0574253 20240402214241.320230814235959.9 85161338880 001021137800001 10.1016/j.jebo.2023.05.040 26 s. P cav_un_epca*02511940167-26812121 (2023)366-391Elsevier Agent-based model Machine learning Simulated method of moments Stepwise selection cav_un_auth*0453094 Žíla Eric UTIA-B Ekonometrie Department of Econometrics E E CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0293468 Kukačka Jiří UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2023/E/kukacka-0574253.pdf https://www.sciencedirect.com/science/article/pii/S0167268123001944?via%3Dihub GA20-14817S GA ČR cav_un_auth*0397554 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. WOS JC 10000 10200 10201 2024 RVO:67985556 https://hdl.handle.net/11104/0344591 S Article Economics C ECONOMICS 2.4 0.5 9 0.01708 1.089 13107 144 1.326 55.5 2.39 3241 Q1 2.100 376 Q2 70.9 Q2 Q2 0.7 Q2 70.9 2023 85161338880 SCOPUS PUBMED 001021137800001 WOS cav_un_epca*0251194 Journal of Economic Behavior & Organization Roč. 212 č. 1 2023 366 391 0167-2681 1879-1751 Elsevier