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

We estimate an agent-based interpretation of the well-known Bass innovation diffusion model. In order to reduce the computational complexity of the estimation procedure, standard ML techniques are used to estimate some parameters as a function of other parameters, which are then estimated by simulated moments. We 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.