IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/103971.html
   My bibliography  Save this paper

Discussion of estimating linearized heterogeneous agent models using panel data

Author

Listed:
  • Den Haan, Wouter J.

Abstract

The techniques proposed in Papp and Reiter (2020) allow the use of cross-sectional and aggregate data observed at different frequencies in the estimation of dynamic stochastic macroeconomic models. However, the question is whether technique is getting ahead of what is sensible in terms of currently available empirical strategies to estimate macroeconomic models which are – without exception – misspecified.

Suggested Citation

  • Den Haan, Wouter J., 2020. "Discussion of estimating linearized heterogeneous agent models using panel data," LSE Research Online Documents on Economics 103971, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:103971
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/103971/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Prescott, Edward C., 1986. "Theory ahead of business-cycle measurement," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 25(1), pages 11-44, January.
    2. Algan, Yann & Allais, Olivier & Den Haan, Wouter J., 2008. "Solving heterogeneous-agent models with parameterized cross-sectional distributions," Journal of Economic Dynamics and Control, Elsevier, vol. 32(3), pages 875-908, March.
    3. repec:hal:spmain:info:hdl:2441/41rhqgovpp8hnq9i7ndtl26ltm is not listed on IDEAS
    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. 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.
    6. Thomas Winberry, 2018. "A method for solving and estimating heterogeneous agent macro models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1123-1151, November.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Juan Carlos Parra-Alvarez & Olaf Posch & Mu-Chun Wang, 2017. "Estimation of Heterogeneous Agent Models: A Likelihood Approach," CESifo Working Paper Series 6717, CESifo.
    3. Schesch, Constantin, 2024. "Pseudospectral methods for continuous-time heterogeneous-agent models," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    4. Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
    5. Felipe Alves & Christian Bustamante & Xing Guo & Katya Kartashova & Soyoung Lee & Thomas Michael Pugh & Kurt See & Yaz Terajima & Alexander Ueberfeldt, 2022. "Heterogeneity and Monetary Policy: A Thematic Review," Discussion Papers 2022-2, Bank of Canada.
    6. Papp, Tamás K. & Reiter, Michael, 2020. "Estimating linearized heterogeneous agent models using panel data," Journal of Economic Dynamics and Control, Elsevier, vol. 115(C).
    7. Marcelo Veracierto, 2020. "Computing Equilibria of Stochastic Heterogeneous Agent Models Using Decision Rule Histories," Working Paper Series WP-2020-05, Federal Reserve Bank of Chicago.
    8. Laura Liu & Mikkel Plagborg‐Møller, 2023. "Full‐information estimation of heterogeneous agent models using macro and micro data," Quantitative Economics, Econometric Society, vol. 14(1), pages 1-35, January.
    9. Xing Guo, 2020. "Identifying Aggregate Shocks with Micro-level Heterogeneity: Financial Shocks and Investment Fluctuation," Staff Working Papers 20-17, Bank of Canada.
    10. 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.
    11. Gouin-Bonenfant, Emilien & Toda, Alexis Akira, 2018. "Pareto Extrapolation: Bridging Theoretical and Quantitative Models of Wealth Inequality," University of California at San Diego, Economics Working Paper Series qt90n2h2bb, Department of Economics, UC San Diego.
    12. Laura Liu & Mikkel Plagborg-M{o}ller, 2021. "Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data," Papers 2101.04771, arXiv.org, revised Jun 2022.
    13. 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.
    14. Stephen J. Terry, 2017. "Alternative Methods for Solving Heterogeneous Firm Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(6), pages 1081-1111, September.
    15. 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.
    16. Ralph Luetticke, 2021. "Transmission of Monetary Policy with Heterogeneity in Household Portfolios," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(2), pages 1-25, April.
    17. Fernández-Villaverde, Jesús & Levintal, Oren, 2024. "The Distributional Effects of Asset Returns," CEPR Discussion Papers 18855, C.E.P.R. Discussion Papers.
    18. 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.
    19. Nils M. Gornemann & Keith Kuester & Makoto Nakajima, 2021. "Doves for the Rich, Hawks for the Poor? Distributional Consequences of Systematic Monetary Policy," Opportunity and Inclusive Growth Institute Working Papers 50, Federal Reserve Bank of Minneapolis.
    20. 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.

    More about this item

    Keywords

    heterogeneous agents; misspecification; solution techniques;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

    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:ehl:lserod:103971. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    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.