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.
Grazzini, J., Richiardi, M., Sella, L., Small sample bias in MSM estimation of agent-based models, in Andrea Tegli, A. T., Simone Alfaran, S. A., Eva Camacho-Cuen, E. C., Miguel Ginés-Vila, M. G. (ed.), Managing Market Complexity, Springer, Berlino 2012: <<LECTURE NOTES IN ECONOMICS AND MATHEMATICAL SYSTEMS>>, 237- 247. 10.1007/978-3-642-31301-1_19 [http://hdl.handle.net/10807/43186]
Small sample bias in MSM estimation of agent-based models
Grazzini, Jakob;
2012
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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