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A Regression-Based Calibration Method for Agent-Based Models

Author

Listed:
  • Siyan Chen

    (Shantou University)

  • Saul Desiderio

    (Shantou University)

Abstract

Because of their complexity, taking agent-based models to the data is still an unresolved issue. In this paper we propose a method to calibrate the model parameters on real data that is based on a novel global sensitivity analysis procedure. The innovative feature of this procedure is that it allows to estimate regression meta-models for the relationship between model parameters and model output without resorting to Monte Carlo simulations to eliminate the effect of randomness. This is achieved by sampling at the same time both the parameters and the seed of the random numbers generator in a random fashion. If correctly specified, the meta-models can be directly used to consistently estimate the average response of the ABM to any parameter vector input by the modeler and, as a consequence, also the distance between real and simulated data. The advantage of the proposed method is twofold: it is very parsimonious in terms of computational time and is relatively easy to implement, being it based on elementary econometric techniques.

Suggested Citation

  • Siyan Chen & Saul Desiderio, 2022. "A Regression-Based Calibration Method for Agent-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 687-700, February.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:2:d:10.1007_s10614-021-10106-9
    DOI: 10.1007/s10614-021-10106-9
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    References listed on IDEAS

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    13. Chen, Siyan & Desiderio, Saul, 2018. "Computational evidence on the distributive properties of monetary policy," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-32.
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    Citations

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    Cited by:

    1. Andrei I. Vlad & Alexei A. Romanyukha & Tatiana E. Sannikova, 2024. "Parameter Tuning of Agent-Based Models: Metaheuristic Algorithms," Mathematics, MDPI, vol. 12(14), pages 1-21, July.
    2. Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
    3. Leonardo Bargigli & Filippo Pietrini, 2024. "Conformism, distinction and heterogeneity in an agent-based model of fads," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(4), pages 807-829, October.
    4. Barde, Sylvain, 2024. "Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
    5. Siyan Chen & Saul Desiderio, 2023. "An agent-based framework for the analysis of the macroeconomic effects of population aging," Journal of Evolutionary Economics, Springer, vol. 33(2), pages 393-427, April.
    6. Karl Naumann-Woleske & Max Sina Knicker & Michael Benzaquen & Jean-Philippe Bouchaud, 2021. "Exploration of the Parameter Space in Macroeconomic Agent-Based Models," Papers 2111.08654, arXiv.org, revised Aug 2022.
    7. Sylvain Barde, 2022. "Bayesian Estimation of Large-Scale Simulation Models with Gaussian Process Regression Surrogates," Studies in Economics 2203, School of Economics, University of Kent.

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    More about this item

    Keywords

    Agent-based models; Calibration; Meta-modeling; Global sensitivity analysis;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • 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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