Calibrating Agent-Based Models with Linear Regressions
Richard Bailey () and
Jens Koed Madsen ()
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Richard Bailey: https://www.geog.ox.ac.uk/staff/rbailey.html
Journal of Artificial Societies and Social Simulation, 2020, vol. 23, issue 1, 7
In this paper, we introduce a simple way to parametrize simulation models by using regularized linear regression. Regressions bypass the three major challenges of calibrating by minimization: selecting the summary statistics, defining the distance function and minimizing it numerically. By substituting regression with classification, we can extend this approach to model selection. We present five example estimations: a statistical fit, a biological individual-based model, a simple real business cycle model, a non-linear biological simulation and heuristics selection in a fishery agent-based model. The outcome is a method that automatically chooses summary statistics, weighs them and uses them to parametrize models without running any direct minimization.
Keywords: Agent-Based Models; Indirect Inference; Estimation; Calibration; Simulated Minimum Distance; Approximate Bayesian Computation (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2019-66-2
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