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Application of Monte Carlo Methods: Computing Heterogeneous Agent Models Without Aggregate Uncertainty


  • Vasilev, Aleksandar


In this paper we solve the benchmark heterogeneous agents model by Aiyagari (1994) using Monte Carlo methods. In addition, the idiosyncratic shocks process is approximated using Tauchen's (1986) method. This we go beyond the 2 by 2 Markov matrix approximation of the AR(1) stochastic process. The code is written in MATLAB. The computation time is much faster than the one written by Heer and Maussner (2008) in FORTRAN. This model also solves Mehra-Prescott's puzzle and generates a risk-free interest rate that is much closer to the one we observe in data.

Suggested Citation

  • Vasilev, Aleksandar, 2009. "Application of Monte Carlo Methods: Computing Heterogeneous Agent Models Without Aggregate Uncertainty," EconStor Research Reports 142159, ZBW - German National Library of Economics.
  • Handle: RePEc:zbw:esrepo:142159

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    References listed on IDEAS

    1. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
    2. S. Rao Aiyagari, 1994. "Uninsured Idiosyncratic Risk and Aggregate Saving," The Quarterly Journal of Economics, Oxford University Press, vol. 109(3), pages 659-684.
    3. Huggett, Mark, 1993. "The risk-free rate in heterogeneous-agent incomplete-insurance economies," Journal of Economic Dynamics and Control, Elsevier, vol. 17(5-6), pages 953-969.
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    More about this item


    Monte Carlo; Aiyagari paper;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques


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