Agent-based model calibration using machine learning surrogates

Francesco Lamperti, Andrea Roventini and Amir Sani

Journal of Economic Dynamics and Control, 2018, vol. 90, issue C, 366-389

Abstract: Efficiently calibrating agent-based models (ABMs) to real data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs by combining machine-learning and intelligent iterative sampling. The proposed approach “learns” a fast surrogate meta-model using a limited number of ABM evaluations and approximates the nonlinear relationship between ABM inputs (initial conditions and parameters) and outputs. Performance is evaluated on the Brock and Hommes (1998) asset pricing model and the “Islands” endogenous growth model Fagiolo and Dosi (2003). Results demonstrate that machine learning surrogates obtained using the proposed iterative learning procedure provide a quite accurate proxy of the true model and dramatically reduce the computation time necessary for large scale parameter space exploration and calibration.

Keywords: Agent based model; Calibration; Machine learning; Surrogate; Meta-model (search for similar items in EconPapers)
JEL-codes: C15 C52 C63 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (72)

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Related works:
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-based model calibration using machine learning surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
Working Paper: Agent-Based Model Calibration using Machine Learning Surrogates (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:90:y:2018:i:c:p:366-389

DOI: 10.1016/j.jedc.2018.03.011

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