-3, January.
    9. Per Krusell & Anthony A. Smith & Jr., 1998. "Income and Wealth Heterogeneity in the Macroeconomy," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 867-896, October.
    10. Levintal, Oren, 2017. "Fifth-order perturbation solution to DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 80(C), pages 1-16.
    11. Christian Bayer & Ralph Luetticke, 2020. "Solving discrete time heterogeneous agent models with aggregate risk and many idiosyncratic states by perturbation," Quantitative Economics, Econometric Society, vol. 11(4), pages 1253-1288, November.
    12. George-Marios Angeletos, 2007. "Uninsured Idiosyncratic Investment Risk and Aggregate Saving," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(1), pages 1-30, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schesch, Constantin, 2024. "Pseudospectral methods for continuous-time heterogeneous-agent models," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    2. Juan Carlos Parra‐Alvarez & Olaf Posch & Mu‐Chun Wang, 2023. "Estimation of Heterogeneous Agent Models: A Likelihood Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 304-330, April.
    3. Boppart, Timo & Krusell, Per & Mitman, Kurt, 2018. "Exploiting MIT shocks in heterogeneous-agent economies: the impulse response as a numerical derivative," Journal of Economic Dynamics and Control, Elsevier, vol. 89(C), pages 68-92.
    4. Zhouzhou Gu & Mathieu Lauri`ere & Sebastian Merkel & Jonathan Payne, 2024. "Global Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models," Papers 2406.13726, arXiv.org.
    5. 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.
    6. Evans, David & Li, Jungang & McGough, Bruce, 2023. "Local rationality," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 216-236.
    7. Francesco Ferlaino, 2024. "Does the financial accelerator accelerate inequalities?," Working Papers 538, University of Milano-Bicocca, Department of Economics.
    8. Juan Carlos Parra-Alvarez & Olaf Posch & Mu-Chun Wang, 2017. "Estimation of Heterogeneous Agent Models: A Likelihood Approach," CESifo Working Paper Series 6717, CESifo.
    9. François Le Grand & Xavier Ragot, 2022. "Managing Inequality Over Business Cycles: Optimal Policies With Heterogeneous Agents And Aggregate Shocks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(1), pages 511-540, February.
    10. SeHyoun Ahn & Greg Kaplan & Benjamin Moll & Thomas Winberry & Christian Wolf, 2018. "When Inequality Matters for Macro and Macro Matters for Inequality," NBER Macroeconomics Annual, University of Chicago Press, vol. 32(1), pages 1-75.
    11. Heejeong Kim, 2022. "Inequality, Disaster risk, and the Great Recession," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 45, pages 187-216, July.
    12. Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
    13. Adrien Auclert & Bence Bardóczy & Matthew Rognlie & Ludwig Straub, 2021. "Using the Sequence‐Space Jacobian to Solve and Estimate Heterogeneous‐Agent Models," Econometrica, Econometric Society, vol. 89(5), pages 2375-2408, September.
    14. Papp, Tamás K. & Reiter, Michael, 2020. "Estimating linearized heterogeneous agent models using panel data," Journal of Economic Dynamics and Control, Elsevier, vol. 115(C).
    15. repec:hal:wpspec:info:hdl:2441/4lhe3u3c38ojohjlcbfaupcjr is not listed on IDEAS
    16. Dobrew, Michael & Gerke, Rafael & Giesen, Sebastian & Röttger, Joost, 2023. "Make-up strategies with incomplete markets and bounded rationality," Discussion Papers 01/2023, Deutsche Bundesbank.
    17. Emmet Hall-Hoffarth, 2023. "Non-linear approximations of DSGE models with neural-networks and hard-constraints," Papers 2310.13436, arXiv.org.
    18. Domeij, David & Ellingsen, Tore, 2015. "Rational Bubbles and Economic Crises: A Quantitative Analysis," SSE Working Paper Series in Economics 2015:1, Stockholm School of Economics.
    19. Takeki Sunakawa, 2020. "Applying the Explicit Aggregation Algorithm to Heterogeneous Macro Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 845-874, March.
    20. Juan Carlos Parra-Alvarez & Olaf Posch & Mu-Chun Wang, 2017. "Identification and estimation of heterogeneous agent models: A likelihood approach," CREATES Research Papers 2017-35, Department of Economics and Business Economics, Aarhus University.
    21. Ivo Bakota, 2023. "Market Clearing and Krusell-Smith Algorithm in an Economy with Multiple Assets," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1007-1045, October.

    More about this item

    Keywords

    Numerical Methods; Distributions; Heterogeneous Agent Models; Linearization;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ajk:ajkdps:311. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ECONtribute Office (email available below). General contact details of provider: https://www.econtribute.de .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.