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Consistent Estimation of Agent-Based Models by Simulated Minimum Distance

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Agent-based (AB) models are considered a promising tool for macroeconomic analysis. However, until estimation of AB models become a common practice, they will not get to the center stage of macroeconomics. Two diculties arise in the estimation of AB models: (i) the criterion function has no simple analytical expression, and (ii) the aggregate properties of the model cannot be analytically understood. The rst one calls for simulation-based estimation techniques; the second requires additional statistical testing in order to ensure that the simulated quantities are consistent estimators of the theoretical quantities. The possibly high number of parameters involved and the non-linearities in the theoretical quantities used for estimation add to the complexity of the problem. As these diculties are also shared, though to a di erent extent, by DSGE models, we rst look at the lessons that can be learned from this literature. We identify simulated minimum distance (SMD) as a practical approach to estimation of AB models, and we discuss the conditions which ensure consistency of SMD estimators in AB models

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  • Grazzini, Jakob & Richiardi, Matteo, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201335, University of Turin.
  • Handle: RePEc:uto:dipeco:201335
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    1. Ross Richardson & Matteo Richiardi & Michael Wolfson, 2015. "We ran one billion agents. Scaling in simulation models," Economics Papers 2015-W05, Economics Group, Nuffield College, University of Oxford.
    2. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
    3. Sander Hoog, 2019. "Surrogate Modelling in (and of) Agent-Based Models: A Prospectus," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1245-1263, March.
    4. G. Rigatos, 2021. "Statistical Validation of Multi-Agent Financial Models Using the H-Infinity Kalman Filter," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 777-798, October.
    5. Matteo G. Richiardi, 2017. "The Future of Agent-Based Modeling," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 271-287, March.
    6. Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.
    7. Oliver Reinhardt & Jason Hilton & Tom Warnke & Jakub Bijak & Adelinde M. Uhrmacher, 2018. "Streamlining Simulation Experiments with Agent-Based Models in Demography," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(3), pages 1-9.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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