Moment set selection for the SMM using simple machine learning

Eric Zila and Jiri Kukacka

Journal of Economic Behavior & Organization, 2023, vol. 212, issue C, 366-391

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 develop a simple machine learning extension reducing arbitrariness and automating the moment 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 estimation 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 restrictions.

Keywords: Agent-based model; Machine learning; Simulated method of moments; Stepwise selection (search for similar items in EconPapers)
JEL-codes: C13 C15 C22 C51 C58 G40 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:212:y:2023:i:c:p:366-391

DOI: 10.1016/j.jebo.2023.05.040

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