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Small sample bias in MSM estimation of agent-based models

In: Managing Market Complexity

Author

Listed:
  • Jakob Grazzini

    (Catholic University of Milan)

  • Matteo Richiardi

    (University of Turin)

  • Lisa Sella

    (Ceris - CNR)

Abstract

Starting from an agent-based interpretation of the well-known Bass innovation diffusion model, we perform a Montecarlo analysis of the performance of a method of simulated moment (MSM) estimator. We show that nonlinearities of the moments lead to a small bias in the estimates in small populations, although our estimates are consistent and converge to the true values as population size increases. Our approach can be generalized to the estimation of more complex agent-based models.

Suggested Citation

  • Jakob Grazzini & Matteo Richiardi & Lisa Sella, 2012. "Small sample bias in MSM estimation of agent-based models," Lecture Notes in Economics and Mathematical Systems, in: Andrea Teglio & Simone Alfarano & Eva Camacho-Cuena & Miguel GinĂ©s-Vilar (ed.), Managing Market Complexity, edition 127, chapter 0, pages 237-247, Springer.
    • Handle: RePEc:spr:lnechp:978-3-642-31301-1_19
    DOI: 10.1007/978-3-642-31301-1_19
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    Cited by:

    1. 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.
    2. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.

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