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Small sample bias in MSM estimation of agent-based models

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Managing Market Complexity

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 662))

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.

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Author information

Authors and Affiliations

  1. Institute of Economic Theory and Quantitative Methods, Catholic University of Milan, via Necchi 5, 20123, Milano, Italy

    Jakob Grazzini

  2. Department of Economics and LABORatorio Revelli, Collegio Carlo Alberto, University of Turin, via Real Collegio 30, Moncalieri, 10024, Torino, Italy

    Matteo Richiardi

  3. Ceris - CNR, via Real Collegio 30, Moncalieri, 10024, Torino, Italy

    Lisa Sella

Authors
  1. Jakob Grazzini
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  2. Matteo Richiardi
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  3. Lisa Sella
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Corresponding author

Correspondence to Jakob Grazzini .

Editor information

Editors and Affiliations

  1. , Economics, Universitat Jaume I, Campus del Riu Sec, Castellon de la Plana, 12071, Spain

    Andrea Teglio

  2. , Economics, Universitat Jaume I, Campus del Riu Sec, Castellon de la Plana, 12071, Spain

    Simone Alfarano

  3. , Economics, Universitat Jaume I, Campus del Riu Sec, Castellon de la Plana, 12071, Spain

    Eva Camacho-Cuena

  4. , Economics, Universitat Jaume I, Campus del Riu Sec, Castellon de la Plana, 12071, Spain

    Miguel Ginés-Vilar

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Grazzini, J., Richiardi, M., Sella, L. (2012). Small sample bias in MSM estimation of agent-based models. In: Teglio, A., Alfarano, S., Camacho-Cuena, E., Ginés-Vilar, M. (eds) Managing Market Complexity. Lecture Notes in Economics and Mathematical Systems, vol 662. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31301-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-31301-1_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31300-4

  • Online ISBN: 978-3-642-31301-1

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