Abstract
This paper addresses the moment selection issue of the simulated method of moments, an estimation technique commonly applied to intractable agent-based models. We de-velop a simple machine learning extension reducing arbitrariness and automating the mo-ment choice.
Two algorithms are proposed: backward stepwise moment elimination and forward stepwise moment selection. The methodology is tested using simulations on a Markov-switching multifractal framework and two popular financial agent-based models with increasing complexity.
We find that both algorithms can identify multiple moment sets that outperform all benchmark sets. Moreover, we achieve considerable in-sample es-timation precision gains of up to 66 percent for agent-based models.
Finally, an out-of-sample empirical exercise with S & P 500 data strongly supports the practical applicability of our methodology as the estimated models pass the validity test of overidentifying re-strictions. & COPY; 2023 Elsevier B.V. All rights reserved.