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Solving Heterogeneous-agent Models with Parameterized Cross-sectional Distributions

Abstract : A new algorithm is developed to solve models with heterogeneous agents and aggregate uncertainty that avoids some disadvantages of the prevailing algorithm that strongly relies on simulation techniques and is easier to implement than existing algorithms. A key aspect of the algorithm is a new procedure that parameterizes the cross-sectional distribution, which makes it possible to avoid Monte Carlo integration. The paper also develops a new simulation procedure that not only avoids cross-sectional sampling variation but is also more than ten times faster than the standard procedure of simulating an economy with a large but finite number of agents. This procedure can help to improve the efficiency of the most popular algorithm in which simulation procedures play a key role.
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Submitted on : Thursday, September 18, 2014 - 1:17:34 PM
Last modification on : Monday, March 21, 2022 - 2:50:42 PM
Long-term archiving on: : Friday, December 19, 2014 - 1:26:26 PM


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  • HAL Id : hal-01065666, version 1
  • SCIENCESPO : 2441/8823



Yann Algan, Olivier Allais, Wouter J den Haan. Solving Heterogeneous-agent Models with Parameterized Cross-sectional Distributions. 2007. ⟨hal-01065666⟩



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