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Computing Dynamic Heterogeneous-Agent Economies: Tracking the Distribution

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  • Grey Gordon

Abstract

Theoretical formulations of dynamic heterogeneous-agent economies typically include a distribution as an aggregate state variable. This paper introduces a method for computing equilibrium of these models by including a distribution directly as a state variable if it is finite-dimensional or a fine approximation of it if it is infinite-dimensional. The method accurately computes equilibrium in an extreme calibration of Huffman's (1987) overlapping-generations economy where quasi-aggregation, the accurate forecasting of prices using a small state space, fails to obtain. The method also accurately solves for equilibrium in a version of Krusell and Smith's (1998) economy wherein quasi-aggregation obtains but households face occasionally binding constraints. The method is demonstrated to be not only accurate but also feasible with equilibria for both economies being computed in under ten minutes in Matlab. Feasibility is achieved by using Smolyak's (1963) sparse-grid interpolation algorithm to limit the necessary number of gridpoints by many orders of magnitude relative to linear interpolation. Accuracy is achieved by using Smolyak's algorithm, which relies on smoothness, only for representing the distribution and not for other state variables such as individual asset holdings.

Suggested Citation

  • Grey Gordon, 2020. "Computing Dynamic Heterogeneous-Agent Economies: Tracking the Distribution," Economic Quarterly, Federal Reserve Bank of Richmond, issue 2Q, pages 61-95.
  • Handle: RePEc:fip:fedreq:88440
    DOI: 10.21144/eq1060202
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    1. Computing Dynamic Heterogeneous-Agent Economies: Tracking the Distribution
      by Christian Zimmermann in NEP-DGE blog on 2011-07-22 18:21:27

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    Cited by:

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    2. Judd, Kenneth L. & Maliar, Lilia & Maliar, Serguei & Valero, Rafael, 2014. "Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 92-123.
    3. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    4. 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.
    5. 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.
    6. Schesch, Constantin, 2024. "Pseudospectral methods for continuous-time heterogeneous-agent models," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    7. Cao, Dan, 2020. "Recursive equilibrium in Krusell and Smith (1998)," Journal of Economic Theory, Elsevier, vol. 186(C).
    8. Dennis, Richard, 2024. "Using a hyperbolic cross to solve non-linear macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).

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    More about this item

    Keywords

    Heterogeneous; Equilibrium; Forecasting;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

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