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Solving heterogeneous-agent models with paramaterized cross-sectional distribution

Author

Listed:
  • Yann Algan

    (PJSE - Paris-Jourdan Sciences Economiques - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, OEP - Organisation et Efficacité de Production - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12)

  • Olivier Allais

    (CORELA - Laboratoire de Recherche sur la Consommation - INRA - Institut National de la Recherche Agronomique)

  • Wouter J. den Haan

    (UvA - University of Amsterdam [Amsterdam] = Universiteit van Amsterdam)

Abstract

A new algorithm is developed to solve models with heterogeneous agents and aggregate uncertainty. Projection methods are the main building blocks of the algorithm and - in contrast to the most popular solution procedure - simulations only play a very minor role. The paper also develops a new simulation procedure that not only avoids cross-sectional sampling variation but is 10 (66) times faster than simulating an economy with 10,000 (100,000) agents. Because it avoids cross-sectional sampling variation, it can generate an accurate representation of the whole cross-sectional distribution. Finally, the paper outlines a set of accuracy tests.

Suggested Citation

  • Yann Algan & Olivier Allais & Wouter J. den Haan, 2008. "Solving heterogeneous-agent models with paramaterized cross-sectional distribution," PSE-Ecole d'économie de Paris (Postprint) halshs-00754295, HAL.
    • Handle: RePEc:hal:pseptp:halshs-00754295
    DOI: 10.1016/j.jedc.2007.03.007
    Note: View the original document on HAL open archive server: https://pjse.hal.science/halshs-00754295
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    References listed on IDEAS

    as
    1. Den Haan, Wouter J, 1996. "Heterogeneity, Aggregate Uncertainty, and the Short-Term Interest Rate," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 399-411, October.
    2. Miao, Jianjun, 2006. "Competitive equilibria of economies with a continuum of consumers and aggregate shocks," Journal of Economic Theory, Elsevier, vol. 128(1), pages 274-298, May.
    3. Christiano, Lawrence J. & Fisher, Jonas D. M., 2000. "Algorithms for solving dynamic models with occasionally binding constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 24(8), pages 1179-1232, July.
    4. Eric R Young, 2005. "Approximate Aggregation," Computing in Economics and Finance 2005 141, Society for Computational Economics.
    5. Bruce Preston & Mauro Roca, 2007. "Incomplete Markets, Heterogeneity and Macroeconomic Dynamics," NBER Working Papers 13260, National Bureau of Economic Research, Inc.
    6. Marcelo L. Veracierto, 2002. "Plant-Level Irreversible Investment and Equilibrium Business Cycles," American Economic Review, American Economic Association, vol. 92(1), pages 181-197, March.
    7. Den Haan, Wouter J., 1997. "Solving Dynamic Models With Aggregate Shocks And Heterogeneous Agents," Macroeconomic Dynamics, Cambridge University Press, vol. 1(2), pages 355-386, June.
    8. Michael Reiter, 2001. "Recursive Solution Of Heterogeneous Agent Models," Computing in Economics and Finance 2001 167, Society for Computational Economics.
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    10. José-Víctor Ríos-Rull, 1997. "Computation of equilibria in heterogeneous agent models," Staff Report 231, Federal Reserve Bank of Minneapolis.
    11. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
    12. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
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    More about this item

    Keywords

    Incomplete markets; Numerical solutions; Projection methods; Simulations;
    All these keywords.

    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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