Working Paper: CEPR ID: DP6062
Authors: Yann Algan; Olivier Allais; Wouter den Haan
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 solution; Projection method; Simulation
JEL Codes: C63; D52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| New algorithm (C69) | Improved efficiency of solving heterogeneous agent models (C51) |
| New algorithm (C69) | More accurate representation of the cross-sectional distribution (D39) |
| New algorithm (C69) | Reduced reliance on Monte Carlo integration (C59) |
| New algorithm (C69) | Streamlined computational process (C88) |
| Simulation procedure avoiding cross-sectional sampling variation (C22) | Faster simulation results (C69) |
| Simulation procedure avoiding cross-sectional sampling variation (C22) | Enhanced accuracy of simulation results (C53) |
| New algorithm (C69) | Cheaper and more efficient simulation of economy with continuum of agents (E19) |