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• W2748617269 abstract "In this chapter, I review the recent developments of the Agent-Based literature with respect to empirical estimation. The methods employed in the literature include Bayesian estimation, simulated minimum distance, simulated maximum likelihood. In the second part, I focus on two distinct problems. The first one is parameter calibration. This approach is indeed useful since Agent-Based Models (ABMs) have typically a large parameter space. The second one regards the possibility of replacing ABMs with a metamodel, that is, a statistical model linking the value of parameters to a set of moments of the simulated data. The metamodels provide the conditional expectation of the moments, which might be used for a variety of purposes, including estimation. In particular, I focus on sensitivity analysis and on the problem of parameter identification." @default.
• W2748617269 created "2017-08-31" @default.
• W2748617269 creator A5040944937 @default.
• W2748617269 date "2017-01-01" @default.
• W2748617269 modified "2023-09-27" @default.
• W2748617269 title "Econometric Methods for Agent-Based Models" @default.
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• W2748617269 doi "https://doi.org/10.1016/b978-0-12-803834-5.00011-4" @default.
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