Solving heterogeneous-agent models with parameterized cross-sectional distributions

Yann Algan, Olivier Allais () and Wouter J. den Haan
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Wouter J. den Haan: UvA - University of Amsterdam [Amsterdam] = Universiteit van Amsterdam, CEPR - Center for Economic Policy Research, London Business School - London Business School

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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.

Keywords: incomplete markets; numerical solutions; projection methods; simulations (search for similar items in EconPapers)
Date: 2006-12
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00589129v1
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Citations: View citations in EconPapers (4)

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Working Paper: Solving heterogeneous-agent models with parameterized cross-sectional distributions (2008)
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Working Paper: Solving Heterogeneous-agent Models with Parameterized Cross-sectional Distributions (2007)
Working Paper: Solving Heterogeneous-agent Models with Parameterized Cross-sectional Distributions (2007)
Working Paper: Solving Heterogeneous-agent Models with Parameterized Cross-sectional Distributions (2006)
Working Paper: Solving Heterogeneous-agent Models with Parameterized Cross-sectional Distributions (2006)
Working Paper: Solving heterogeneous-agent models with parameterized cross-sectional distributions (2006)
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