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Indirect estimation of agent-based models.An application to a simple diffusion model

Author

Listed:
  • Jacob Grazzini
  • Matteo Richiardi
  • Lisa Sella

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 estimator. We show that nonlinearities of the moments lead to a small bias in the estimates in small populations, and prove that 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. However, a trade-off emerges between model inadequacy and data inadequacy. This is particularly severe when only aggregate information is available, as common with diffusion data.

Suggested Citation

  • Jacob Grazzini & Matteo Richiardi & Lisa Sella, 2012. "Indirect estimation of agent-based models.An application to a simple diffusion model," LABORatorio R. Revelli Working Papers Series 118, LABORatorio R. Revelli, Centre for Employment Studies.
    • Handle: RePEc:cca:wplabo:118
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    2. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Introduction - Improving the Toolbox: New Advances in Agent-Based and Computational Models," SciencePo Working papers Main hal-01053562, HAL.
    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. Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.
    5. Michael Neugart & Matteo G. Richiardi, 2012. "Agent-based models of the labor market," LABORatorio R. Revelli Working Papers Series 125, LABORatorio R. Revelli, Centre for Employment Studies.
    6. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Improving the toolbox. New advances in Agent-based and Computational Models," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 7-13.
    7. repec:hal:spmain:info:hdl:2441/dcditnq6282sbu1u151qe5p7f is not listed on IDEAS
    8. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    9. Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.
    10. repec:hal:spmain:info:hdl:2441/53r60a8s3kup1vc9l564k4686 is not listed on IDEAS

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    More about this item

    Keywords

    diffusion model; method of simulated moments; estimation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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