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Estimation of Ergodic Agent-Based Models by Simulated Minimum Distance

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  • Jakob Grazzini

    () (Catholic University of Milan, Dept of Economics and Finance)

  • Matteo Richiardi

    () (Institute for New Economic Thinking, Nuffield College, Oxford and Collegio Carlo Alberto)

Abstract

Two diculties arise in the estimation of AB models: (i) the criterion function has no simple analytical expression, (ii) the aggregate properties of the model cannot be analytically understood. In this paper we show how to circumvent these diculties and under which conditions ergodic models can be consistently estimated by simulated minimum distance techniques, both in a long-run equilibrium and during an adjustment phase.

Suggested Citation

  • Jakob Grazzini & Matteo Richiardi, 2014. "Estimation of Ergodic Agent-Based Models by Simulated Minimum Distance," Economics Papers 2014-W07, Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:1407
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    Cited by:

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    3. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.
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    6. 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.
    7. Lux, Thomas, 2017. "Estimation of agent-based models using sequential Monte Carlo methods," Economics Working Papers 2017-07, Christian-Albrechts-University of Kiel, Department of Economics.
    8. Francesco Lamperti, 2016. "Empirical Validation of Simulated Models through the GSL-div: an Illustrative Application," LEM Papers Series 2016/18, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    9. Giovanni Dosi & Marcelo C. Pereira & Andrea Roventini & Maria Enrica Virgillito, 2016. "The Effects of Labour Market Reforms upon Unemployment and Income Inequalities: an Agent Based Model," Sciences Po publications 2016-24, Sciences Po.
    10. Alexandru Mandes & Peter Winker, 2017. "Complexity and model comparison in agent based modeling of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 469-506, October.
    11. Jaqueson K. Galimberti & Nicolas Suhadolnik & Sergio Silva, 2017. "Cowboying Stock Market Herds with Robot Traders," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 393-423, October.
    12. Guerini, Mattia & Napoletano, Mauro & Roventini, Andrea, 2018. "No man is an Island: The impact of heterogeneity and local interactions on macroeconomic dynamics," Economic Modelling, Elsevier, vol. 68(C), pages 82-95.
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    14. Creel, Michael, 2017. "Neural nets for indirect inference," Econometrics and Statistics, Elsevier, vol. 2(C), pages 36-49.
    15. Matteo Richiardi, 2016. "Editorial," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 1-4.
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    19. Michael Creel, 2016. "Neural Nets for Indirect Inference," Working Papers 942, Barcelona Graduate School of Economics.
    20. Sylvain Barde & Sander van der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Studies in Economics 1712, School of Economics, University of Kent.
    21. Francesco Lamperti, 2015. "An Information Theoretic Criterion for Empirical Validation of Time Series Models," LEM Papers Series 2015/02, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
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    Keywords

    Agent-based Models; Consistent Estimation; Method of Simulated Moments.;

    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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