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Solving the incomplete markets model with aggregate uncertainty using parameterized cross-sectional distributions

Author

Listed:
  • Yann Algan

    (Sciences Po - Sciences Po)

  • Olivier Allais

    (ALISS - Alimentation et sciences sociales - INRA - Institut National de la Recherche Agronomique)

  • Wouter J den Haan

    (Department of Economics - UvA - Universiteit van Amsterdam = University of Amsterdam)

Abstract

This note describes how the incomplete markets model with aggregate uncertainty in Den Haan et al. [Comparison of solutions to the incomplete markets model with aggregate uncertainty. Journal of Economic Dynamics and Control, this issue] is solved using standard quadrature and projection methods. This is made possible by linking the aggregate state variables to a parameterized density that describes the cross-sectional distribution. A simulation procedure is used to find the best shape of the density within the class of approximating densities considered. This note compares several simulation procedures in which there is—as in the model—no cross-sectional sampling variation.

Suggested Citation

  • Yann Algan & Olivier Allais & Wouter J den Haan, 2010. "Solving the incomplete markets model with aggregate uncertainty using parameterized cross-sectional distributions," Sciences Po Economics Publications (main) hal-02662044, HAL.
    • Handle: RePEc:hal:spmain:hal-02662044
    DOI: 10.1016/j.jedc.2009.03.010
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-02662044v1
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    Citations

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    Cited by:

    1. Mordecai Kurz & Maurizio Motolese & Giulia Piccillo & Howei Wu, 2015. "Monetary Policy with Diverse Private Expectations," Discussion Papers 15-004, Stanford Institute for Economic Policy Research.
    2. Vasco M. Carvalho & Basile Grassi, 2019. "Large Firm Dynamics and the Business Cycle," American Economic Review, American Economic Association, vol. 109(4), pages 1375-1425, April.
    3. Den Haan, Wouter J., 2010. "Comparison of solutions to the incomplete markets model with aggregate uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 34(1), pages 4-27, January.
    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. Wouter J Den Haan & Pontus Rendahl & Markus Riegler, 2018. "Unemployment (Fears) and Deflationary Spirals," Journal of the European Economic Association, European Economic Association, vol. 16(5), pages 1281-1349.
    6. 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.
    7. Bhagath Cheela & André DeHon & Jesús Fernández‐Villaverde & Alessandro Peri, 2025. "Programming FPGAs for economics: An introduction to electrical engineering economics," Quantitative Economics, Econometric Society, vol. 16(1), pages 49-87, January.
    8. Pichler, Paul, 2011. "Solving the multi-country Real Business Cycle model using a monomial rule Galerkin method," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 240-251, February.
    9. Karsten O. Chipeniuk, 2020. "Optimal Grid Selection for the Numerical Solution of Dynamic Stochastic Optimization Problems," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 883-928, December.
    10. Emoto, Masakazu & Sunakawa, Takeki, 2021. "Applying the explicit aggregation algorithm to heterogeneous agent models in continuous time," Economics Letters, Elsevier, vol. 206(C).
    11. Bayer, Christian & Luetticke, Ralph, 2018. "Solving heterogeneous agent models in discrete time with many idiosyncratic states by perturbation methods," CEPR Discussion Papers 13071, C.E.P.R. Discussion Papers.
    12. Den Haan, Wouter J., 2010. "Assessing the accuracy of the aggregate law of motion in models with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 34(1), pages 79-99, January.
    13. Michael C. Hatcher & Eric M. Scheffel, 2016. "Solving the Incomplete Markets Model in Parallel Using GPU Computing and the Krusell–Smith Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 569-591, December.
    14. 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.
    15. Chipeniuk, Karsten O. & Katz, Nets Hawk & Walker, Todd B., 2022. "Households, auctioneers, and aggregation," European Economic Review, Elsevier, vol. 141(C).
    16. Juan M. Morelli & Pablo Ottonello & Diego J. Perez, 2022. "Global Banks and Systemic Debt Crises," Econometrica, Econometric Society, vol. 90(2), pages 749-798, March.
    17. Den Haan, Wouter J. & Rendahl, Pontus, 2010. "Solving the incomplete markets model with aggregate uncertainty using explicit aggregation," Journal of Economic Dynamics and Control, Elsevier, vol. 34(1), pages 69-78, January.
    18. Andrei Jirnyi & Vadym Lepetyuk, 2011. "A reinforcement learning approach to solving incomplete market models with aggregate uncertainty," Working Papers. Serie AD 2011-21, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    19. 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.

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    Keywords

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D52 - Microeconomics - - General Equilibrium and Disequilibrium - - - Incomplete Markets

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