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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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    8. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Introduction. Improving the Toolbox: New Advances in Agent-Based and Computational Models," Sciences Po publications info:hdl:2441/53r60a8s3ku, Sciences Po.
    6. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Introduction. Improving the Toolbox," Post-Print hal-01053562, HAL.

    More about this item

    Keywords

    diffusion model; method of simulated moments; estimation;

    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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